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• Cyber Physical Systems Security.-Risk Management for CPS Security.-Wireless Sensor Network Security for Cyber Physical Systems.-WSN Security mechanisms for CPS.- ICS/SCADA System Security for CPS.-Embedded Systems Security for Cyber Physical Systems.-Distributed Control Systems Security for CPS.-Standards for CPS.
• (source: Nielsen Book Data)

This book is a pioneering yet primary general reference resource on cyber physical systems and their security concerns. Providing a fundamental theoretical background, and a clear and comprehensive overview of security issues in the domain of cyber physical systems, it is useful for students in the fields of information technology, computer science, or computer engineering where this topic is a substantial emerging area of study. .
(source: Nielsen Book Data)

• Modelling of Underwater Robots.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.
• (source: Nielsen Book Data)

A classic in underwater robotics. One of the first volumes in the "Springer Tracts in Advanced Robotics" series, it has been a bestseller through the previous three editions. Fifteen years after the publication of the first edition, the fourth edition comes to print. The book addresses the main control aspects in underwater manipulation tasks. With respect to the third edition, it has been revised, extended and some concepts better clustered. The mathematical model with significant impact on the control strategy is discussed. The problem of controlling a 6-degrees-of-freedoms autonomous underwater vehicle is investigated and a survey of fault detection/tolerant strategies for unmanned underwater vehicles is provided. Inverse kinematics, dynamic and interaction control for underwater vehicle-manipulator systems are then discussed. The code used to generate most of the numerical simulations is made available and briefly discussed.
(source: Nielsen Book Data)

• Aerial Vehicles progress On Pico Air Vehicles.- Perception and Mapping.- Planning.- Systems and Integration.- Control.
• (source: Nielsen Book Data)

This volume presents a collection of papers presented at the 15th International Symposium of Robotic Research (ISRR). ISRR is the biennial meeting of the International Foundation of Robotic Research (IFRR) and its 15th edition took place in Flagstaff, Arizona on December 9 to December 12, 2011. As for the previous symposia, ISRR 2011 followed up on the successful concept of a mixture of invited contributions and open submissions. Therefore approximately half of the 37 contributions were invited contributions from outstanding researchers selected by the IFRR officers and the program committee, and the other half were chosen among the open submissions after peer review. This selection process resulted in a truly excellent technical program which featured some of the very best of robotic research. The program was organized around oral presentation in a single-track format and included for the first time a small number of interactive presentations. The symposium contributions contained in this volume report on a variety of new robotics research results covering a broad spectrum including perception, manipulation, grasping, vehicles and design, navigation, control and integration, estimation and SLAM. .
(source: Nielsen Book Data)

• Locomotion.- Haptics.- Manipulation.- Perception.- Human-Robot Interaction.- Mapping and Localization.- Mechanisms.- Perception and Planning.- Sensor Networks.- Many-Robot Systems.- Interactive Presentations.- Plenary Talk.
• (source: Nielsen Book Data)

The International Symposium on Experimental Robotics (ISER) is a series of bi-annual meetings which are organized in a rotating fashion around North America, Europe and Asia/Oceania. The goal of ISER is to provide a forum for research in robotics that focuses on novelty of theoretical contributions validated by experimental results. The meetings are conceived to bring together, in a small group setting, researchers from around the world who are in the forefront of experimental robotics research. This unique reference presents the latest advances across the various fields of robotics, with ideas that are not only conceived conceptually but also explored experimentally. It collects robotics contributions on the current developments and new directions in the field of experimental robotics, which are based on the papers presented at the 14th ISER held on June 15-18, 2014 in Marrakech and Essaouira, Morocco. This present fourteenth edition of Experimental Robotics edited by M. Ani Hsieh, Oussama Khatib, and Vijay Kumar offers a collection of a broad range of topics in field and human-ce ntered robotics.
(source: Nielsen Book Data)

• Motivation.- Closing the Loop: Placing Ontology Usage Analysis in the Ontology Development and Deployment Lifecycle.- Ontology Usage Analysis Framework (OUSAF).- Identification Phase : Ontology Usage Network Analysis Framework (OUN-AF).- Investigation Phase: Empirical Analysis of Domain Ontology Usage (EMP-AF).
• (source: Nielsen Book Data)

This unique book succinctly summarizes the need to measure how ontologies (one of the building blocks of the Semantic Web) are currently being utilized, providing insights for various stakeholders. Where possible it improves and reuses terms in existing vocabularies/ontologies, as recommended by the Linked Data community. Recent advances in the Semantic Web have led to a proliferation of Resource Description Framework (RDF) data, which employ ontologies to semantically describe the information on the Web making it equally understandable for both humans and machines. However, to create a network effect, it is important that selective ontologies are used by more data publishers to improve the value of that ontology. For this to happen, it is vital to discover what is being used from an ontology to semantically annotate the information on the Web specific to a given domain. Answers to such basic but crucial questions can only be achieved by ascertaining how ontologies in the current semantic web are being utilized and adopted. The proposed frameworks to obtain such insights are explained with real-world examples to provide a clear and detailed description of ontology usage analysis. Both theoretical and practical, the book is of value to academics and professionals working in industry. Specifically, it is of primary interest to researchers, graduate students and practitioners in the area of the Semantic Web and its various real-world applications.
(source: Nielsen Book Data)

• Part I Global Robot Path Planning
• 1.
• Introduction to Mobile Robot Path Planning
• 2.
• Background on Artificial Intelligence Algorithms for Global Path Planning
• 3.
• Design and Evaluation of Intelligent Global Path Planning Algorithms 4.
• Integration of Global Path Planners in ROS
• 5.
• Robot Path Planning using Cloud Computing for Large Grid Maps
• Part II Multi-Robot Task Allocation
• 6.
• General Background on Multi-Robot Task Allocation
• 7.
• Different Approaches to Solve the MRTA Problem
• 8.
• Performance Analysis of the MRTA Approaches for Autonomous Mobile Robot .
• (source: Nielsen Book Data)

This book presents extensive research on two main problems in robotics: the path planning problem and the multi-robot task allocation problem. It is the first book to provide a comprehensive solution for using these techniques in large-scale environments containing randomly scattered obstacles. The research conducted resulted in tangible results both in theory and in practice. For path planning, new algorithms for large-scale problems are devised and implemented and integrated into the Robot Operating System (ROS). The book also discusses the parallelism advantage of cloud computing techniques to solve the path planning problem, and, for multi-robot task allocation, it addresses the task assignment problem and the multiple traveling salesman problem for mobile robots applications. In addition, four new algorithms have been devised to investigate the cooperation issues with extensive simulations and comparative performance evaluation. The algorithms are implemented and simulated in MATLAB and Webots.
(source: Nielsen Book Data)

• Part I. Multi-Robots Systems
• Part II. Data Fusion, Localization and Mapping
• Part III. Security and Dependability
• Part IV. Mobility.

This book compiles some of the latest research in cooperation between robots and sensor networks. Structured in twelve chapters, this book addresses fundamental, theoretical, implementation and experimentation issues. The chapters are organized into four parts namely multi-robots systems, data fusion and localization, security and dependability, and mobility.

• Preface
• Special Topics in Pattern Recognition with Applications in Nonprofileration
• Manufacturing Feature Recognition for Mould & Die Designs: Current Status & Future Directions
• Pattern-Recognition Receptors of Oral Epithelia
• Generating-Kernel Based Nonlinear Feature Extraction Methods
• Damage Assessment Based on Pattern Recognition
• Artificial Intelligence Techniques for Assisting the Decision of Making or Postponing the Embryo Transfer
• New Perspectives on a Pattern Recognition Algorithm Based on Haken's Synergetic Computer Network- With a Comment on Artificial Intelligence & Physical Intelligence
• Active Contours for Real Time Applications
• Class Distribution Estimation in Imprecise Domains Based on Supervised Learning
• Quantitative Bioimage Analysis Using Pattern Recognition
• Advances in Mining Emerging Patterns for Supervised Classification
• On the Geometrical Aspect of Biometric Authentication
• Pattern Recognition as a New Method of Numerical Research of the Concrete Dynamic System
• Pattern Recognition from ICA Mixture Modeling
• Index.
• (source: Nielsen Book Data)

Pattern recognition involves tasks such as pattern identification, feature extraction from them and pattern classification in categories, based on the extracted features. This book presents topical research in the study of pattern recognition, including pattern recognition applications in non-proliferation; pattern recognition of machine feature identifications; pattern recognition receptors of oral epithelia; construction damage assessment based on pattern recognition; Haken's pattern recognition algorithm as applied in engineering sciences; active contours for real time applications and quantitative bioimage analysis using pattern recognition.
(source: Nielsen Book Data)

• Humanoid Robotics.- Time-Varying Affective Response for Humanoid Robots.- The Co-simulation of Humanoid Robot Based on Solidworks, ADAMS and Simulink.- From RoboNova to HUBO: Platforms for Robot Dance.- BunnyBot: Humanoid Platform for Research and Teaching.- Teen Sized Humanoid Robot: Archie.- Interdisciplinary Construction and Implementation of a Human Sized Humanoid Robot by Master Students.- Human Robot Interaction.- Safety Aspects in a Human-Robot Interaction Scenario: A Human Worker Is Co-operating with an Industrial Robot.- Integration of a RFID System in a Social Robot.- A Practical Study on the Design of a User-Interface Robot Application.- Infrared Remote Control with a Social Robot.- BlogRobot: Mobile Terminal for Blog Browse Using Physical Representation.- An Exploratory Investigation into the Effects of Adaptation in Child-Robot Interaction.- Devious Chatbots - Interactive Malware with a Plot.- Towards Better Human Robot Interaction: Understand Human Computer Interaction in Social Gaming Using a Video-Enhanced Diary Method.- Promotion of Efficient Cooperation by Sharing Environment with an Agent Having a Body in Real World.- Interaction Design for a Pet-Like Remote Control.- Experiences with a Barista Robot, FusionBot.- Mutually Augmented Cognition.- How Humans Optimize Their Interaction with the Environment: The Impact of Action Context on Human Perception.- Development of a Virtual Presence Sharing System Using a Telework Chair.- PLEXIL-DL: Language and Runtime for Context-Aware Robot Behaviour.- Ambient Intelligence in a Smart Home for Energy Efficiency and Eldercare.- Education and Entertainment.- Intelligent Technologies for Edutainment Using Multiple Robots.- Remote Education Based on Robot Edutainment.- Not Just "Teaching Robotics" but "Teaching through Robotics".- A Proposal of Autonomous Robotic Systems Educative Environment.- Mechatronics Education: From Paper Design to Product Prototype Using LEGO NXT Parts.- Fostering Development of Students' Collective and Self-efficacy in Robotics Projects.- From an Idea to a Working Robot Prototype: Distributing Knowledge of Robotics through Science Museum Workshops.- Teaching Electronics through Constructing Sensors and Operating Robots.- Learning from Analogies between Robotic World and Natural Phenomena.- Integrating Robot Design Competitions into the Curriculum and K-12 Outreach Activities.- Teamwork and Robot Competitions in the Undergraduate Program at the Copenhagen University College of Engineering.- Cooperative Robotics.- Multiagents System with Dynamic Box Change for MiroSot.- Multi Block Localization of Multiple Robots.- Soty-Segment: Robust Color Patch Design to Lighting Condition Variation.- Task-Based Flocking Algorithm for Mobile Robot Cooperation.- Analysis of Spatially Limited Local Communication for Multi-Robot Foraging.- AMiRESot - A New Robot Soccer League with Autonomous Miniature Robots.- Robotic System Design.- BeBot: A Modular Mobile Miniature Robot Platform Supporting Hardware Reconfiguration and Multi-standard Communication.- System Design for Semi-automatic AndroSot.- Learning, Optimization, Communication.- Extended TA Algorithm for Adapting a Situation Ontology.- An Integer-Coded Chaotic Particle Swarm Optimization for Traveling Salesman Problem.- USAR Robot Communication Using ZigBee Technology.
• (source: Nielsen Book Data)

th This volume is an edition of the papers selected from the 12 FIRA RoboWorld C- gress, held in Incheon, Korea, August 16-18, 2009. The Federation of International Robosoccer Association (FIRA - www. fira. net) is a non-profit organization, which organizes robotic competitions and meetings around the globe annually. The RoboSoccer competitions started in 1996 and FIRA was - tablished on June 5, 1997. The Robot Soccer competitions are aimed at promoting the spirit of science and technology to the younger generation. The congress is a forum in which to share ideas and future directions of technologies, and to enlarge the human networks in robotics area. The objectives of the FIRA Cup and Congress are to explore the technical dev- opment and achievement in the field of robotics, and provide participants with a robot festival including technical presentations, robot soccer competitions and exhibits - der the theme "Where Theory and Practice Meet. " th Under the umbrella of the 12 FIRA RoboWorld Incheon Congress 2009, six int- national conferences were held for greater impact and scientific exchange: th * 6 International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS) th * 5 International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE) * International Conference on Social Robotics (ICSR) * International Conference on Advanced Humanoid Robotics Research (ICAHRR) * International Conference on Entertainment Robotics (ICER) * International Robotics Education Forum (IREF) This volume consists of selected quality papers from the six conferences.
(source: Nielsen Book Data)

• Introduction Rational Agents Strategic Games Coordination Partial Observability Mechanism Design Learning.
• (source: Nielsen Book Data)

Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Clusters and robots
• 3. Cluster programming
• 4. Robot motion
• 5. Sensors
• 6. Mapping and localization
• 7. Vision and tracking
• 8. Learning landmarks
• 9. Robot architectures
• Appendix I: Summary of OpenMPI man page for mpirun
• Appendix II: MPI datatypes
• Appendix III: MPI reduction operations
• Appendix IV: MPI application programmer interface.

In this book, we look at how cluster technology can be leveraged to build better robots. Algorithms and approaches in key areas of robotics and computer vision, such as map building, target tracking, action selection and landmark learning, are reviewed and cluster implementations for these are presented.The objective of the book is to give professionals working in the beowulf cluster or robotics and computer vision fields a concrete view of the strong synergy between the areas as well as to spur further fruitful exploitation of this connection. The book is written at a level appropriate for an advanced undergraduate or graduate student. The key concepts in robotics, computer vision and cluster computing are introduced before being used to make the text useful to a wide audience in these fields.
(source: Nielsen Book Data)

• Title page; Preface; Contents; Part I: General Purpose Applications of AI; Supervised Machine Learning: A Review of Classification Techniques; Dimension Reduction and Data Visualization Using Neural Networks; Recommender System Technologies Based on Argumentation; Knowledge Modelling Using UML Profile for Knowledge-Based Systems Development; A Semantic-Based Navigation Approach for Information Retrieval in the Semantic Web; Ontology-Based Management of Pervasive Systems; A DIYD (Do It Yourself Design) e-Commerce System for Vehicle Design Based on Ontologies and 3D Visualization.

Provides insights on how computer engineers can implement artificial intelligence (AI) in real world applications. This book presents practical applications of AI.

• Part 1: Preparing for the Future of Work
• Chapter 1: Will Robots Replace You?
• Chapter 2: Technology Definitions
• Part 2: Robots are Working
• Chapter 3: Robotic Process Automation
• Chapter 4: Robots in Teams
• Chapter 5: Robots Without Arms
• Part 3: Making Sense for Robots and Society
• Chapter 6: Robots in a World of Data
• Chapter 7: Robots in Society
• Chapter 8: Work in the Future.

Understand the current and future research into technologies that underpin the increasing capabilities of automation technologies and their impact on the working world of the future. Rapid advances in automation and robotics technologies are often reported in the trade and general media, often relying on scary headlines such as "Jobs Lost to Robots." It is certainly true that work will change with the advent of smarter and faster automated workers; however, the scope and scale of the changes is still unknown. Automation may seem to be here already, but we are only at the early stages. Automation and Collaborative Robotics explores the output of current research projects that are improving the building blocks of an automated world. Research into collaborative robotics (cobotics) is merging digital, audio, and visual data to generate a commonly held view between cobots and their human collaborators. Low-power machine learning at the edge of the network can deliver decision making on cobots or to their manipulations. Topics covered in this book include: Robotic process automation, chatbots, and their impact in the near future The hype of automation and headlines leading to concerns over the future of work Component technologies that are still in the research labs Foundational technologies and collaboration that will enable many tasks to be automated with human workers being re-skilled and displaced rather than replaced What You Will Learn Be aware of the technologies currently being researched to improve or deliver automation Understand the impact of robotics, other automation technologies, and the impact of AI on automation Get an idea of how far we are from implementation of an automated future Know what work will look like in the future with the deployment of these technologies Who This Book Is For Technical and business managers interested in the future of automation and robotics, and the impact it will have on their organizations, customers, and the business world in general.
(source: Nielsen Book Data)

Extract patterns and knowledge from your data in easy way using MATLAB About This Book * Get your first steps into machine learning with the help of this easy-to-follow guide * Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB * Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn * Learn the introductory concepts of machine learning. * Discover different ways to transform data using SAS XPORT, import and export tools, * Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. * Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. * Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. * Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. * Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
(source: Nielsen Book Data)

• Frontmatter
• Preface
• Contents
• List of Contributors
• 1. Feature selection in biomedical signal classification process and current software implementations / Jovic, Alan
• 2. An overview of skin lesion segmentation, features engineering, and classification / Sabri, My Abdelouahed / Filali, Youssef / Ennouni, Assia / Yahyaouy, Ali / Aarab, Abdellah
• 3. Brain tumor image segmentation and classification using SVM, CLAHE, and ARKFCM / Ishita, Banerjee / Madhumathy, P. / Kavitha, N.
• 4. Coronary Heart Disease prediction using genetic algorithm based decision tree / Sivaranjani, Reddi / Naresh, Vankamamidi S. / Murthy, Nistala V.E.S.
• 5. Intelligent approach for retinal disease identification / Rajyaguru, Vipul C. / Vithalani, Chandresh H. / Thanki, Rohit M.
• 6. Speech separation for interactive voice systems / Wiem, Belhedi / Anouar, Ben Messaoud Mohamed / Aïcha, Bouzid
• 7. Machine vision for human-machine interaction using hand gesture recognition / Verma, Varnita / Rajput, Anshuman / Chauhan, Piyush / Rathore, Harshit / Goyal, Piyush / Gupta, Mukul Kumar
• Index

Intelligent prediction and decision support systems are based on signal processing, computer vision (CV), machine learning (ML), software engineering (SE), knowledge based systems (KBS), data mining, artificial intelligence (AI) and include several systems developed from the study of expert systems (ES), genetic algorithms (GA), artificial neural networks (ANN) and fuzzy-logic systems The use of automatic decision support systems in design and manufacturing industry, healthcare and commercial software development systems has the following benifits: Cost savings in companies, due to employment of expert system technology. Fast decision making, completion of projects in time and development of new products. Improvement in decision making capability and quality. Usage of Knowledge database and Preservation of expertise of individuals Eases complex decision problems. Ex: Diagnosis in Healthcare To address the issues and challenges related to development, implementation and application of automatic and intelligent prediction and decision support systems in domains such as manufacturing, healthcare and software product design, development and optimization, this book aims to collect and publish wide ranges of quality articles such as original research contributions, methodological reviews, survey papers, case studies and/or reports covering intelligent systems, expert prediction systems, evaluation models, decision support systems and Computer Aided Diagnosis (CAD).
(source: Nielsen Book Data)

• pt. I. Technology. Robust layered control system for a mobile robot
• Robot that walks: emergent behaviors from a carefully evolved network
• Learning a distributed map representation based on navigation behaviors
• New approaches to robotics. pt. II. Philosophy. Intelligence without representation
• Planning is just a way of avoiding figuring out what to do next
• Elephants don't play chess
• Intelligence without reason.

Until the mid-1980s, AI researchers assumed that an intelligent system doing high-level reasoning was necessary for the coupling of perception and action. In this traditional model, cognition mediates between perception and plans of action. Realizing that this core AI, as it was known, was illusory, Rodney A. Brooks turned the field of AI on its head by introducing the behavior-based approach to robotics. The cornerstone of behavior-based robotics is the realization that the coupling of perception and action gives rise to all the power of intelligence and that cognition is only in the eye of an observer. Behavior-based robotics has been the basis of successful applications in entertainment, service industries, agriculture, mining, and the home. It has given rise to both autonomous mobile robots and more recent humanoid robots such as Brooks' Cog. This book represents Brooks' initial formulation of and contributions to the development of the behavior-based approach to robotics. It presents all of the key philosophical and technical ideas that put this "bottom-up" approach at the forefront of current research in not only AI but all of cognitive science.
(source: Nielsen Book Data)

• Introduction, C.H. Chen
• combined neural-net/knowledge-based adaptive systems for large scale dynamic control, A.D.C. Holden and S.C. Suddarth
• a connectionist incremental expert system combining production systems and associative memory, H.F. Yin and P. Liang
• optimal hidden units for two-layer nonlinear feedforward networks, T.D. Sanger
• an incremental fine adjustment algorithm for the design of optimal interpolating networks, S.K. Sin and R.J.P. deFigueiredo
• on the asymptotic properties of recurrent neural networks for optimization, J. Wang
• a real-time image segmentation system using a connectionist classifier architecture, W.E. Blanz and S.L. Gish
• segmentation of ultrasonic images with neural network technology's on automatic active sonar classifier development, T.B. Haley
• on the relationships between statistical pattern recognition and artificial neural networks, C.H. Chen.
• (source: Nielsen Book Data)

The revitalization of neural network research in the past few years has already had a great impact on research and development in pattern recognition and artificial intelligence. Although neural network functions are not limited to pattern recognition, there is no doubt that a renewed progress in pattern recognition and its applications now critically depends on neural networks. This volume specially brings together outstanding original research papers in the area and aims to help the continued progress in pattern recognition and its applications.
(source: Nielsen Book Data)

• Chapter 1 Memristor Emulators A Note on Modeling.-
• Chapter 2 A Simple Oscillator using Memristor.-
• Chapter 3 A Hyperjerk Memristive System with Hidden Attractors.-
• Chapter 4 A Memristive System with Hidden Attractors and its Engineering Application.-
• Chapter 5 Adaptive Control, Synchronization and Circuit Simulation of a Memristor-Based.-
• Chapter 6 Modern System Design using Memristors.-
• Chapter 7 RF/Microwave Applications of Memristors.-
• Chapter 8 Theory, Modeling and Design of Memristor-Based Min-Max Circuits.-
• Chapter 9 Analysis of a 4-D Hyperchaotic Fractional-Order Memristive System with Hidden Attractors.-
• Chapter 10 Adaptive Control and Synchronization of a Memristor-Based Shinriki's System.
• (source: Nielsen Book Data)

This book reports on the latest advances in and applications of memristors, memristive devices and systems. It gathers 20 contributed chapters by subject experts, including pioneers in the field such as Leon Chua (UC Berkeley, USA) and R.S. Williams (HP Labs, USA), who are specialized in the various topics addressed in this book, and covers broad areas of memristors and memristive devices such as: memristor emulators, oscillators, chaotic and hyperchaotic memristive systems, control of memristive systems, memristor-based min-max circuits, canonic memristors, memristive-based neuromorphic applications, implementation of memristor-based chaotic oscillators, inverse memristors, linear memristor devices, delayed memristive systems, flux-controlled memristive emulators, etc. Throughout the book, special emphasis is given to papers offering practical solutions and design, modeling, and implementation insights to address current research problems in memristors, memristive devices and systems. As such, it offers a valuable reference book on memristors and memristive devices for graduate students and researchers with a basic knowledge of electrical and control systems engineering.
(source: Nielsen Book Data)

• Efficient Multi-Robot Motion Planning for Unlabeled Discs in Simple Polygons.- Navigation of Distinct Euclidean Particles via Hierarchical Clustering.- Coalition Formation Games for Dynamic Multirobot Tasks.- Active Control Strategies for Discovering And Localizing Devices with Range-Only Sensors.- Aggressive Moving Obstacle Avoidance Using a Stochastic Reachable Set Based Potential Field.- Distributed Range-Based Relative Localization of Robot.- Computing Large Convex Regions of Obstacle-Free Space through Semidefinite Programming.- A Region-Based Strategy for Collaborative Roadmap Construction.- Efficient Sampling-based Approaches to Optimal Path Planning in Complex Cost Spaces.- Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions.- FFRob: An efficient heuristic for task and motion planning.- Fast Nearest Neighbor Search in SE(3) for Sampling-Based Motion Planning.- Trackability with Imprecise Localization.- Kinodynamic RRTs with Fixed Time Step and Best-Input Extension are not Probabilistically Complete.-Featureless Motion Vector-based Simultaneous Localization, Planar Surface Extraction, and Moving Obstacle Tracking.- Sparse Methods for Efficient Asymptotically Optimal KinodynamicPlanning.- Adaptive Informative Path Planning in Metric Spaces.- The Feasible Transition Graph: Encoding Topology and Manipulation Constraints for Multirobot Push-Planning.- Predict Collision Among Rigid and Articulated Obstacles with Unknown Motion.- Asymptotically Optimal Stochastic Motion Planning with Temporal Goals.- Resolution-Exact Algorithms for Link Robots.- Optimal Trajectories for Planar Rigid Bodies with Switching Costs.- Maximum-Reward Motion in a Stochastic Environment: The Nonequilibrium Statistical Mechanics Perspective.- Optimal Path Planning in Cooperative Heterogeneous Multi-robot Delivery Systems.- Composing Dynamical Systems to Realize Dynamic Robotic Dancing.-The Lion and Man Game on Convex Terrains.- RRT-X: Real-Time Motion Planning/Replanning for Environments with Unpredictable Obstacles.- Orienting Parts with Shape Variation.- Smooth and Dynamically Stable Navigation of Multiple Human-Like Robots.- Scaling up Gaussian Belief Space Planning through Covariance-Free Trajectory Optimization and Automatic Differentiation.- Planning Curvature and Torsion Constrained Ribbons in 3D with Application to Intracavitary Brachytherapy.- A Quadratic Programming Approach to Quasi-Static Whole-Body Manipulation.- On-Line Coverage of Planar Environments by a Battery Powered Autonomous Mobile Robot.- Finding a needle in an exponential haystack: Discrete RRT for Exploration of Implicit Roadmaps in Multi-Robot Motion Planning.- Stochastic Extended LQR: Optimization-Based Motion Planning Under Uncertainty.- An Approximation Algorithm for Time Optimal Multi-Robot Routing Decidability of Robot Manipulation Planning: Three Disks in the Plane.- A Topological Perspective on Cycling Robots for Full Tree Coverage.- Towards arranging and tightening knots and unknots with fixtures.- Asymptotically Optimal Feedback Planning: FMM Meets Adaptive Mesh Refinement.- Online Task Planning and Control for Aerial Robots with Fuel Constraints in Winds.-Pebble Motion on Graphs with Rotations: Efficient Feasibility Tests and Planning Algorithms.
• (source: Nielsen Book Data)

This carefully edited volume is the outcome of the eleventh edition of the Workshop on Algorithmic Foundations of Robotics (WAFR), which is the premier venue showcasing cutting edge research in algorithmic robotics. The eleventh WAFR, which was held August 3-5, 2014 at Bogazici University in Istanbul, Turkey continued this tradition. This volume contains extended versions of the 42 papers presented at WAFR. These contributions highlight the cutting edge research in classical robotics problems (e.g. manipulation, motion, path, multi-robot and kinodynamic planning), geometric and topological computation in robotics as well novel applications such as informative path planning, active sensing and surgical planning. This book - rich by topics and authoritative contributors - is a unique reference on the current developments and new directions in the field of algorithmic foundations.
(source: Nielsen Book Data)

• Introduction.- Radon Transform based Automatic Posture Recognition in Ballet Dance.- Fuzzy Image Matching Based Posture Recognition in Ballet Dance.- Gesture Driven Fuzzy Interface System For Car Racing Game.- Type-2 Fuzzy Classifier based Pathological Disorder Recognition.- Probabilistic Neural Network based Dance Gesture Recognition.- Differential Evolution based Dance Composition.- EEG-Gesture based Artificial Limb Movement for Rehabilitative Applications.- Conclusions and Future Directions.- Index.
• (source: Nielsen Book Data)

This book presents a thorough analysis of gestural data extracted from raw images and/or range data with an aim to recognize the gestures conveyed by the data. It covers image morphological analysis, type-2 fuzzy logic, neural networks and evolutionary computation for classification of gestural data. The application areas include the recognition of primitive postures in ballet/classical Indian dances, detection of pathological disorders from gestural data of elderly people, controlling motion of cars in gesture-driven gaming and gesture-commanded robot control for people with neuro-motor disability. The book is unique in terms of its content, originality and lucid writing style. Primarily intended for graduate students and researchers in the field of electrical/computer engineering, the book will prove equally useful to computer hobbyists and professionals engaged in building firmware for human-computer interfaces. A prerequisite of high school level mathematics is sufficient to understand most of the chapters in the book. A basic background in image processing, although not mandatory, would be an added advantage for certain sections.
(source: Nielsen Book Data)

• Vocabularies, Schemas, Ontologies
• Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment
• RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration
• VocBench: a Web Application for Collaborative Development of Multilingual Thesauri
• Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning
• Reasoning
• A Context-Based Semantics for SPARQL Property Paths over the Web
• Distributed and Scalable OWL EL Reasoning
• Large scale rule-based Reasoning using a Laptop
• RDF Digest: Efficient Summarization of RDF/S KBs
• Linked Data
• A Comparison of Data Structures to Manage URIs on the Web of Data
• Heuristics for Fixing Common Errors in Deployed schema.org Microdata
• Semantic Web and Web Science
• LOD-based Disambiguation of Named Entities in @tweets through Context enrichment
• Knowledge Enabled Approach to Predict the Location of Twitter Users
• Semantic Data Management, Big data, Scalability
• A Compact In-Memory Dictionary for RDF data
• Quality Assessment of Linked Datasets using Probabilistic Approximations
• Cooperative Techniques for SPARQL Query Relaxation in RDF Databases
• HDT-MR: A Scalable Solution for RDF Compression with HDT and MapReduce
• Processing Aggregate Queries in a Federation of SPARQL Endpoints
• A survey of HTTP caching implementations on the open Semantic Web
• Query Execution Optimization for Clients of Triple Pattern Fragments
• Natural Language Processing and Information Retrieval LIME: the Metadata Module for OntoLex
• Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text
• HAWK Hybrid Question Answering using Linked Data
• Machine Learning
• Automating RDF Dataset Transformation and Enrichment
• Semi-supervised Instance Matching Using Boosted Classifiers
• Assigning Semantic Labels to Data Sources
• Inductive Classification through Evidence-based Models and their Ensembles
• Mobile Web, Internet of Things and Semantic Streams
• Standardized and Efficient RDF Encoding for Constrained Embedded Networks
• Services, Web APIs, and the Web of Things SPSC: Efficient Composition of Semantic Services in Unstructured P2P Networks
• Data as a Service: The Semantic Web Redeployed
• Cognition and Semantic Web
• On Coherent Indented Tree Visualization of RDF Graphs
• Gagg: A Graph Aggregation Operator
• FrameBase: Representing N-ary Relations using Semantic Frames
• Human Computation and Crowdsourcing
• Towards hybrid NER: a study of content and crowdsourcing-related performance factors
• Ranking Entities in the Age of Two Webs, An Application to Semantic Snippets
• In-Use Industrial Track
• Troubleshooting and Optimizing Named Entity Resolution Systems in the Industry
• Using Ontologies For Modeling Virtual Reality Scenarios
• Supporting Open Collaboration in Science through Explicit and Linked Semantic Description of Processes
• Crowdmapping Digital Social Innovation with Linked data
• Desperately searching for travel offers? Formulate better queries with some help from Linked Data
• Towards the Linked Russian Heritage Cloud: Data enrichment and Publishing
• From Symptoms to Diseases
• Creating the Missing Link
• Using semantic web technologies for enterprise architecture analysis
• PADTUN
• Using Semantic Technologies in Tunnel Diagnosis and Maintenance Domain.

This book constitutes the refereed proceedings of the 12th Extended Semantic Web Conference, ESWC 2014, held in Anissaras, Portoroz, Slovenia, in May/June 2015. The 43 revised full papers presented together with three invited talks were carefully reviewed and selected from 164 submissions. This program was completed by a demonstration and poster session, in which researchers had the chance to present their latest results and advances in the form of live demos. In addition, the PhD Symposium program included 12 contributions, selected out of 16 submissions. The core tracks of the research conference were complemented with new tracks focusing on linking machine and human computation at web scale (cognition and Semantic Web, Human Computation and Crowdsourcing) beside the following subjects Vocabularies, Schemas, Ontologies, Reasoning, Linked Data, Semantic Web and Web Science, Semantic Data Management, Big data, Scalability, Natural Language Processing and Information Retrieval, Machine Learning, Mobile Web, Internet of Things and Semantic Streams, Services, Web APIs, and the Web of Things, Cognition and Semantic Web, Human Computation and Crowdsourcing, and In-Use Industrial Track as well.

• Introduction
• Kinematic Geometry of the PnP Robots
• Differential Kinematics of the Robots
• Kinematic Performance Criteria
• Elastostatics and Stiffness
• Rigid Multibody Dynamics
• Robot Elastodynamics
• Design optimization of parallel PnP Robots.

This book discusses the parametric modeling, performance evaluation, design optimization and comparative study of the high-speed, parallel pick-and-place robots. It collects the modeling methodology, evaluation criteria and design guidelines for parallel PnP robots to provide a systematic analysis method for robotic developers. Furthermore, it gathers the research results previously scattered in many prestigious international journals and conference proceedings and methodically edits them and presents them in a unified form. The book is of interest to researchers, R&D engineers and graduate students in industrial parallel robotics who wish to learn the core principles, methods, algorithms, and applications.
(source: Nielsen Book Data)

Extract patterns and knowledge from your data in easy way using MATLAB About This Book * Get your first steps into machine learning with the help of this easy-to-follow guide * Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB * Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn * Learn the introductory concepts of machine learning. * Discover different ways to transform data using SAS XPORT, import and export tools, * Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. * Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. * Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. * Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. * Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
(source: Nielsen Book Data)

• Introduction.- Problem Description.- Background.- Related Work.- The PLASTIC Algorithms.- Theoretical Analysis of PLASTIC.- Empirical Evaluation.- Discussion and Conclusion.
• (source: Nielsen Book Data)

This book is devoted to the encounter and interaction of agents such as robots with other agents and describes how they cooperate with their previously unknown teammates, forming an Ad Hoc team. It presents a new algorithm, PLASTIC, that allows agents to quickly adapt to new teammates by reusing knowledge learned from previous teammates. PLASTIC is instantiated in both a model-based approach, PLASTIC-Model and a policy-based approach, PLASTIC-Policy. In addition to reusing knowledge learned from previous teammates, PLASTIC also allows users to provide expert-knowledge and can use transfer learning (such as the new Two Stage Transfer algorithm) to quickly create models of new teammates when it has some information about its new teammates. The effectiveness of the algorithm is demonstrated on three domains, ranging from multi-armed bandits to simulated robot soccer games.
(source: Nielsen Book Data)

• A Study of Energy Reduction Strategies in Renewable Hybrid Grid.- Classification and Watermarking of Brain Tumor using Artificial and Convolutional Neural Networks.- A Proposal for a Deep Learning Model to Enhance Student Guidance and Reduce Dropout.- EduBot: An Unsupervised Domain-Specific Chatbot for Educational Institutions.- SQL Generation from Natural Language using Supervised Learning and Recurrent Neural Networks.- Toward Intelligent Solution to Identify Learner Attitude from Source Code.
• (source: Nielsen Book Data)

This book gathers selected papers from Artificial Intelligence and Industrial Applications (A2IA'2020), the first installment of an annual international conference organized by ENSAM-Meknes at Moulay Ismail University, Morocco. The 29 papers presented here were carefully reviewed and selected from 141 submissions by an international scientific committee. They address various aspects of artificial intelligence such as digital twin, multiagent systems, deep learning, image processing and analysis, control, prediction, modeling, optimization and design, as well as AI applications in industry, health, energy, agriculture, and education. The book is intended for AI experts, offering them a valuable overview and global outlook for the future, and highlights a wealth of innovative ideas and recent, important advances in AI applications, both of a foundational and practical nature. It will also appeal to non-experts who are curious about this timely and important subject.
(source: Nielsen Book Data)

• Introduction
• Monitoring of Weld Pool Surface with Active Vision
• Visual Sensing of 3D Weld Pool Geometry with Passive Vision Image
• Penetration prediction with machine learning models
• Penetration Control for Bead-on plate welding
• Penetration Detection of narrow U-groove welding
• Lack of fusion detection inside narrow U-groove
• Conclustions and recommendations.

This book describes the application of vision-sensing technologies in welding processes, one of the key technologies in intelligent welding manufacturing. Gas tungsten arc welding (GTAW) is one of the main welding techniques and has a wide range of applications in the manufacturing industry. As such, the book also explores the application of AI technologies, such as vision sensing and machine learning, in GTAW process sensing and feature extraction and monitoring, and presents the state-of-the-art in computer vision, image processing and machine learning to detect welding defects using non-destructive methods in order to improve welding productivity. Featuring the latest research from ORNL (Oak Ridge National Laboratory) using digital image correlation technology, this book will appeal to researchers, scientists and engineers in the field of advanced manufacturing.
(source: Nielsen Book Data)

• Cognitive Systems Introduction.- Component Science.- Architecture and Representations.- The Sensorimotor Approach in CoSy: The Example of Dimensionality Reduction.- Categorical Perception.- Semantic Modelling of Space.- Planning and Failure Detection.- Multi-modal Learning.- Situated Dialogue Processing for Human-Robot Interaction.- Integration and Systems.- The PlayMate System.- The Explorer System.- Lessons Learnt from Scenario-Based Integration.- Summary and Outlook.- Cross-Disciplinary Reflections: Philosophical Robotics.- Lessons and Outlook.
• (source: Nielsen Book Data)

Design of cognitive systems for assistance to people poses a major challenge to the fields of robotics and artificial intelligence. The Cognitive Systems for Cognitive Assistance (CoSy) project was organized to address the issues of i) theoretical progress on design of cognitive systems ii) methods for implementation of systems and iii) empirical studies to further understand the use and interaction with such systems. To study, design and deploy cognitive systems there is a need to considers aspects of systems design, embodiment, perception, planning and error recovery, spatial insertion, knowledge acquisition and machine learning, dialog design and human robot interaction and systems integration. The CoSy project addressed all of these aspects over a period of four years and across two different domains of application - exploration of space and task / knowledge acquisition for manipulation. The present volume documents the results of the CoSy project. The CoSy project was funded by the European Commission as part of the Cognitive Systems Program within the 6th Framework Program.
(source: Nielsen Book Data)

• Foreword
• 1. The Semantic Web
• 2. Web Knowledge
• 3. Reactive Agents
• 4. Practical Reasoning and Deductive Agents
• 5. Reasoning on the Web
• 6. Agent Communication
• 7. Semantic Web Services
• 8. Conclusions
• Index.
• (source: Nielsen Book Data)

This highly topical text considers the construction of the next generation of the Web, called the Semantic Web. This will enable computers to automatically consume Web-based information, overcoming the human-centric focus of the Web as it stands at present, and expediting the construction of a whole new class of knowledge-based applications that will intelligently utilise Web content. The text is structured into three main sections on knowledge representation techniques, reasoning with multi-agent systems, and knowledge services. For each of these topics, the text provides an overview of the state-of-the-art techniques and the popular standards that have been defined. Numerous small programming examples are given, which demonstrate how the benefits of the Semantic Web technologies can be realised at the present time. The main theoretical results underlying each of the technologies are presented, and the main problems and research issues which remain are summarised. Based on a course on 'Multi-Agent Systems and the Semantic Web' taught at the University of Edinburgh, this text is ideal for final-year undergraduate and graduate students in Mathematics, Computer Science, Artificial Intelligence, and Logic and researchers interested in Multi-Agent Systems and the Semantic Web.
(source: Nielsen Book Data)

• A Fuzzy Ontology Based Approach To Support Product Eco-Design.-
• A Genetic-based SVM Approach for Quality Data Classification.- Towards a platform to implement an intelligent and predictive maintenance in the context of industry.- Towards a Prediction Analysis in an Industrial Context.- Methodology for Implementation of Industry . Technologies in Supply Chain for SMEs.- A Deep Reinforcement Learning DRL Decision Model For Heating Process Parameters Identification In Automotive Glass Manufacturing
• Analytic Hierarchy Process AHP for supply chain . risks management.- SmartDFRelevance: a Holonic Agent based System for Engineering Industrial Projects in Concurrent Engineering Context.- A Cyber-Physical Warehouse Management System Architecture in an.- Industry . context.- PLM and Smart technologies for product and supply chain design.- Production systems simulation considering Non-Productive Times and Human Factors.- Distributed and Embedded System to Control Traffic Collision Based on Artificial Intelligence.- The Emergence and Decision Support in Complex System with Fuzzy Logic Control.- Markov Decision Processes with Discounted Costs over a Finite Horizon: Action Elimination.- Robust adaptive fuzzy path tracking control of Differential Wheeled Mobile Robot.- Deep Learning approach for Automated Guided Vehicle System.- Path Planning Using Particle Swarm Optimization And Fuzzy Logic
• Prediction of Robot localization states using Hidden Markov Models.- A New Approach for Multi-Agent Reinforcement Learning.- Recommender System for Most Relevant k Pick-Up Points.- Feature Detection and Tracking for Visual Effects: Augmented Reality and Video Stabilization.- Spectral image recognition using artificial dynamic neural network in information resonance mode.- U-Net Based Model for Obfuscated Human Faces Reconstruction.- A Machine Learning Assistant for Choosing Operators and Tuning Their Parameters in Image Processing Tasks.- Convergence and parameters setting of continuous Hopfield neural networks applied to image restoration problem.- The Ibn Battouta Air Traffic Control Corpus with Real Life ADS-B and METAR data.- Weed Recognition System For Low-Land Rice Precision Farming Using Deep Learning Approach.- A Comparative Study Between Mixture and Stepwise Regression to Model The Parameters Of The Composting Process.- Deep Learning Based Sponge Gourd Diseases Recognition For Commercial Cultivation in Bangladesh.- Exploitation of vegetation indices and Random Forest for cartography of rosemary cover: application to Gourrama region, Morocco.
• (source: Nielsen Book Data)

This book gathers the refereed proceedings of the Artificial Intelligence and Industrial Applications (A2IA'2020), the first installment of an annual international conference organized by the ENSAM-Meknes at Moulay Ismail University, Morocco. The 30 papers presented here were carefully reviewed and selected from 141 submissions by an international scientific committee. They address various aspects of artificial intelligence such as smart manufacturing, smart maintenance, smart supply chain management, supervised learning, unsupervised learning, reinforcement learning, graph-based and semi-supervised learning, neural networks, deep learning, planning and optimization, and other AI applications. The book is intended for AI experts, offering them a valuable overview of the status quo and a global outlook for the future, with many new and innovative ideas and recent important developments in AI applications, both of a foundational and practical nature. It will also appeal to non-experts who are curious about this timely and important subject.
(source: Nielsen Book Data)

• Pattern Recognition Principles.- Patterns of Go gaming by Ising model.- A Novel Criterion to Obtain the Best Feature Subset from Filter Ranking Methods.- Class-specific Reducts vs. Classic Reducts in a Rule-based Classifier: A Case Study.- On the Construction of a Specific Algebra for Composing Tonal Counterpoint.- The Impact of Basic Matrix Dimension on the Performance of Algorithms for Computing Typical Testors.- Fast Convex Hull by a Geometric Approach.- An Experimental Study on Ant Colony Optimization Hyper-heuristics for Solving the Knapsack Problem.- A Linear Time Algorithm for Computing #2SAT for Outerplanar 2-CNF Formulas.- Improving the List of Clustered Permutation on Metric Spaces for Similarity Searching on Secondary Memory.- Modelling 3-Coloring of Polygonal Trees via Incremental Satisfiability.- Deep Learning, Neural Networks and Associative Memories.- Performance Analysis of Deep Neural Networks for Classification of Gene-Expression Microarrays.- Extreme Points of Convex Polytopes Derived from Lattice Autoassociative Memories.- A Comparison of Deep Neural Network Algorithms for Recognition of EEG Motor Imagery Signals.- Learning Word and Sentence Embeddings using a Generative Convolutional Network.- Dense Captioning of Natural Scenes in Spanish.- Automated Detection of Hummingbirds in Images: a Deep Learning Approach.- Data Mining.- Patterns in Poor Learning Engagement in Students While They are Solving Mathematics Exercises in an Affective Tutoring System Related to Frustration.- Pattern Discovery in Mixed Data Bases.- Image Clustering based on Frequent Approximate Subgraph Mining.- Validation of Semantic Relation of Synonymy in Domain Ontologies using Lexico-Syntactic Patterns and Acronyms.- Computer Vision.- Scene Text Segmentation Based on Local Image Phase Information and MSER Method.- A Lightweight Library for Augmented Reality Applications.- Point Set Matching with Order Type.- Including Foreground and Background Information in Maya Hieroglyph Representation.- A Fast Algorithm for Robot Localization using Multiple Sensing Units.- Improving Breast Mass Classification through Kernel Methods and the Fusion of Clinical Data and Image Descriptors.- An Improved Stroke Width Transform to Detect Race Bib Numbers.- Scaled CCR Histogram for Scale-invariant Texture Classification.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 10th Mexican Conference on Pattern Recognition, MCPR 2018, held in Puebla, Mexico, in June 2018. The 28 papers presented in this volume were carefully reviewed and selected from 44 submissions. They were organized in topical sections named: pattern recognition principles; deep learning, neural networks and associative memories; data mining; and computer vision. .
(source: Nielsen Book Data)

• Discrete-Time Signals and Circuits Fundamentals.- Introduction to Adaptive Signal Processing.- Optimal Linear Filter Theory.- Least Squares Method.- First Order Adaptive Algorithms.- Second Order Adaptive Algorithms.- Block and Transform Domain Algorithms.- Linear Prediction and Recursive Order Algorithms.- Discrete Space-Time Filtering.- Appendix A: Linear Algebra Foundation.- Appendix B: Non Linear Programming Fundamentals.-Appendix C: Random Variables, Stochastic Processes and Estimation Theory.
• (source: Nielsen Book Data)

This book is an accessible guide to adaptive signal processing methods that equips the reader with advanced theoretical and practical tools for the study and development of circuit structures and provides robust algorithms relevant to a wide variety of application scenarios. Examples include multimodal and multimedia communications, the biological and biomedical fields, economic models, environmental sciences, acoustics, telecommunications, remote sensing, monitoring and in general, the modeling and prediction of complex physical phenomena. The reader will learn not only how to design and implement the algorithms but also how to evaluate their performance for specific applications utilizing the tools provided. While using a simple mathematical language, the employed approach is very rigorous. The text will be of value both for research purposes and for courses of study.
(source: Nielsen Book Data)

• Cognitive Radio Architecture
• Collaborative Adaptation
• Signaling Options
• Agent Communication Language
• An Example: Collaborative Link Adaptation
• Knowledge and Inference
• Cognitive Radio Ontology
• Implementation of Collaborative Link Optimization
• Evaluations.

This book provides an introduction to software-defined radio and cognitive radio, along with methodologies for applying knowledge representation, semantic web, logic reasoning and artificial intelligence to cognitive radio, enabling autonomous adaptation and flexible signaling. Readers from the wireless communications and software-defined radio communities will use this book as a reference to extend software-defined radio to cognitive radio, using the semantic technology described. Readers with a background in semantic web and artificial intelligence will find in this book the application of semantic web and artificial intelligence technologies to wireless communications. For readers in networks and network management, this book presents a new approach to enable interoperability, collaborative optimization and flexible adaptation of network components. Provides a comprehensive ontology covering the core concepts of wireless communications using a formal language; Presents the technical realization of using a formal language to exchange control messages, achieving autonomous adaptation of a communications link; Describes an architecture that enables radios to use a formal language to send inquiries and requests to other nodes, accept, interpret and execute such requests using their local policies and modify their own parameters.

• Foreword. Introduction. Part I: Measuring Success. 1 Why Understanding Your Web Traffic Is Important to Your Business. 2 Available Methodologies. 3 Where Google Analytics Fits. Part II: Using Google Analytics Reports. 4 Using the Google Analytics Interface. 5 Top 10 Reports Explained Part III: Implementing Google Analytics. 6 Getting Started. 7 Advanced Implementation. 8 Best Practices Configuration Guide. 9 Google Analytics Hacks. Part IV: Using Visitor Data to Drive Website Improvement. 10 Focusing on Key Performance Indicators. 11 Real-World Tasks. 12 Integrating Google Analytics Data with Third-Party Systems. Appendix A Regular Expression Overview. Understanding the Fundamentals. Regex Examples. Appendix B Useful Tools. Tools to Audit Your GATC Deployment. Firefox Add-ons. Desktop Helper Applications. Appendix C Recommended Further Reading. Books on Web Analytics and Related Areas. Web Resources. Blog Roll for Web Analytics. Index.
• (source: Nielsen Book Data)

Packed with insider tips and tricks, this how-to guide is fully revised to cover the latest version of Google Analytics and shows you how to implement proven Web analytics methods and concepts. This second edition of the bestselling Advanced Web Metrics with Google Analytics is the perfect book for marketers, vendors, consultants, and Webmasters who want to learn the installation, configuration, tracking techniques, and best practices of Google Analytics.* Google Analytics is a free tool that measures Web site effectiveness and helps users better understand how web site performance; this book is a detailed usage guide written by one of the software's original creators* Explains what filters keep data accurate, how to measure Flash usage and tag for e-mail marketing, and what visitor segmentation provides the most useful feedback* Examines principles and practices of Web analytics, then shows how to use GA's reports and how to track dynamic Web pages, banners, outgoing links, and contact forms* Discusses advanced setups for configuring goals and filters, how to integrate GA with third-party systems, and how to leverage the new API Advanced Web Metrics with Google Analytics, Second Edition is valuable for both novice and experienced users of Google Analytics.
(source: Nielsen Book Data)

• PREFACE. NOTATION.
• 1 Introduction. 1.1 Learning and Statistical Estimation. 1.2 Statistical Dependency and Causality. 1.3 Characterization of Variables. 1.4 Characterization of Uncertainty. 1.5 Predictive Learning versus Other Data Analytical Methodologies.
• 2 Problem Statement, Classical Approaches, and Adaptive Learning. 2.1 Formulation of the Learning Problem. 2.1.1 Objective of Learning. 2.1.2 Common Learning Tasks. 2.1.3 Scope of the Learning Problem Formulation. 2.2 Classical Approaches. 2.2.1 Density Estimation. 2.2.2 Classification. 2.2.3 Regression. 2.2.4 Solving Problems with Finite Data. 2.2.5 Nonparametric Methods. 2.2.6 Stochastic Approximation. 2.3 Adaptive Learning: Concepts and Inductive Principles. 2.3.1 Philosophy, Major Concepts, and Issues. 2.3.2 A Priori Knowledge and Model Complexity. 2.3.3 Inductive Principles. 2.3.4 Alternative Learning Formulations. 2.4 Summary.
• 3 Regularization Framework. 3.1 Curse and Complexity of Dimensionality. 3.2 Function Approximation and Characterization of Complexity. 3.3 Penalization. 3.3.1 Parametric Penalties. 3.3.2 Nonparametric Penalties. 3.4 Model Selection (Complexity Control). 3.4.1 Analytical Model Selection Criteria. 3.4.2 Model Selection via Resampling. 3.4.3 Bias-Variance Tradeoff. 3.4.4 Example of Model Selection. 3.4.5 Function Approximation versus Predictive Learning. 3.5 Summary.
• 4 Statistical Learning Theory. 4.1 Conditions for Consistency and Convergence of ERM. 4.2 Growth Function and VC Dimension. 4.2.1 VC Dimension for Classification and Regression Problems. 4.2.2 Examples of Calculating VC Dimension. 4.3 Bounds on the Generalization. 4.3.1 Classification. 4.3.2 Regression. 4.3.3 Generalization Bounds and Sampling Theorem. 4.4 Structural Risk Minimization. 4.4.1 Dictionary Representation. 4.4.2 Feature Selection. 4.4.3 Penalization Formulation. 4.4.4 Input Preprocessing. 4.4.5 Initial Conditions for Training Algorithm. 4.5 Comparisons of Model Selection for Regression. 4.5.1 Model Selection for Linear Estimators. 4.5.2 Model Selection for k-Nearest-Neighbor Regression. 4.5.3 Model Selection for Linear Subset Regression. 4.5.4 Discussion. 4.6 Measuring the VC Dimension. 4.7 VC Dimension, Occam's Razor, and Popper's Falsifiability. 4.8 Summary and Discussion.
• 5 Nonlinear Optimization Strategies. 5.1 Stochastic Approximation Methods. 5.1.1 Linear Parameter Estimation. 5.1.2 Backpropagation Training of MLP Networks. 5.2 Iterative Methods. 5.2.1 EM Methods for Density Estimation. 5.2.2 Generalized Inverse Training of MLP Networks. 5.3 Greedy Optimization. 5.3.1 Neural Network Construction Algorithms. 5.3.2 Classification and Regression Trees. 5.4 Feature Selection, Optimization, and Statistical Learning Theory. 5.5 Summary.
• 6 Methods for Data Reduction and Dimensionality Reduction. 6.1 Vector Quantization and Clustering. 6.1.1 Optimal Source Coding in Vector Quantization. 6.1.2 Generalized Lloyd Algorithm. 6.1.3 Clustering. 6.1.4 EM Algorithm for VQ and Clustering. 6.1.5 Fuzzy Clustering. 6.2 Dimensionality Reduction: Statistical Methods. 6.2.1 Linear Principal Components. 6.2.2 Principal Curves and Surfaces. 6.2.3 Multidimensional Scaling. 6.3 Dimensionality Reduction: Neural Network Methods. 6.3.1 Discrete Principal Curves and Self-Organizing Map Algorithm. 6.3.2 Statistical Interpretation of the SOM Method. 6.3.3 Flow-Through Version of the SOM and Learning Rate Schedules. 6.3.4 SOM Applications and Modifications. 6.3.5 Self-Supervised MLP. 6.4 Methods for Multivariate Data Analysis. 6.4.1 Factor Analysis. 6.4.2 Independent Component Analysis. 6.5 Summary.
• 7 Methods for Regression. 7.1 Taxonomy: Dictionary versus Kernel Representation. 7.2 Linear Estimators. 7.2.1 Estimation of Linear Models and Equivalence of Representations. 7.2.2 Analytic Form of Cross-Validation. 7.2.3 Estimating Complexity of Penalized Linear Models. 7.2.4 Nonadaptive Methods. 7.3 Adaptive Dictionary Methods. 7.3.1 Additive Methods and Projection Pursuit Regression. 7.3.2 Multilayer Perceptrons and Backpropagation. 7.3.3 Multivariate Adaptive Regression Splines. 7.3.4 Orthogonal Basis Functions and Wavelet Signal Denoising. 7.4 Adaptive Kernel Methods and Local Risk Minimization. 7.4.1 Generalized Memory-Based Learning. 7.4.2 Constrained Topological Mapping. 7.5 Empirical Studies. 7.5.1 Predicting Net Asset Value (NAV) of Mutual Funds. 7.5.2 Comparison of Adaptive Methods for Regression. 7.6 Combining Predictive Models. 7.7 Summary.
• 8 Classification. 8.1 Statistical Learning Theory Formulation. 8.2 Classical Formulation. 8.2.1 Statistical Decision Theory. 8.2.2 Fisher's Linear Discriminant Analysis. 8.3 Methods for Classification. 8.3.1 Regression-Based Methods. 8.3.2 Tree-Based Methods. 8.3.3 Nearest-Neighbor and Prototype Methods. 8.3.4 Empirical Comparisons. 8.4 Combining Methods and Boosting. 8.4.1 Boosting as an Additive Model. 8.4.2 Boosting for Regression Problems. 8.5 Summary.
• 9 Support Vector Machines. 9.1 Motivation for Margin-Based Loss. 9.2 Margin-Based Loss, Robustness, and Complexity Control. 9.3 Optimal Separating Hyperplane. 9.4 High-Dimensional Mapping and Inner Product Kernels. 9.5 Support Vector Machine for Classification. 9.6 Support Vector Implementations. 9.7 Support Vector Regression. 9.8 SVM Model Selection. 9.9 Support Vector Machines and Regularization. 9.10 Single-Class SVM and Novelty Detection. 9.11 Summary and Discussion.
• 10 Noninductive Inference and Alternative Learning Formulations. 10.1 Sparse High-Dimensional Data. 10.2 Transduction. 10.3 Inference Through Contradictions. 10.4 Multiple-Model Estimation. 10.5 Summary.
• 11 Concluding Remarks. Appendix A: Review of Nonlinear Optimization. Appendix B: Eigenvalues and Singular Value Decomposition. References. Index.
• (source: Nielsen Book Data)

An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.
(source: Nielsen Book Data)

• PREFACE. NOTATION.
• 1 Introduction. 1.1 Learning and Statistical Estimation. 1.2 Statistical Dependency and Causality. 1.3 Characterization of Variables. 1.4 Characterization of Uncertainty. 1.5 Predictive Learning versus Other Data Analytical Methodologies.
• 2 Problem Statement, Classical Approaches, and Adaptive Learning. 2.1 Formulation of the Learning Problem. 2.1.1 Objective of Learning. 2.1.2 Common Learning Tasks. 2.1.3 Scope of the Learning Problem Formulation. 2.2 Classical Approaches. 2.2.1 Density Estimation. 2.2.2 Classification. 2.2.3 Regression. 2.2.4 Solving Problems with Finite Data. 2.2.5 Nonparametric Methods. 2.2.6 Stochastic Approximation. 2.3 Adaptive Learning: Concepts and Inductive Principles. 2.3.1 Philosophy, Major Concepts, and Issues. 2.3.2 A Priori Knowledge and Model Complexity. 2.3.3 Inductive Principles. 2.3.4 Alternative Learning Formulations. 2.4 Summary.
• 3 Regularization Framework. 3.1 Curse and Complexity of Dimensionality. 3.2 Function Approximation and Characterization of Complexity. 3.3 Penalization. 3.3.1 Parametric Penalties. 3.3.2 Nonparametric Penalties. 3.4 Model Selection (Complexity Control). 3.4.1 Analytical Model Selection Criteria. 3.4.2 Model Selection via Resampling. 3.4.3 Bias-Variance Tradeoff. 3.4.4 Example of Model Selection. 3.4.5 Function Approximation versus Predictive Learning. 3.5 Summary.
• 4 Statistical Learning Theory. 4.1 Conditions for Consistency and Convergence of ERM. 4.2 Growth Function and VC Dimension. 4.2.1 VC Dimension for Classification and Regression Problems. 4.2.2 Examples of Calculating VC Dimension. 4.3 Bounds on the Generalization. 4.3.1 Classification. 4.3.2 Regression. 4.3.3 Generalization Bounds and Sampling Theorem. 4.4 Structural Risk Minimization. 4.4.1 Dictionary Representation. 4.4.2 Feature Selection. 4.4.3 Penalization Formulation. 4.4.4 Input Preprocessing. 4.4.5 Initial Conditions for Training Algorithm. 4.5 Comparisons of Model Selection for Regression. 4.5.1 Model Selection for Linear Estimators. 4.5.2 Model Selection for k-Nearest-Neighbor Regression. 4.5.3 Model Selection for Linear Subset Regression. 4.5.4 Discussion. 4.6 Measuring the VC Dimension. 4.7 VC Dimension, Occam's Razor, and Popper's Falsifiability. 4.8 Summary and Discussion.
• 5 Nonlinear Optimization Strategies. 5.1 Stochastic Approximation Methods. 5.1.1 Linear Parameter Estimation. 5.1.2 Backpropagation Training of MLP Networks. 5.2 Iterative Methods. 5.2.1 EM Methods for Density Estimation. 5.2.2 Generalized Inverse Training of MLP Networks. 5.3 Greedy Optimization. 5.3.1 Neural Network Construction Algorithms. 5.3.2 Classification and Regression Trees. 5.4 Feature Selection, Optimization, and Statistical Learning Theory. 5.5 Summary.
• 6 Methods for Data Reduction and Dimensionality Reduction. 6.1 Vector Quantization and Clustering. 6.1.1 Optimal Source Coding in Vector Quantization. 6.1.2 Generalized Lloyd Algorithm. 6.1.3 Clustering. 6.1.4 EM Algorithm for VQ and Clustering. 6.1.5 Fuzzy Clustering. 6.2 Dimensionality Reduction: Statistical Methods. 6.2.1 Linear Principal Components. 6.2.2 Principal Curves and Surfaces. 6.2.3 Multidimensional Scaling. 6.3 Dimensionality Reduction: Neural Network Methods. 6.3.1 Discrete Principal Curves and Self-Organizing Map Algorithm. 6.3.2 Statistical Interpretation of the SOM Method. 6.3.3 Flow-Through Version of the SOM and Learning Rate Schedules. 6.3.4 SOM Applications and Modifications. 6.3.5 Self-Supervised MLP. 6.4 Methods for Multivariate Data Analysis. 6.4.1 Factor Analysis. 6.4.2 Independent Component Analysis. 6.5 Summary.
• 7 Methods for Regression. 7.1 Taxonomy: Dictionary versus Kernel Representation. 7.2 Linear Estimators. 7.2.1 Estimation of Linear Models and Equivalence of Representations. 7.2.2 Analytic Form of Cross-Validation. 7.2.3 Estimating Complexity of Penalized Linear Models. 7.2.4 Nonadaptive Methods. 7.3 Adaptive Dictionary Methods. 7.3.1 Additive Methods and Projection Pursuit Regression. 7.3.2 Multilayer Perceptrons and Backpropagation. 7.3.3 Multivariate Adaptive Regression Splines. 7.3.4 Orthogonal Basis Functions and Wavelet Signal Denoising. 7.4 Adaptive Kernel Methods and Local Risk Minimization. 7.4.1 Generalized Memory-Based Learning. 7.4.2 Constrained Topological Mapping. 7.5 Empirical Studies. 7.5.1 Predicting Net Asset Value (NAV) of Mutual Funds. 7.5.2 Comparison of Adaptive Methods for Regression. 7.6 Combining Predictive Models. 7.7 Summary.
• 8 Classification. 8.1 Statistical Learning Theory Formulation. 8.2 Classical Formulation. 8.2.1 Statistical Decision Theory. 8.2.2 Fisher's Linear Discriminant Analysis. 8.3 Methods for Classification. 8.3.1 Regression-Based Methods. 8.3.2 Tree-Based Methods. 8.3.3 Nearest-Neighbor and Prototype Methods. 8.3.4 Empirical Comparisons. 8.4 Combining Methods and Boosting. 8.4.1 Boosting as an Additive Model. 8.4.2 Boosting for Regression Problems. 8.5 Summary.
• 9 Support Vector Machines. 9.1 Motivation for Margin-Based Loss. 9.2 Margin-Based Loss, Robustness, and Complexity Control. 9.3 Optimal Separating Hyperplane. 9.4 High-Dimensional Mapping and Inner Product Kernels. 9.5 Support Vector Machine for Classification. 9.6 Support Vector Implementations. 9.7 Support Vector Regression. 9.8 SVM Model Selection. 9.9 Support Vector Machines and Regularization. 9.10 Single-Class SVM and Novelty Detection. 9.11 Summary and Discussion.
• 10 Noninductive Inference and Alternative Learning Formulations. 10.1 Sparse High-Dimensional Data. 10.2 Transduction. 10.3 Inference Through Contradictions. 10.4 Multiple-Model Estimation. 10.5 Summary.
• 11 Concluding Remarks. Appendix A: Review of Nonlinear Optimization. Appendix B: Eigenvalues and Singular Value Decomposition. References. Index.
• (source: Nielsen Book Data)

An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.
(source: Nielsen Book Data)

• Theoretical computer science.- Artificial intelligence.- Microeconomics.- Problems at the intersection of computation, game theory and economics. .
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed proceedings of the 13th International Conference on Web and Internet Economics, WINE 2017, held in Bangalore, India, in December 2017. The 28 full and 6 short papers presented were carefully reviewed and selected from 89 submissions. The papers reflect the work of researchers in theoretical computer science, artificial intelligence, and microeconomics who have joined forces to tackle problems at the intersection of computation, game theory and economics. .
(source: Nielsen Book Data)

• Chapter 1. Introduction.-
• Chapter 2. Sparse Linear Model.-
• Chapter 3. Blind Demixing.-
• Chapter 4. Sparse Blind Demixing.-
• Chapter 5. Shuffled Linear Regression.-
• Chapter 6. Learning Augmented Methods.-
• Chapter 7. Conclusions and Discussions.-
• Chapter 8. Appendix. .
• (source: Nielsen Book Data)

• 4.5.1 Optimization on Product Manifolds
• 4.5.2 Smoothed Riemannian Optimization
• 4.5.3 Simulation Results
• 4.6 Summary
• References
• 5 Shuffled Linear Regression
• 5.1 Joint Data Decoding and Device Identification
• 5.2 Problem Formulation
• 5.3 Maximum Likelihood Estimation Based Approaches
• 5.3.1 Sorting Based Algorithms
• 5.3.2 Approximation Algorithm
• 5.4 Algebraic-Geometric Approach
• 5.4.1 Eliminating Π via Symmetric Polynomials
• 5.4.2 Theoretical Analysis
• 5.4.2.1 Exact Data
• 5.4.2.2 Corrupted Data
• 5.4.3 Algebraically Initialized Expectation-Maximization

The recent developments in wireless communications, networking, and embedded systems have driven various innovative Internet of Things (IoT) applications, e.g., smart cities, mobile healthcare, autonomous driving and drones. A common feature of these applications is the stringent requirements for low-latency communications. Considering the typical small payload size of IoT applications, it is of critical importance to reduce the size of the overhead message, e.g., identification information, pilot symbols for channel estimation, and control data. Such low-overhead communications also help to improve the energy efficiency of IoT devices. Recently, structured signal processing techniques have been introduced and developed to reduce the overheads for key design problems in IoT networks, such as channel estimation, device identification, and message decoding. By utilizing underlying system structures, including sparsity and low rank, these methods can achieve significant performance gains. This book provides an overview of four general structured signal processing models: a sparse linear model, a blind demixing model, a sparse blind demixing model, and a shuffled linear model, and discusses their applications in enabling low-overhead communications in IoT networks. Further, it presents practical algorithms based on both convex and nonconvex optimization approaches, as well as theoretical analyses that use various mathematical tools.
(source: Nielsen Book Data)

• Swarm and multi-robotic systems.- Human-robot interaction.- Robotic learning and imitation.- Robot navigation, planning and safety.- Humanoid and bio-inspired robots.- Mobile robots and vehicles.- Robot testing and design.- Detection and recognition.- Learning and adaptive behaviours.- Interaction.- Soft and reconfigurable robots.- Service and industrial robots.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 18th Annual Conference on Towards Autonomous Robotics, TAROS 2017, held in Guildford, UK, in July 2017. The 43 revised full papers presented together with 13 short papers were carefully reviewed and selected from 66 submissions. The papers discuss robotics research drawn from a wide and diverse range of topics, such as swarm and multi-robotic systems; human-robot interaction; robotic learning and imitation; robot navigation, planning and safety; humanoid and bio-inspired robots; mobile robots and vehicles; robot testing and design; detection and recognition; learning and adaptive behaviours; interaction; soft and reconfigurable robots; and service and industrial robots.
(source: Nielsen Book Data)

• Introduction.- Background and Literature Survey.- VLSI Architecture for Fast Three Step Search Algorithm.- Parallel Architecture for Successive Elimination Block Matching Algorithm.- Fast One-Bit Transformation Architectures.- Efficient Pixel Truncation Algorithm and Architecture.- Introduction to Scalable Image and Video Coding.- Forward Plans.- 8 Forward Plans.- A Matlab Programs.- B Verilog Modules.
• (source: Nielsen Book Data)

The need of video compression in the modern age of visual communication cannot be over-emphasized. This monograph will provide useful information to the postgraduate students and researchers who wish to work in the domain of VLSI design for video processing applications. In this book, one can find an in-depth discussion of several motion estimation algorithms and their VLSI implementation as conceived and developed by the authors. It records an account of research done involving fast three step search, successive elimination, one-bit transformation and its effective combination with diamond search and dynamic pixel truncation techniques. Two appendices provide a number of instances of proof of concept through Matlab and Verilog program segments. In this aspect, the book can be considered as first of its kind. The architectures have been developed with an eye to their applicability in everyday low-power handheld appliances including video camcorders and smartphones.
(source: Nielsen Book Data)

• Introduction Motivation Shape recognition Proposed method Objectives Assumptions and input data Book organization
• Preliminary knowledge Statistics Probability Random variable Expected value Variance and deviation Covariance and correlation Moment-generating function Fourier transform Structural and syntactic pattern recognition Introduction Grammar-based passing method Graph-based matching methods Neural network Architecture Learning process Summary
• Review of shape recognition techniques 2D shape recognition Shape representation Shape recognition approaches Logo recognition Statistical approach Syntactic/structural approach Neural network Hybrid approach Polygonal approximation Indexing Matching Distance measure Hausdorff distance Summary
• System overview Preprocessing Polygonal approximation Indexing Matching
• Polygonal approximation Feature point detection overview Dynamic two-strip algorithm The proposed method Results Comparison with other methods Summary
• Logo indexing Normalization Indexing Reference angle indexing (filter 1) Line orientation indexing (filters 2 and 3) Experimental results Summary
• Logo matching Hausdorff distance Modified LHD (MLHD) Experimental results Matching results Degradation analysis Results analysis with respect to the LHD and the MHD Discussion and comparison with other methods Summary
• Applications Mobile visual search with GetFugu Using logo recognition for anti-phishing and Internet brand monitoring The LogoTrace library Real-time vehicle logo recognition Summary
• Conclusion Book summary Contribution Future work Book conclusion References
• Appendix Test images Appendix Results of feature point detection
• Index.
• (source: Nielsen Book Data)

Used by companies, organizations, and even individuals to promote recognition of their brand, logos can also act as a valuable means of identifying the source of a document. E-business applications can retrieve and catalog products according to their logos. Governmental agencies can easily inspect goods using smart mobile devices that use logo recognition techniques. However, because logos are two-dimensional shapes of varying complexity, the recognition process can be challenging. Although promising results have been found for clean logos, they have not been as robust for noisy logos. Logo Recognition: Theory and Practice is the first book to focus on logo recognition, especially under noisy conditions. Beginning with an introduction to fundamental concepts and methods in pattern and shape recognition, it surveys advances in logo recognition. The authors also propose a new logo recognition system that can be used under adverse conditions such as broken lines, added noise, and occlusion. The proposed system introduces a novel polygonal approximation, a robust indexing scheme, and a new line segment Hausdorff distance (LHD) matching method that can handle more distortion and transformation types than previous techniques. In the first stage, raw logos are transformed into normalized line segment maps. In the second stage, effective line pattern features are used to index the database to generate a moderate number of likely models. In the third stage, an improved LHD measure screens and generates the best matches. A comprehensive overview of logo recognition, the book also presents successful applications of the technology and suggests directions for future research.
(source: Nielsen Book Data)

• 1. Introduction.-
• 2. Machine Learning Methods for Spatial and Temporal Parameter Estimation.-
• 3. Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms.-
• 4. Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing and Biomedicine.-
• 5. Advances in Deep Learning for Hyperspectral Image Analysis - Addressing Challenges Arising in Practical Imaging Scenarios.-
• 6. Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis.-
• 7. Supervised, Semi Supervised and Unsupervised Learning for Hyperspectral Regression.-
• 8. Sparsity-based Methods for Classification.-
• 9. Multiple Kernel Learning for Hyperspectral Image Classification.-
• 10. Low Dimensional Manifold Model in Hyperspectral Image Reconstruction.-
• 11. Deep Sprase Band Selection for Hyperspectral Face Recognition.-
• 12. Detection of Large-Scale and Anomalous Changes.-
• 13. Recent Advances in Hyperspectral Unmixing Using Sparse Techniques and Deep Learning.-
• 14. Chapter Hyperspectral-Multispectral Image Fusion Enhancement Based on Deep Learning.-
• 15. Automatic Target Detection for Sparse Hyperspectral Images.
• (source: Nielsen Book Data)

This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful. .
(source: Nielsen Book Data)

• Low-level preprocessing, surveillance systems
• Tracking, robotics
• Identification, detection, recognition
• Behavior, activities, crowd analysis.

This book constitutes the refereed proceedings of the 4th Chinese Conference, IVS 2016, held in Beijing, China, in October 2016. The 19 revised full papers presented were carefully reviewed and selected from 45 submissions. The papers are organized in topical sections on low-level preprocessing, surveillance systems; tracking, robotics; identification, detection, recognition; behavior, activities, crowd analysis.

• Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces / Muhammad Muzzamil Luqman, Jean-Yves Ramel and Josep Lladós
• Feature Grouping and Selection Over an Undirected Graph / Sen Yang, Lei Yuan, Ying-Cheng Lai, Xiaotong Shen and Peter Wonka, et al.
• Median Graph Computation by Means of Graph Embedding into Vector Spaces / Miquel Ferrer, Itziar Bardají, Ernest Valveny, Dimosthenis Karatzas and Horst Bunke
• Patch Alignment for Graph Embedding / Yong Luo, Dacheng Tao and Chao Xu
• Improving Classifications Through Graph Embeddings / Anirban Chatterjee, Sanjukta Bhowmick and Padma Raghavan
• Learning with ℓ1-Graph for High Dimensional Data Analysis / Jianchao Yang, Bin Cheng, Shuicheng Yan, Yun Fu and Thomas Huang
• Graph-Embedding Discriminant Analysis on Riemannian Manifolds for Visual Recognition / Sareh Shirazi, Azadeh Alavi, Mehrtash T. Harandi and Brian C. Lovell
• A Flexible and Effective Linearization Method for Subspace Learning / Feiping Nie, Dong Xu, Ivor W. Tsang and Changshui Zhang
• A Multi-graph Spectral Framework for Mining Multi-source Anomalies / Jing Gao, Nan Du, Wei Fan, Deepak Turaga and Srinivasan Parthasarathy, et al.
• Graph Embedding for Speaker Recognition / Z.N. Karam and W.M. Campbell.₉

Graph Embedding for Pattern Analysis covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the latest advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph, and graph in vector spaces. Real-world applications of these theories are spanned broadly in dimensionality reduction, subspace learning, manifold learning, clustering, classification, and feature selection. A selective group of experts contribute to different chapters of this book which provides a comprehensive perspective of this field.

• 1. The vision of sociable robots
• 2. Robot in society: a question of interface
• 3. Insights from developmental psychology
• 4. Designing sociable robots
• 5. The physical robot
• 6. The vision system
• 7. The auditory system
• 8. The motivation system
• 9. The behavior system
• 10. Facial animation and expression
• 11. Expressive vocalization system
• 12. Social constraints on animate vision
• 13. Grand challenges of building sociable robots.

Cynthia Breazeal here presents her vision of the sociable robot of the future, a synthetic creature and not merely a sophisticated tool. A sociable robot will be able to understand us, to communicate and interact with us, to learn from us and grow with us. It will be socially intelligent in a humanlike way. Eventually sociable robots will assist us in our daily lives, as collaborators and companions. Because the most successful sociable robots will share our social characteristics, the effort to make sociable robots is also a means for exploring human social intelligence and even what it means to be human. Breazeal defines the key components of social intelligence for these machines and offers a framework and set of design issues for their realization. Much of the book focuses on a nascent sociable robot she designed named Kismet. Breazeal offers a concrete implementation for Kismet, incorporating insights from the scientific study of animals and people, as well as from artistic disciplines such as classical animation. This blending of science, engineering, and art creates a lifelike quality that encourages people to treat Kismet as a social creature rather than just a machine. The book includes a CD-ROM that shows Kismet in action.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Mammographic Mass Segmentation Using Fuzzy C-means and Decision Trees
• 1 Introduction
• 2 Segmentation of Masses in Mammograms Using Fuzzy C-means and Decision Trees
• 2.1 Fuzzy C-means Based on Gray Levels Histogram
• 2.2 Reduction of False Positive ROIs
• 2.3 Feature Extraction
• 2.4 Binary Decision Tree
• 3 Experimentation
• 3.1 Results and Discussion
• 4 Conclusions
• References
• Refining the Pose: Training and Use of Deep Recurrent Autoencoders for Improving Human Pose Estimation
• 1 Introduction

• 2 Deep Architecture for 3D Human Pose Refinement
• 2.1 Denoising Recurrent Autoencoder
• 2.2 Convolutional Network for Pose Prediction
• 2.3 Pose Refinement Training
• 2.4 Cost Function
• 3 Experiments
• 3.1 Evaluation on HumanEva-I
• 3.2 Evaluation on Human 3.6 Million
• 3.3 Ablation Experiments
• 3.4 Conclusions
• References
• How Can Deep Neural Networks Be Generated Efficiently for Devices with Limited Resources?
• 1 Introduction
• 2 Background
• 3 Parameter Pruning
• 4 Quantization
• 5 Low-Rank Factorization
• 6 Compact Network Design
• 7 Neural Model Deployment

• 7.1 Compact Network Design
• 7.2 Training and Pruning
• 7.3 Quantize Model
• 7.4 Inference Optimization
• 8 Conclusion
• References
• Controlling a Smartphone with Brain-Computer Interfaces: A Preliminary Study
• 1 Introduction
• 2 Subjects and Methods
• 2.1 Acquisition
• 2.2 Processing
• 2.3 Application
• 2.4 Evaluation Procedure
• 3 Results
• 4 Discussion
• 5 Conclusion
• References
• Capturing Industrial Machinery into Virtual Reality
• 1 Introduction
• 2 Related Work
• 3 System Design
• 3.1 Initialisation
• 3.2 Capturing Images
• 3.3 Calibration
• 3.4 Visualisation

• 4 Results
• 5 Conclusion
• References
• Leishmaniasis Parasite Segmentation and Classification Using Deep Learning
• 1 Introduction
• 2 Data
• 3 Method
• 4 Results
• 5 Conclusions
• References
• Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset
• 1 Introduction
• 2 The JNU Dataset
• 3 Automatic Pedestrian Detection
• 4 Automatic Data Association
• 5 Evaluation
• 6 Conclusion
• References
• Shape and Appearance Based Sequenced Convnets to Detect Real-Time Face Attributes on Mobile Devices
• 1 Introduction
• 2 Related Work

• 3 Datasets and Data Preparation
• 3.1 FER-2013 and FER+ Datasets
• 3.2 Data Preprocessing
• 4 Proposed CNN Architecture
• 4.1 Sequenced CNN Models
• 4.2 Face Heatmap Image Construction
• 4.3 CNN Models
• 4.4 Learning with a Shape Heatmap Image
• 5 Results and Applications
• 5.1 Effects of Data Preparation and Alignment
• 5.2 Combining Face Shape and Appearance with VGG
• 5.3 Combining Face Shape and Appearance with Mobilenet
• 5.4 Implementation
• 6 Conclusions
• References
• Image Colorization Using Generative Adversarial Networks
• 1 Introduction

This book constitutes the refereed proceedings of the 10th International Conference on Articulated Motion and Deformable Objects, AMDO 2018, held in Palma de Mallorca, Spain, in July 2018. The 12 papers presented were carefully reviewed and selected from 26 submissions. The papers address the following topics: advanced computer graphics and immersive videogames; human modeling and animation; human motion analysis and tracking; 3D human reconstruction and recognition; multimodal user interaction and applications; ubiquitous and social computing; design tools; input technology; programming user interfaces; 3D medical deformable models and visualization; deep learning methods for computer vision and graphics; and multibiometric.
(source: Nielsen Book Data)

• The DARPA Robotics Challenge Finals: Results and Perspectives.- Robot System of DRC-HUBO+ and Control Strategy of Team KAIST in DARPA Robotics Challenge Finals.- Team IHMC's Lessons Learned from the DARPA Robotics Challenge: Finding Data in the Rubble.- Developing a Robust Disaster Response Robot: CHIMP and the Robotics Challenge.- DRC Team NimbRo Rescue: Perception and Control for Centaur-like Mobile Manipulation Robot Momaro.- Team RoboSimian: Semi-autonomous Mobile Manipulation at the 2015 DARPA Robotics Challenge Finals.- Director: A User Interface Designed for Robot Operation with Shared Autonomy.- Achieving Reliable Humanoid Robot Operations in the DARPA Robotics Challenge: Team WPI-CMU's Approach.- Team DRC-Hubo@UNLV in 2015 DARPA Robotics Challenge Finals.- Team SNU's Control Strategies to Enhancing Robot's Capability: Lessons from the DARPA Robotics Challenge Finals 2015.- Team THOR's Entry in the DARPA Robotics Challenge Finals 2015.- Collaborative Autonomy Between High-level Behaviors and Human Operators for Control of Complex Tasks with Different Humanoid Robots.- WALK-MAN Humanoid Platform.- An Architecture for Human-Guided Autonomy: Team TROOPER at the DARPA Robotics Challenge Finals.- Team VALOR's ESCHER: A Novel Electromechanical Biped for the DARPA Robotics Challenge.- Perspectives on Human-Robot Team Performance from an Evaluation of the DARPA Robotics Challenge.- What Happened at the DARPA Robotics Challenge Finals.
• (source: Nielsen Book Data)

The DARPA Robotics Challenge was a robotics competition that took place in Pomona, California USA in June 2015. The competition was the culmination of 33 months of demanding work by 23 teams and required humanoid robots to perform challenging locomotion and manipulation tasks in a mock disaster site. The challenge was conceived as a response to the Japanese Fukushima nuclear disaster of March 2011. The Fukushima disaster was seen as an ideal candidate for robotic intervention since the risk of exposure to radiation prevented human responders from accessing the site. This volume, edited by Matthew Spenko, Stephen Buerger, and Karl Iagnemma, includes commentary by the organizers, overall analysis of the results, and documentation of the technical efforts of 15 competing teams. The book provides an important record of the successes and failures involved in the DARPA Robotics Challenge and provides guidance for future needs to be addressed by policy makers, funding agencies, and the robotics research community. Many of the papers in this volume were initially published in a series of special issues of the Journal of Field Robotics. We have proudly collected versions of those papers in this STAR volume. .
(source: Nielsen Book Data)

• Development of an Industry 4.0 demonstrator using Sequence Planner and ROS2.- ROS2 for ROS1 users.- Multi-Robot SLAM framework for ROS with Efficient Information Sharing.- Agile experimentation of robot swarms in large scale.- Lessons learned building a self-driving car on top of ROS.- Landing a UAV on a moving platform using a front facing camera.- Integrating the Functional Mock-up Interface with ROS and Gazebo.- An ARVA sensor simulator.- ROS Implementation for Untethered Microrobot Manipulation.- ClegS: A package to develop C-legged robots.- Video frames selection method for 3D Reconstruction depending on ROS-based monocular SLAM.- ROS Rescue: Fault Tolerance System for ROS.
• (source: Nielsen Book Data)

This book is the fifth volume in the successful book series Robot Operating System: The Complete Reference. The objective of the book is to provide the reader with comprehensive coverage on the Robot Operating System (ROS), which is currently considered to be the primary development framework for robotics applications, and the latest trends and contributing systems. The content is divided into six parts. Pat I presents for the first time the emerging ROS 2.0 framework, while Part II focuses on multi-robot systems, namely on SLAM and Swarm coordination. Part III provides two chapters on autonomous systems, namely self-driving cars and unmanned aerial systems. In turn, Part IV addresses the contributions of simulation frameworks for ROS. In Part V, two chapters explore robotic manipulators and legged robots. Finally, Part VI presents emerging topics in monocular SLAM and a chapter on fault tolerance systems for ROS. Given its scope, the book will offer a valuable companion for ROS users and developers, helping them deepen their knowledge of ROS capabilities and features. .
(source: Nielsen Book Data)

• Object manipulation and locomotion.- Soft and bioinspired robotics.- Path planning and autonomous vehicles.- Robotics vision and teleoperation.- HRI, assistive and medical robotics.- Swarm robotics.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 19th Annual Conference on Towards Autonomous Robotics, TAROS 2018, held in Bristol, UK, in July 2018. The 38 full papers presented together with 14 short papers were carefully reviewed and selected from 68 submissions. The papers focus on presentation and discussion of the latest results and methods in autonomous robotics research and applications. The conference offers a friendly environment for robotics researchers and industry to take stock and plan future progress.
(source: Nielsen Book Data)

• Current research in the field of robotics
• Educational activities in the field of robotics
• Interaction between robots and humans.

This book includes the thoroughly refereed proceedings of the 18th Annual RoboCup International Symposium, held in Joao Pessoa, Brazil, in July 2014. The 36 revised papers were carefully reviewed and selected from 66 submis-sions and include 11 champion-team papers, three special-track papers on open-source hardware and software, nine papers on the advancement of the RoboCup leagues track, and three best papers. The contributions present current research and educational activities in the field of robotics and artificial intelligence with a special focus on the interaction between robots and humans.

• Part I Holonic and Agent-based Industrial Automation Systems.- Part II Service-oriented Management and Control of Manufacturing Systems.- Part III Distributed Modelling for Safety and Security in Industrial Systems.- Part IV Complexity, Big Data and Virtualization in Computing-oriented Manufacturing.- Part V Adaptive, Bio-inspired and Self-organized Multi-Agent Systems for Manufacturing.- Part VI Physical Internet Simulation, Modelling and Control.
• (source: Nielsen Book Data)

This volume gathers the peer reviewed papers presented at the 4th edition of the International Workshop "Service Orientation in Holonic and Multi-agent Manufacturing - SOHOMA'14" organized and hosted on November 5-6, 2014 by the University of Lorraine, France in collaboration with the CIMR Research Centre of the University Politehnica of Bucharest and the TEMPO Laboratory of the University of Valenciennes and Hainaut-Cambresis. The book is structured in six parts, each one covering a specific research line which represents a trend in future manufacturing: (1) Holonic and Agent-based Industrial Automation Systems; (2) Service-oriented Management and Control of Manufacturing Systems; (3) Distributed Modelling for Safety and Security in Industrial Systems; (4) Complexity, Big Data and Virtualization in Computing-oriented Manufacturing; (5) Adaptive, Bio-inspired and Self-organizing Multi-Agent Systems for Manufacturing and (6) Physical Internet Simulation, Modelling and Control. There is a clear orientation of the SOHOMA'14 workshop towards complexity, which is a common view of all six parts. There is need for a framework allowing the development of manufacturing cyber physical systems including capabilities for complex event processing and data analytics which are expected to move the manufacturing domain closer towards cloud manufacturing within contextual enterprises. Recent advances in sensor, communication and intelligent computing technologies made possible the Internet connectivity of the physical world: the Physical Internet, where not only documents and images are created, shared, or modified in the cyberspace, but also the physical resources and products interact over Internet and make decisions based on shared communication.
(source: Nielsen Book Data)

• Real time Implementation of Articial Neural Network in FPGA Platform.- Design of a 0,18um CMOS Inductor - based Bandpass Microwave Active Filter.- Improvement of the Authentication on in-Vehicle Controller Area Networks.- Comparative Evaluation of Speech Recognition systems Based on Different Toolkits.- Stability Analysis and H Performance for Nonlinear Fuzzy Networked Control Systems with Time-Varying Delay.- Driver Behavior assistance in Road Intersections.- Performance of CE-OFDM System over AWGN and Frequency-Selective Channels.- Towards a Dynamical Adaptive Finite Volume Core for Urban Flow Simulations.- An Efficiency Study of Adaptive Median Filtering for Image Denoising, Based on A Hardware Implementation.- Processing Time and Computing Resources Optimization in a Mobile Edge Computing Node.- Viscous Dissipation and Heat Source/Sink Effects on Convection Heat Transfer in a Saturated Porous Medium.- Fast and Efficient Decoding Algorithm Developed From Concatenation between A Symbol-By-Symbol Decoder and A Decoder Based On Syndrome Computing and Hash Techniques.- SWIMAC: An Ultra-Low Power Consumption MAC Protocol for IR-UWB based WSN.- An Optimal Design of a Short-Channel RF Low Noise Amplifier Using a Swarm Intelligence Techniques.
• (source: Nielsen Book Data)

• 4 Conclusion
• References
• Design of a 0.18 μm CMOS Inductor-Based Bandpass Microwave Active Filter
• 1 Introduction
• 2 Synthesis of a Grounded CMOS Active Inductor
• 3 Procedure of Designing Chebyshev Microwave Bandpass Filters
• 4 Example of a Third-Order Chebyshev Bandpass Filter
• 5 Conclusion
• References
• Improvement of the Authentication on In-Vehicle Controller Area Networks
• 1 Introduction
• 2 Development of an Authentication Method
• 2.1 Principles
• 2.2 Current Technique
• 2.3 Proposed Improvements
• 3 Hash Algorithm
• 4 Conclusion
• References

This book gathers selected research papers presented at the First International Conference on Embedded Systems and Artificial Intelligence (ESAI 2019), held at Sidi Mohamed Ben Abdellah University, Fez, Morocco, on 2-3 May 2019. Highlighting the latest innovations in Computer Science, Artificial Intelligence, Information Technologies, and Embedded Systems, the respective papers will encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.
(source: Nielsen Book Data)

• Pattern recognition algorithms.- Reproducible research frameworks.- Reproducible research results.- Previous works on reproducible research.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Reproducible Research in Pattern Recognition, RRPR 2016, held in Cancun, Mexico, in December 2016. The 12 revised full papers, among them 2 invited talks, presented were carefully reviewed and selected from 16 submissions. They focus on pattern recognition algorithms; reproducible research frameworks; reproducible research results, previous works on reproducible research.
(source: Nielsen Book Data)

• Features, Learning, and Classifiers. Toward Computer-Aided Interpretation of Situations / Juliusz L. Kulikowski
• Classification of Multi-dimensional Distributions Using Order Statistics Criteria / A. Thomas, B. John Oommen
• Knowledge Extraction from Graph-Based Structures in Conceptual Design / Grażyna Ślusarczyk
• Estimation of the Relations of: Equivalence, Tolerance and Preference on the Basis of Pairwise Comparisons / Leszek Klukowski
• Generalized Constraint Design of Linear-Phase FIR Digital Filters / Norbert Henzel, Jacek M. Leski
• Reduced Kernel Extreme Learning Machine / Wanyu Deng, Qinghua Zheng, Kai Zhang
• Using Positional Information in Modeling Inflorescence Discs / Malgorzata Prolejko
• A New Method for Random Initialization of the EM Algorithm for Multivariate Gaussian Mixture Learning / Wojciech Kwedlo
• Heuristic Optimization Algorithms for a Tree-Based Image Dissimilarity Measure / Bartłomiej Zieliński, Marcin Iwanowski.

• Model of Syntactic Recognition of Distorted String Patterns with the Help of GDPLL(k)-Based Automata / Janusz Jurek, Tomasz Peszek
• Decision Rules in Simple Decision Systems over Ontological Graphs / Krzysztof Pancerz
• Nonlinear Extension of the IRLS Classifier Using Clustering with Pairs of Prototypes / Michal Jezewski, Jacek M. Leski
• Separable Linearization of Learning Sets by Ranked Layer of Radial Binary Classifiers / Leon Bobrowski, Magdalena Topczewska
• Hidden Markov Models For Two-Dimensional Data / Janusz Bobulski
• Methods of Learning Classifier Competence Applied to the Dynamic Ensemble Selection / Maciej Krysmann, Marek Kurzynski
• The Method of Improving the Structure of the Decision Tree Given by the Experts / Robert Burduk
• Biometrics. Face Detection and Recognition under Heterogeneous Database Based on Fusion of Catadioptric and PTZ Vision Sensors / Aditya Raj [and others]
• Eigenfaces, Fisherfaces, Laplacianfaces, Marginfaces -- How to Face the Face Verification Task / Maciej Smiatacz.

• A Content Based Feature Combination Method for Face Recognition / Madeena Sultana, Marina Gavrilova
• Face Recognition Based on Sequence of Images / Jacek Komorowski, Przemyslaw Rokita
• Statistical Analysis in Signature Recognition System Based on Levenshtein Distance / Malgorzata Palys, Rafal Doroz, Piotr Porwik
• A Computational Assessment of a Blood Vessel's Roughness / Tomasz Wesolowski, Krzysztof Wrobel
• Performance Benchmarking of Different Binarization Techniques for Fingerprint-Based Biometric Authentication / Soharab Hossain Shaikh, Khalid Saeed, Nabendu Chaki
• Biometric Features Selection with k-Nearest Neighbours Technique and Hotelling Adaptation Method / Piotr Porwik, Rafal Doroz
• Data Stream Classification and Big Data Analytics. Predictive Regional Trees to Supplement Geo-Physical Random Fields / Annalisa Appice, Sonja Pravilovic, Donato Malerba
• Extending Bagging for Imbalanced Data / Jerzy Błaszczyński, Jerzy Stefanowski, Łukasz Idkowiak.

• Rule Chains for Visualizing Evolving Fuzzy Rule-Based Systems / Sascha Henzgen, Marc Strickert, Eyke Hüllermeier
• Recovery Analysis for Adaptive Learning from Non-stationary Data Streams / Ammar Shaker, Eyke Hüllermeier
• Analysis of Roles and Groups in Blogosphere / Bogdan Gliwa, Anna Zygmunt, Jarosław Koźlak
• Knowledge Generalization from Long Sequence of Execution Scenarios / Radosław Z. Ziembiński
• Incremental Learning and Forgetting in One-Class Classifiers for Data Streams / Bartosz Krawczyk, Michał Woźniak
• Comparable Study of Statistical Tests for Virtual Concept Drift Detection / Piotr Sobolewski, Michał Woźniak
• Image Processing and Computer Vision. An Experimental Comparison of Fourier-Based Shape Descriptors in the General Shape Analysis Problem / Katarzyna Gościewska, Dariusz Frejlichowski
• Extraction of the Foreground Regions by Means of the Adaptive Background Modelling Based on Various Colour Components for a Visual Surveillance System / Dariusz Frejlichowski [and others].

• Repeatability Measurements for 2D Interest Point Detectors on 3D Models / Simon R. Lang, Martin H. Luerssen, David M.W. Powers
• Extended Investigations on Skeleton Graph Matching for Object Recognition / Jens Hedrich [and others]
• Low-Level Image Features for Stamps Detection and Classification / Paweł Forczmański, Andrzej Markiewicz
• Stochastic Approximation to Reconstruction of Vector-Valued Images / Dariusz Borkowski
• Image Segmentation with Use of Cross-Entropy Clustering / Marek Śmieja, Jacek Tabor
• Detection of Disk-Like Particles in Electron Microscopy Images / P. Spurek, J. Tabor, E. Zając
• A GPU Accelerated Local Polynomial Approximation Algorithm for Efficient Denoising of MR Images / Artur Klepaczko
• Altair: Automatic Image Analyzer to Assess Retinal Vessel Caliber / Gabino Verde [and others]
• Real-Time Wrist Localization in Hand Silhouettes / Tomasz Grzejszczak, Jakub Nalepa, Michal Kawulok
• An s-layered Grade Decomposition of Images / Maria Grzegorek.

• System-Level Hardware Implementation of Simplified Low-Level Color Image Descriptor / Paweł Forczmański, Piotr Dziurzański
• Reconstruction of Head Surface Model from Single Scan / Krzysztof Skabek, Dawid Łapczynski
• The Effectiveness of Matching Methods for Rectified Images / Pawel Popielski, Zygmunt Wrobel, Robert Koprowski
• The Print-Scan Problem in Printed Steganography of Face Images / Włodzimierz Kasprzak, Maciej Stefańczyk, Jan Popiołkiewicz
• Phototool Geometry Verification / Jarosław Zdrojewski, Adam Marchewka
• Structure from Motion in Three -- Dimensional Modeling of Human Head / Anna Wójcicka, Zygmunt Wróbel
• A Short Overview of Feature Extractors for Knuckle Biometrics / Michał Choraś
• Three-Stage Method of Text Region Extraction from Diagram Raster Images / Jerzy Sas, Andrzej Zolnierek
• Medical Applications. Time Series of Fuzzy Sets in Classification of Electrocardiographic Signals / Jacek M. Leski, Norbert Henzel.

• Interpolation Procedure in Filtered Backprojection Algorithm for the Limited-Angle Tomography / Aleksander Denisiuk
• Classification of Uterine Electrical Activity Patterns for Early Detection of Preterm Birth / Janusz Jezewski [and others]
• Diagnosis of Bipolar Disorder Based on Principal Component Analysis and SVM / M. Termenon [and others]
• Genetic Algorithms in EEG Feature Selection for the Classification of Movements of the Left and Right Hand / Izabela Rejer
• On the Use of Programmed Automata for a Verification of ECG Diagnoses / Mariusz Flasiński, Piotr Flasiński, Ewa Konduracka
• Blood Flow Modeling in a Synthetic Cylindrical Vessel for Validating Methods of Vessel Segmentation in MRA Images / Grzegorz Dwojakowski, Artur Klepaczko, Andrzej Materka
• Swarm Optimization and Multi-level Thresholding of Cytological Images for Breast Cancer Diagnosis / Marek Kowal [and others]
• Detecting Overlapped Nuclei Regions in the Feulgen-Stained Cytological Smears / Bogusław D. Piętka, Annamonika Dulewicz.

• Density Invariant Detection of Osteoporosis Using Growing Neural Gas / Igor T. Podolak, Stanisław K. Jastrzębski
• Cost Sensitive Hierarchical Classifiers for Non-invasive Recognition of Liver Fibrosis Stage / Bartosz Krawczyk [and others]
• Miscellaneous Applications. A Blinking Measurement Method for Driver Drowsiness Detection / Belhassen Akrout, Walid Mahdi
• Description of Human Activity Using Behavioral Primitives / Piotr Augustyniak
• How to Become Famous? Motives in Scientific Social Networks / Adam Matusiak, Mikołaj Morzy
• AdaBoost for Parking Lot Occupation Detection / Radovan Fusek [and others]
• Blink Detection Based on the Weighted Gradient Descriptor / Krystian Radlak, Bogdan Smolka
• Touchless Input Interface for Disabled / Adam Nowosielski, Łukasz Chodyła
• Architecture of the Semantically Enhanced Intellectual Property Protection System / Dariusz Ceglarek
• Validation of Clustering Techniques for User Group Modeling / Danuta Zakrzewska.

• Parking Lot Occupancy Detection Using Computational Fluid Dynamics / Tomas Fabian
• Human Fall Detection Using Kinect Sensor / Michal Kepski, Bogdan Kwolek
• Evaluation of Various Techniques for SQL Injection Attack Detection / Michał Choraś, Rafał Kozik
• Task Allocation in Distributed Mesh-Connected Machine Learning System: Simplified Busy List Algorithm with Q-Learning Based Queuing / Agnieszka Majkowska, Dawid Zydek, Leszek Koszałka
• Power Saving Algorithms for Mobile Networks Using Classifiers Ensemble / Rafal Lysiak, Marek Kurzynski
• Hardware Implementation of Fourier Transform for Real Time EMG Signals Recognition / Andrzej R. Wolczowski [and others]
• Data Preprocessing with GPU for DBSCAN Algorithm / Piotr Cal, Michał Woźniak
• Pattern Recognition and Image Processing in Robotics. Structured Light Techniques for 3D Surface Reconstruction in Robotic Tasks / M. Rodrigues [and others]
• Multi-modal People Detection from Aerial Video / Helen Flynn, Stephen Cameron.

• The Classification of the Terrain by a Hexapod Robot / Adam Schmidt, Krzysztof Walas
• Robust Registration of Kinect Range Data for Sensor Motion Estimation / Michał Nowicki, Piotr Skrzypczyński
• Utilization of Depth and Color Information in Mobile Robotics / Maciej Stefańczyk, Konrad Bojar, Włodzimierz Kasprzak
• Speech and Word Recognition. Texture-Based Text Detection in Digital Images with Wavelet Features and Support Vector Machines / Marcin Grzegorzek [and others]
• Automatic Disordered Syllables Repetition Recognition in Continuous Speech Using CWT and Correlation / Ireneusz Codello [and others]
• Evaluation of the Document Classification Approaches / Michal Hrala, Pavel Král
• The Prolongation-Type Speech Non-fluency Detection Based on the Linear Prediction Coefficients and the Neural Networks / Adam Kobus [and others].

This book is a study of computer recognition systems. It contains articles contributed by experts of pattern recognition, and reports on current research with respect to both methodology and applications. Topics include: biometrics; data stream classification and big data analytics; features, learning, and classifiers; image processing and computer vision; medical applications; pattern recognition and image processing in robotics; speech and word recognition.

• Awareness Platform.- Aalto Observatory on Digital Valuation Systems.- A Novel Lexicon-Based Approach in Determining Sentiment in Financial Data Using Learning Automata.- A Hybrid Recommendation System Based on Density-Based Clustering.- Computing Platform for Virtual Economic Activities Index.- Data Based Stock Portfolio Construction Using Computational Intelligence.- YourDataStories: Transparency and Corruption Fighting Through Data Interlinking and Visual Exploration.- The Maker Movement and the Disruption of the Producer-Consumer Relation.- Open Data: Creating Communities and Practices for a New Common.- The Case for Collaborative Policy Experimentation Using Advanced Geospatial Data Analytics and Visualisation.- An Engagement-Related Behaviour Change Approach for SavingFood in Greece.- Developing a Social Innovation Methodology in the Web 2.0 Era.- Code Hunting Games: a Mixed Reality Multiplayer Treasure Hunt Through a Conversational Interface.- Politician - an Imitation Game.- Towards Open Domain Chatbots - a GRU Architecture for Data Driven Conversations.- Creating Dialogues Using Argumentation and Social Practices.- An Overview of Open-Source Chatbots Social Skills.
• (source: Nielsen Book Data)

This book constitutes the refereed post-conference proceedings of 4 workshops, held at the 4th International Conference on Internet Science, Thessaloniki, Greece, in November 2017: the Second International Workshop on the Internet for Financial Collective Awareness and Intelligence, IFIN 2017, the International Workshop on Data Economy 2017, the International Workshop on Digital Technology to Support Social Innovation, DSI 2017, and the International Workshop on Chatbot Research and Design, CONVERSATIONS 2017. The 17 full papers presented together with one short paper were carefully reviewed and selected from 27 submissions. The contributions of the IFIN workshop focus on a multidisciplinary dialogue on how to use the internet to promote financial awareness and capability among citizens whereas the papers of the Data Economy workshop show how online data change economy and business. The aim of the DSI workshop was to collect the lessons learned from different platforms and settings, and to understand the requirements and challenges for building and using digital platforms to effectively engage broad participation in the social innovation process. The papers of the Conversations workshop explore the brave new world of human-computer communication through natural language, gathering latest developments in chatbots research and design.
(source: Nielsen Book Data)

• Rol based on MATLAB and the real-time controller
• Design and Analysis of Motor Control System for Drilling Fluid Continuous Wave Generator Based on Improved Active Disturbance Rejection Control and Hysteresis Current Control
• Trajectory Planning based on Optimal Control and Exact Derivatives
• Co-simulation Based on ADAMS and Simulink for Direct Yaw Moment Control System of 4WD-EV
• LI Zhanfeng, Du Shuang. Simulation Analysis of PID Closed-Loop Control of Current of SBW
• Characteristic Analysis and Disturbance Control of Hydraulic Transmission System for Driving Torque Extraction Electric Power Generation in Coal Sampling Robot
• Appearance-Based Gaze Tracking: A Brief Review
• Language and Robotics: Complex Sentence Understanding
• Dynamic Motion Planning Algorithm in Human-Robot Collision Avoidance
• Dynamics modeling of a 2-DOFs mechanism with rigid joint and flexible joint
• Haptic Joystick Impedance Control with Gravity Compensation
• Landmark-Based Virtual Path Estimation for Assisted UAV FPV Tele-Operation with Augmented Reality
• Concurrent Probabilistic Motion Primitives for Obstacle Avoidance and Human-Robot Collaboration e System of the Assistive Gait Training Robot
• A Noninvasive Calibration-free and Model-free Surgical Robot for Automatic Fracture Reduction
• Force Modeling of Tool-tissue Interaction Force during Suturing
• Minimally invasive instrument joint design based on variable stiffness of transmission efficiency
• An improved arti cial potential eld method for mobile robots using environmental information
• Towards End-to-End Speech Recognition with Deep Multipath Convolutional Neural Networks
• Robot Intelligent Trajectory Planning based on PCM guided Reinforcement Learning
• Fast Robot Motor Skill Acquisition Based on Bayesian inspired Policy Improvement
• Control of Nameplate Pasting Robot for Sand Mold Based on Deep Reinforcement Learning
• Robot Intelligent Trajectory Planning based on PCM guided Reinforcement Learning
• Way-Point Tracking Control of Underactuated USV Based on GPC Path Planning
• Neural Networks-Based PID Precision Motion Control of a Piezo-Actuated Microinjector
• Force/Motion Hybrid Control of Three Link Constrained Manipulator Using Sliding Mode
• Development of workshop management system for assembly production process
• Dynamic Scheduling of Dual-Resource Constrained Blocking Job Shop
• Study on No-wait Flexible Flow Shop Scheduling with Multi-Objective
• Dynamic Behavior Analysis and Multi-sensor Modal Information Fusion for Robotic Milling System
• Adaptive Impedance Control for Robotic Polishing with an Intelligent Digital Compliant Grinder
• Path Planning of UAV-UGV Heterogeneous Robot System in Road Network
• Automatic programming for dual robots to grinding intersecting curve
• A HoloLens Based Augmented Reality Navigation System for Minimally Invasive Total Knee Arthroplasty
• R-3RPS robot-based mathematical modeling for a military flight simulator
• Innovation Ability Cultivation Quality Evaluation Model of Machinery Postgraduate by Mechatronics Engineering
• DOREP: an educational experiment platform for robot cont.

The volume set LNAI 11740 until LNAI 11745 constitutes the proceedings of the 12th International Conference on Intelligent Robotics and Applications, ICIRA 2019, held in Shenyang, China, in August 2019. The total of 378 full and 25 short papers presented in these proceedings was carefully reviewed and selected from 522 submissions. The papers are organized in topical sections as follows: Part I: collective and social robots; human biomechanics and human-centered robotics; robotics for cell manipulation and characterization; field robots; compliant mechanisms; robotic grasping and manipulation with incomplete information and strong disturbance; human-centered robotics; development of high-performance joint drive for robots; modular robots and other mechatronic systems; compliant manipulation learning and control for lightweight robot. Part II: power-assisted system and control; bio-inspired wall climbing robot; underwater acoustic and optical signal processing for environmental cognition; piezoelectric actuators and micro-nano manipulations; robot vision and scene understanding; visual and motional learning in robotics; signal processing and underwater bionic robots; soft locomotion robot; teleoperation robot; autonomous control of unmanned aircraft systems. Part III: marine bio-inspired robotics and soft robotics: materials, mechanisms, modelling, and control; robot intelligence technologies and system integration; continuum mechanisms and robots; unmanned underwater vehicles; intelligent robots for environment detection or fine manipulation; parallel robotics; human-robot collaboration; swarm intelligence and multi-robot cooperation; adaptive and learning control system; wearable and assistive devices and robots for healthcare; nonlinear systems and control. Part IV: swarm intelligence unmanned system; computational intelligence inspired robot navigation and SLAM; fuzzy modelling for automation, control, and robotics; development of ultra-thin-film, flexible sensor s, and tactile sensation; robotic technology for deep space exploration; wearable sensing based limb motor function rehabilitation; pattern recognition and machine learning; navigation/localization. Part V: robot legged locomotion; advanced measurement and machine vision system; man-machine interactions; fault detection, testing and diagnosis; estimation and identification; mobile robots and intelligent autonomous systems; robotic vision, recognition and reconstruction; robot mechanism and design. Part VI: robot motion analysis and planning; robot design, development and control; medical robot; robot intelligence, learning and linguistics; motion control; computer integrated manufacturing; robot cooperation; virtual and augmented reality; education in mechatronics engineering; robotic drilling and sampling technology; automotive systems; mechatronics in energy systems; human-robot interaction.

• Information Systems Applications (incl.
• Internet).- Information Storage and Retrieval.- Software Engineering Artificial Intelligence (incl. Robotics).- Systems and Data Security.- Computer Appl. in Administrative Data
• Processing Ontologies.- Databases.- Semantics.- Cooperative Information Systems.-Cloud Computing.- Trust, Security, Privacy And Risk
• Management.
• (source: Nielsen Book Data)

This double volumes LNCS 11229-11230 constitutes the refereed proceedings of the Confederated International Conferences: Cooperative Information Systems, CoopIS 2018, Ontologies, Databases, and Applications of Semantics, ODBASE 2018, and Cloud and Trusted Computing, C&TC, held as part of OTM 2018 in October 2018 in Valletta, Malta. The 64 full papers presented together with 22 short papers were carefully reviewed and selected from 173 submissions. The OTM program every year covers data and Web semantics, distributed objects, Web services, databases, informationsystems, enterprise workflow and collaboration, ubiquity, interoperability, mobility, grid and high-performance computing.
(source: Nielsen Book Data)

This book constitutes the refereed post-conference proceedings of the 11th International Seminar on Speech Production, ISSP 2017, held in Tianjin, China, In October 2017. The 20 revised full papers included in this volume were carefully reviewed and selected from 68 submissions. They cover a wide range of speech science fields including phonology, phonetics, prosody, mechanics, acoustics, physiology, motor control, neuroscience, computer science and human interaction. The papers are organized in the following topical sections: emotional speech analysis and recognition; articulatory speech synthesis; speech acquisition; phonetics; speech planning and comprehension, and speech disorder.
(source: Nielsen Book Data)

• Next generation community engagement.- Online policy, politics and co-creation.- Understanding and empowering digital citizens.- Data-driven research and design.- Social media and online interaction.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 4th International Conference on Internet Science held in Thessaloniki, Greece, in November 2017. The 34 papers presented were carefully reviewed and selected for inclusion in this volume. They were organized in topical sections named: next generation community engagement; online policy, politics and co-creation; understanding and empowering digital citizens; data-driven research and design; social media and online interaction.
(source: Nielsen Book Data)

• OnTheMove 2017 Keynotes.- International Conference on Cooperative Information Systems (CoopIS) 2017.- Cloud and Trusted Computing (C&TC) 2017.- International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE) 2017.
• (source: Nielsen Book Data)

This double volumes LNCS 10573-10574 constitutes the refereed proceedings of the Confederated International Conferences: Cooperative Information Systems, CoopIS 2017, Ontologies, Databases, and Applications of Semantics, ODBASE 2017, and Cloud and Trusted Computing, C&TC, held as part of OTM 2017 in October 2017 in Rhodes, Greece. The 61 full papers presented together with 19 short papers were carefully reviewed and selected from 180 submissions. The OTM program every year covers data and Web semantics, distributed objects, Web services, databases, information systems, enterprise workflow and collaboration, ubiquity, interoperability, mobility, grid and high-performance computing.
(source: Nielsen Book Data)

• Robotics.- Artificial intelligence.- Environment perception.- State estimation.- Pattern recognition.- Sensor fusion.- Action planning.- Control theory.- Robot learning.- Human-robot interaction.- Multi-agent systems.- Simultaneous localization and mapping.- Object manipulation.- Locomotion.- RoboCup.- Robot competition. .
• (source: Nielsen Book Data)

This book includes the post-conference proceedings of the 20th RoboCup International Symposium, held in Leipzig, Germany, in July 2016. In addition to the 38 contributions to the symposium, selected from 63 submissions, the book also contains 15 champion papers of teams winning individual leagues of the RoboCup 2016 competition, the Amazon Picking Challenge, and the Harting Open Source Award. The papers present current research in the fields of robotics and artificial intelligence with a special focus to robot hardware and software, environment perception, action planning and control, robot learning, multi-robot systems, and human-robot interaction.
(source: Nielsen Book Data)

• Collective Awareness and Crowdsourcing Platforms
• Collaboration, Privacy and Conformity in Virtual/Social Environments
• Internet Interoperability, Freedom and Data Analysis
• Smart Cities and Sociotechnical Systems.

This book constitutes the proceedings of the Third International Conference on Internet Science held in Florence, Italy, in September 2016. The 25 papers presented were carefully reviewed and selected for inclusion in this volume. They were organized in topical sections named: collective awareness and crowdsourcing platforms¸ collaboration, privacy and conformity in virtual/social environments; internet interoperability, freedom and data analysis; smart cities and sociotechnical systems.

• 1. A Comprehensive Introduction to Photometric 3D-Reconstruction.-
• 2. Perspective Shape from Shading an Exposition on Recent Works with New Experiments.-
• 3. RGBD-Fusion: Depth Refinement for Diffuse and Specular Objects.-
• 4. Non-Rigid Structure from Motion and Shading.-
• 5. On the Well-Posedness of Uncalibrated Photometric Stereo Under General Lighting.-
• 6. Recent Progress in Shape from Polarization.-
• 7. Estimating Facial Aging using Light Scattering Photometry.
• (source: Nielsen Book Data)

This book presents the latest advances in photometric 3D reconstruction. It provides the reader with an overview of the state of the art in the field, and of the latest research into both the theoretical foundations of photometric 3D reconstruction and its practical application in several fields (including security, medicine, cultural heritage and archiving, and engineering). These techniques play a crucial role within such emerging technologies as 3D printing, since they permit the direct conversion of an image into a solid object. The book covers both theoretical analysis and real-world applications, highlighting the importance of deepening interdisciplinary skills, and as such will be of interest to both academic researchers and practitioners from the computer vision and mathematical 3D modeling communities, as well as engineers involved in 3D printing. No prior background is required beyond a general knowledge of classical computer vision models, numerical methods for optimization, and partial differential equations. .
(source: Nielsen Book Data)

• OnTheMove 2017 Keynotes.- International Conference on Cooperative Information Systems (CoopIS) 2017.- Cloud and Trusted Computing (C&TC) 2017.- International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE) 2017.
• (source: Nielsen Book Data)

This double volumes LNCS 10573-10574 constitutes the refereed proceedings of the Confederated International Conferences: Cooperative Information Systems, CoopIS 2017, Ontologies, Databases, and Applications of Semantics, ODBASE 2017, and Cloud and Trusted Computing, C&TC, held as part of OTM 2017 in October 2017 in Rhodes, Greece. The 61 full papers presented together with 19 short papers were carefully reviewed and selected from 180 submissions. The OTM program every year covers data and Web semantics, distributed objects, Web services, databases, information systems, enterprise workflow and collaboration, ubiquity, interoperability, mobility, grid and high-performance computing.
(source: Nielsen Book Data)

• Pattern recognition and artificial intelligence techniques.- Image processing and analysis.- Robotics and remote sensing.- Natural language processing and recognition.- Applications of pattern recognition.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 9th Mexican Conference on Pattern Recognition, MCPR 2017, held in Huatulco, Mexico, in June 2017. The 29 revised full papers presented were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on pattern recognition and artificial intelligence techniques, image processing and analysis, robotics and remote sensing, natural language processing and recognition, applications of pattern recognition.
(source: Nielsen Book Data)

• Mobile web data analytics.- Big spatial data and urban computing.- Graph data management and analytics.- Mobility analytics from spatial and social data.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-conference proceedings of the First APWeb-WAIM 2017 Workshops, held jointly with the First International Joint Conference APWeb-WAIM 2017, held in Beijing, China, in July 2017. The 25 full papers presented were carefully reviewed and selected from 40 submissions. The papers originating from six workshops present cutting-edge ideas, results, experiences, techniques, and tools from all aspects of web data management with the focus on mobile web data analytics; big spatial data and urban computing; graph data management and analytics; mobility analytics from spatial and social data.
(source: Nielsen Book Data)

• Communication in Soccer Simulation: On the Use of Wiretapping Opponent Teams.- Multi-Robot Fast-Paced Coordination With Leader Election.- Visual SLAM-Based Localization and Navigation for Service Robots: The Pepper Case.- Visual Mesh: Real-time Object Detection Using Constant Sample Density.- Fast Multi-Scale fHOG Feature Extraction Using Histogram Downsampling.- Combining Simulations and Real-Robot Experiments for Bayesian Optimization of Bipedal Gait Stabilization.- Learning Skills for Small Size League RoboCup.- Real-time Scene Understanding Using Deep Neural Networks for RoboCup SPL.- Training a RoboCup Striker Agent via Transferred Reinforcement Learning.- Playing Soccer Without Colors in the SPL: A Convolutional Neural Network.- End-to-End Deep Imitation Learning: Robot Soccer Case Study.- Designing Convolutional Neural Networks Using a Genetic Approach for Ball Detection.- ImageTagger: An Open Source Online Platform for Collaborative Image Labeling.- Mimicking an Expert Team Through the Learning of Evaluation Functions from Action Sequences.- Jetson, Where Is the Ball? Using Neural Networks for Ball Detection at RoboCup 2017.- Bridging the Gap - On a Humanoid Robotics Rookie League.- Context Aware Robot Architecture, Application to the Robocup@Home Challenge.- From Commands to Goal-based Dialogs: A Roadmap to Achieve Natural Language Interaction in RoboCup@Home.-RoboCupSimData: Software and Data for Machine Learning from RoboCup Simulation League.- Generation of Laser-Quality 2D Navigation Maps from RGB-D Sensors.- Towards Long-Term Memory for Social Robots: Proposing a New Challenge for the RoboCup@Home.- eEVA: Real-Time Web-Based Affective Agents for Human-Robot Interface.- Evaluation of Situations in RoboCup 2D Simulations Using Soccer Field Images.- Near Real-Time Object Recognition for Pepper Based on Deep Neural Networks Running on a Backpack.- Multimodal Movement Activity Recognition Using a Robot's Proprioceptive Sensors.- Survey of Rescue Competitions and Proposal of New Standard Task from Ordinary Tasks.- Adjusted Bounded Weighted Policy Learner.- Towards Real-Time Ball Localization Using CNNs.- Deep Learning for Semantic Segmentation on Minimal Hardware.- RoboCup Junior in the Hunter Region: Driving the Future of Robotic STEM Education Distributed Circumnavigation Control with Dynamic Spacings for a Heterogeneous Multi-Robot System.- Prediction of a Ball Trajectory for the Humanoid Robots: A Friction-Based Study.- RoboCup SSL 2018 Champion Team Paper.- Tech United Eindhoven Middle Size League Winner 2018.- Ichiro Robots Winning RoboCup 2018 Humanoid TeenSize Soccer Competitions.- NimbRo Robots Winning RoboCup 2018 Humanoid AdultSize Soccer Competitions.- HELIO
• S2018: RoboCup 2018 Soccer Simulation 2D League Champion.- UT Austin Villa: RoboCup 2018 3D Simulation League Champions.- Integrating the Latest Artificial Intelligence Algorithms in the RoboCup Rescue Simulation framework.- Robust and Flexible System Architecture for Facing the RoboCup Logistics League Challenge.- RoboCup@Work 2018 Team AutonOHM.- homer@UniKoblenz: Winning Team of the RoboCup@Home Open Platform League 2018.- ToBI - Team of Bielefeld: Enhancing the Robot Capabilities of the Social Standard Platform Pepper. .
• (source: Nielsen Book Data)

This book includes the post-conference proceedings of the 22nd RoboCup International Symposium, held in Montreal, QC, Canada, in June 2018. The 32 full revised papers and 11 papers from the winning teams presented were carefully reviewed and selected from 51 submissions. This book highlights the approaches of champion teams from the competitions and documents the proceedings of the 22nd annual RoboCup International Symposium. Due to the complex research challenges set by the RoboCup initiative, the RoboCup International Symposium offers a unique perspective for exploring scientific and engineering principles underlying advanced robotic and AI systems.
(source: Nielsen Book Data)

This book constitutes the proceedings of the 11th International Conference on Internet and Distributed Computing Systems, IDCS 2018, held in Tokyo, Japan, in October 2018. The 21 full papers presented together with 5 poster and 2 short papers in this volume were carefully reviewed and selected from 40 submissions. This conference desired to look for inspiration in diverse areas (e.g., infrastructure and system design, software development, big data, control theory, artificial intelligence, IoT, self-adaptation, emerging models, paradigms, applications and technologies related to Internet-based distributed systems) to develop new ways to design and mange such complex and adaptive computation resources. .
(source: Nielsen Book Data)

• Active Compliance Control of the RED Hand: A PID Control Approach
• A Wearable Automated System to Quantify Parkinsonian Symptoms Enabling Closed Loop Deep Brain Stimulation
• Systematic and Realistic Testing in Simulation of Control Code for Robots in Collaborative Human-Robot Interactions
• Congratulations, it's a boy! Bench-marking children's perceptions of the Robokind Zeno-R25
• A Bioinspired Approach to Vision
• An Overview of the Ongoing Humanoid Robot Project LARMbot
• Force and Topography Reconstruction using GP and MOR for the TACTIP Soft Sensor System
• Agent-based Autonomous Systems and Abstraction Engines: Theory meets Practice
• Experimental evaluation of a multi-modal user interface for a robotic Service
• Enhancing Autonomy in VTOL Aircraft Based on Symbolic Computation Algorithms
• Development of an Intelligent Robotic Rein for Haptic Control and Interaction with Mobile Machines
• Infrastructure Mapping in Well-Structured Environments Using MAV
• Probabilistic Model Checking of Ant-Based Positionless Swarming
• Testing, Verification and Improvements of Timeliness in ROS Processes
• The Psi Swarm: A low-cost robotics platform and its use in an education setting
• Multi Robot Cooperative Area Coverage, Case Study: Spraying
• A Step Toward Mobile Robots Autonomy: Energy Estimation Models
• Toward Performance Guarantee for Autonomous Mobile Robotic Mission: An Approach for Hardware and Software Resources Management
• Using Google Glass in Human-Robot Swarm Interaction
• A virtual viscoelastic based aggregation model for self-organization of swarm robots system
• A Portable Active Binocular Robot Vision Architecture for Scene Exploration
• Kinematic Analysis of the Human Thumb with Foldable Palm
• Private Cloud Deployment Model in Open-Source Mobile Robots Ecosystem
• Efficient People-Searching Robot
• Accurate and Versatile Automation of Industrial Kitting Operations with SkiROS
• Introducing a 3D Physics Simulator Plugin for the ARGoS Robot Simulator
• Co-operative use of Marine Autonomous Systems to enhance navigational accuracy of Autonomous Underwater Vehicles
• Human Management of a Robotic Swarm
• Texture Recognition Using Force Sensitive Resistors
• First Experiences towards Potential Impact of an Outdoor Shopping Assistant
• Evaluating Multi-Robot Teamwork in Parameterised Environments
• How to build and customize a high-resolution 3D laserscanner using off-the-shelf components
• Effects of residual charge on the performance of Electro-adhesive grippers
• A Data Set for Fault Detection Research on Component-Based Robotic Systems.

This book constitutes the refereed proceedings of the 17th Annual Conference on Towards Autonomous Robotics, TAROS 2016, held in Sheffield UK, in June/July 2016. The 23 revised full papers presented together with 15 short papers were carefully reviewed and selected from 56 submissions. The overall program covers various aspects of robotics, including navigation, planning, sensing and perception, flying and swarm robots, ethics, humanoid robotics, human-robot interaction, and social robotics.

• Biological and cognitive vision
• Hardware-implemented and real-time vision systems
• High-level vision
• Learning and adaptation
• Robot vision.- Vision systems applications.

This book constitutes the refereed proceedings of the 10th International Conference on Computer Vision Systems, ICVS 2015, held in Copenhagen, Denmark, in July 2015. The 48 papers presented were carefully reviewed and selected from 92 submissions. The paper are organized in topical sections on biological and cognitive vision; hardware-implemented and real-time vision systems; high-level vision; learning and adaptation; robot vision; and vision systems applications.

• Sections on motion and reconstruction.- Mathematical foundations and image processing.- Biomedical image analysis and applications.- Human pose analysis
• Recognition and scene understanding.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 37th German Conference on Pattern Recognition, GCPR 2015, held in Aachen, Germany, in October 2015. The 45 revised full papers and one Young Researchers Forum presented were carefully reviewed and selected from 108 submissions. The papers are organized in topical sections on motion and reconstruction; mathematical foundations and image processing; biomedical image analysis and applications; human pose analysis; recognition and scene understanding.
(source: Nielsen Book Data)

• to Pattern Recognition and Classification in Medical and Astrophysical Images.- Image Standardization and Enhancement.- Intensity and Region-Based Feature Recognition in Solar Images.- Advanced Feature Recognition and Classification Using Artificial Intelligence Paradigms.- Feature Recognition and Classification Using Spectral Methods.
• (source: Nielsen Book Data)

This book presents innovative techniques in recognition and classification of astrophysical and medical images. Coverage includes: image standardization and enhancement; region-based methods for pattern recognition in medical and astrophysical images; advanced information processing using statistical methods; and feature recognition and classification using spectral method.
(source: Nielsen Book Data)

• Conversational Agents in Healthcare: Using QCA to Explain Patients Resistance to Chatbots for Medication
• An Approach for Ex-Post-Facto Analysis of Knowledge Graph-Driven Chatbots
• the DBpedia Chatbot
• Privacy Concerns in Chatbot Interactions
• Creating Humanlike Chatbots: What Chatbot Developers Could Learn from Webcare Employees in Adopting a Conversational Human Voice
• The Conversational Agent "Emoty" Perceived by People with Neurodevelopmental Disorders: Is It a Human or a Machine
• Gender Bias in Chatbot Design
• Conversational Web Interaction: Proposal of a Dialog-Based Natural Language Interaction Paradigm for the Web
• Designing Chatbots for Guiding Online Peer Support Conversations for Adults with ADHD
• Towards Chatbots to Support Bibliotherapy Preparation and Delivery
• CivicBots
• Chatbots for Supporting Youth in Societal Participation
• Using Theory of Mind to Assess Users Sense of Agency in Social Chatbots
• Exploring Age Differences in Motivations for and Acceptance of Chatbot Communication in a Customer Service Context
• Improving Conversations: Lessons Learnt from Manual Analysis of Chatbot Dialogues
• Conversational Repair in Chatbots for Customer Service: The Effect of Expressing Uncertainty and Suggesting Alternatives
• Working Together with Conversational Agents: The Relationship of Perceived Cooperation with Service Performance Evaluation
• Chatbots for the Information Acquisition at Universities
• A Students View on the Application Area
• A Configurable Agent to Advance Peers Productive Dialogue in MOOCs
• Small Talk Conversations and the Long-Term Use of Chatbots in Educational Settings
• Experiences from a Field Study.

This book constitutes the refereed proceedings of the Third International Workshop on Chatbot Research and Design, CONVERSATIONS 2019, held in Amsterdam, The Netherlands, in November 2019. The 18 revised full papers presented in this volume were carefully reviewed and selected from 31 submissions. The papers are grouped in the following topical sections: user and communication studies user experience and design, chatbots for collaboration, chatbots for customer service, and chatbots in education.

• Structured Tensor Decompositions and Applications.- Matrix and Tensor Factorizations.- ICA Methods.- Nonlinear Mixtures.- Audio Data and Methods.- Signal Separation Evaluation Campaign.- Deep Learning and Data-driven Methods.- Advances in Phase Retrieval and Applications.- Sparsity-Related Methods.- Biomedical Data and Methods.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018, held in Guildford, UK, in July 2018.The 52 full papers were carefully reviewed and selected from 62 initial submissions. As research topics the papers encompass a wide range of general mixtures of latent variables models but also theories and tools drawn from a great variety of disciplines such as structured tensor decompositions and applications; matrix and tensor factorizations; ICA methods; nonlinear mixtures; audio data and methods; signal separation evaluation campaign; deep learning and data-driven methods; advances in phase retrieval and applications; sparsity-related methods; and biomedical data and methods.
(source: Nielsen Book Data)

• Biometrics.- Computer Vision Application.- Deep Learning.- Document Analysis.- Face Recognition and Analysis.- Feature Extraction and Selection.- Machine Learning.- Object Detection and Tracking.- Performance Evaluation and Database.- Remote Sensing.
• (source: Nielsen Book Data)

The four-volume set LNCS 11056, 110257, 11258, and 11073 constitutes the refereed proceedings of the First Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018, held in Guangzhou, China, in November 2018. The 179 revised full papers presented were carefully reviewed and selected from 399 submissions. The papers have been organized in the following topical sections: Part I: Biometrics, Computer Vision Application. Part II: Deep Learning. Part III: Document Analysis, Face Recognition and Analysis, Feature Extraction and Selection, Machine Learning. Part IV: Object Detection and Tracking, Performance Evaluation and Database, Remote Sensing.
(source: Nielsen Book Data)

• Queueing Models with Retrials.- Queueing Models with Variants of Service.- Queueing Models for Cloud Computing.- Queueing Models for Wireless Communication.- Optimization in Queueing Models.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 13th International Conference on Queueing Theory and Network Applications, QTNA 2018, held in Tsukuba, Japan in July 2018.The 8 full papers together with 10 short papers included in this volume were carefully reviewed and selected from 57 initial submissions. All the papers to be presented disseminate the latest results covering up-to-date research fields such as performance modeling and analysis of telecommunication systems, retrial and vacation queueing models, optimization of queueing systems, modeling of social systems, application of machine learning in queueing models.
(source: Nielsen Book Data)

• Behavior Analysis.- Human Motion Recognition.- Application Datasets.
• (source: Nielsen Book Data)

This book constitutes the revised selected papers of the Second International Workshop on Understanding Human Activities through 3D Sensors, UHA3DS 2016, that was held in conjunction with the 23rd International Conference on Pattern Recognition, ICPR 2016, held in Cancun, Mexico, in December 2016. The 9 revised full papers were carefully reviewed and selected from 12 submissions. The papers are organized in topical sections on Behavior Analysis, Human Motion Recognition, and Application Datasets.
(source: Nielsen Book Data)

• The Two-point Fano and Ideal Binary Clutters.- On Scheduling Coflows.- Integrality Gaps of Integer Knapsack Problems.- An Improved Integrality Gap for the Calinescu-Karloff-Rabani Relaxation for Multiway Cut.- Approximation of Corner Polyhedra with Families of Intersection Cuts.- The Structure of the Infinite Models in Integer Programming.- Mixed-integer Linear Representability, Disjunctions, and Variable Elimination.- Deterministic Fully Dynamic Approximate Vertex Cover and Fractional Matching in O(1) Amortized Update Time.- Cutting Planes from Wide Split Disjunctions.- The Salesman's Improved Tours for Fundamental Classes.- The Heterogeneous Capacitated k-Center Problem.- Local Guarantees in Graph Cuts and Clustering.- Verifying Integer Programming Results.- Long term Behavior of Dynamic Equilibria in uid Queuing Networks.- A 4/5 - Approximation Algorithm for the Maximum Traveling Salesman Problem.- Minimizing Multimodular Functions and Allocating Capacity in Bike-sharing Systems.- Compact, Provably-Good LPs for Orienteering and Regret-Bounded Vehicle Routing.- Discrete Newton's Algorithm for Parametric Submodular Function Minimization.- Stochastic Online Scheduling on Unrelated Machines.- Online Matroid Intersection: Beating Half for Random Arrival.- Number Balancing is as Hard as Minkowski's Theorem and Shortest Vector.- An Improved Deterministic Rescaling for Linear Programming Algorithms.- Min-Max Theorems for Packing and Covering Odd (u
• v)-trails.- Breaking 1 - 1/e Barrier for Non-preemptive Throughput Maximization.- A Quasi-Polynomial Approximation for the Restricted Assignment Problem.- Adaptive Submodular Ranking.- On the Notions of Facets, Weak Facets, and Extreme Functions of the Gomory-Johnson Infinite Group Problem.- Minimum Birkhoff-von Neumann Decomposition.- Maximum Matching in the Online Batch-Arrival Model.- Budget Feasible Mechanisms on Matroids.- Deterministic Discrepancy Minimization Via the Multiplicative Weight Update Method.- Mixed-integer Convex Representability.- High Degree Sum of Squares Proofs, Bienstock-Zuckerberg Hierarchy and Chvatal-Gomory Cuts.- Enumeration of Integer Points in Projections of Unbounded Polyhedral.- Excluded t-factors in Bipartite Graphs: A Unified Framework for Nonbipartite Matchings and Restricted 2-matchings.- Equilibrium Computation in Atomic Splittable Singleton Congestion Games. .
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 19th International Conference on Integer Programming and Combinatorial Optimization, IPCO 2017, held in Waterloo, IN, Canada, in June 2017. The 36 full papers presented were carefully reviewed and selected from 125 submissions. The conference is a forum for researchers and practitioners working on various aspects of integer programming and combinatorial optimization. The aim is to present recent developments in theory, computation, and applications in these areas. The scope of IPCO is viewed in a broad sense, to include algorithmic and structural results in integer programming and combinatorial optimization as well as revealing computational studies and novel applications of discrete optimization to practical problems.
(source: Nielsen Book Data)

• Biometrics.- Computer Vision Application.- Deep Learning.- Document Analysis.- Face Recognition and Analysis.- Feature Extraction and Selection.- Machine Learning.- Object Detection and Tracking.- Performance Evaluation and Database.- Remote Sensing.
• (source: Nielsen Book Data)

The four-volume set LNCS 11056, 110257, 11258, and 11073 constitutes the refereed proceedings of the First Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018, held in Guangzhou, China, in November 2018. The 179 revised full papers presented were carefully reviewed and selected from 399 submissions. The papers have been organized in the following topical sections: Part I: Biometrics, Computer Vision Application. Part II: Deep Learning. Part III: Document Analysis, Face Recognition and Analysis, Feature Extraction and Selection, Machine Learning. Part IV: Object Detection and Tracking, Performance Evaluation and Database, Remote Sensing.
(source: Nielsen Book Data)

• Algorithms.- Biometrics and pattern recognition applications.- Data analysis and information retrieval.-Engineering of enterprise software products.- Industrial management and other applications.- Modelling and optimization.- Various aspects of computer security.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 16th IFIP TC8 International Conference on Computer Information Systems and Industrial Management, CISIM 2017, held in Bialystok, Poland, in June 2017. The 60 regular papers presented together with 5 keynotes were carefully reviewed and Selected from 85 submissions. They are organized in the following topical sections: algorithms; biometrics and pattern recognition applications; data analysis and information retrieval; engineering of enterprise software products; industrial management and other applications; modelling and optimization; various aspects of computer security.
(source: Nielsen Book Data)

• Dimensionality reduction.- Manifold learning and embedding methods.-Dissimilarity representations.- Graph-theoretic methods.- Model selection, classification and clustering.- Semi and fully supervised learning methods.- Shape analysis.- Spatio-temporal pattern recognition.- Structural matching.- Text and document analysis.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural Syntactic, and Statistical Pattern Recognition, S+SSPR 2016, consisting of the International Workshop on Structural and Syntactic Pattern Recognition SSPR, and the International Workshop on Statistical Techniques in Pattern Recognition, SPR. The 51 full papers presented were carefully reviewed and selected from 68 submissions. They are organized in the following topical sections: dimensionality reduction, manifold learning and embedding methods; dissimilarity representations; graph-theoretic methods; model selection, classification and clustering; semi and fully supervised learning methods; shape analysis; spatio-temporal pattern recognition; structural matching; text and document analysis.
(source: Nielsen Book Data)

• Visualization and big data.- Machine learning and computer vision.- Computer graphics.- Virtual reality.
• (source: Nielsen Book Data)

This book constitute the refereed proceedings of the Fourth International Conference on Advances in Visual Informatics, IVIC 2015, held in Selangor, Malaysia, in November 2015. The five keynotes and 45 papers presented were carefully reviewed and selected from various submissions. The papers are organized in four tracks on visualization and big data; machine learning and computer vision; computer graphics; virtual reality.
(source: Nielsen Book Data)

• Pattern Recognition and Machine Learning
• Spatiotemporal Stacked Sequential Learning for Pedestrian Detection
• Social Signaling Descriptor for Group Behaviour Analysis
• A New Smoothing Method for Lexicon-Based Handwritten Text Keyword Spotting
• Empirical Evaluation of Different Feature Representations for Social Circles Detection
• Source-Target-Source Classification Using Stacked Denoising Autoencoders
• On the Impact of Distance Metrics in Instance-Based Learning Algorithms
• Multi-class Boosting for Imbalanced Data
• Computer Vision
• Object Discovery Using CNN Features in Egocentric Videos
• Prototype Generation on Structural Data Using Dissimilarity Space Representation: A Case of Study
• Estimation and Tracking of Partial Planar Templates to Improve VSLAM
• Human Centered Scene Understanding Based on 3D Long-Term Tracking Data
• Fast Head Pose Estimation for Human-Computer Interaction
• Structured Light System Calibration for Perception in Underwater Tanks
• Dense 3D SLAM in Dynamic Scenes Using Kinect
• Scene Recognition Invariant to Symmetrical Reflections and Illumination Conditions in Robotics
• System for Medical Mask Detection in the Operating Room Through Facial Attributes
• Image and Signal Processing
• New Method for Obtaining Optimal Polygonal Approximations
• Noise Decomposition Using Polynomial Approximation
• Color Correction for Image Stitching by Monotone Cubic Spline Interpolation
• Structured Output Prediction with Hierarchical Loss Functions for Seafloor Imagery Taxonomic Categorization
• Escaping Path Approach with Extended Neighborhood for Speckle Noise Reduction
• Adaptive Line Matching for Low-Textured Images
• Unsupervised Approximation of Digital Planar Curves
• On the Modification of Binarization Algorithms to Retain Grayscale Information for Handwritten Text Recognition
• Applications
• Improving the Minimum Description Length Inference of Phrase-Based Translation Models
• A Kinect-Based System to Assess Lymphedema Impairments in Breast Cancer Patients
• Arabic Writer Identification Using Local Binary Patterns (LBP) of Handwritten Fragments
• A Fuzzy C-Means Algorithm for Fingerprint Segmentation
• Word-Graph Based Applications for Handwriting Documents: Impact of Word-Graph Size on Their Performances
• Temporal Segmentation of Digital Colposcopies
• Goal-Driven Phenotyping Through Spectral Imaging for Grape Aromatic Ripeness Assessment
• Medical Image
• Robust 3D Active Shape Model for the Segmentation of the Left Ventricle in MRI
• Kernel-Based Feature Relevance Analysis for ECG Beat Classification
• Spatial-Dependent Similarity Metric Supporting Multi-atlas MRI Segmentation
• Color Detection in Dermoscopy Images Based on Scarce Annotations
• Pattern Recognition and Machine Learning
• Spectral Clustering Using Friendship Path Similarity
• R-Clustering for Egocentric Video Segmentation
• Applying Basic Features from Sentiment Analysis for Automatic Irony Detection
• Exploiting the Bin-Class Histograms for Feature Selection on Discrete Data
• Time-Series Prediction Based on Kernel Adaptive Filtering with Cyclostationary Codebooks
• Latent Topic Encoding for Content-Based Retrieval
• Dissimilarity-Based Learning from Imbalanced Data with Small Disjuncts and Noise
• Binary and Multi-class Parkinsonian Disorders Classification Using Support Vector Machines
• Peripheral Nerve Segmentation Using Speckle Removal and Bayesian Shape Models
• Measuring Scene Detection Performance
• Threshold of Graph-Based Volumetric Segmentation
• Brain Neural Data Analysis with Feature Space Defined by Descriptive Statistics
• Extremely Overlapping Vehicle Counting
• Sentence Clustering Using Continuous Vector Space Representation
• Online Learning of Stochastic Bi-automaton to Model Dialogues
• Single-Channel Separation Between Stationary and Non-stationary Signals Using Relevant Information
• Computer Vision
• A New Trajectory Based Motion Segmentation Benchmark Dataset (UdG-MS15)
• Escritoire: A Multi-touch Desk with e-Pen Input for Capture, Management and Multimodal Interactive Transcription of Handwritten Documents
• Person Enrollment by Face-Gait Fusion
• Homographic Class Template for Logo Localization and Recognition
• Multimodal Object Recognition Using Random Clustering Trees
• Videogrammetry System for Wind Turbine Vibration Monitoring
• Canonical Views for Scene Recognition in Mobile Robotics
• Crater Detection in Multi-ring Basins of Mercury
• Iterative Versus Voting Method to Reach Consensus Given Multiple Correspondences of Two Sets
• Extracting Categories by Hierarchical Clustering Using Global Relational Features
• A Calibration Algorithm for Multi-camera Visual Surveillance Systems Based on Single-View Metrology
• 3D-Guided Multiscale Sliding Window for Pedestrian Detection
• A Feature-Based Gaze Estimation Algorithm for Natural Light Scenarios
• Image and Signal Processing
• Fast Simple Linear Iterative Clustering by Early Candidate Cluster Elimination
• Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture Decomposition
• PPG Beat Reconstruction Based on Shape Models and Probabilistic Templates for Signals Acquired with Conventional Smartphones
• Peripheral Nerves Segmentation in Ultrasound Images Using Non-linear Wavelets and Gaussian Processes
• Improving Diffusion Tensor Estimation Using Adaptive and Optimized Filtering Based on Local Similarity
• Dimension Reduction of Hyperspectral Image with Rare Event Preserving
• Applications
• Clustering of Strokes from Pen-Based Music Notation: An Experimental Study
• Image Analysis-Based Automatic Detection of Transmission Towers Using Aerial Imagery
• A Sliding Window Framework for Word Spotting Based on Word Attributes
• Combining Statistical and Semantic Knowledge for Sarcasm Detection in Online Dialogues
• Estimating Fuel Consumption from GPS Data
• A Bag-of-phonemes Model for Homeplace Classification of Mandarin Speakers
• A Gaussian Process Emulator for Estimating the Volume of Tissue Activated During Deep Brain Stimulation
• Genetic Seam Carving: A Genetic Algorithm Approach for Content-Aware Image Retargeting
• Analysis of Expressiveness of Portuguese Sign Language Speakers
• Automatic Eye Localization; Multi-block LBP vs. Pyramidal LBP Three-Levels Image Decomposition for Eye Visual Appearance Description
• Clinical Evaluation of an Automatic Method for Segmentation and Characterization of the Thoracic Aorta with and Without Aneurysm Patients
• Combined MPEG7 Color Descriptors for Image Classification: Bypassing the Training Phase
• A Multi-platform Graphical Software for Determining Reproductive Parameters in Fishes Using Histological Image Analysis.

This book constitutes the proceedings of the 7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015, held in Santiage de Compostela, Spain, in June 2015. The 83 papers presented in this volume were carefully reviewed and selected from 141 submissions. They were organized in topical sections named: Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; Applications; Medical Image; Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; and Applications.

• A Novel Data Representation based on a Second-Order Dissimilarity Measure.- Characterizing Multiple Instance Datasets.- Supervised learning of diffiusion distance to improve histogram matching.- Similarity Analysis from Limiting Quantum Walks.- Introducing Negative Evidence in Ensemble Clustering.- Dissimilarity representations for low-resolution face recognition.- Deep metric learning using Triplet network.- Cluster Merging Based on Dominant Sets.- An Adaptive Radial Basis Function Kernel for Support Vector Data Description.- Robust initialization for learning Latent Dirichlet Allocation.- Unsupervised Motion Segmentation Using Metric Embedding of Features.- Transitive Assignment Kernels for Structural Classification.- Large scale Indefinite Kernel Fisher Discriminant.- Similarity-based User Identification across Social Networks.- Dominant-Set Clustering Using Multiple Affinity Matrices.- Distance-Based Network Recovery under Feature Correlation.- Discovery of salient low-dimensional dynamical structure using Hopfield Networks.- On Geodesic Exponential Kernels.- A Matrix Factorization Approach to Graph Compression.- A Geometrical Approach to Find Corresponding Patches in 3D Medical Surfaces.- Similarities, SDEs, and Most Probable Paths.- Can the optimum similarity matrix be selected before clustering for graph-based approaches?.- Approximate spectral clustering with utilized similarity information fusing geodesic based hybrid distance measures.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the Third International Workshop on Similarity Based Pattern Analysis and Recognition, SIMBAD 2015, which was held in Copenahgen, Denmark, in October 2015. The 15 full and 8 short papers presented were carefully reviewed and selected from 30 submissions.The workshop focus on problems, techniques, applications, and perspectives: from supervisedto unsupervised learning, from generative to discriminative models, and fromtheoretical issues to empirical validations.
(source: Nielsen Book Data)

• Clustering and Quantum Methods.- Motion and Tracking.- Image Processing and Segmentation.- Color, Shading and Reflectance of Light.- Propagation and Time-evolution.- Inference, Labeling, and Relaxation.
• (source: Nielsen Book Data)

This volume constitutes the refereed proceedings of the 11th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2017, held in Venice, Italy, in October/November 2017. The 37 revised full papers were carefully reviewed and selected from 51 submissions. The papers are organized in topical sections on Clustering and Quantum Methods; Motion and Tracking; Image Processing and Segmentation; Color, Shading and Reflectance of Light; Propagation and Time-evolution; and Inference, Labeling, and Relaxation.
(source: Nielsen Book Data)

• Biometrics.- Computer Vision Application.- Deep Learning.- Document Analysis.- Face Recognition and Analysis.- Feature Extraction and Selection.- Machine Learning.- Object Detection and Tracking.- Performance Evaluation and Database.- Remote Sensing.
• (source: Nielsen Book Data)

The four-volume set LNCS 11056, 110257, 11258, and 11073 constitutes the refereed proceedings of the First Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018, held in Guangzhou, China, in November 2018. The 179 revised full papers presented were carefully reviewed and selected from 399 submissions. The papers have been organized in the following topical sections: Part I: Biometrics, Computer Vision Application. Part II: Deep Learning. Part III: Document Analysis, Face Recognition and Analysis, Feature Extraction and Selection, Machine Learning. Part IV: Object Detection and Tracking, Performance Evaluation and Database, Remote Sensing.
(source: Nielsen Book Data)

• Discrete geometry and topology.- Tilings and patterns.- Grammars, models and other technical tools for image analysis.- Image segmentation.- Classification.- Reconstruction.- Compression.- Texture analysis.-Bioimaging.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 18th International Workshop on Combinatorial Image Analysis, IWCIA 2017, held in Plovdiv, Bulgaria, in June 2017. The 27 revised full papers presented were carefully reviewed and selected from 47 submissions. The workshop is organized in topical sections of theoretical foundations and theory of applications, namely: discrete geometry and topology; tilings and patterns; grammars, models and other technical tools for image analysis; image segmentation, classification; reconstruction; compression; texture analysis; bioimaging.
(source: Nielsen Book Data)

• Pattern Recognition and Machine Learning.- Computer Vision.- Image and Signal Processing.- Medical Image.-Applications.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 8th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2017, held in Faro, Portugal, in June 2017. The 60 regular papers presented in this volume were carefully reviewed and selected from 86 submissions. They are organized in topical sections named: Pattern Recognition and Machine Learning; Computer Vision; Image and Signal Processing; Medical Image; and Applications.
(source: Nielsen Book Data)

• Image processing technology
• Image analysis and understanding
• Computer vision and pattern recognition
• Big data mining, computer graphics and VR.-Image technology application.

This book constitutes the refereed proceedings of the 11th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2016, held in Beijing, China in July 2016. The 27 papers presented were carefully reviewed and selected from 69 submissions. They provide a forum for sharing progresses in the areas of image processing technology; image analysis and understanding; computer vision and pattern recognition; big data mining, computer graphics and VR; as well as image technology applications.

• Learning sequential data with the help of linear systems
• A spiking neural network for personalised modelling of Electrogastogrophy (EGG)
• Improving generalization abilities of maximal average margin classifiers
• Finding small sets of random Fourier features for shift-invariant kernel approximation
• Incremental construction of low-dimensional data representations
• Soft-constrained nonparametric density estimation with artificial neural networks
• Density based clustering via dominant sets
• Co-training with credal models
• Interpretable classifiers in precision medicine: feature selection and multi-class categorization
• On the evaluation of tensor-based representations for optimum-pathforest classification
• On the harmony search using quaternions
• Learning parameters in deep belief networks through firefly algorithm
• Towards effective classification of imbalanced data with convolutional neural networks
• On CPU performance optimization of restricted Boltzmann machine and convolutional RBM
• Comparing incremental learning strategies for convolutional neural networks
• Approximation of graph edit distance by means of a utility matrix
• Time series classification in reservoir- and model-space: a comparison
• Objectness scoring and detection proposals in forward-Looking sonar images with convolutional neural networks
• Background categorization for automatic animal detection in aerial videos using neural networks
• Predictive segmentation using multichannel neural networks in Arabic OCR system
• Quad-tree based image segmentation and feature extraction to recognize online handwritten Bangla characters
• A hybrid recurrent neural network/dynamic probabilistic graphical model predictor of the disulfide bonding state of cysteines from the primary structure of proteins
• Using radial basis function neural networks for continuous anddiscrete pain estimation from bio-physiological signals
• Active learning for speech event detection in HCI
• Emotion recognition in speech with deep learning architectures
• On gestures and postural behavior as a modality in ensemble methods
• Machine learning driven heart rate detection with camera photoplethysmography in time domain.

This book constitutes the refereed proceedings of the 7th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2016, held in Ulm, Germany, in September 2016. The 25 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 32 submissions for inclusion in this volume. The workshop will act as a major forum for international researchers and practitioners working in all areas of neural network- and machine learning-based pattern recognition to present and discuss the latest research, results, and ideas in these areas.

• Robotics
• Computer vision
• Basic theory of pattern recognition
• Image and video processing
• Speech and language
• Emotion recognition.

The two-volume set CCIS 662 and CCIS 663 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition, CCPR 2016, held in Chengdu, China, in November 2016. The 121 revised papers presented in two volumes were carefully reviewed and selected from 199 submissions. The papers are organized in topical sections on robotics; computer vision; basic theory of pattern recognition; image and video processing; speech and language; emotion recognition.

• Big and Smart Data
• A Parallel Approach for Decision Trees Learning from Big Data Streams
• Industry 4.0
• Potentials for Creating Smart Products: Empirical Research Results
• Evaluating New Approaches of Big Data Analytics Frameworks
• Automated Equation Formulation for Causal Loop Diagrams
• An Empirical Study on Spreadsheet Shortcomings from an Information Systems Perspective
• A Mark Based-Temporal Conceptual Graphs for Enhancing Big Data Management and Attack Scenario Reconstruction
• Semantic Technologies
• Ontology-Based Creation of 3D Content in a Service-Oriented Environment
• Modeling and Querying Sensor Services Using Ontologies
• Validation and Calibration of Dietary Intake in Chronic Kidney Disease: An Ontological Approach
• Content Retrieval and Filtering
• Evaluation of the Dynamic Construct Competition Miner for an eHealth System
• Dynamic Reconfiguration of Composite Convergent Services Supported by Multimodal Search
• On Extracting Information from Semi-structured Deep Web Documents
• A Novel Approach to Web Information Extraction
• Business Process Management and Mining
• Mining Multi-variant Process Models from Low-Level Logs
• On Improving the Maintainability of Compliance Rules for Business Processes
• Evolutionary Computation Based Discovery of Hierarchical Business Process Models
• Collaboration
• Empowering End-Users to Collaboratively Structure Processes for Knowledge Work
• Social Collaboration Solutions as a Catalyst of Consumer Trust in Service Management Platforms
• A Research Study
• The Relative Advantage of Collaborative Virtual Environments and Two-Dimensional Websites in Multichannel Retail
• Enterprise Architecture and Business-IT-Alignment
• Combining Business Processes and Cloud Services: A Marketplace for Processlets
• The Nature and a Process for Development of Enterprise Architecture Principles
• Interrelations Between Enterprise Modeling Focal Areas and Business and IT Alignment Domains
• Specific BIS Applications
• Multi-dimensional Performance Measurement in Complex Networks
• Model and Integration for Logistics Chain Management
• Engaging Facebook Users in Brand Pages: Different Posts of Marketing-Mix Information
• Context Variation for Service Self-contextualization in Cyber-Physical Systems
• EPOs, an Early Warning System for Post-merger IS Integration
• Open Data for BIS
• The Adoption of Open Data and Open API Telecommunication Functions by Software Developers
• The Use of Open API in Travel Planning Application
• Tap & Plan Use Case.

This book contains the refereed proceedings of the 18th International Conference on Business Information Systems, BIS 2015, held in Poznań, Poland, in June 2015. The BIS conference series follows trends in academic and business research; thus, the theme of the BIS 2015 conference was "Making Big Data Smarter." Big data is now a fairly mature concept, recognized and widely used by professionals in both research and industry. Together, they work on developing more adequate and efficient tools for data processing and analyzing, thus turning "big data" into "smart data." The 26 revised full papers were carefully reviewed and selected from 70 submissions. In addition, two invited papers are included in this book. They are grouped into sections on big and smart data, semantic technologies, content retrieval and filtering, business process management and mining, collaboration, enterprise architecture and business−IT alignment, specific BIS applications, and open data for BIS.

• Intro
• Preface
• Organization
• Contents
• Invited Talk
• Digital Analytical Geometry: How Do I Define a Digital Analytical Object?
• 1 Introduction
• 2 Digitization
• 2.1 Notations
• 2.2 General Remarks on Digitizations
• 2.3 Morphological Digitizations
• 3 Analytical Characterization of Digital Objects
• 3.1 Direct Defined Analytical Digital Objects
• 3.2 Digitized Analytical Objects
• 4 Conclusion and Perspectives
• References
• Theoretical Foundations of Combinatorial Image Analysis
• Digital Geometry and Topology

• Fuzzy Connectedness Segmentation: A Brief Presentation of the Literature
• 1 Introduction
• 2 Basic Definitions
• 3 A Simple Multi Object Fuzzy Segmentation (MOFS) Algorithm
• 4 Robustness of Fuzzy Connectedness Segmentations
• 5 Unified Theory of FC Segmentations
• 6 Conclusion
• References
• Equivalent Sequential and Parallel Subiteration-Based Surface-Thinning Algorithms
• 1 Introduction
• 2 Basic Notions and Results
• 3 Parallel and Sequential 6-Subiteration Surface-Thinning Algorithms
• 4 Verification
• 5 Conclusions
• References

• Relative Convex Hull Determination from Convex Hulls in the Plane
• 1 Introduction
• 2 Preliminaries
• 3 Definition and Properties of the Relative Convex Hull
• 4 Previous Algorithms of Determining the Relative Convex Hull for Simple Polygons in the Plane
• 5 A New Algorithm of Determining the Relative Convex Hull for Simple Polygons in the Plane
• 5.1 Vertex Lists, Convex Hull Determination and Cavity Detection
• 5.2 Processing of One Cavity
• 5.3 Detection and Processing of Subsequent Cavities
• 5.4 Pseudocode, Implementation, and Complexity
• 6 Conclusion and Future Work

• On the Connectivity and Smoothness of Discrete Spherical Circles
• 1 Introduction
• 1.1 Motivation
• 1.2 Definitions and Terminologies
• 2 Discrete Spheres, Types, Ordering
• 3 Discrete Spherical Circles
• 3.1 Discretization Classes
• 3.2 Smoothness of Spherical Circles
• 3.3 Algorithm for Discrete Spherical Circle
• 4 Results and Discussion
• References
• Optimal Consensus Set for nD Fixed Width Annulus Fitting
• 1 Introduction
• 2 Annulus Fitting
• 2.1 Annulus Characterization
• 2.2 nD Annular Characterizations
• 3 Fitting Algorithm
• 3.1 Dual Space and Annulus Fitting in nD

This volume constitutes the refereed proceedings of the 17th International Workshop on Combinatorial Image Analysis, IWCIA 2015, held in Kolkata, India, in November 2015. The 24 revised full papers and 2 invited papers presented were carefully reviewed and selected from numerous submissions. The workshop provides theoretical foundations and methods for solving problems from various areas of human practice. In contrast to traditional approaches to image analysis which implement continuous models, float arithmetic and rounding, combinatorial image analysis features discrete modelsusing integer arithmetic. The developed algorithms are based on studying combinatorial properties of classes of digital images, and often appear to be more efficient and accurate than those based on continuous models.

• Cover; Title page; Preface; Contents;
• 1. Introduction to Neural Networks for Instrumentation, Measurement, and Industrial Applications;
• 2. The Fundamentals of Measurement Techniques;
• 3. Neural Networks in Intelligent Sensors and Measurement Systems for Industrial Applications;
• 4. Neural Networks in System Identification;
• 5. Neural Techniques in Control;
• 6. Neural Networks for Signal Processing in Measurement Analysis and Industrial Applications: the Case of Chaotic Signal Processing.

This work aims to disseminate theoretical and practical knowledge about neural networks in measurement, instrumentation and the related industrial applications. It also creates a consciousness about the effectiveness of these techniques as well as the measurement problems in industrial environments.

• Deep Learning Methods for Image Guidance in Radiation Therapy Intentional Image Similarity Search.- Sttructured (De)composable Representations Trained with Neural Networks.- Long Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling.- Improving Accuracy and Efficiency of Object Detection Algorithms using Multiscale Feature Aggregation Plugins.- Abstract Echo State Networks.- Minimal Complexity Support Vector Machines.- Named Entity Disambiguation at Scale.- Geometric Attention for Prediction of Differential Properties in 3D Point Clouds.- How (Not) to Measure Bias in Face Recognition Networks.-Feature Extraction: A Time Window Analysis based on the X-ITE Pain Database.- Pain Intensity Recognition - An Analysis of Short-Time Sequences in a Real-World Scenario.- A deep learning approach for efficient registration of dual view mammography.- Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology.- Applications of Generative Adversarial Networks to Dermatologic Imaging.- Typing Plasmids with Distributed Sequence Representation.- KP-YOLO: a modification of YOLO algorithm for the keypoint-based detection of QR Codes.- Using Mask R-CNN for Image-Based Wear Classification of Solid Carbide Milling and Drilling Tools.- A Hybrid Deep Learning Approach For Forecasting Air Temperature.- Using CNNs to optimize numerical simulations in geotechnical engineering.- Going for 2D or 3D? Investigating various Machine Learning Approaches for Peach Variety Identification.- A Transfer Learning End-to-End Arabic Text-To-Speech (TTS) Deep Architecture.- ML-Based Trading Models: An investigation during COVID-19 pandemic crisis.- iNNvestigate-GUI - Explaining Neural Networks Through an Interactive Visualization Tool.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications.
(source: Nielsen Book Data)

• Pattern Recognition Techniques.- Image Processing and Analysis.- Computer Vision.- Industrial and Medical Applications of Pattern Recognition.- Natural Language Processing and Recognition.- Artificial Intelligence Techniques and Recognition.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 12th Mexican Conference on Pattern Recognition, MCPR 2020, which was due to be held in Morelia, Mexico, in June 2020. The conference was held virtually due to the COVID-19 pandemic. The 31 papers presented in this volume were carefully reviewed and selected from 67 submissions. They were organized in the following topical sections: pattern recognition techniques; image processing and analysis; computer vision; industrial and medical applications of pattern recognition; natural language processing and recognition; artificial intelligence techniques and recognition.
(source: Nielsen Book Data)

• Biometrics.- Computer Vision Application.- Deep Learning.- Document Analysis.- Face Recognition and Analysis.- Feature Extraction and Selection.- Machine Learning.- Object Detection and Tracking.- Performance Evaluation and Database.- Remote Sensing.
• (source: Nielsen Book Data)

The four-volume set LNCS 11056, 110257, 11258, and 11073 constitutes the refereed proceedings of the First Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2018, held in Guangzhou, China, in November 2018. The 179 revised full papers presented were carefully reviewed and selected from 399 submissions. The papers have been organized in the following topical sections: Part I: Biometrics, Computer Vision Application. Part II: Deep Learning. Part III: Document Analysis, Face Recognition and Analysis, Feature Extraction and Selection, Machine Learning. Part IV: Object Detection and Tracking, Performance Evaluation and Database, Remote Sensing.
(source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 15th International Semantic Web Conference, ESWC 2018, held in Heraklion, Crete, Greece. The 48 revised full papers presented were carefully reviewed and selected from 179 submissions. The papers cover a large range of topics such as logical modelling and reasoning, natural language processing, databases and data storage and access, machine learning, distributed systems, information retrieval and data mining, social networks, and Web science and Web engineering. .
(source: Nielsen Book Data)

• Pattern Recognition and Artificial Intelligent Techniques.- Image Processing and Analysis.- Robotics and Computer Vision.- Natural Language Processing and Recognition.- Applications of Pattern Recognition.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 7th Mexican Conference on Pattern Recognition, MCPR 2015, held in Mexico City Mexico, in June 2015. The 30 revised full papers presented were carefully reviewed and selected from 63 submissions. The papers are organized in topical sections on pattern recognition and artificial intelligence; image processing and analysis; robotics and computer vision; natural language processing and recognition; and applications of pattern recognition.
(source: Nielsen Book Data)

• Semi-supervised Learning Algorithm for Binary Relevance Multilabel Classification
• On the Information Diffusion between Web-based Social Networks
• Generic Private Social Network for Knowledge Management
• Analysis of Event Logs: Behavioral Graphs
• Delineating Worker-Centered Organizational Work: Blending BPMS and Social Software Features
• Compliance of the LinkedIn Privacy Policy with the Principles of the ISO 29100:2011 Standard
• The Use of Social Tagging in Social Business Process Management SocIoS API: A Data Aggregator for Accessing User Generated Content from Online Social Networks
• Integrating Social Media and Open Data in a Cloud-based Platform for Public Sector Advertising
• A Survey on Approaches to Modeling Artifact-centric Business Processes
• A Connectivity-Based Recommendation Approach for Data Service Mashups
• Using Incentives to Analyze Social Web Services Behaviors Creating and Modelling Personal Socio-Economic Networks in On-Line Banking
• Cloud-based Video Monitoring Framework: An Approach based on Software-Defined Networking for Addressing Scalability Problems
• Context-awareness in Task Automation Services by Distributed Event Processing
• Data Streams Quality Evaluation for the Generation of Alarms in Health Domain
• Multilayer and Multi-agent Data Fusion in WSN
• Strategies for Data Quality Monitoring in Business Processes Quality Improvement Framework for Business Oriented Geo-Spatial Data.

This book constitutes the revised selected papers of the combined workshops on Web Information Systems Engineering, WISE 2014, held in Thessaloniki, Greece, in October 2014. The 19 selected papers presented were carefully revised and report from the four workshops: computational social networks, IWCSN 2014, enterprise social networks, Org2 2014, personalization and context-awareness in cloud and service computing, PCS 2014, and data quality and trust in big data, QUAT 2014.

• Algebraic Theory, Max-Plus and Max-Min Mathematics.- Discrete Geometry and Discrete Topology.- Watershed and Graph-based Segmentation.- Trees and Hierarchies.- Topological and Graph-based Clustering, Classification and Filtering.- Connected Operators and Attribute Filters.- PDE-based Morphology.- Scale-Space Representations and Nonlinear Decompositions.- Computational Morphology.- Object Detection.- Biomedical, Material Science and Physical Applications.
• (source: Nielsen Book Data)

This book contains the refereed proceedings of the 13th International Symposium on Mathematical Morphology, ISMM 2017, held in Fontainebleau, France, in May 2017. The 36 revised full papers presented together with 4 short papers were carefully reviewed and selected from 53 submissions. The papers are organized in topical sections on algebraic theory, max-plus and max-min mathematics; discrete geometry and discrete topology; watershed and graph-based segmentation; trees and hierarchies; topological and graph-based clustering, classification and filtering; connected operators and attribute filters; PDE-based morphology; scale-space representations and nonlinear decompositions; computational morphology; object detection; and biomedical, material science and physical applications.
(source: Nielsen Book Data)