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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)