%{search_type} search results

• 1. Introduction to modern Fortran
• 2. Array-valued functions
• 3. Mathematical abstractions
• 4. Memory management
• 5. An interface problem
• 6. Interfacing to C: SQLite as an example
• 7. Graphics, GUIs, and the internet
• 8. Unit testing
• 9. Code reviews
• 10. Robust implementation of several simple algorithms
• 11. Object-oriented programming
• 12. Parallel programming
• Appendix A. Tools for development and maintenance
• Appendix B. Caveats.
• (source: Nielsen Book Data)

From its earliest days, the Fortran programming language has been designed with computing efficiency in mind. The latest standard, Fortran 2008, incorporates a host of modern features, including object-orientation, array operations, user-defined types, and provisions for parallel computing. This tutorial guide shows Fortran programmers how to apply these features in twenty-first-century style: modular, concise, object-oriented, and resource-efficient, using multiple processors. It offers practical real-world examples of interfacing to C, memory management, graphics and GUIs, and parallel computing using MPI, OpenMP, and coarrays. The author also analyzes several numerical algorithms and their implementations and illustrates the use of several open source libraries. Full source code for the examples is available on the book's website.
(source: Nielsen Book Data)

• Preface xxix Acknowledgments xxxi Trademarks and Copyrights xxxiii Copyright Permissions xxxv About the Companion Website xxxix
• Chapter 1 Introduction to Microcontrollers and This Book 1 1.1 Microcontroller Configuration and Structure 2 1.2 The ARM Cortex M4 Microcontroller System 3 1.3 The TM4C123GH6PM Microcontroller Development Tools and Kits 4 1.4 Outstanding Features About This Book 5 1.5 Who This Book Is For 5 1.6 What This Book Covers 6 1.7 How This Book Is Organized and How to Use This Book 8 1.8 How to Use the Source Code and Sample Projects 9 1.9 Instructors and Customers Supports 11
• Chapter 2 ARM Microcontroller Architectures 13 2.1 Overview and Introduction 13 2.2 Introduction to ARM Cortex-M4 MCU 15 2.3 The Memory Architecture 27 2.4 The Nested Vectored Interrupt Controller (NVIC) Architecture 34 2.5 The Debug Architecture 37 2.6 Introduction to Tiva C Series ARM Cortex-M4 MCU-TM4C123GH6PM 38 2.7 Introduction to Tiva C Series LaunchPad TM4C123GXL Evaluation Board 72 2.8 Introduction to EduBASE ARM Trainer 77 2.9 Chapter Summary 77
• Chapter 3 ARM Microcontroller Development Kits 83 3.1 Overview and Introduction 83 3.2 The Entire Tiva TM4C123G-based Development System 84 3.3 Download and Install Development Suite and Specified Firmware 86 3.4 Introduction to the Integrated Development Environment Keil MDK Version5 87 3.5 Embedded Software Development Procedure 127 3.6 The Keil ARM-MDK Vision5 Debugger and Debug Process 128 3.7 The TivaWare for C Series Software Suite 140 3.8 The TivaWare for C Series Utilities and Other Supports 147 3.9 Program Examples 151 3.10 Chapter Summary 152
• Chapter 4 ARM Microcontroller Software and Instruction Set 155 4.1 Overview and Introduction 155 4.2 Introduction to ARM Cortex-M4 Software Development Structure 156 4.3 Introduction to ARM Cortex-M4 Assembly Instruction Set 157 4.4 ARMdD Cortex-M4 Software Development Procedures 196 4.5 Using C Language to Develop ARM Cortex-M4 Microcontroller Applications 197 4.6 Chapter Summary 243
• Chapter 5 ARM Microcontroller Interrupts and Exceptions 261 5.1 Overview and Introduction 261 5.2 Exceptions and Interrupts in the ARM Cortex-M4 MCU System 263 5.3 Exceptions and Interrupts in the TM4C123GH6PM Microcontroller System 273 5.4 Developing GPIO Port Interrupt Projects to Handle GPIO Interrupts 285 5.5 Comparison Among Four Interrupt Programming Methods 317 5.6 Chapter Summary 318
• Chapter 6 ARM Microcontroller Memory System 333 6.1 Overview and Introduction 333 6.2 Memory Architecture in the TM4C123GH6PM MCU System 334 6.3 Memory Map in TM4C123GH6PM MCU System 361 6.4 Bit-Band Operations 362 6.5 Memory Requirements and Memory Properties 370 6.6 Memory System Programming Methods 375 6.7 Memory System Programming Projects 380 6.8 Chapter Summary 420
• Chapter 7 ARM Cortex-M4 Parallel I/O Ports Programming 433 7.1 Overview and Introduction 433 7.2 GPIO Module Architecture and GPIO Port Configuration 434 7.3 GPIO Port Control Registers 437 7.4 On-Board Keypad Interface Programming Project 440 7.5 Analog-to-Digital Converter Programming Project 446 7.6 PWM-Controlled DC and Step Motors Programming Project 486 7.7 The PWM API Functions in the TivaWare Peripheral Driver Library 521 7.8 Chapter Summary 525
• Chapter 8 ARM Cortex-M4 Serial I/O Ports Programming 547 8.1 Overview and Introduction 547 8.2 GPIO Module Architecture and GPIO Port Configuration 548 8.3 Synchronous Serial Interface (SSI) 551 8.4 Inter-Integrated Circuit (I2C) Interface 611 8.5 Universal Asynchronous Receivers/Transmitters (UARTs) 642 8.6 Chapter Summary 668
• Chapter 9 ARM Cortex-M4 Timer and USB Programming 691 9.1 Overview and Introduction 691 9.2 General-Purpose Timers 692 9.3 Watchdog Timers 732 9.4 Universal Serial Bus (USB) Controller 743 9.5 Chapter Summary 788
• Chapter 10 ARM Cortex-M4 Other Peripherals Programming 805 10.1 Overview and Introduction 805 10.2 The Controller Area Network (CAN) 805 10.3 The Quadrature Encoder Interface (QEI) 847 10.4 The Continuous and Discrete PID Closed-Loop Control System 871 10.5 The Fuzzy Logic Closed-Loop Control System 887 10.6 The Analog Comparators 899 10.7 Chapter Summary 908
• Chapter 11 ARM Floating Point Unit (FPU) 927 11.1 Overview and Introduction 927 11.2 Three Types of the Floating-Point Data 928 11.3 The FPU in the Cortex-M4 MCU 934 11.4 Implementing the Floating-Point Unit 938 11.5 Chapter Summary 946
• Chapter 12 ARM Memory Protection Unit (MPU) 951 12.1 Overview and Introduction 951 12.2 Implementation of the MPU 952 12.3 Initialization and Configuration of the MPU 959 12.4 Building A Practical Example MPU Project 960 12.5 The API Functions Provided by the TivaWare Peripheral Driver Library 964 12.5.3 The MPU Interrupt Handler Control API Functions 968 12.6 Chapter Summary 969 Homework 970 Index 975 About the Author 987.
• (source: Nielsen Book Data)

This book covers both the fundamentals, as well as practical techniques in designing and building microcontrollers in industrial and commercial applications. * Examples included in this book have been compiled, built, and tested * Includes Both ARM(R) assembly and C codes * Direct Register Access (DRA) model and the Software Driver (SD) model programming techniques and discussed If you are an instructor and adopted this book for your course, please email ieeeproposals@wiley.com to get access to the instructor files for this book.
(source: Nielsen Book Data)

• Foreword xi Introduction xiii Part I
• Chapter 1 Meet the BBC micro:bit 3 A Tour of the Board 3 Breaking It Down 5 Display 6 Buttons 7 Processor 8 Radio 9 Accelerometer 10 Compass 11 Input-Output Pins 12 Micro-USB Port 13 Battery Connector 14
• Chapter 2 Getting Started with the BBC micro:bit 17 Handling the BBC micro:bit 17 Powering the BBC micro:bit 18 USB Power 18 Battery Power 20 Greetings from the BBC micro:bit 23 Signs of Life 24 Testing the Buttons 24 Motion Gaming 24 Get Coding 25 Resetting the BBC micro:bit 25
• Chapter 3 Programming the BBC micro:bit 27 USB Connectivity 27 Drag-and-Drop 29 Automatic Flashing 31 The Code Editor 32 Downloading Your Program 33 About Flash Memory 38 Part II
• Chapter 4 Programming Languages 41 About Programming Languages 41 The Three Main BBC micro:bit Languages 42 JavaScript Blocks 43 JavaScript 44 Python 45 Comparing Programming Languages 46 Choosing a Programming Language 48 Other Programming Languages 49
• Chapter 5 JavaScript Blocks 51 Introducing the JavaScript Blocks Editor 51 Program 1: 'Hello, World!' 54 Loops 57 Program 2: Button Inputs 58 Multiple Buttons 60 Program 3: Touch Inputs 61 Variables 62 Program 4: The Temperature Sensor 65 Formatting the Output 67 Program 5: The Compass Sensor 67 Program 6: The Accelerometer Sensor 70 Delays 73 Reading Raw Accelerometer Data 74 Program 7: The Fruit Catcher Game 76 The Setup 77 The Main Program Loop 78 Conditional Loops 80 Conditional Statements 82 The Control Events 84 Further Steps 86
• Chapter 6 JavaScript 87 Introducing the JavaScript Editor 88 Program 1: 'Hello, World!' 90 Loops 93 Program 2: Button Inputs 94 Multiple Buttons 97 Program 3: Touch Inputs 98 Variables 99 Program 4: The Temperature Sensor 102 Formatting the Output 104 Program 5: The Compass Sensor 104 Program 6: The Accelerometer Sensor 107 Delays 109 Reading Raw Accelerometer Data 110 Program 7: The Fruit Catcher Game 112 The Setup 113 The Main Program Loop 115 The Conditional Loops 116 The Conditional Statements 117 The Control Events 120 Further Steps 123
• Chapter 7 Python 125 Introducing the Python Editor 126 Program 1: 'Hello, World!' 128 Loops 132 Program 2: Button Inputs 133 Multiple Buttons 136 Program 3: Touch Inputs 137 Variables 138 Program 4: The Temperature Sensor 141 Formatting the Output 142 Program 5: The Compass Sensor 143 Program 6: The Accelerometer Sensor 145 Delays 147 Reading Raw Accelerometer Data 148 Program 7: The Fruit Catcher Game 150 The Setup 150 The Main Program Loop 153 Conditional Loops 154 Conditional Statements 155 Drawing the Sprites 156 Finishing the Program 157 Further Steps 160 Part III
• Chapter 8 The Wireless BBC micro:bit 163 The BBC micro:bit Radio 163 Program 1: One-to-One Communication 164 Program 2: One-to-Many Communication 167 Program 3: Radio Groups 169 Testing the Group Feature 171 Using the BBC micro:bit with a Smartphone or Tablet 173
• Chapter 9 The BBC micro:bit and the Raspberry Pi 175 About the Raspberry Pi 176 Connecting the Raspberry Pi to the BBC micro:bit 177 Reading from the BBC micro:bit 180 Using the BBC micro:bit Display 186 Practical Example: A CPU Monitor 189
• Chapter 10 Building Circuits 193 Electronic Equipment 194 The Input-Output Pins 196 The Large Pins 197 The Small Pins 199 Serial Peripheral Interface (SPI) 201 Inter-Integrated Circuit (I2C) 201 Universal Asynchronous Receiver/Transmitter (UART) 201 Your First Circuits 202 Reading from a Button Input 202 Reading Resistor Colour Codes 205 Writing to an LED Output 207 Fading an LED via PWM 211 Reading an Analogue Input 213
• Chapter 11 Extending the BBC micro:bit 217 Extending via Breakout Boards 217 Kitronik Edge Connector Breakout Board 218 ScienceScope Micro:bit Breakout Board 219 Proto-Pic Bread:Bit 220 Proto-Pic Exhi:Bit 220 Robotics and the BBC micro:bit 222 Kitronik Line-Following Buggy 222 Kitronik Motor Driver Board 223 Technology Will Save Us Micro:Bot 224 4tronix Bit:Bot 225 BinaryBots 226 Other BBC micro:bit Add-Ons 227 Kitronik Mi:Power 227 Proto-Pic Micro:Pixel Board 228 Proto-Pic Simon:Says Board 229 4tronix Bit:2:Pi Board 230 Kitronik Mi:Pro Protector and Mi:Power Cases 231
• Chapter 12 The Wearable BBC micro:bit 233 Advantages of the Wearable BBC micro:bit 234 Conductive Thread 235 Using Conductive Thread 237 The Rain-Sensing Hat 241 Building the Hat 242 Mounting the BBC micro:bit 244 The Rain-Sensing Program 245 Battery Power 246
• Chapter 13 Additional Resources 249 The Micro:bit Educational Foundation 249 Official Teaching Resources 251 Third-Party Teaching Resources 252 The Institution of Engineering and Technology 252 Computing At School 253 Micro:bit for Primary Schools 253 TES Magazine 255 Code Clubs 256 Part IV Appendix A JavaScript Blocks Recipes 259 Appendix B JavaScript Recipes 267 Appendix C Python Recipes 275 Appendix D Pin-Out Listing 285 Index 289.
• (source: Nielsen Book Data)

The go-to guide to getting started with micro:bit and exploring all of the mini-computer's amazing capabilities The micro:bit is a pocket-sized electronic development platform built with education in mind. It was developed by the BBC in partnership with Microsoft and other major tech companies to provide kids with a fun, easy, inexpensive way to develop their digital skills. With it, kids (and grownups) can learn basic programming and coding while having fun making virtual pets, developing games, and a whole lot more. Written by internationally bestselling tech author Gareth Halfacree and endorsed by the Micro:bit Foundation, the micro:bit User Guide contains what you need to know to get up and running fast with the micro:bit. Learn everything from taking your first steps with the software to writing your own programs. You'll also learn how to expand its capabilities with add-ons through easy-to-follow, step-by-step instructions. * Configure your micro:bit and develop your digital skills * Write code in Microsoft PXT, Python, JavaScript, and more * Discover the motion detector and compass * Connect the micro:bit to a computer, Raspberry Pi, or your smartphone * Build your own circuits and create hardware The micro:bit User Guide is your go-to source for learning all the secrets of the micro:bit. Whether you're just beginning or have some experience, this book allows you to dive right in and experience everything the micro:bit has to offer.
(source: Nielsen Book Data)
The go-to guide to getting started with the BBC micro:bit and exploring all of its amazing capabilities. The BBC micro:bit is a pocket-sized electronic development platform built with education in mind. It was developed by the BBC in partnership with major tech companies, communities, and educational organizations to provide kids with a fun, easy, inexpensive way to develop their digital skills. With it, kids (and grownups) can learn basic programming and coding while having fun making virtual pets, developing games, and a whole lot more. Written by internationally bestselling tech author Gareth Halfacree and endorsed by the Micro:bit Foundation, The Official BBC micro:bit User Guide contains what you need to know to get up and running fast with the BBC micro:bit. Learn everything from taking your first steps with the BBC micro:bit to writing your own programs. You'll also learn how to expand its capabilities with add-ons through easy-to-follow, step-by-step instructions. Set up your BBC micro:bit and develop your digital skills Write code in JavaScript Blocks, JavaScript, and Python Discover the BBC micro:bit's built-in sensors Connect the BBC micro:bit to a Raspberry Pi to extend its capabilities Build your own circuits and create hardware The Official BBC micro:bit User Guide is your go-to source for learning all the secrets of the BBC micro:bit. Whether you're just beginning or have some experience, this book allows you to dive right in and experience everything the BBC micro:bit has to offer.
(source: Nielsen Book Data)

• Introduction xiEverado REYES-GARCIA
• Chapter 1. From Controversies to Decision-making: Between Argumentation and Digital Writing 1Orelie DESFRICHES-DORIA 1.1. Introduction 1 1.2. Hypertexts and hypermedia 2 1.3. From decision-making to the study of controversies 3 1.3.1. Definition of the concept of controversy 3 1.3.2. Shifts from one situation to another 4 1.3.3. Controversy representation 5 1.3.4. Some controversy visualization and processing tools and methods 7 1.4. Detailed presentation of Vesta Cosy 9 1.5. What is the content of argument representations? 14 1.5.1. Interactions between the two fields 14 1.5.2. Theoretical approaches to argumentation 16 1.5.3. Hypermedia structure in the process of decision-making map construction with Vesta Cosy 19 1.6. Application of Vesta Cosy to controversy analysis 22 1.6.1. Characterization of the nature of a controversy 22 1.6.2. Methodological principles of controversy analysis 24 1.7. New digital writings with hypermedia 29 1.7.1. Extension of reasoning and paradigm shift 29 1.7.2. Hyperlinked content according to diversified details 30 1.7.3. Disorientation, hypernarrativity and interactions 32 1.8. Conclusion 33 1.9. Bibliography 33
• Chapter 2. Training in Digital Writing Through the Prism of Tropisms: Case Studies and Propositions 37Stephane CROZAT 2.1. Abstract 37 2.2. Introduction 37 2.3. Issue: theoretical approach to digital technology 38 2.3.1. The possibility of mechanizing intellectual labor 38 2.3.2. Digitization of content 39 2.3.3. "It has been manipulated": manipulation as a source of digital content 40 2.3.4. "And it will be again": manipulation as the future of digital content 41 2.4. Proposition: tropisms of digital content 42 2.4.1. The concept of tropism 42 2.4.2. Modeling of functional tendencies of digital objects 44 2.5. Detailed description of tropisms 44 2.5.1. Abstraction: it has been coded and will be recoded 44 2.5.2. Addressing: it has been found and will be found again 45 2.5.3. Connection: it has been transmitted and will be retransmitted 46 2.5.4. Duplication: it has been copied and will be recopied 46 2.5.5. Transformation: it has been changed and will be changed again 47 2.5.6. Universality: it has been integrated and will be reintegrated 48 2.6. Application: training in digital technology with tropisms 48 2.6.1. Training in ordinary digital writing at the University of Technology of Compiegne (UTC) 48 2.6.2. BABA strings (abstraction and polymorphism) 49 2.6.3. SolSys string (staging, hypertextualization) 51 2.6.4. BD string (transclusion, interactivity) 53 2.7. Case study: training in digital writing at IFCAM 53 2.7.1. Introduction to training 53 2.7.2. Training scenario 54 2.7.3. An experience to increase awareness using Etherpad 54 2.7.4. Understanding the properties of digital technology and theoretical content 56 2.7.5. Assignment 1: analysis of practices 57 2.7.6. Part two: reading and writing, second assignment (critical observation) 57 2.8. Perspective: a MOOC "digital literacy" project 57 2.8.1. Defining information literacy 58 2.8.2. Defining digital technology 59 2.8.3. Issue: teaching information literacy 60 2.8.4. Components of teaching information literacy 61 2.8.5. Format: challenges of MOOCs 62 2.8.6. Proposition: content and scenario for an information literacy MOOC 64 2.9. Conclusion and perspectives 65 2.10. Acknowledgments 66 2.11. Further reading 66 2.12. Bibliography 67
• Chapter 3. Assessing the Design of Hypermedia Interfaces: Differing Perspectives 69Maria Ines LAITANO 3.1. Man-machine interaction 70 3.1.1. Fundamental principles of usability 70 3.1.2. Cognitive engineering 72 3.2. Mediated human activity 74 3.2.1. The Danish school 76 3.2.2. Instrumental psychology 78 3.3. Meaningful systems 80 3.3.1. Semiotic engineering 80 3.3.2. The sociocognitive model 84 3.3.3. Semiotic scenario 86 3.4. Three mediations: three ways of evaluating a design? 88 3.5. Bibliography 93
• Chapter 4. Experience Design: Explanation and Best Practices 97Leslie MATTE GANET 4.1. Several problems identified with interface creation 99 4.1.1. Users have difficulty too often 99 4.1.2. An awkward practice of Experience Design 99 4.1.3. A difficult beginning for Experience Design in France 100 4.1.4. Ill-defined jobs 101 4.1.5. Manufacturers at various XD maturity levels 102 4.2. What is good Experience Design? 104 4.3. How does Experience Design work? 106 4.3.1. A method, more than a result 106 4.3.2. Focused on humans 106 4.3.3. A transformed project management 106 4.3.4. New professions 108 4.3.5. Tools in DX 112 4.4. A powerful approach 114 4.4.1. XD protects from rejection 114 4.4.2. XD allows for an important gain in time 115 4.4.3. The XD facilitator 116 4.5. Example of XD contribution to an industrial project 116 4.5.1. Creating the Website with classic project management 117 4.5.2. Revising the Website with XD project management 121 4.6. How can we improve the quality of Experience Design in the ICT industries? 124 4.6.1. A team with an open mind and empathy 124 4.6.2. Co-design, creativity, ideation and respiration 124 4.6.3. Good skills for appropriate responsibilities 125 4.6.4. The systematic presence of the user and going into the field 126 4.6.5. No longer using the term UX 126 4.7. Conclusion 127 4.8. Bibliography . 128
• Chapter 5. Designing Authoring Software Environments for the Interactive Arts: An Overview of Mobilizing.js 131Dominique CUNIN 5.1. Research context: artistic practices of interactivity 131 5.1.1. Art and technique in the face of the digital 131 5.1.2. An idea: an authoring software environment 134 5.2. Computer graphics, game engine, art engine? 138 5.2.1. Reusability 138 5.2.2. Game engine: when the metaphor and the objective design the tool 140 5.2.3. Programming for the interactive arts: toward complexity 142 5.2.4. Art engine, an authoring environment possibility? 149 5.3. Mobilizing.js: an attempt at a multi-paradigmatic authoring software environment 151 5.3.1. Artistic technical conduct and critical technical practice 153 5.3.2. An engine with many speeds 157 5.4. Structure and results of Mobilizing.js 163 5.4.1. Overview of a technical sequence 163 5.4.2. Constructing interactivities 170 5.4.3. Interactive, immersive and collaborative system 175 5.5. Conclusion 181 5.6. Bibliography 182
• Chapter 6. Clues. Anomalies. Understanding. Detecting Underlying Assumptions and Expected Practices in the Digital Humanities through the AIME Project 185Donato RICCI, Robin DE MOURAT, Christophe LECLERCQ and Bruno LATOUR 6.1. Abstract 185 6.2. Introduction 186 6.3. AIME and its digital humanities set-up 188 6.4. Methodology: multiplying listening devices 193 6.5. Anomaly family #1: displacements in acknowledging on-and-offline practices ecosystem 197 6.6. Anomaly family #2: interface-driven methodology and its encounters with scholarly publics 199 6.7. Anomaly family #3: the shock of collaboration's ethoses 204 6.8. Qualifying anomalies for a better understanding of Digital Humanities projects 207 6.9. Bibliography 209 List of Authors 213 Index 215.
• (source: Nielsen Book Data)

This book aims at exploring and illustrating the different ways in which hypermedia systems and tools are designed according to those aspects. The design and visualization schemes included in any system will be related to the variety of social and technical complexities confronted by researchers in social, communication, humanities, art and design.
(source: Nielsen Book Data)

• Chapter 1. The Science of Biogeography 1 1.1. Introduction 1 1.2. Island biogeography 3 1.3. Influence factors for biogeography 6
• Chapter 2. Biogeography and Biological Optimization 11 2.1. A mathematical model of biogeography 11 2.2. Biogeography as an optimization process 16 2.3. Biological optimization 19 2.3.1. Genetic algorithms 19 2.3.2. Evolution strategies 20 2.3.3. Particle swarm optimization 21 2.3.4. Artificial bee colony algorithm 22 2.4. Conclusion 23
• Chapter 3. A Basic BBO Algorithm 25 3.1. BBO definitions and algorithm 25 3.1.1. Migration 26 3.1.2. Mutation 27 3.1.3. BBO implementation 27 3.2. Differences between BBO and other optimization algorithms 35 3.2.1. BBO and genetic algorithms 35 3.2.2. BBO and other algorithms 36 3.3. Simulations 37 3.4. Conclusion 44
• Chapter 4. BBO Extensions 45 4.1. Migration curves 45 4.2. Blended migration 49 4.3. Other approaches to BBO 51 4.4. Applications 56 4.5. Conclusion 59
• Chapter 5. BBO as a Markov Process 61 5.1. Markov definitions and notations 61 5.2. Markov model of BBO 72 5.3. BBO convergence 79 5.4. Markov models of BBO extensions 90 5.5. Conclusions 99
• Chapter 6. Dynamic System Models of BBO 103 6.1. Basic notation 103 6.2. Dynamic system models of BBO 105 6.3. Applications to benchmark problems 119 6.4. Conclusions 122
• Chapter 7. Statistical Mechanics Approximations of BBO 123 7.1. Preliminary foundation 123 7.2. Statistical mechanics model of BBO 128 7.2.1. Migration 128 7.2.2. Mutation 134 7.3. Further discussion 141 7.3.1. Finite population effects 141 7.3.2. Separable fitness functions 142 7.4. Conclusions 143
• Chapter 8. BBO for Combinatorial Optimization 145 8.1. Traveling salesman problem 147 8.2. BBO for the TSP 148 8.2.1. Population initialization 148 8.2.2. Migration in the TSP 150 8.2.3. Mutation in the TSP 157 8.2.4. Implementation framework 159 8.3. Graph coloring 163 8.4. Knapsack problem 165 8.5. Conclusion 167
• Chapter 9. Constrained BBO 169 9.1. Constrained optimization 170 9.2. Constraint-handling methods 172 9.2.1. Static penalty methods 172 9.2.2. Superiority of feasible points 173 9.2.3. The eclectic evolutionary algorithm 174 9.2.4. Dynamic penalty methods 174 9.2.5. Adaptive penalty methods 176 9.2.6. The niched-penalty approach 177 9.2.7. Stochastic ranking 178 9.2.8. -level comparisons 178 9.3. BBO for constrained optimization 179 9.4. Conclusion 185
• Chapter 10. BBO in Noisy Environments 187 10.1. Noisy fitness functions 188 10.2. Influence of noise on BBO 190 10.3. BBO with re-sampling 193 10.4. The Kalman BBO 196 10.5. Experimental results 199 10.6. Conclusion 201
• Chapter 11. Multi-objective BBO 203 11.1. Multi-objective optimization problems 204 11.2. Multi-objective BBO 211 11.2.1. Vector evaluated BBO 211 11.2.2. Non-dominated sorting BBO 213 11.2.3. Niched Pareto BBO 216 11.2.4. Strength Pareto BBO 218 11.3. Real-world applications 223 11.3.1. Warehouse scheduling model 223 11.3.2. Optimization of warehouse scheduling 229 11.4. Conclusion 231
• Chapter 12. Hybrid BBO Algorithms 233 12.1. Opposition-based BBO 234 12.1.1. Opposition definitions and concepts 234 12.1.2. Oppositional BBO 236 12.1.3. Experimental results 238 12.2. BBO with local search 240 12.2.1. Local search methods 240 12.2.2. Simulation results 245 12.3. BBO with other EAs 247 12.3.1. Iteration-level hybridization 247 12.3.2. Algorithm-level hybridization 250 12.3.3. Experimental results 254 12.4. Conclusion 256 Appendices 259 Appendix A. Unconstrained Benchmark Functions 261 Appendix B. Constrained Benchmark Functions 265 Appendix C. Multi-objective Benchmark Functions 289 Bibliography 309 Index 325.
• (source: Nielsen Book Data)

Evolutionary computation algorithms are employed to minimize functions with large number of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is based on the science of biogeography, which researches the migration patterns of species. These migration paradigms provide the main logic behind BBO. Due to the cross-disciplinary nature of the optimization problems, there is a need to develop multiple approaches to tackle them and to study the theoretical reasoning behind their performance. This book explains the mathematical model of BBO algorithm and its variants created to cope with continuous domain problems (with and without constraints) and combinatorial problems.
(source: Nielsen Book Data)

• Introduction Perspectives on data science for software engineering Software analytics and its application in practice Seven principles of inductive software engineering: What we do is different The need for data analysis patterns (in software engineering) From software data to software theory: The path less traveled Why theory matters Success Stories/Applications Mining apps for anomalies Embrace dynamic artifacts Mobile app store analytics The naturalness of software Advances in release readiness How to tame your online services Measuring individual productivity Stack traces reveal attack surfaces Visual analytics for software engineering data Gameplay data plays nicer when divided into cohorts A success story in applying data science in practice There's never enough time to do all the testing you want The perils of energy mining: measure a bunch, compare just once Identifying fault-prone files in large industrial software systems A tailored suit: The big opportunity in personalizing issue tracking What counts is decisions, not numbers-Toward an analytics design sheet A large ecosystem study to understand the effect of programming languages on code quality Code reviews are not for finding defects-Even established tools need occasional evaluation Techniques Interviews Look for state transitions in temporal data Card-sorting: From text to themes Tools! Tools! We need tools! Evidence-based software engineering Which machine learning method do you need? Structure your unstructured data first!: The case of summarizing unstructured data with tag clouds Parse that data! Practical tips for preparing your raw data for analysis Natural language processing is no free lunch Aggregating empirical evidence for more trustworthy decisions If it is software engineering, it is (probably) a Bayesian factor Becoming Goldilocks: Privacy and data sharing in "just right" conditions The wisdom of the crowds in predictive modeling for software engineering Combining quantitative and qualitative methods (when mining software data) A process for surviving survey design and sailing through survey deployment Wisdom Log it all? Why provenance matters Open from the beginning Reducing time to insight Five steps for success: How to deploy data science in your organizations How the release process impacts your software analytics Security cannot be measured Gotchas from mining bug reports Make visualization part of your analysis process Don't forget the developers! (and be careful with your assumptions) Limitations and context of research Actionable metrics are better metrics Replicated results are more trustworthy Diversity in software engineering research Once is not enough: Why we need replication Mere numbers aren't enough: A plea for visualization Don't embarrass yourself: Beware of bias in your data Operational data are missing, incorrect, and decontextualized Data science revolution in process improvement and assessment? Correlation is not causation (or, when not to scream "Eureka!") Software analytics for small software companies: More questions than answers Software analytics under the lamp post (or what star trek teaches us about the importance of asking the right questions) What can go wrong in software engineering experiments? One size does not fit all While models are good, simple explanations are better The white-shirt effect: Learning from failed expectations Simpler questions can lead to better insights Continuously experiment to assess values early on Lies, damned lies, and analytics: Why big data needs thick data The world is your test suite.
• (source: Nielsen Book Data)

Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community's leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid.
(source: Nielsen Book Data)

• Introduction Perspectives on data science for software engineering Software analytics and its application in practice Seven principles of inductive software engineering: What we do is different The need for data analysis patterns (in software engineering) From software data to software theory: The path less traveled Why theory matters Success Stories/Applications Mining apps for anomalies Embrace dynamic artifacts Mobile app store analytics The naturalness of software Advances in release readiness How to tame your online services Measuring individual productivity Stack traces reveal attack surfaces Visual analytics for software engineering data Gameplay data plays nicer when divided into cohorts A success story in applying data science in practice There's never enough time to do all the testing you want The perils of energy mining: measure a bunch, compare just once Identifying fault-prone files in large industrial software systems A tailored suit: The big opportunity in personalizing issue tracking What counts is decisions, not numbers-Toward an analytics design sheet A large ecosystem study to understand the effect of programming languages on code quality Code reviews are not for finding defects-Even established tools need occasional evaluation Techniques Interviews Look for state transitions in temporal data Card-sorting: From text to themes Tools! Tools! We need tools! Evidence-based software engineering Which machine learning method do you need? Structure your unstructured data first!: The case of summarizing unstructured data with tag clouds Parse that data! Practical tips for preparing your raw data for analysis Natural language processing is no free lunch Aggregating empirical evidence for more trustworthy decisions If it is software engineering, it is (probably) a Bayesian factor Becoming Goldilocks: Privacy and data sharing in "just right" conditions The wisdom of the crowds in predictive modeling for software engineering Combining quantitative and qualitative methods (when mining software data) A process for surviving survey design and sailing through survey deployment Wisdom Log it all? Why provenance matters Open from the beginning Reducing time to insight Five steps for success: How to deploy data science in your organizations How the release process impacts your software analytics Security cannot be measured Gotchas from mining bug reports Make visualization part of your analysis process Don't forget the developers! (and be careful with your assumptions) Limitations and context of research Actionable metrics are better metrics Replicated results are more trustworthy Diversity in software engineering research Once is not enough: Why we need replication Mere numbers aren't enough: A plea for visualization Don't embarrass yourself: Beware of bias in your data Operational data are missing, incorrect, and decontextualized Data science revolution in process improvement and assessment? Correlation is not causation (or, when not to scream "Eureka!") Software analytics for small software companies: More questions than answers Software analytics under the lamp post (or what star trek teaches us about the importance of asking the right questions) What can go wrong in software engineering experiments? One size does not fit all While models are good, simple explanations are better The white-shirt effect: Learning from failed expectations Simpler questions can lead to better insights Continuously experiment to assess values early on Lies, damned lies, and analytics: Why big data needs thick data The world is your test suite.
• (source: Nielsen Book Data)

Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community's leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid.
(source: Nielsen Book Data)

• Cover; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Preface;
• Chapter 1: Introduction to Machine learning; Machine learning; Definition; Core Concepts and Terminology; What is learning?; Data; Labeled and unlabeled data; Tasks; Algorithms; Models; Data and inconsistencies in Machine learning; Under-fitting; Over-fitting; Data instability; Unpredictable data formats; Practical Machine learning examples; Types of learning problems; Classification; Clustering; Forecasting, prediction or regression; Simulation; Optimization

• Supervised learningUnsupervised learning; Semi-supervised learning; Reinforcement learning; Deep learning; Performance measures; Is the solution good?; Mean squared error (MSE); Mean absolute error (MAE); Normalized MSE and MAE (NMSE and NMAE); Solving the errors: bias and variance; Some complementing fields of Machine learning; Data mining; Artificial intelligence (AI); Statistical learning; Data science; Machine learning process lifecycle and solution architecture; Machine learning algorithms; Decision tree based algorithms; Bayesian method based algorithms; Kernel method based algorithms

• Clustering methodsArtificial neural networks (ANN); Dimensionality reduction; Ensemble methods; Instance based learning algorithms; Regression analysis based algorithms; Association rule based learning algorithms; Machine learning tools and frameworks; Summary; Chapter 2: Machine learning and Large-scale datasets; Big data and the context of large-scale Machine learning; Functional versus Structural
• A methodological mismatch; Commoditizing information; Theoretical limitations of RDBMS; Scaling-up versus Scaling-out storage; Distributed and parallel computing strategies

• Machine learning: Scalability and PerformanceToo many data points or instances; Too many attributes or features; Shrinking response time windows
• need for real-time responses; Highly complex algorithm; Feed forward, iterative prediction cycles; Model selection process; Potential issues in large-scale Machine learning; Algorithms and Concurrency; Developing concurrent algorithms; Technology and implementation options for scaling-up Machine learning; MapReduce programming paradigm; High Performance Computing (HPC) with Message Passing Interface (MPI)

• Language Integrated Queries (LINQ) frameworkManipulating datasets with LINQ; Graphics Processing Unit (GPU); Field Programmable Gate Array (FPGA); Multicore or multiprocessor systems; Summary; Chapter 3: An Introduction to Hadoop's Architecture and Ecosystem; Introduction to Apache Hadoop; Evolution of Hadoop (the platform of choice); Hadoop and its core elements; Machine learning solution architecture for big data (employing Hadoop); The Data Source layer; The Ingestion layer; The Hadoop Storage layer; The Hadoop (Physical) Infrastructure layer
• supporting appliance

Tackle the real-world complexities of modern machine learning with innovative, cutting-edge, techniques About This Book * Fully-coded working examples using a wide range of machine learning libraries and tools, including Python, R, Julia, and Spark * Comprehensive practical solutions taking you into the future of machine learning * Go a step further and integrate your machine learning projects with Hadoop Who This Book Is For This book has been created for data scientists who want to see machine learning in action and explore its real-world application. With guidance on everything from the fundamentals of machine learning and predictive analytics to the latest innovations set to lead the big data revolution into the future, this is an unmissable resource for anyone dedicated to tackling current big data challenges. Knowledge of programming (Python and R) and mathematics is advisable if you want to get started immediately. What You Will Learn * Implement a wide range of algorithms and techniques for tackling complex data * Get to grips with some of the most powerful languages in data science, including R, Python, and Julia * Harness the capabilities of Spark and Hadoop to manage and process data successfully * Apply the appropriate machine learning technique to address real-world problems * Get acquainted with Deep learning and find out how neural networks are being used at the cutting-edge of machine learning * Explore the future of machine learning and dive deeper into polyglot persistence, semantic data, and more In Detail Finding meaning in increasingly larger and more complex datasets is a growing demand of the modern world. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. Machine learning uses complex algorithms to make improved predictions of outcomes based on historical patterns and the behaviour of data sets. Machine learning can deliver dynamic insights into trends, patterns, and relationships within data, immensely valuable to business growth and development. This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data. This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naive Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory-and mystery-out of even the most advanced machine learning methodologies. Style and approach A practical data science tutorial designed to give you an insight into the practical application of machine learning, this book takes you through complex concepts and tasks in an accessible way. Featuring information on a wide range of data science techniques, Practical Machine Learning is a comprehensive data science resource.
(source: Nielsen Book Data)

• Introduction ix
• Chapter 1. Elements of the General Configuration and Adaptive Landscape of Collective Intelligences 1 1.1. The intertwined narratives of tangible utopias and brilliant futures 1 1.2. Intelligence is "always already collective and machined" 5 1.3. Collective intelligences in the weaving of data 9 1.4. Semiotics and statistics 13 1.5. Data cities and human becomings: the new milieus of intelligence 17 1.5.1. Open Data (OD): a heterogeneous movement, the contribution to novel forms of knowledge in question 22 1.6. Coupling OD/big data/data mining 32 1.7. The semantic web as intellectual technology 34 1.8. Toward understanding onto-ethologies 42 1.9. Marketing intelligences: data and graphs in the heat of passions 50 1.10. Personal data: private property as an open and unstable process 59 1.11. The figures of the network 64 1.12. Machinic interfaces: social subjection and enslavement 67 1.13. Collective intelligences and anthropological concerns 70 1.14. Toward a new encyclopedic state: first overview 74 1.15. Controversies and boundaries 78 1.16. The milieus of intelligence and knowledge 84 1.17. Which criteria for writings? 86 1.18. Collective intelligences of usage and doxic collective intelligences: the status of short forms 90 1.19. Collective intelligences, self-organization, "swarm" intelligences 92 1.20. Short forms, relinkage, relaunching 99 1.21. Insomniac commentary as a catastrophic correction of short forms 100 1.22. Twitter as a Markovian Territory: a few remarks 103
• Chapter 2. Post- and Transhumanist Horizons 107 2.1. Some bioanthropotechnical transformations 107 2.2. What to do with our brain? 113 2.3. About transhumanism and speculative posthumanism 122 2.4. Epigenetic and epiphylogenetic plasticity 125 2.5. Speculative uncertainties 127 2.6. Trans- and posthumanism as they present themselves 152
• Chapter 3. Fragmented Encyclopedism 169 3.1. Collective intelligences and the encyclopedic problem 169 3.2. The political utopia in store 170 3.3. Encyclopedism and digital publishing modes 174 3.4. A new documentary process 176 3.5. Fragmented encyclopedism: education/interfaces 190 3.6. Encyclopedism and correlations 192 3.6.1. "Correlation is enough": the Anderson controversy, and the J. Gray paradigm and their limits 192 3.7. "Perplication" in knowledge 198 3.7.1. Doxic tension in fragmented encyclopedism and format accordingly 198 3.8. Networks of the digital environment 199 3.8.1. Variations of speed and slowness at the center of encyclopedic pragmatics 200 3.9. Knowledge and thought in fragmented encyclopedism 201 3.10. What criteriology for encyclopedic writings? 202 3.11. Borders in fragmented encyclopedism: autoimmune disorders and disagreement 205 3.12. Fragmented encyclopedism: a habitat for controversies? 207 3.13. Encyclopedism according to the semantic and sociosemantic web (ontologies and web): mapping(s) and semantic levels 209 3.14. From ontologies to "onto-ethologies" and assemblages 212 3.15. Fragmented encyclopedism in the digital age: metalanguage and combinatorial 214 3.15.1. Encyclopedism and doxic immanence field: the proliferation of short forms 216 3.16. From fragmented encyclopedism to gaseous encyclopedism 217 Bibliography 219 Index 233Conclusion.
• (source: Nielsen Book Data)

There is a great transformation of the production of knowledge and intelligibility. The "digital fold of the world" (with the convergence of NBIC) affects the collective assemblages of "thought", of research. The aims of these assemblages are also controversial issues. From a general standpoint, these debates concern "performative science and performative society". But one emerges and strengthens that has several names: transhumanism, post-humanism, speculative post-humanism. It appears as a great narration, a large story about the future of our existence, facing our entry into the Anthropocene. It is also presented as a concrete utopia with an anthropological and technical change. In this book, we proposed to show how collective intelligences stand in the middle of the coupling of ontological horizons and of the "process of bio-technical maturation".
(source: Nielsen Book Data)

• Preface
• Transit Passenger Origin Inference Using Smart Card Data & GPS Data
• Knowledge Extraction from an Automated Formative Evaluation Based on Odala approach Using the Weka Tool
• Modeling Nations Failure via Data Mining Techniques
• An Evolutionary Self-Adaptive Algorithm for Mining Association Rules
• Index.
• (source: Nielsen Book Data)

Data mining is an area of research where appropriate methodological research and technical means are experienced to produce useful knowledge from different types of data. Data mining techniques use a broad family of computationally intensive methods that include decision trees, neural networks, rule induction, machine learning and graphic visualisation. This book discusses the principles, applications and emerging challenges of data mining.
(source: Nielsen Book Data)

• Introduction
• Supervised learning
• Bayesian decision theory
• Parametric methods
• Multivariate methods
• Dimensionality reduction
• Clustering
• Nonparametric methods
• Decision trees
• Linear discrimination
• Multilayer perceptrons
• Local models
• Kernel machines
• Graphical models
• Brief contents
• Hidden markov models
• Bayesian estimation
• Combining multiple learners
• Reinforcement learning
• Design and analysis of machine learning experiments.

A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.
(source: Nielsen Book Data)

• About the Authors IX Preface XI 1 Basics of Machine Vision 1 1.1 Digital Images 1 1.1.1 Grayscale Image 1 1.1.2 Binary Image 2 1.1.3 Color Image 3 1.2 Components of Imaging System 5 1.2.1 Camera 6 1.2.2 Camera Bus: The Method to Connect PC and Camera 10 1.2.3 Lens 13 1.2.4 Lighting 15 2 Image Acquisition with LabVIEW 17 2.1 Acquiring Images with MAX 17 2.2 Acquiring Images Using LabVIEW 19 2.2.1 IMAQdx Functions 19 2.2.2 Image Management Functions 21 2.2.3 Block Diagram for Image Acquisition 23 2.2.4 Image Acquisition from Example 23 2.2.5 Vision Acquisition Express 26 3 Particle Analysis 33 3.1 Particle Analysis Using Vision Assistant 34 3.1.1 Image Acquisition Using Vision Assistant 35 3.1.2 Image Processing Functions 37 3.1.3 Setting a ROI (Region of Interest) 38 3.1.4 Binary Image Conversion 40 3.1.5 Morphology 43 3.1.6 Particle Analysis 44 3.2 LabVIEW Code Creation Using Vision Assistant 47 3.2.1 Block Diagram of Created LabVIEW Code 50 3.2.2 Image Type Modification 54 3.3 LabVIEW Code Modification 55 3.3.1 SubVI for Particle Analysis 55 3.4 Particle Analysis Using Vision Express 67 3.4.1 Vision Acquisition Express 67 3.4.2 Vision Assistant Express 68 3.5 Conversion of Pixels to Real-World Units 71 4 Edge Detection 75 4.1 Edge Detection via Vision Assistant 75 4.2 LabVIEW Code for Edge Detection 78 4.3 VI for Real-Time-Based Edge Detection 81 4.4 The Use of Vision Assistant Express for Real-Time Edge Detection 85 5 Pattern Matching 89 5.1 Pattern Matching Using Vision Assistant 90 5.2 LabVIEW Code Creation and Modification 96 5.3 Main VI for Pattern Matching 99 5.4 Vision Assistant Express 101 6 Color Pattern Matching 105 6.1 Color Pattern Matching Using Vision Assistant Express 105 6.1.1 Vision Acquisition Express 107 6.1.2 Vision Assistant Express 108 6.1.3 Main VI 112 7 Dimension Measurement 117 7.1 Dimension Measurement Using Vision Assistant Express 117 7.1.1 Find Circular Edge Function 119 7.1.2 Clamp Function 119 7.1.3 Caliper Function 123 7.2 VI Creation for Dimension Measurement 126 7.2.1 Vision Assistant Express VI for Dimension Measurement 126 7.2.2 ROI Array 127 7.2.3 Front Panel of Main VI 129 7.2.4 Block Diagram of the Main VI 130 8 Dimension Measurement Using Coordinate System 135 8.1 Measurement Based on a Reference Coordinate System Using Vision Assistant Express 135 8.1.1 Pattern Matching 137 8.1.2 Coordinate System 138 8.1.3 Dimension Measurement Using the Clamp Function 141 8.1.4 Measurement of Circle Edge 142 8.2 Conversion of Vision Assistant Express to a Standard VI 145 9 Geometric Matching 149 9.1 Geometric Matching Using Vision Assistant Express 150 9.1.1 Geometric Matching for Circles 151 9.1.2 Geometric Matching for Ellipses 155 9.2 VI Creation for Geometric Matching 158 9.3 Shape Detection 159 10 Binary Shape Matching 165 10.1 Accessing Previously Saved Images with Vision Acquisition Express 166 10.2 Binary Shape Matching Using Vision Assistant 168 10.2.1 Binary Template Images 169 10.2.2 Binary Shape Matching 170 10.3 Overlay VI Creation for Shape Matching 172 10.4 VI for Binary Shape Matching 173 11 OCR (Optical Character Recognition) 177 11.1 OCR Using Vision Assistant 177 11.1.1 Character Training Using Vision Assistant 177 11.1.2 Character Identification Using Vision Assistant 181 11.2 VI for OCR 185 11.2.1 VI Creation for OCR Using Vision Assistant 185 11.2.2 SubVI for OCR 185 11.2.3 Main VI 187 12 Binary Particle Classification 191 12.1 Vision Acquisition Express to Load Image Files 192 12.2 Vision Assistant Express for Classification 194 12.2.1 Train for Particle Classification 194 12.2.2 VI Creation 199 12.3 VI Modification 200 12.4 Overlay for Classification 204 12.5 Main VI for Classification 206 13 Contour Analysis 209 13.1 Contour Analysis 210 13.1.1 Image Acquisition Using a USB Camera 210 13.1.2 Contour Analysis Using Vision Assistant 212 13.1.3 Defect Detection Using Curvature 215 13.1.4 Defect Detection by Comparing Contours 216 13.1.5 VI Creation 219 13.2 VIs for Contour Analysis 219 13.2.1 Main VI 219 13.2.2 Overlay for Defects 222 13.2.3 Perspective Errors in Images 225 14 Image Calibration and Correction 227 14.1 Method for Creating an Image Correction File 227 14.1.1 Image Acquisition 228 14.1.2 New Calibration File 228 14.2 Image Correction 234 14.2.1 Image Correction Using Vision Assistant Express 234 14.2.2 VI Creation for Image Correction 237 15 Saving and Reading Images 241 15.1 Saving Image 241 15.2 Image Read from File 245 15.2.1 IMAQ Readfile 245 15.2.2 Example of Reading Image from Image Files 246 16 AVI File Write and Read 249 16.1 AVI File Creation Using Image Files 249 16.2 AVI File Creation Based on Real-Time Image Acquisition 251 16.3 Read Frame from AVI Files 252 17 Tracking 255 17.1 Tracking with the Use of Vision Assistant 255 17.2 VI Creation for Tracking Objects 259 18 LabVIEW Machine Vision Applications* 263 18.1 Semiconductor Manufacturing 263 18.2 Automobile Industry 264 18.3 Medical and Bio Applications 266 18.4 Inspection 268 18.5 Industrial Printing 269 19 Student Projects 271 Project
• 1: Noncontact Motion Measurement and its Analysis 271 Project
• 2: Intelligent Surveillance Camera 271 Project
• 3: Driving a LEGO NXT Car (LEGO Mindstorm) with Finger Motion 273 Project
• 4: Piano Keyboard Using Machine Vision 273 Index 275.
• (source: Nielsen Book Data)

For both students and engineers in R&D, this book explains machine vision in a concise, hands-on way, using the Vision Development Module of the LabView software by National Instruments. Following a short introduction to the basics of machine vision and the technical procedures of image acquisition, the book goes on to guide readers in the use of the various software functions of LabView's machine vision module. It covers typical machine vision tasks, including particle analysis, edge detection, pattern and shape matching, dimension measurements as well as optical character recognition, enabling readers to quickly and efficiently use these functions for their own machine vision applications. A discussion of the concepts involved in programming the Vision Development Module rounds off the book, while example problems and exercises are included for training purposes as well as to further explain the concept of machine vision. With its step-by-step guide and clear structure, this is an essential reference for beginners and experienced researchers alike.
(source: Nielsen Book Data)

• Chapter 1 Problem Representation
• Chapter 2 Hierarchy and Multi-granular Computing
• Chapter 3 Information Synthesis in Multi-granular Computing
• Chapter 4 Reasoning in Multi-granular Computing
• Chapter 5 Automatic Spatial Planning
• Chapter 6 Statistical Heuristic Search
• Chapter 7 the Expansion of Quotient Space Theory Addenda A: Some Concepts and Properties of Point Set Topology Addenda B: Some Concepts and Properties of Integral and Statistical Inference.
• (source: Nielsen Book Data)

Quotient Space Based Problem Solving provides an in-depth treatment of hierarchical problem solving, computational complexity, and the principles and applications of multi-granular computing, including inference, information fusing, planning, and heuristic search.
(source: Nielsen Book Data)

• 1. Introduction
• 2. A plan of attack
• 3. What to measure
• 4. Tuning algorithms, tuning code
• 5. The toolbox
• 6. Creating analysis-friendly data
• 7. Data analysis.
• (source: Nielsen Book Data)

Computational experiments on algorithms can supplement theoretical analysis by showing what algorithms, implementations and speed-up methods work best for specific machines or problems. This book guides the reader through the nuts and bolts of the major experimental questions: What should I measure? What inputs should I test? How do I analyze the data? To answer these questions the book draws on ideas from algorithm design and analysis, computer systems, and statistics and data analysis. The wide-ranging discussion includes a tutorial on system clocks and CPU timers, a survey of strategies for tuning algorithms and data structures, a cookbook of methods for generating random combinatorial inputs, and a demonstration of variance reduction techniques. The book can be used by anyone who has taken a course or two in data structures and algorithms. A companion website, AlgLab (www.cs.amherst.edu/alglab) contains downloadable files, programs and tools for use in experimental projects.
(source: Nielsen Book Data)

• 1. Introduction of Real-time Image Processing.
• 2. Hardware Architectures for Real-time Processing.
• 3. Rapid Prototyping of Parallel Reconfigurable Instruction Set Processor for Efficient Real-Time Image Processing.
• 4. Exploration of High-Level Synthesis Technique.
• 5. CDMS4HLS: A Novel Source- To-Source Compilation Strategy for HLS-Based FPGA Design.
• 6. Embedded Implementation of VHR Satellite Image Segmentation.
• 7. Real-time Image Processing with Very High-level Synthesis.
• (source: Nielsen Book Data)

• Introduction of Real-time Image processing Description of hardware architecture for real-time processing Optimization strategies for real-time image processing Application
• 1: Level set optimization methods for real-time image segmentation Application
• 2: Real-time image skin lesion assessment with light-tissue interaction based method Future perspectives: Hardware implementation of real-time multispectral image processing within heterogeneous architectures?
• (source: Nielsen Book Data)

In the field of image processing, many applications require real-time execution, particularly those in the domains of medicine, robotics and transmission, to name but a few. Recent technological developments have allowed for the integration of more complex algorithms with large data volume into embedded systems, in turn producing a series of new sophisticated electronic architectures at affordable prices. This book performs an in-depth survey on this topic. It is primarily written for those who are familiar with the basics of image processing and want to implement the target processing design using different electronic platforms for computing acceleration. The authors present techniques and approaches, step by step, through illustrative examples. This book is also suitable for electronics/embedded systems engineers who want to consider image processing applications as sufficient imaging algorithm details are given to facilitate their understanding.
(source: Nielsen Book Data)

• 1. Rudiments of Human Visual System (HVS) Features
• .2. Computational Color Constancy Algorithms.
• 3. Retinex-like Algorithms for Color Imageâ ¨ Processing.
• 4. Variational Formulation ofâ ¨ Histogram Equalization.
• 5. Perceptually-inspired Variational Models for Color Enhancement in the RGB Space. .
• (source: Nielsen Book Data)

Color is a sensation generated both by the interaction of the visual sensors in the eyes with the natural environment and by the elaboration of visual information by higher brain functions.This book presents the mathematical framework needed to deal with several models of color processing of digital images. The book starts with a short yet exhaustive introduction to the basic phenomenological features of color vision, which are constantly used throughout the book. The discussion of computational issues starts with color constancy, which is dealt with in a rigorous and self-contained mathematical setting. Then, the original Retinex model and its numerous variants are introduced and analyzed with direct discrete equations. The remainder of the book is dedicated to the variational analysis of Retinex-like models, contextualizing their action with respect to contrast enhancement. .
(source: Nielsen Book Data)

Learn Chef Provisioning like a boss and finally own your infrastructure About This Book * This is the first Chef book focused on provisioning infrastructure as its sole task. The book offers a clear solution to a specific pain point: learn to make your system work faster. * Learning better approaches to load balancing and parallelization with this book will save you time * By mastering the techniques in this book, you will know how to run an entire fleet of machines without breaking a sweat * This book is more helpful than the documentation ( https://docs.chef.io/provisioning.html), with a stronger guiding voice and clearer explanations and use cases Who This Book Is For This book is for Software Engineers, System Administrators, or DevOps Engineers who need to quickly deliver reliably consistent infrastructure at scale. You are expected to have intermediate experience with Chef and Ruby and will be reading this book to advance your knowledge and take your skillset to the next level. What You Will Learn * Use best practices to describe your entire infrastructure as code * Automate and document every aspect of your network, from the hardware of individual nodes to software, middleware, and all containers and clouds * Create a perfect model system * Make the best possible use of your resources and avoid redundancy * Deliver on the promise of Infrastructure as Code * Scale with ease by properly provisioning their infrastructure * Use the best Test Driven Development methodologies In Detail This book will show you the best practices to describe your entire infrastructure as code. With the help of this book you can expand your knowledge of Chef because and implement robust and scalable automation solutions. You can automate and document every aspect of your network, from the hardware to software, middleware, and all your containers. You will become familiar with the Chef's Chef Provisioning tool. You will be able to make a perfect model system where everything is represented as code beneath your fingertips. Make the best possible use of your resources, and deliver infrastructure as code, making it as versionable, testable and repeatable as application software Style and approach By dedicating a whole book solely to the question of provisioning, this book will teach administrators to use Chef as a birds-eye lens for their entire system. It will moves you away from the specifics of each machine and its automations and instead will teach you them how to approach the entire cluster as something different than the sum of its parts. By focusing on infrastructure as code as its own project, the book offers elegant, time-saving solutions for a perfectly described and automated network.
(source: Nielsen Book Data)

• Cover ; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface;
• Chapter 1: Welcome to Machine Learning Using the .NET Framework; What is machine learning?; Why .NET?; What version of the .NET Framework are we using?; Why write your own?; Why open data?; Why F#?; Getting ready for machine learning; Setting up Visual Studio; Learning F#; Third-party libraries; Math.NET; Accord.NET; Numl; Summary;
• Chapter 2: AdventureWorks Regression; Simple linear regression; Setting up the environment; Preparing the test data; Standard deviation

• Pearson's CorrelationLinear regression; Math.NET; Regression try 1; Regression try 2; Accord.NET; Regression; Regression evaluation using RSME; Regression and the real world; Regression against actual data; AdventureWorks app; Setting up the environment; Updating the existing web project; Implementing the regression; Summary;
• Chapter 3: More AdventureWorks Regression; Introduction to multiple linear regression; Intro example; Keep adding x variables?; AdventureWorks data; Add multiple regression to our production application; Considerations when using multiple x variables

• Adding a third x variable to our modelLogistic regression; Intro to logistic regression; Adding another x variable; Applying a logistic regression to AdventureWorks data; Categorical data; Attachment point; Analyzing results of the logistic regression; Adding logistic regression to the application; Summary; Chapter 4: Traffic Stops
• Barking Up the Wrong Tree?; The scientific process; Open data; Hack-4-Good; FsLab and type providers; Data exploration; Visualization; Decision trees; Accord; numl; Summary; Chapter 5: Time Out
• Obtaining Data; Overview; SQL Server providers; Non-type provider

• SqlProviderDeedle; MicrosoftSqlProvider; SQL Server type provider wrap up; Non SQL type providers; Combining data; Parallelism; JSON type provider
• authentication; Summary; Chapter 6: AdventureWorks Redux
• k-NN and Naïve Bayes Classifiers; k-Nearest Neighbors (k-NN); k-NN example; Naïve Bayes; Naïve Bayes in action; One thing to keep in mind while using Naïve Bayes; AdventureWorks; Getting the data ready; k-NN and AdventureWorks data; Naïve Bayes and AdventureWorks data; Making use of our discoveries; Getting the data ready; Expanding features; Summary

• Chapter 7: Traffic Stops and Crash Locations
• When Two Datasets Are Better Than OneUnsupervised learning; k-means; Principle Component Analysis (PCA); Traffic stop and crash exploration; Preparing the script and the data; Geolocation analysis; PCA ; Analysis summary; The Code-4-Good application; Machine learning assembly; The UI; Adding distance calculations; Augmenting with human observations; Summary; Chapter 8: Feature Selection and Optimization; Cleaning data; Selecting data; Collinearity; Feature selection; Normalization; Scaling; Overfitting and cross validation

• Cross validation
• train versus test

Master the art of machine learning with .NET and gain insight into real-world applications About This Book * Based on .NET framework 4.6.1, includes examples on ASP.NET Core 1.0 * Set up your business application to start using machine learning techniques * Familiarize the user with some of the more common .NET libraries for machine learning * Implement several common machine learning techniques * Evaluate, optimize and adjust machine learning models Who This Book Is For This book is targeted at .Net developers who want to build complex machine learning systems. Some basic understanding of data science is required. What You Will Learn * Write your own machine learning applications and experiments using the latest .NET framework, including .NET Core 1.0 * Set up your business application to start using machine learning. * Accurately predict the future using regressions. * Discover hidden patterns using decision trees. * Acquire, prepare, and combine datasets to drive insights. * Optimize business throughput using Bayes Classifier. * Discover (more) hidden patterns using KNN and Naive Bayes. * Discover (even more) hidden patterns using K-Means and PCA. * Use Neural Networks to improve business decision making while using the latest ASP.NET technologies. * Explore "Big Data", distributed computing, and how to deploy machine learning models to IoT devices - making machines self-learning and adapting * Along the way, learn about Open Data, Bing maps, and MBrace In Detail .Net is one of the widely used platforms for developing applications. With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their .Net applications smarter. Also, .NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines. This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product. Forming a base with the regression model, you will start using machine learning libraries available in .NET framework such as Math.NET, Numl.NET and Accord.NET with the help of a sample application. You will then move on to writing multiple linear regressions and logistic regressions. You will learn what is open data and the awesomeness of type providers. Next, you are going to address some of the issues that we have been glossing over so far and take a deep dive into obtaining, cleaning, and organizing our data. You will compare the utility of building a KNN and Naive Bayes model to achieve best possible results. Implementation of Kmeans and PCA using Accord.NET and Numl.NET libraries is covered with the help of an example application. We will then look at many of issues confronting creating real-world machine learning models like overfitting and how to combat them using confusion matrixes, scaling, normalization, and feature selection. You will now enter into the world of Neural Networks and move your line of business application to a hybrid scientific application. After you have covered all the above machine learning models, you will see how to deal with very large datasets using MBrace and how to deploy machine learning models to Internet of Thing (IoT) devices so that the machine can learn and adapt on the fly. Style and approach This book will guide you in learning everything about how to tackle the flood of data being encountered these days in your .NET applications with the help of popular machine learning libraries offered by the .NET framework.
(source: Nielsen Book Data)

A quick and concise guide to Angular 2 Components About This Book * First look to the Angular 2 Components architecture * Creating your own Angular 2 Component * Integrating your components with third party components Who This Book Is For If you are a front-end developer with some experience in Angular and want to understand Angular 2 Components, and easily put it to use to create powerful user interfaces and views, then this book is for you What You Will Learn * Break your application into reusable dynamic components * Take advantage of TypeScript in Angular 2 * Migrate your Angular 1 directive to an Angular 2 Component * Understand the Angular 2 component structure and APIs * Hook to component life cycle events * Bind dynamic data to your component properties * Communicate with other components using events * Compose complicated UIs from simple components In Detail This book is a concise guide to Angular 2 Components and is based on the stable version of Angular 2. You will start with learning about the Angular 2 Components architecture and how components differ from Angular directives in Angular 1. You will then move on to quickly set up an Angular 2 development environment and grasp the basics of TypeScript. With this strong foundation in place, you will start building components. The book will teach you, with an example, how to define component behavior, create component templates, and use the controller of your component. You will also learn how to make your components communicate with each other. Once you have built a component, you will learn how to extend it by integrating third-party components with it. By the end of the book, you will be confident with building and using components for your applications. Style and approach A step-by-step guide covering features and working of Angular 2 Components along with the process for creating your own components.
(source: Nielsen Book Data)

Create scalable machine learning applications to power a modern data-driven business using Spark 2.x About This Book * Get to the grips with the latest version of Apache Spark * Utilize Spark's machine learning library to implement predictive analytics * Leverage Spark's powerful tools to load, analyze, clean, and transform your data Who This Book Is For If you have a basic knowledge of machine learning and want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and Python languages. What You Will Learn * Get hands-on with the latest version of Spark ML * Create your first Spark program with Scala and Python * Set up and configure a development environment for Spark on your own computer, as well as on Amazon EC2 * Access public machine learning datasets and use Spark to load, process, clean, and transform data * Use Spark's machine learning library to implement programs by utilizing well-known machine learning models * Deal with large-scale text data, including feature extraction and using text data as input to your machine learning models * Write Spark functions to evaluate the performance of your machine learning models In Detail This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business. Style and approach This practical tutorial with real-world use cases enables you to develop your own machine learning systems with Spark. The examples will help you combine various techniques and models into an intelligent machine learning system.
(source: Nielsen Book Data)