Automatic control engineering Books

Book SynopsisAdvanced technologies such as robotics, 5G mobile communications, IoT, cloud computing and wireless sensor networks have had a huge impact and influence on manufacturing, with an increased collaboration between humans and smart systems. As the manufacturing process becomes more automated using real-time data, communication systems, Artificial Intelligence (AI) techniques and robotics feed data back into the manufacturing process. This enables the design of products that are more customized and personal, and leads to a more competitive, efficient and value-added production process by reacting more quickly to technical or human errors to avoid product and system damage while increasing workplace safety, and reducing waste, pollution, and associated costs. This edited book covers challenges, concepts, systems, architectures, technologies, and design characteristics of human-machine cooperation and interaction systems in smart manufacturing environments using state of the art technologies including AI, 5G, IoTs, Blockchains, CPS, sensing, automation and robotics. The book is aimed at researchers and engineers working on the applications of robotics and automation, HMI, HCI, CPS, sensing, information and communications technology, data science, ML/DL/AI, AR/VR, cybersecurity and electronics. It is also a useful reference for advanced students and lecturers in these fields, and will appeal to manufacturers and automation system developers.Table of Contents Chapter 1: Introduction to HMI - current and future, systems, features, and benefits Human-machine interfaces in smart manufacturing Chapter 2: Human-machine interaction (HMI) technology - Malaysia National Technology Roadmap Industry4WRD leading the human intelligence transformation in smart manufacturing Chapter 3: Challenges and impact of human-machine interaction systems in smart manufacturing Chapter 4: Robotics and autonomous systems in smart manufacturing Chapter 5: Artificial intelligence implementations in HMI for smart manufacturing Chapter 6: 5G and beyond environment for smart manufacturing Chapter 7: Drone supports applications in smart manufacturing Chapter 8: VoIP technology in manufacturing Chapter 9: Industrial Internet of Things solutions in smart manufacturing Chapter 10: Metal powder bed fusion: an overview on processes, materials, and challenges Chapter 11: 3D processing for human-machine interaction and additive manufacturing Chapter 12: Augmented reality technology in smart manufacturing Chapter 13: Extended reality on smart manufacturing Chapter 14: Intelligent transportation systems Chapter 15: Optical fibres for data interoperability and real-time production tracking in medical manufacturing Chapter 16: Human-Machine Interface for Healthcare Technology Manufacturing Chapter 17: Smart manufacturing workplace safety with virtual training, AR and haptic technologies Chapter 18: Blockchain technology in smart manufacturing Chapter 19: Reducing waste and pollution with automation and CPS in manufacturing Chapter 20: Smart manufacturing workplace safety with virtual training, AR, MR and haptic technologies Chapter 21: Conserving environment using resources wisely with reduction of waste and pollution: exemplary initiatives for Education 4.0 Chapter 22: Conserving cultural heritage, monitoring health and safety in the environment integrating technology: issues, challenges and the way forward Chapter 23: Rethinking and redesigning strategies related to IR4.0 to bridge the gap of human resource development in ICT industries and smart manufacturing Chapter 24: Summary

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Book SynopsisThe Arduino platform provides a virtually limitless range of creative opportunities to musicians who are interested to explore new technologies. In Arduino for Musicians, Brent Edstrom provides a comprehensive guide to the underlying technologies enabling the creation of custom instruments that respond to light, touch, breath, and other forms of control.Trade Review"I heartily recommend this book as a way to get started with Arduino or electronics even for non-musicians. But if you ever wanted to make your own Moog synth or Theremin (and who hasn't) then you will love this book."--Dr. Simon Monk, author and maker "A comprehensive and easy to use guide for everything you may need to know about how to use the Arduino for musical applications. The book is well-organized, allowing both technical novices and experienced music technologists to find the information and guidance they may be looking for."--Gil Weinberg, Professor and Director, Georgia Tech Center for Music Technology "Edstrom has developed a very useful resource for musicians interested in using microcomputer controllers and software tools in Arduino for Musicians. This publication, organized in three sections, serves not only as an introduction for those just learning the world of the Arduino, but also for those learning to use the open-source electronics platform to apply creative applications of the core concepts for the more advanced projects. The intermediate and advanced sections are written in a casual narrative style with well-organized sequencing. The book is a welcome addition as a resource or required text for undergraduate or graduate students enrolled in music technology or related courses in multimedia performance."--G. David Peters, Professor and Head of Graduate Studies, Music and Arts Technology, Indiana University - IUPUITable of ContentsTable of Contents Forward Chapter 1 Getting Started Chapter 2 Introduction To Programming Chapter 3 Introduction To Electronics Chapter 4 Interfacing With Arduino Chapter 5 Music Instrument Digital Interface (MIDI) I/O Chapter 6 Real-Time Input: Musical Expression Chapter 7 Music-Making Shields Chapter 8 Programming Part II Chapter 9 Audio Output and Sound Synthesis Chapter 10 Audio Input Chapter 11 Finalizing Projects Chapter 12 Standalone Arduino Chapter 13 MIDI Hand Drum Project Chapter 14 Stella Synthesizer Project Chapter 15 Step Sequencer Project Chapter 16 Emöte MIDI Controller Notes Bibliography Appendix A: MIDI Control Changes Appendix B: MMC Commands Appendix C: Introduction to Bit Twiddling Index

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Book SynopsisDiscover all the amazing things you can do with Arduino Arduino is a programmable circuit board that is being used by everyone from scientists, programmers, and hardware hackers to artists, designers, hobbyists, and engineers in order to add interactivity to objects and projects and experiment with programming and electronics.Table of ContentsIntroduction 1 Part I: Getting Started with Arduino Projects 7 Chapter 1: Exploring the World of Arduino 9 Chapter 2: Setting Up Your Workspace and Tools 19 Chapter 3: Understanding the Basics 41 Part II: Basic Arduino Projects 63 Chapter 4: The All-Seeing Eye 65 Chapter 5: Making a Light Pet 85 Chapter 6: Making a Scrolling Sign 107 Chapter 7: Building an Arduino Clock 127 Part III: The Interactive Home and Garden 153 Chapter 8: Building a Keypad Entry System 155 Chapter 9: Building an RFID Tag Reader 181 Chapter 10: Building an Automated Garden 201 Chapter 11: Building a Tweeting Pet Door 221 Chapter 12: Building a Home Sensing Station 243 Part IV: Advanced Arduino Projects 273 Chapter 13: Building a GPS Data Logger 275 Chapter 14: Building a Remote-Controlled Car 299 Chapter 15: Building an LED Cube 323 Part V: The Part of Tens 349 Chapter 16: Ten Great Arduino Resources 351 Chapter 17: Ten Troubleshooting Tips 359 Index 369

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Book SynopsisSpace debris and asteroid impacts pose a very real, very near-term threat to Earth. In order to help study and mitigate these risks, the Stardust program was formed in 2013. This training and research network was devoted to developing and mastering techniques such as removal, deflection, exploitation, and tracking. This book is a collection of many of the topics addressed at the Final Stardust Conference, describing the latest in asteroid monitoring and how engineering efforts can help us reduce space debris. It is a selection of studies bringing together specialists from universities, research institutions, and industry, tasked with the mission of pushing the boundaries of space research with innovative ideas and visionary concepts.Topics covered by the Symposium: Orbital and Attitude Dynamics Modeling Long Term Orbit and Attitude Evolution Particle Cloud Modeling and Simulation Collision and Impact Modelling and Simulation, Re-entry Modeling and Simulation Asteroid Origins and Characterization Orbit and Attitude Determination Impact Prediction and Risk Analysis, Mission Analysis-Proximity Operations, Active Removal/Deflection Control Under Uncertainty, Active Removal/Deflection Technologies, and Asteroid Manipulation Table of ContentsPreface.- Section 1: Mission to Asteroids.- Chapter 1: Optimization of Asteroid Capture Missions using Earth Resonant Encounters.- Chapter 2: Evaluating Proximity Operations through High-fidelity Asteroid Deflection Evaluation Software (HADES).- Chapter 3: Prediction of Orbital Parameters for Undiscovered Potentially Hazardous Asteroids Using Machine Learning.- Section 2: Orbit and Uncertainty Propagation.- Chapter 4: Exploring Sensitivity of Orbital Dynamics with Respect to Model Truncation. The Frozen Orbits Approach.- Chapter 5: A Parametric Study of the Orbital Lifetime of Super GTO and SSTO Orbits Based on Semi-Analytical Integration.- Chapter 6: On the Use of Positive Polynomials for the Estimation of Upper and Lower Expectations in Orbital Dynamics.- Section 3: Space Debris Monitoring, Mitigation, and Removal.- Chapter 7: Trajectory Generation Method for Robotic Free-Floating Capture of a Non- Cooperative, Tumbling Target.- Chapter 8: Taxonomy of LEO Space Debris Population for ADR Capture Methods Selection.- Chapter 9: Remote Sensing for Planar Electrostatic Characterization using the Multi-Sphere Method.- Chapter 10: Active Debris Removal and Space Debris Mitigation using Hybrid Propulsion Solutions.- Chapter 11: The Puzzling Case of the Deep-Space Debris WT1190F: A Test Bed for Advanced SSA Techniques.- Chapter 12: Development of a Debris Index.- Section 4: Re-Entry Analysis and Design for Demise.- Chapter 13: A Multidisciplinary Approach of Demisable Tanks’ Re-Entry.- Chapter 14: Design-for-Demise Analysis using the SAM Destructive Re-Entry Model.- Chapter 15: Low-Fidelity Modelling for Aerodynamic Characteristics of Re-Entry Objects.- Chapter 16: Re-Entry Predictions of Potentially Dangerous Uncontrolled Satellites: Challenges and Civil Protection Applications.- Chapter 17: Uncertainty Quantification for Destructive Re-Entry Risk Analysis – JAXA Perspective.

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Book SynopsisMultivariable Feedback Control: Analysis and Design, Second Edition presents a rigorous, yet easily readable, introduction to the analysis and design of robust multivariable control systems.Table of Contents1 Introduction1 1.1 The process of control system design 1 1.2 The control problem 2 1.3 Transfer functions 3 1.4 Scaling 5 1.5 Deriving linear models 7 1.6 Notation 10 2 Classical Feedback Control 15 2.1 Frequency response 15 2.2 Feedback control 20 2.3 Closed-loop stability 26 2.4 Evaluating closed-loop performance 28 2.5 Controller design 40 2.6 Loop shaping 42 2.7 IMC design procedure and PID control for stable plants 54 2.8 Shaping closed-loop transfer functions 59 2.9 Conclusion 65 3 Introduction to Multivariable Control 67 3.1 Introduction 67 3.2 Transfer functions for MIMO systems 68 3.3 Multivariable frequency response analysis 71 3.4 Relative gain array (RGA) 82 3.5 Control of multivariable plants 91 3.6 Introduction to multivariable RHP-zeros 96 3.7 Introduction to MIMO robustness 98 3.8 General control problem formulation 104 3.9 Additional exercises 115 3.10 Conclusion 117 4 Elements of Linear System Theory 119 4.1 System descriptions 119 4.2 State controllability and state observability 127 4.3 Stability 134 4.4 Poles 135 4.5 Zeros 138 4.6 Some important remarks on poles and zeros 141 4.7 Internal stability of feedback systems 144 4.8 Stabilizing controllers 148 4.9 Stability analysis in the frequency domain 150 4.10 System norms 156 4.11 Conclusion 162 5 Limitations on Performance In Siso Systems 163 5.1 Input–output controllability 163 5.2 Fundamental limitations on sensitivity 167 5.3 Fundamental limitations: bounds on peaks 172 5.4 Perfect control and plant inversion 180 5.5 Ideal ISE optimal control 181 5.6 Limitations imposed by time delays 182 5.7 Limitations imposed by RHP-zeros 183 5.8 Limitations imposed by phase lag 191 5.9 Limitations imposed by unstable (RHP) poles 192 5.10 Performance requirements imposed by disturbances and commands 198 5.11 Limitations imposed by input constraints 199 5.12 Limitations imposed by uncertainty 203 5.13 Summary: controllability analysis with feedback control 206 5.14 Summary: controllability analysis with feedforward control 209 5.15 Applications of controllability analysis 210 5.16 Conclusion 219 6 Limitations on Performance In Mimo Systems 221 6.1 Introduction 221 6.2 Fundamental limitations on sensitivity 222 6.3 Fundamental limitations: bounds on peaks 223 6.4 Functional controllability 232 6.5 Limitations imposed by time delays 233 6.6 Limitations imposed by RHP-zeros 235 6.7 Limitations imposed by unstable (RHP) poles 238 6.8 Performance requirements imposed by disturbance s238 6.9 Limitations imposed by input constraints 240 6.10 Limitations imposed by uncertainty 242 6.11 MIMO input–output controllability 253 6.12 Conclusion 258 7 Uncertainty And Robustness for Siso Systems 259 7.1 Introduction to robustness 259 7.2 Representing uncertainty 260 7.3 Parametric uncertainty 262 7.4 Representing uncertainty in the frequency domain 265 7.5 SISO robust stability 274 7.6 SISO robust performance 281 7.7 Additional exercises 287 7.8 Conclusion 288 8 Robust Stability And Performance Analysis For Mimo Systems 289 8.1 General control configuration with uncertainty 289 8.2 Representing uncertainty 290 8.3 Obtaining P, N and M 298 8.4 Definitions of robust stability and robust performance 299 8.5 Robust stability of the M Δ-structure 301 8.6 Robust stability for complex unstructured uncertainty 302 8.7 Robust stability with structured uncertainty: motivation 305 8.8 The structured singular value 306 8.9 Robust stability with structured uncertainty 313 8.10 Robust performance 316 8.11 Application: robust performance with input uncertainty 320 8.12 μ-synthesis and DK-iteration 328 8.13 Further remarks on μ 336 8.14 Conclusion 338 9 Controller Design 341 9.1 Trade-offs in MIMO feedback design 341 9.2 LQG control 344 9.3 H2 and H∞ control 352 9.4 H∞ loop-shaping design 364 9.5 Conclusion 381 10 Control Structure Design 383 10.1 Introduction 383 10.2 Optimal operation and control 385 10.3 Selection of primary controlled outputs 388 10.4 Regulatory control layer 403 10.5 Control configuration elements 420 10.6 Decentralized feedback control 429 10.7 Conclusion 454 11 Model Reduction 455 11.1 Introduction 455 11.2 Truncation and residualization 456 11.3 Balanced realizations 457 11.4 Balanced truncation and balanced residualization 458 11.5 Optimal Hankel norm approximation 459 11.6 Reduction of unstable models 462 11.7 Model reduction using Matlab 462 11.8 Two practical examples 463 11.9 Conclusion 471 12 Linear Matrix Inequalities 473 12.1 Introduction to LMI problems473 12.2 Types of LMI problems 476 12.3 Tricks in LMI problems 479 12.4 Case study: anti-windup compensator synthesis 484 12.5 Conclusion 490 13 Case Studies 491 13.1 Introduction 491 13.2 Helicopter control 492 13.3 Aero-engine control 500 13.4 Distillation process 509 13.5 Conclusion 514 A Matrix Theory And Norms 515 A.1 Basics 515 A.2 Eigenvalues and eigenvectors 518 A.3 Singular value decomposition 520 A.4 Relative gain array 526 A.5 Norms 530 A.6 All-pass factorization of transfer function matrices 541 A.7 Factorization of the sensitivity function 542 A.8 Linear fractional transformations 543 B Project Work And Sample Exam 547 B.1 Project work 547 B.2 Sample exam 548 Bibliography 553 Index 563

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Book SynopsisThis book highlights the developments, discoveries, and practical and advanced experiences related to responsive distributed computing and how it can support the deployment of trajectory-based applications in smart systems. Smart Trajectories: Metamodeling, Reactive Architecture for Analytics and Smart Applications deals with the representation and manipulation of smart trajectories in various applications and scenarios. Presented in three parts, the book first discusses the foundation and principles for spatial information systems, complex event processing, and building a reactive architecture. Next, the book discusses modeling and architecture in relation to smart trajectory metamodeling, mining and big trajectory data, and clustering trajectories. The final section discusses advanced applications and trends in the field, including congestion trajectory analytics and real-time Big Data analytics in cloud ecosystems. Metamodeling, distributed architectures, reactive Table of Contents1. Intelligent Distributed Computing Paradigm. 2. Multi Micro-Agent System Middleware Model based on Event Sourcing and CQRS Patterns. 3. Intelligent Distributed Computing Paradigm: Emergence, Challenges and Future Research Directions. 4. Emerging Paradigm of Urban Computing: Challenges, Applications, and Future Research Directions. 5. Complex Event Processing Architectures for Smart City Applications. 6. Portunus: Enhancing smart city application connectivity with complex space-time events distributed system. 7. Smart Trajectories Metamodeling. 8. A Type Level Trajectory Framework. 9. A Distributed Reactive Trajectory Framework for Nearby Events Discovery. 10. A Multidimensional Trajectory Model in the Context of Mobile Crowd Sensing. 11. Trajectory mining based on Process mining in RORO terminals: Performance-Driven Analysis to support trajectories redesign. 12. Aspects from mobility data in fog/cloud era: Directions from a pilot case study of Hazmat transportation telemonitoring in urban area. 13. Utility assessment of line-of-sight traffic jam and queue detection in urban environments for intelligent road vehicles. 14. Risky Trajectory Prediction for Safe Walkability under Intuitionistic Fuzzy Environment. 15. A real-time reactive Service Oriented Architecture for safe urban walkability. 16. Safest Trajectories for Pedestrian using Distributed Architecture based on Spatial Risk Analysis and Voronoï Spatial Accessibility. 17. Towards a predictive simulation framework of accidents risks for pedestrians based on distributed artificial intelligence and intuitionist fuzzy modeling. 18. Trajectory to a new shape of organizational structure, Enterprise Architect and Organizational Audit for Governance of Information Systems Processes. 19. Dynamic detection of fuzzy sub-congested urban traffic networks. 20. Multi-Agent Modeling for Pedestrian Risk Assessment.

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Book SynopsisReinforcement Learning (RL) is a branch of Artificial Intelligence (AI) that teaches agents to learn optimal behavior through interaction, feedback, and long-term goals. After decades of research, RL has matured into a powerful technology driving real-world innovation; it is now used in areas such as robotics, energy systems, finance, and autonomous vehicles.Yet, for many, RL feels inaccessible, buried under dense mathematics and complex theory. This book changes that. It is designed to help newcomers start applying RL as quickly as possible through a classical pedagogical approach: many small, focused examples that build intuition and practical skill step by step.Featuring:â Essential concepts explained from the ground upâ Code-based examples that reveal how algorithms work in practiceâ Worked examples by hand to strengthen intuition, just like in engineering or mathematicsâ Language-agnostic guidance, easily followed using Python, Java, or C++Even readers without coding or university-level mathematics backgrounds will gain valuable insight into the fascinating world of RL - insight that may become a critical differentiator in the age of AI. Whether you are a student or professional, Reinforcement Learning Explained will give you the tools and confidence to explore one of AIâs most exciting frontiers.

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Book SynopsisThis book offers readers a fresh perspective on intelligence, its alignment with human society, and the role AI plays in shaping our collective future. By exploring AI across three perspectives, external AI, internal AI, and societal AI, the book addresses the evolving challenges each stage presents.Reconceiving AI: The World as an Apple or a Blue Orange explores the diverse nature of intelligence and addresses the alignment problem by questioning the types of intelligence we seek in AI. By examining neurotypical human emotions such as love, empathy, and remorse, and intrinsic moral imperatives, it proposes a new form of intelligence, neurodivergent yet harmoniously aligned with human society. The book draws on CÃzanneâs still-life paintings to illustrate the relational and substantive nature of objects within his world models and uses Paul Ãluardâs poem The World Is a Blue Orange to explore how imagination and creativity can deepen our understanding of these models. Ãluardâs surreal imagery challenges our conventional understanding of reality, prompting us to consider whether AI could adopt similarly creative, non-linear ways of perceiving and representing the world. The exploration continues with moral and ethical dilemmas found in Eric Rohmerâs Six Moral Tales and Krzysztof KieÅlowskiâs Dekalog films, shedding light on how these principles should be reflected in the AI systems of the future. Finally, the book offers an in-depth analysis of generative AI technologies, particularly large language models and multimodal systems, evaluating their scientific limitations and their potential to advance AI toward a truly intelligent system.This book equips readers with the tools to understand AIâs potential impact on society and its ethical and scientific challenges. It will be useful to developers, researchers, students, and general readers. The book provides an accessible, thought-provoking exploration of how AI can evolve and what we should expect from it.

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Book SynopsisA leader in the data economy explains how we arrived at AI—and how we can navigate its future. In The Datapreneurs, Bob Muglia helps us understand how innovation in data and information technology have led us to AI—and how this technology must shape our future. The long-time Microsoft executive, former CEO of Snowflake, and current tech investor maps the evolution of the modern data stack and how it has helped build today’s economy and society. And he explains how humanity must create a new social contract for the artificial general intelligence (AGI)—autonomous machines intelligent as people—that he expects to arrive in less than a decade. Muglia details his personal experience in the foundational years of computing and data analytics, including with Bill Gates and Sam Altman, the CEO of OpenAI, the creator of ChatGPT, and others that are not household names—yet. He builds upon Isaac Asimov’s Laws of Robotics to explore the moral, ethical, and legal implications of today’s smart machines, and how a combination of human and machine intelligence could create an era of progress and prosperity where all the people on Earth can have what they need and want without destroying our natural environment.The Datapreneurs is a call to action. AGI is surely coming. Muglia believes that tech business leaders, ethicists, policy leaders, and even the general public must collaborate answer the short- and long-term questions raised by its emergence. And he argues that we had better get going, because advances are coming so fast that society risks getting caught flatfooted—with potentially disastrous consequences. Trade Review"Muglia rightly refocuses us on the people, not just the algorithms and machines, behind the next wave of artificial intelligence." —Satya Nadella, Chairman and CEO, Microsoft “As a values-based leader, Bob brings a unique point of view that inspires others to solve today’s greatest technical challenges.” —Mark C Thompson, chief executive leadership coach and New York Times bestselling author Bob remained at the forefront of data technology throughout his career, and his informed perspective makes this book extremely compelling.”—Paul Maritz, technology investor, former CEO of VMware, and former group vice president at Microsoft "As the Information Age evolves into the Age of AI, data has never been more critical to the enterprise or, dare I say, humanity. There is nobody better than Bob to frame the important conversations about the data road ahead."—Brad Gerstner, Serial Entrepreneur and Founder / CEO of Altimeter Capital

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Book SynopsisDr. Katsuhiko Ogata graduated from the University of Tokyo (BS), earned an MS degree from the University of Illinois, and his Ph.D from the University of California, Berkeley. He is Professor Emeritus at the University of Minnesota.Table of ContentsContents Preface Chapter 1 Introduction to Control Systems 1–1 Introduction 1–2 Examples of Control Systems 1–3 Closed-Loop Control versus Open-Loop Control 1–4 Outline of the Book Chapter 2 Mathematical Modeling of Control Systems 2–1 Introduction 2–2 Transfer Function and impulse Response Function 2–3 Atomatic Control Systems 2–4 Modeling in state space 2–5 State-Space Representation of Scalar Differential Equation System 2–6 Transformation of Mathematical models with MATLAB 2–7 Linearization of Nonlinear Mathematical Models Example Problems and Solutions Problems Chapter 3 Mathematical Modeling of Mechanical Systems and Electrical Systems 3–1 Introduction 3–2 Mathematical Modeling of Mechanical Systems 3–3 Mathematical Modeling of Electrical Systems Example Problems and Solutions Problems Chapter 4 Mathematical Modeling of Fluid Systems and Thermal Systems 4–1 Introduction 4–2 Liquid-Level Systems 4–3 Pneumatic Systems 4–4 Hydraulic Systems 4–5 Thermal Systems Example Problems and Solutions Problems Chapter 5 Transient and Steady-State Response Analyses 5–1 Introduction 5–2 First-Order Systems 5–3 Second-Order Systems 5–4 Higher Order Systems 5–5 Transient-Response Analysis with MATLAB 5–6 Routh's Stability Criterion 5–7 Effects of Integral and Derivative Control Actions on System Performance 5–8 Steady-State Errors in Unity-Feedback Control Systems Example Problems and Solutions Problems Chapter 6 Control Systems Analysis and design by the Root-Locus Method 6–1 Introduction 6–2 Root-Locus Plots 6–3 plotting Root Loci with MATLAB 6–4 Root-Locus Plots of Positive Feedback Systems 6–5 Root-Locus Approach to control Systems Design 6–6 Lead Compensation 6–7 Lag Compensation 6–8 Lag-Lead Compensation Example Problems and Solutions Problems Chapter 7 Control Systems Analysis and Design by the Frequency Response Method 7–1 Introduction 7–2 Bode Digrams 7–3 Polar Plots 7–4 Log-Magnitude-versus-Phase plots 7–5 Nyquist Stability Criterion 7–6 Stability Analysis 7–7 Relative Stability Analysis 7–8 Closed-Loop Frequency Response of Unity-feedback Systems 7–9 Experimental Determination of Transfer functions 7–10 Control Systems design by Frequency Response Approach 7–11 Lead Compensation 7–12 Lag Compensation 7–13 Lag-Lead Compensation Example Problems and Solutions Problems Chapter 8 PID Controllers and Modified PID Controllers 8–1 Introduction 8–2 Ziegler- Nichols Rules for tuning PID controllers 8–3 Design of PID Controllers with Frequency Response Approach 8–4 Design of PID Controllers with Computational Optimization Approach 8–5 Modification of PID Control Schemes 8–6 Two-Degrees-of-freedom PID Control Schemes 8–7 Zero Placement Approach to Improve Response Example Problems and Solutions Problems Chapter 9 Control Systems Analysis in State Space 9–1 Introduction 9–2 State-space Representations of Transfer-Function Systems 9–3 Transformation of System Models with MATLAB 9–4 Solving the Time-Invariant State Equation 9–5 Some Useful Results in vector-Matrix Analysis 9–6 Controllability 9–7 Observability Example Problems and Solutions Problems Chapter 10 Control Systems Design of in State Space 10–1 Introduction 10–2 Pole Placement 10–3 Solving Pole-Placement Problems with MATLAB 10–4 Design of Servo Systems 10–5 State Observers 10–6 Design of Regulator Systems with Observers 10–7 Design of Control Systems with Observers 10–8 Quadratic Optimal Regulator Systems 10–9 Robust Control Solutions Example Problems and Solutions Problems Appendix A Appendix B Appendix C References Index

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Book SynopsisFrom a review of the first edition: Modern Data Science with R is rich with examples and is guided by a strong narrative voice. What's more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician).Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions.The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to undTrade Review"This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data." (Hunter Glanz Cal Poly San Luis Obispo)"Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills." (Lynn Collen, St. Cloud Stat Univ.)"I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much…I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R…and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course." (Matthew Beckman, Penn State University)"[...] To answer a wide range of modern research questions, this book by Baumer, Kaplan, and Horton features an excellent introduction to data wrangling, visualization, statistical modeling, machine learning, and other advanced statistical applications through the RStudio environment following the tidyverse syntax. [...] Overall, Modern Data Science with R, 2nd edition serves as an excellent introductory resource to help develop techniques to extract, transform, visualize, and learn from datasets through the R environment. It focuses on implementing those techniques in R and does not provide a theoretical background for the discussed methods. The book will be a perfect reference for a broad audience ranging from undergraduates in data science courses to advanced graduate students and professionals from a variety of research fields."-Kohma Arai and Vyacheslav Lyubchich, in Technometrics, July 2022"Overall, I enjoyed reading this book. The authors were very good at creating a complete tool for studying data science. Therefore, I recommend this book, for its content, writing, and organization, to graduate students in data science and statistics. I also recommend the book to professionals who should prepare themselves for the challenges they are going to face in the future with the voluminous and heterogenous amount of data that should be timely analyzed to extract meaningful information to guide action."-Georgios Nikolopoulos, in ISCB News, June 2022"The authors have successfully completed the job of choosing the content with relevant topics and, deciding the extent of knowledge to be delivered, and finally, putting them in an understandable sequence. This is a well-written book and does not cover much theory. .. The book’s second edition contents are updated, expanded, revised, split, rewritten and rearranged compared to the first edition. The key changes are the use of recently developed R packages, .... (and) updated exercises in the chapters ..."-Shalabh,in Journal of the Royal Statistical Society Series A, August 2021"[This book] provides an excellent basis for statisticians who want to dig deeper into, for example, data handling, for computer scientists who aim to strengthen their knowledge of statistical methods as well as for all other researchers who are interested in data science in general. ... Each section is structured as an interplay between R-code and explanatory text for understanding. The division into several stand-alone segments is an advantage, because the reader may easily choose the section she or he is interested in without missing relevant information. A key feature of the book is its focus on different example data sets that are available via R-packages or from URLs that are embedded in the text. These data sets are used to illustrate the methodology presented using R-code. Their availability allows the reader to reproduce the code while working with the book. ... It can be warmly recommended to practical researchers who seek a comprehensive overview of different topics in data science with focus on implementations in R."-Annika Hoyer, in Biometrical Journal, August 2021"This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data." -Hunter Glanz, Cal Poly San Luis Obispo"Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills." -Lynn Collen, St. Cloud Stat University"I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much…I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R…and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course." -Matthew Beckman, Penn State University"The authors have covered almost all aspects of data science, a revolutionary field that marries elements of computational thinking and traditional statistical theory. The book can thus equip the readers with the necessary knowledge and skills to extract data from a variety of sources, restructure observations in a form that allows analysis, store data in efficient databases, and work effectively on massive and complex data sets in order to produce actionable information."- Georgios Nikolopoulos, University of Cyprus, ISCB Book Reviews, June 2022.Table of ContentsI Part I: Introduction to Data Science. 1. Prologue: Why data science? 2. Data visualization. 3. A grammar for graphics. 4. Data wrangling on one table. 5. Data wrangling on multiple tables. 6. Tidy data. 7. Iteration. 8. Data science ethics. II. Part II: Statistics and Modeling. 9. Statistical foundations. 10. Predictive modeling. 11. Supervised learning. 12. Unsupervised learning. 13. Simulation. III Part III: Topics in Data Science. 14. Dynamic and customized data graphics. 15. Database querying using SQL. 16. Database administration. 17. Working with spatial data. 18.Geospatial computations. 19. Text as data. 20. Network science. IV Part IV: Appendices.

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Book SynopsisThe design, construction, and upkeep of infrastructure is comprised of a multitude of dimensions spanning a highly complex paradigm of interconnected opportunities and challenges. While traditional methods fall short of adequately accounting for such complexity, artificial intelligence (AI) presents novel and out-of-the-box solutions that effectively tackle the growing demands of our infrastructure. The convergence between AI and civil engineering is an emerging frontier with tremendous potential.The book is likely to provide a boost to the state of infrastructure engineering by fostering a new look at civil engineering that capitalizes on AI as its main driver. It highlights the ongoing push to adopt and leverage AI to realize contemporary, intelligent, safe, and resilient infrastructure. The book comprises interdisciplinary and novel works from across the globe. It presents findings from innovative efforts supplemented with physical tests, numerical simulations, and case stTable of Contents1. Convolutional Neural Networks and Applications on Civil Infrastructure 2. Identifying Non-linearity in Construction Workers' Personality: Safety Behaviour Predictive Relationship Using Neural Network and Linear Regression Modelling 3. Machine Learning Framework for Predicting Failure Mode and Flexural Capacity of FRP-Reinforced Beams 4. A Novel Formulation for Estimating Compressive Strength of High Performance Concrete Using Gene Expression Programming 5. Implementation of Data-Driven Approaches for Condition Assessment of Structures and Analyzing Complex Data 6. Automatic Detection of Surface Thermal Cracks in Structural Concrete with Numerical Correlation Analysis 7. State-of-the-Art Research in the Area of Artificial Intelligence with Specific Consideration to Civil Infrastructure, Construction Engineering and Management, and Safety 8. Artificial Intelligence in Concrete Materials: A Scientometric View 9. Active Learning Kriging-Based Reliability for Assessing the Safety of Structures: Theory and Application 10. A Bayesian Estimation Technique for Multilevel Damage Classification in DBHM 11. Machine learning and IoT Data for Concrete Performance Testing and Analysis 12. Knowledge-enhanced Deep Learning for Efficient Response Estimation of Nonlinear Structures 13. Damage Detection in Reinforced Concrete Girders by Finite Element and Artificial Intelligence Synergy 14. Deep Learning in Transportation Cyber-Physical Systems 15. Artificial Intelligence in the Construction Industry: Theory and Emerging Applications for the Future of Work 16. The Use of Machine Learning in Heat Transfer Analysis for Structural Fire Engineering Applications 17. Using Artificial Intelligence to Derive Temperature Dependent Mechanical Properties of Ultra-High Performance Concrete 18. Smart Tunnel Fire Safety Management by Sensor Network and Artificial Intelligence

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Book SynopsisBrain and Behavior Computing offers insights into the functions of the human brain. This book provides an emphasis on brain and behavior computing with different modalities available such as signal processing, image processing, data sciences, statistics further it includes fundamental, mathematical model, algorithms, case studies, and future research scopes. It further illustrates brain signal sources and how the brain signal can process, manipulate, and transform in different domains allowing researchers and professionals to extract information about the physiological condition of the brain. Emphasizes real challenges in brain signal processing for a variety of applications for analysis, classification, and clustering. Discusses data sciences and its applications in brain computing visualization. Covers all the most recent tools for analysing the brain and it's working. Describes brain modeling and all possTable of Contents1. Simulation Tools for Brain Signal Analysis 2. Processing Techniques and Analysis of Brain Sensor Data Using Electroencephalography 3. Application of Machine-Learning Techniques in Electroencephalography Signals 4. Revolution of Brain Computer Interface: An Introduction 5. Signal Modeling Using Spatial Filtering and Matching Wavelet Feature Extraction for Classification of Brain Activity Pattern 6. Study and Analysis of the Visual P300 Speller on Neurotypical Subjects 7. Effective Brain Computer Interface Based on the Adaptive-Rate Processing and Classification of Motor Imagery Tasks 8. EEG-Based BCI Systems for Neurorehabilitation Applications 9. Scalp EEG Classification Using TQWT-Entropy Features for Epileptic Seizure Detection 10. An Efficient Single-Trial Classification Approach for Devanagari Script-Based Visual P300 Speller Using Knowledge Distillation and Transfer Learning 11. Deep Learning Algorithms for Brain Image Analysis 12. Evolutionary Optimization Based Two Dimensional Elliptical FIR Filters for Skull Stripping in Brain Imaging and Disorder Detection 13. EEG-Based Neurofeedback Game for Focus Level Enhancement 14. Detecting K-Complexes in Brain Signals Using WSST2-DETOKS 15. Directed Functional Brain Networks: Characterization of Information Flow Direction during Cognitive Function Using Non-Linear Granger Causality 16. Student Behavior Modeling and Context Acquisition: A Ubiquitous Learning Framework

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Book SynopsisThis book provides an extended overview and fundamental knowledge in industrial automation, while building the necessary knowledge level for further specialization in advanced concepts of industrial automation. It covers a number of central concepts of industrial automation, such as basic automation elements, hardware components for automation and process control, the latch principle, industrial automation synthesis, logical design for automation, electropneumatic automation, industrial networks, basic programming in PLC, and PID in the industry.Table of ContentsIntroduction to Automation. Hardware Components for Automation and Process Control. Industrial Automation Synthesis. Logical Design of Industrial Automation. Basic Components of Electro Pneumatic Automation. Industrial Networks. Basic Programming Principles of PLCs Author. PID Control in the Industry.

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Book SynopsisFocused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies.The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendationTable of ContentsGraphs in Social and Digital Media. Mathematical Preliminaries: Graphs and Matrices. Algebraic Graph Analysis. Web Search Based on Ranking. Label Propagation and Information Diffusion in Graphs. Graph-Based Pattern Classification and Dimensionality Reduction. Matrix and Tensor Factorization with Recommender System Applications. Multimedia Social Search Based on Hypergraph Learning. Graph Signal Processing in Social Media. Big Data Analytics for Social Networks. Semantic Model Adaptation for Evolving Big Social Data. Big Graph Storage, Processing and Visualization.

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Book SynopsisThis book is written in a clear and thorough way to cover both the traditional and modern uses of artificial intelligence and soft computing. It gives an in-depth look at mathematical models, algorithms, and real-world problems that are hard to solve in MATLAB. The book is intended to provide a broad and in-depth understanding of fuzzy logic controllers, genetic algorithms, neural networks, and hybrid techniques such as ANFIS and the GA-ANN model.Features: A detailed description of basic intelligent techniques (fuzzy logic, genetic algorithm and neural network using MATLAB) A detailed description of the hybrid intelligent technique called the adaptive fuzzy inference technique (ANFIS) Formulation of the nonlinear model like analysis of ANOVA and response surface methodology Variety of solved problems on ANOVA and RSM Case studies of above mentioned intelligent techniques on the different process control systems This book can be used as a handbook and a guide for students of all engineering disciplines, operational research areas, computer applications, and for various professionals who work in the optimization area.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisAugmenting Public Relations examines how existing technologies used in Public Relations (PR) are being significantly augmented because of the advent of Artificial Intelligence (AI). The book describes the opportunities and pitfalls of AI, recent and emerging technologies, and projections in their development, offering an introduction to practitioners on how they, too, can create their own AI-enhanced tools.The developments in augmented, virtual and meta-reality, aided by AI, have now become serious contenders for commercial communication, and the ability to harness this visual capability is explained in some detail. As is the ability for practitioners to automatically monitor and feed websites using Application Programming Interfaces (APIs). The book also considers computer games as a form of communication, and the evolving application of games supported by AI. In recent years, the PR monitoring industry has deployed AI to search for content of interest to clients ac

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Book SynopsisIs a computer simulation of a brain sufficient to make it intelligent? Do you need consciousness to have intelligence? Do you need to be alive to have consciousness? This book has a dual purpose. First, it provides a multi-disciplinary research survey across all branches of neuroscience and AI research that relate to this book's mission of bringing AI research closer to building a human-level AI (HLAI) system. It provides an encapsulation of key ideas and concepts, and provides all the references for the reader to delve deeper; much of the survey coverage is of recent pioneering research. Second, the final part of this book brings together key concepts from the survey and makes suggestions for building HLAI. This book provides accessible explanations of numerous key concepts from neuroscience and artificial intelligence research, including: The focus on visual processing and thinking and the possible role of brain lateralization toward visual thinking and intelligence.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisWidely used across industrial and manufacturing automation, Programmable Logic Controllers (PLCs) perform a broad range of electromechanical tasks with multiple input and output arrangements, designed specifically to cope in severe environmental conditions such as automotive and chemical plants. Programmable Logic Controllers: A Practical Approach using CoDeSys is a hands-on guide to rapidly gain proficiency in the development and operation of PLCs based on the IEC 61131-3 standard. Using the freely-available* software tool CoDeSys, which is widely used in industrial design automation projects, the author takes a highly practical approach to PLC design using real-world examples. The design tool, CoDeSys, also features a built in simulator/soft PLC enabling the reader to undertake exercises and test the examples. Key features: Introduces to programming techniques using IEC 61131-3 guidelines in the five PLC-recognised programming languages. FocusesTable of ContentsPreface xiv Part One Hardware 1 1 About PLCs 3 1.1 History 4 1.1.1 More Recent Developments 6 1.2 Structure 7 1.2.1 Inputs and Outputs 10 1.3 PLC Operation 13 1.3.1 Process Knowledge 14 1.3.2 Standard Operations 16 1.3.3 Cyclic, Freewheeling, or Event‐Controlled Execution 18 1.4 Test Problems 19 2 Digital Signals and Digital Inputs and Outputs 20 2.1 Introduction 20 2.2 Terminology 21 2.2.1 Discrete, Digital, Logical, and Binary 21 2.2.2 Sensors, Transducers, and Transmitters 22 2.3 Switches 24 2.3.1 Limit Switches 24 2.3.2 Safety Devices 24 2.3.3 Magnetic Switches 25 2.4 Logical Sensors 26 2.4.1 Inductive Sensors 27 2.4.2 Capacitive Sensors 29 2.4.3 Photocells 30 2.4.4 Ultrasonic Sensors 33 2.4.5 Rotating Sensors (Encoders) 34 2.4.6 Other Detection Principles and Sensors 37 2.5 Connection of Logical Sensors 39 2.5.1 Sink/Source 41 2.5.2 Selecting a Sensor with the Proper Type of Output 43 2.6 Properties of Discrete Inputs 44 2.7 Discrete Actuators 45 2.7.1 Relays and Contactors 46 2.7.2 Solenoids and Magnetic Valves 47 2.7.3 Transistor Outputs versus Relay Outputs 49 2.8 Test Problems 50 3 Analog Signals and Analog I/O 52 3.1 Introduction 52 3.2 Digitalization of Analog Signals 53 3.2.1 Filtering 53 3.2.2 A/D Conversion 55 3.3 Analog Instrumentation 58 3.3.1 About Sensors 58 3.3.2 Standard Signal Formats 59 3.3.3 On the 4–20 mA Standard 59 3.3.4 Some Other Properties of Sensors 61 3.4 Temperature Sensors 61 3.4.1 Thermocouple 61 3.4.2 PT100/NI1000 62 3.4.3 Thermistors 64 3.5 Connection 64 3.5.1 About Noise, Loss, and Cabling 64 3.5.2 Connecting Sensors 67 3.5.3 Connection of a PT100 (RTD) 68 3.5.4 Connecting Thermocouples 72 3.6 Properties of Analog Input Modules 72 3.6.1 Measurement Ranges and Digitizing: Resolution 72 3.6.2 Important Properties and Parameters 74 3.7 Analog Output Modules and Standard Signal Formats 75 3.8 Test Problems 76 Part Two Methodic 79 4 Structured Design 81 4.1 Introduction 81 4.2 Number Systems 82 4.2.1 The Decimal Number Systems 82 4.2.2 The Binary Number System 82 4.2.3 The Hexadecimal Number System 83 4.2.4 Binary‐Coded Decimal Numbers 85 4.2.5 Conversion between Number Systems 86 4.3 Digital Logic 87 4.4 Boolean Design 91 4.4.1 Logical Functional Expressions 91 4.4.2 Boolean Algebra 93 4.5 Sequential Design 97 4.5.1 Flowchart 97 4.5.2 Example: Flowchart for Mixing Process 99 4.5.3 Example: Flowchart for an Automated Packaging Line 101 4.5.4 Sequence Diagrams 107 4.5.5 Example: Sequence Diagram for the Mixing Process 110 4.5.6 Example: Batch Process 112 4.6 State‐Based Design 113 4.6.1 Why Use State Diagrams? 114 4.6.2 State Diagrams 114 4.6.3 Example: Batch Process 117 4.6.4 Example: Level Process 118 4.6.5 Example: Packing Facility for Apples 121 4.7 Summary 124 4.8 Test Problems 125 Part Three IEC 61131‐3 131 5 Introduction to Programming and IEC 61131‐3 133 5.1 Introduction 133 5.1.1 Weaknesses in Traditional PLCs 134 5.1.2 Improvements with IEC 61131‐3 136 5.1.3 On Implementation of the Standard 137 5.2 Brief Presentation of the Languages 138 5.2.1 ST 138 5.2.2 FBD 138 5.2.3 LD 139 5.2.4 IL 139 5.2.5 SFC 141 5.3 Program Structure in IEC 61131‐3 141 5.3.1 Example of a Configuration 145 5.4 Program Processing 146 5.4.1 Development of Programming Languages 146 5.4.2 From Source Code to Machine Code 147 5.5 Test Problems 151 6 IEC 61131‐3: Common Language Elements 152 6.1 Introduction 152 6.2 Identifiers, Keywords, and Comments 153 6.2.1 Identifiers 153 6.2.2 Keywords 154 6.2.3 Comments 154 6.3 About Variables and Data Types 156 6.4 Pragmas and Literals 156 6.4.1 Literal 157 6.5 Data Types 158 6.5.1 Numerical and Binary Data Types 158 6.5.2 Data Types for Time and Duration 161 6.5.3 Text Strings 163 6.5.4 Generic Data Types 164 6.5.5 User‐Defined Data Types 166 6.6 Variables 169 6.6.1 Conventional Addressing 170 6.6.2 Declaration of Variables with IEC 61131‐3 171 6.6.3 Local Versus Global Variables 174 6.6.4 Input and Output Variables 175 6.6.5 Other Variable Types 176 6.7 Direct Addressing 176 6.7.1 Addressing Structure 176 6.7.2 I/O‐Addressing 178 6.8 Variable versus I/O‐Addresses 179 6.8.1 Unspecified I/O‐Addresses 179 6.9 Declaration of Multielement Variables 180 6.9.1 Arrays 181 6.9.2 Data Structures 182 6.10 Test Problems 184 7 Functions 187 7.1 Introduction 187 7.2 On Functions 188 7.3 Standard Functions 189 7.3.1 Assignment 190 7.4 Boolean Operations 191 7.5 Arithmetic Functions 192 7.5.1 Overflow 193 7.6 Comparison 194 7.7 Numerical Operations 195 7.7.1 Priority of Execution 196 7.8 Selection 197 7.9 Type Conversion 197 7.10 Bit‐String Functions 199 7.11 Text‐String Functions 200 7.12 Defining New Functions 202 7.13 EN/ENO 203 7.14 Test Problems 204 8 Function Blocks 206 8.1 Introduction 206 8.1.1 The Standard’s FBs 207 8.2 Declaring and Calling FBs 207 8.3 FBs for Flank Detection 208 8.4 Bistable Elements 209 8.5 Timers 210 8.6 Counters 211 8.6.1 Up‐Counter 212 8.6.2 Down‐Counter 212 8.6.3 Up/Down‐Counter 212 8.7 Defining New FBs 213 8.7.1 Encapsulation of Code 214 8.7.2 Other Nonstandardized FBs 216 8.8 Programs 217 8.8.1 Program Calls 218 8.8.2 Execution Control 219 8.9 Test Problems 220 Part Four Programming 221 9 Ladder Diagram (LD) 223 9.1 Introduction 223 9.2 Program Structure 224 9.2.1 Contacts and Conditions 225 9.2.2 Coils and Actions 226 9.2.3 Graphical Elements: An Overview 227 9.3 Boolean Operations 227 9.3.1 AND/OR‐Conditions 227 9.3.2 Set/Reset Coils 230 9.3.3 Edge Detecting Contacts 233 9.3.4 Example: Control of a Mixing Process 234 9.4 Rules for Execution 237 9.4.1 One Output: Several Conditions 237 9.4.2 The Importance of the Order of Execution 238 9.4.3 Labels and Jumps 239 9.5 Use of Standard Functions in LD 240 9.6 Development and Use of FBs in LD 242 9.7 Structured Programming in LD 244 9.7.1 Flowchart versus RS‐Based LD Code 248 9.7.2 State Diagrams versus RS‐Based LD Code 253 9.8 Summary 259 9.9 Test Problems 260 10 Function Block Diagram (FBD) 262 10.1 Introduction 262 10.2 Program Structure 263 10.2.1 Concepts 264 10.3 Execution Order and Loops 264 10.3.1 Labels and Jumps 265 10.4 User‐Defined Functions and FBs 266 10.5 Integer Division 268 10.6 Sequential Programming with FBD 271 10.7 Test Problems 273 11 Structured Text (ST) 278 11.1 Introduction 278 11.2 ST in General 279 11.2.1 Program Structure 280 11.3 Standard Functions and Operators 281 11.3.1 Assignment 282 11.4 Calling FBs 283 11.4.1 Flank Detection and Memories 284 11.4.2 Timers 287 11.4.3 Counters 288 11.5 IF Statements 288 11.6 CASE Statements 290 11.7 ST Code Based upon State Diagrams 292 11.7.1 Example: Code for the Level Process 295 11.8 Loops 298 11.8.1 WHILE … DO… END_WHILE 298 11.8.2 FOR … END_FOR 299 11.8.3 REPEAT … END_REPEAT 300 11.8.4 The EXIT Instruction 300 11.9 Example: Defining and Calling Functions 301 11.10 Test Problems 302 12 Sequential Function Chart (SFC) 306 12.1 Introduction 306 12.1.1 SFC in General 307 12.2 Structure and Graphics 307 12.2.1 Overview: Graphic Symbols 309 12.2.2 Alternative Branches 309 12.2.3 Parallel Branches 311 12.3 Steps 312 12.3.1 Step Addresses 313 12.3.2 SFC in Text Form (for Those Specially Interested…) 314 12.4 Transitions 314 12.4.1 Alternative Definition of Transitions 315 12.5 Actions 317 12.5.1 Action Types 318 12.5.2 Action Control 319 12.5.3 Alternative Declaration and Use of Actions 321 12.6 Control of Diagram Execution 322 12.7 Good Design Technique 323 12.8 Test Problems 326 13 Examples 331 13.1 Example 1: PID Controller Function Block: Structured Text 331 13.2 Example 2: Sampling: SFC 333 13.2.1 List of Variables 334 13.2.2 Possible Solution 334 13.3 Example 3: Product Control: SFC 337 13.3.1 Functional Description 338 13.3.2 List of Variables 338 13.3.3 Possible Solution 339 13.4 Example 4: Automatic Feeder: ST/SFC/FBD 342 13.4.1 Planning and Structuring 344 13.4.2 Alternative 1: SFC 345 13.4.3 Alternative 2: ST/FBD 347 Part Five Implementation 351 14 CODESYS 2.3 353 14.1 Introduction 353 14.2 Starting the Program 354 14.2.1 The Contents of a Project 356 14.3 Configuring the (WAGO) PLC 357 14.4 Communications with the PLC 360 14.4.1 The Gateway Server 361 14.4.2 Local Connection via Service Cable 362 14.4.3 Via Ethernet 363 14.4.4 Communication with a PLC Connected to a Remote PC 364 14.4.5 Testing Communications 365 14.5 Libraries 365 14.6 Defining a POU 367 14.7 Programming in FBD/LD 368 14.7.1 Declaring Variables 369 14.7.2 Programming with FBD 371 14.7.3 Programming with LD 372 14.8 Configuring Tasks 375 14.9 Downloading and Testing Programs 376 14.9.1 Debugging 377 14.10 Global Variables and Special Data Types 379 15 CODESYS Version 3.5 381 15.1 Starting a New Project 381 15.1.1 Device 382 15.1.2 Application 384 15.2 Programming and Programming Units (POUs) 386 15.2.1 Declaration of Variables 388 15.3 Compiling and Running the Project 389 15.3.1 Start Gateway Server and PLS and Set Up Communications 390 15.4 Test Problems 393 Bibliography 395 Index 396

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Book SynopsisMany professionals and students in engineering, science, business, and other application fields need to develop Windows-based and web-enabled information systems to store and use data for decision support, without help from professional programmers. However, few books are available to train professionals and students who are not professional programmers to develop these information systems. Developing Windows-Based and Web-Enabled Information Systems fills this gap, providing a self-contained, easy-to-understand, and well-illustrated text that explores current concepts, methods, and software tools for developing Windows-based and web-enabled information systems. Written in an easily accessible style, the book details current concepts, methods, and software tools for Windows-based and web-enabled information systems that store and use data. It is self-contained with easy-to-understand small examples to walk through concepts and implementation details along with large-scTrade Review"This book takes a comprehensive approach to cover the topics of information systems and their development. At the same time it has detailed examples to help readers at different levels. … detailed examples and case studies makes it a good textbook and reference for readers of diverse backgrounds."—Xiangyang Li, Johns Hopkins University"The main strength of this book is that it is written by industrial engineering professors that understand how to present important data management concepts to engineers that may not have a computer science background. Most books on data management and information systems are written with the computing professional in mind. This book finally gives engineers an understandable approach to learning the fundamental data management concepts that are relevant to engineering applications."—Susan D Urban, Texas Tech University"The book is provides a comprehensive introduction to the concepts of information systems. Furthermore, this book has struck the right balance of having sufficient business case concepts for implementing information systems as well as including important technical theories on designing databases."—Eugene Rex L. Jalao, Ph.D., University of the Philippines Diliman"… the most comprehensive book to introduce information system. … covers all the topics related to information system from theoretical background to practical applications. … organized very well and considers the diversity of the readers which will attract a huge number of audiences. … the best textbook for information system related class at both the undergraduate and graduate levels. … covers lots of complex case studies."—Mengqi Hu, Mississippi State UniversityTable of ContentsFoundations of Information Systems. Boolean algebra and digital logic circuits. Digital data representation. Computer system software. Network system software. Information Assurance. An Overview of Information Systems for Window and Web Applications. Database Design and Development. Data Modeling: E-R and EE-R Modeling. Data modeling: Relational Data Modeling and Normalization. SQL. Access. MySQL. Object-Oriented Database. Windows Application Development. Visual Basic Programming I. Visual Basic Programming II. Visual Basic Programming III. Windows Forms and Controls with VBA. Database connectivity with VBA. Windows Forms and Controls with VB.NET. Database connectivity with VB.NET. Web Application Development. Chapter 20. Web applications. Web services. Working with XML I. Working with XML II. Case Studies. A Business Application - Use of VB A and Database. An Engineering Application - Use of VB. NET and Database. A Science Application - Use of ASP.NET and Database.

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Book SynopsisGraph Theory and Its Applications, Third Edition is the latest edition of the international, bestselling textbook for undergraduate courses in graph theory, yet it is expansive enough to be used for graduate courses as well. The textbook takes a comprehensive, accessible approach to graph theory, integrating careful exposition of classical developments with emerging methods, models, and practical needs. The authorsâ unparalleled treatment is an ideal text for a two-semester course and a variety of one-semester classes, from an introductory one-semester course to courses slanted toward classical graph theory, operations research, data structures and algorithms, or algebra and topology.Features of the Third Edition Expanded coverage on several topics (e.g., applications of graph coloring and tree-decompositions) Provides better coverage of algorithms and algebraic and topological graph theory than any otherTable of ContentsIntroduction to Graph Models Graphs and Digraphs. Common Families of Graphs. Graph Modeling Applications. Walks and Distance. Paths, Cycles, and Trees. Vertex and Edge Attributes. Structure and Representation Graph Isomorphism. Automorphism and Symmetry. Subgraphs. Some Graph Operations. Tests for Non-Isomorphism. Matrix Representation. More Graph Operations. Trees Characterizations and Properties of Trees. Rooted Trees, Ordered Trees, and Binary Trees. Binary-Tree Traversals. Binary-Search Trees. Huffman Trees and Optimal Prefix Codes. Priority Trees. Counting Labeled Trees. Counting Binary Trees. Spanning Trees Tree Growing. Depth-First and Breadth-First Search. Minimum Spanning Trees and Shortest Paths. Applications of Depth-First Search. Cycles, Edge-Cuts, and Spanning Trees. Graphs and Vector Spaces. Matroids and the Greedy Algorithm. Connectivity Vertex and Edge-Connectivity. Constructing Reliable Networks. Max-Min Duality and Menger’s Theorems. Block Decompositions. Optimal Graph Traversals Eulerian Trails and Tours. DeBruijn Sequences and Postman Problems. Hamiltonian Paths and Cycles. Gray Codes and Traveling Salesman Problems. Planarity and Kuratowski’s Theorem Planar Drawings and Some Basic Surfaces. Subdivision and Homeomorphism. Extending Planar Drawings. Kuratowski’s Theorem. Algebraic Tests for Planairty. Planarity Algorithm. Crossing Numbers and Thickness. Graph Colorings Vertex-Colorings. Map-Colorings. Edge-Colorings. Factorization. Special Digraph Models Directed Paths and Mutual Reachability. Digraphs as Models for Relations. Tournaments. Project Scheduling. Finding the Strong Components of a Digraph. Network Flows and Applications Flows and Cuts in Networks. Solving the Maximum-Flow Problem. Flows and Connectivity. Matchings, Transversals, and Vertex Covers. Graph Colorings and Symmetry Automorphisms of Simple Graphs. Equivalence Classes of Colorings. Appendix

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Book SynopsisThis cookbook is perfect for anyone who wants to experiment with the popular Arduino microcontroller and programming environment. Updated for Arduino 1.8, the third edition includes more than 200 tips and techniques for building a variety of objects and prototypes

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Book SynopsisMake: Drones will help the widest possible audience understand how drones work by providing several DIY drone projects based on the world's most popular robot controller--the Arduino. The information imparted in this book will show Makers how to build better drones and be better drone pilots, and incidentally it will have applications in almost any robotics project. Why Arduino? Makers know Arduinos and their accessories, they are widely available and inexpensive, and there is strong community support. Open source flight-control code is available for Arduino, and flying is the hook that makes it exciting, even magical, for so many people. Arduino is not only a powerful board in its own right, but it's used as the controller of most inexpensive 3d printers, many desktop CNCs, and the majority of open source drone platforms.

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Book SynopsisThis textbook provides a comprehensive, but tutorial, introduction to robotics, computer vision, and control. It is written in a light but informative conversational style, weaving text, figures, mathematics, and lines of code into a cohesive narrative. Over 1600 code examples show how complex problems can be decomposed and solved using just a few simple lines of code. This edition is based on MATLAB® and a number of MathWorks® toolboxes. These provide a set of supported software tools for addressing a broad range of applications in robotics and computer vision. These toolboxes enable the reader to easily bring the algorithmic concepts into practice and work with real, non-trivial, problems. For the beginning student, the book makes the algorithms accessible, the toolbox code can be read to gain understanding, and the examples illustrate how it can be used. The code can also be the starting point for new work, for practitioners, students, or researchers, by writing programs based on toolbox functions. Two co-authors from MathWorks have joined the writing team and bring deep knowledge of these MATLAB toolboxes and workflows.Table of ContentsIntroduction.-Foundations: Representing Position and Orientation.- Time and Motion.- Mobile Robotics: Mobile Robot Vehicles.- Navigation.- Localization and Mapping.- Robot Manipulators: Robot Arm Kinematics.- Manipulator Velocity.- Dynamics and Control.- Computer Vision: Light and Color.- Images and Image Processing,- Image Feature Extraction,- Image Formation.- Using Multiple Images.- Installing the Toolboxes.- Linear Algebra.- Geometry.- Lie Groups and Algebras.

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Book SynopsisThe book provides a complete overview of the SIMATIC automation system and the TIA Portal with the engineering tool STEP 7. "Automating with SIMATIC" addresses all those who - want to get an overview of the components of the system and their features, - wish to familiarize themselves with the topic of programmable logic controllers, or - intend to acquire basic knowledge about configuration, programming and interaction of the SIMATIC components. At first, the book introduces the hardware of SIMATIC S7-1200, S7-300, S7-400 and S7-1500, including the ET 200 peripheral modules. This is followed by describing the work with STEP 7 in the programming languages LAD, FBD, STL, SCL and S7-Graph, and offline testing with S7-PLCSIM. The next section describes the structure of the user program, which is followed by the illustration of the data communication between the controllers of the automation system as well as with the peripheral devices by use of the bus systems Profinet and Profibus. The book closes with a survey of the devices for operator control and process monitoring and their configuration software. Table of ContentsController STEP 7 Programming Language User Programm Communication Control and Process Monitoring.

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Book Synopsis

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Book SynopsisThis book presents a unique examination of mobile robots and embedded systems, from introductory to intermediate level. It is structured in three parts, dealing with Embedded Systems (hardware and software design, actuators, sensors, PID control, multitasking), Mobile Robot Design (driving, balancing, walking, and flying robots), and Mobile Robot Applications (mapping, robot soccer, genetic algorithms, neural networks, behavior-based systems, and simulation). The book is written as a text for courses in computer science, computer engineering, IT, electronic engineering, and mechatronics, as well as a guide for robot hobbyists and researchers.Table of ContentsPart I, Embedded Systems and Programming Environments.- Robots and Controllers.- Central Processing Unit.- Sensors.- Actuators.- Actuators.- Control.- Multitasking.- Wireless Communication.- Raspberry Pi.- Arduino.- Part II, Mobile Robot Design.- Driving Robots.- Omnidirectional Robots.- Balancing Robots.- Walking Robots.- Autonomous Planes.- Autonomous Vessels and Underwater Vehicles.- Robot Manipulators.- Simulation Systems.- Part III, Mobile Robot Applications.- Localization and Navigation.- Maze Exploration.- Map Generation.- Real-Time Image Processing.- Robot Soccer.- Neural Networks.- Evolutionary Computing Approaches.- Behavior-Based Systems.- Evolution of Walking Gaits.- Automotive Systems.- Outlook.- Appendices, Index.

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Book SynopsisThis book introduces intelligent manufacturing system planning, design, and implementation, through the deep integration of the Internet, big data, artificial intelligence, and manufacturing process, to promote the transformation and upgrading of enterprises. This book shows the implementation of intelligent manufacturing process with 12 benchmarking enterprises, discusses the planning, implementation, and control of intelligent manufacturing system technology and method of theory, and analyzes the five hierarchies of intelligent manufacturing system, the five stages of life cycle, and five kinds of intelligent depth. The content can cultivate the reader's vocational ability to develop intelligent solutions and implementation based on complex, uncertain environment needs. This book will be interesting and useful to a wide readership in the various fields of management, information science, and engineering science.Table of ContentsBackground, basic concept and method.- Spatial hierarchy dimension of Intelligent manufacturing.- Life cycle dimension of Intelligent manufacturing.- Intelligent feature dimension of Intelligent manufacturing.- Comprehensive implementation path of Intelligent manufacturing.- Future research and development direction.

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Book SynopsisThe fast and easy way to pick out, set up, and learn to fly your drone Ready to soar into the world of unmanned aircraft? Drones For Dummies introduces you to the fascinating world of UAVs.Table of ContentsIntroduction 1 Part I: Getting Started with Drones 7 Chapter 1: Drones 1019 Chapter 2: Picking the Drone That’s Right for You 27 Chapter 3: Picking the Best Camera for Your Needs 47 Chapter 4: Finding Support and Resources 63 Part II: Before You Fly 79 Chapter 5: Setting Up Your Drone 81 Chapter 6: Staying Safe with Your Drone 101 Chapter 7: Knowing the Law 111 Part III: Miracle of Flight 123 Chapter 8: Controlling Your Drone 125 Chapter 9: Flight Basics 137 Chapter 10: Choosing When and Where To Fly 155 Chapter 11: Maintaining Your Drone 169 Part IV: Aerial Photos and Videos 183 Chapter 12: Capturing Beautiful Photos and Video 185 Chapter 13: Working with Shaky Footage 199 Chapter 14: Working with Aerial Images and Footage 213 Part V: The Part of Tens 227 Chapter 15: Ten Things You Shouldn’t Do with Your Drone 229 Chapter 16: Ten Commercial Uses for Drones 237 Chapter 17: Ten FAA Regulatory Implications 247 Appendix A 255 Index 261

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Book SynopsisAtmel's AVR microcontrollers are the chips that power Arduino, and are the go-to chip for many hobbyist and hardware hacking projects. In this book you'll set aside the layers of abstraction provided by the Arduino environment and learn how to program AVR microcontrollers directly.

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Book SynopsisThe workbenches of hobbyists, hackers, and makers have become overrun with microcontrollers, computers-on-a-chip that power homebrewed video games, robots, toys, and more. In Making Things Talk, Tom Igoe, one of the creators of Arduino, shows how to make these gadgets talk. Whether you need to connect some sensors to the Internet or create a device that can interact wirelessly with other creations, this book shows you what you need. Although they are powerful, the projects in this book are inexpensive to build: the Arduino microcontroller board itself ranges from around $25 to $40. The networking hardware covered here includes Ethernet, Wi-Fi, Bluetooth, and can be had for $25 to $50. Fully updated for the latest Arduino hardware and software, this book lets you combine microcontrollers, sensors, and networking hardware to make things... and make them talk to each other!

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Book SynopsisPublisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.So Many Fiendishly Fun Ways to Use the Latest Arduino Boards!Fully updated throughout, this do-it-yourself guide shows you how to program and build fascinating projects with the Arduino Uno and Leonardo boards and the Arduino 1.0 development environment. 30 Arduino Projects for the Evil Genius, Second Edition, gets you started right away with the simplified C programming you need to know and demonstrates how to take advantage of the latest Arduino capabilities.You'll learn how to attach an Arduino board to your computer, program it, and connect electronics to it to create your own devious devices. A bonus chapter uses the special USB keyboard/mouse-impersonation feature exclusive to the Arduino Leonardo.30 Arduino Projects for the Evil GTable of ContentsIntroductionCh 1. Quick StartCh 2. A Tour of ArduinoCh 3. LED ProjectsCh 4. More LED ProjectsCh 5. Sensor ProjectsCh 6. Light ProjectsCh 7. Sound ProjectsCh 8. Power ProjectsCh 9. Miscellaneous ProjectsCh 10. USB Projects with the LeonardoCh 11. Design and Build Your Own ProjectsAppendix: Components and Supplies

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Book SynopsisNew prospects for biomedical and healthcare engineering are being created by the rapid development of Robotic and Artificial Intelligence techniques. Innovative technologies such as Artificial Intelligence, Deep Learning, Robotics, and IoT are currently under huge influence in today's modern world. For instance, a micro-nano robot allows us to study the fundamental problems at a cellular scale owing to its precise positioning and manipulation ability; the medical robot paves a new way for the low-invasive and high-e?cient clinical operation, and rehabilitation robotics is able to improve the rehabilitative e?cacy of patients. This book aims at exhibiting the latest research achievements, ?ndings, and ideas in the ?eld of robotics in biomedical and healthcare engineering, primarily focusing on the walking assistive robot, telerobotic surgery, upper/lower limb rehabilitation, and radiosurgery. As a result, a wide range of robots are being developed to serve a variety of roles within tTable of ContentsChapter 1 IoT-Integrated Robotics in the Health Sector Chapter 2 Microrobots and Nanorobots in the Refinement of Modern Healthcare Practices Chapter 3 Communicable Diseases and COVID-19: A Complementary and Holistic Care with Robotic Renaissance Chapter 4 ASBGo: A Smart Walker for Ataxic Gait and Posture Assessment, Monitoring, and Rehabilitation Chapter 5 Analyzing and Comparing MLP, CNN, and LSTM for Classification of Heart Arrhythmia Using ECG Scans Chapter 6 AI-Powered Robotics and COVID-19: Challenges and Opportunities Chapter 7 Analyze App Health for Ensuring Better Decision-Making and Improved Secure Outcomes Chapter 8 Intelligent Robots in the Disease Recovery Process Using a Whale Optimization-Based Feature Selection and Classification Model Chapter 9 Biomedical Healthcare Robot Movement Control Using an EEG-Based Brain–Computer Interface with an Optimized Kernel Extreme Learning Machine

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisFocuses on the quantitative approaches necessary to computer-integrated manufacturing systems, and integrates major topics covering all phases of the production control cycle: production information processing and flow, production planning, forecasting, material requirements planning and monetary control, and scheduling. This new edition features a compendium set of 11 user-friendly computer programs for the IBM PC that enhance the teaching power of the text, allowing readers to solve real-life problems. Among programs included are growth forecasting, aggregate planning, material requirements planning, lot sizing and inventory control, and limited-resource scheduling. The chapters on scheduling give particularly thorough coverage on this difficult subject. Solutions are clearly presented, with many examples and exercises included in the text.Table of ContentsRole of Production. Production Control Information Flow. CAD/CAM and Production Control. Forecasting--The Key to PC. Aggregate Planning. Material Requirement Planning. Lost Sizing Concepts. Sequence Scheduling. Linear Balancing--A Key to Automation. Project Planning and Resource Constrained Scheduling. Personnel Scheduling. Appendices.

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Book SynopsisThis volume''s focus on the design of computer controlled systems features computational tools that can be applied directly and are explained with simple paper-and-pencil calculations. The use of computational tools is balanced by strong emphasis on control system principles and ideas. Extensive pedagogical aids include worked examples, MATLAB macros, and a solutions manual.

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Book SynopsisIncludes integrated circuit (IC) and system-level design issues as well as keeping its classic "wire lead" material. This title also includes topics such as: Wires, Resistors, Capacitors, Inductors; Resonant Circuits: Resonance, Insertion Loss; Filter Design: High-pass, Bandpass, Band-rejection; and more.Table of Contents1: Components2: Resonant Circuits3: Filter Design4: Impedance Matching5: The Transistor at Radio Frequencies6: Small-Signal RF Amplifier Design7: RF Power Amplifiers8: RF Wireless Front-End Design9: RF Design Software

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Book SynopsisDeterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way. A Deterministic View of Learning in Dynamic EnvironmentsThe authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to aTable of ContentsIntroduction. RBF Networks and the PE Condition. Locally Accurate Identification of Nonlinear Systems. Learning from Closed-Loop Neural Control. Rapid Recognition of Dynamical Patterns. Deterministic Learning using Output Measurements. Applications of Deterministic Learning. Conclusions.

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Book SynopsisThis book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio.A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia and advertisements, image generation, image translation,text-to-images synthesis, video synthesis, generating high-resolution images, drug discovery, etc.Features: Presents a comprehensive guide on how to use GAN for images and videos. Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network, Intrusion detection using GAN Highlights the inclusion of gaming effects using deep learning methods Examines the significant technological advancements in GAN and its real-world application. Discusses as GAN challenges and optimal solutions The book addresses scientific aspects for a wider audience such as junior and senior engineering, undergraduate and postgraduate students, researchers, and anyone interested in the trends development and opportunities in GAN and Deep Learning.The material in the book can serve as a reference in libraries, accreditation agencies, government agencies, and especially the academic institution of higher education intending to launch or reform their engineering curriculum

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Book SynopsisThe process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results. Trade Review"The book is timely and needed. The interest in all things 'data science' morphed into everybody pretending to do, or know, Machine Learning. Kuhn and Johnson happen to actually know this—as evidenced by their earlier and still-popular tome entitled ‘Applied Predictive Modeling.’ The proposed ‘Feature Engineering and Selection’ builds on this and extends it. I expect it to become as popular with a wide reach as both a textbook, self-study material, and reference."~Dirk Eddelbuettel, University of Illinois at Urbana-Champaign"As a reviewer, it has been exciting and edifying to see this book develop into what is likely to become one of the foundational works on feature engineering. It is launching propitiously on the current tide of interest in both interpretable models and AutoML."~Robert Horton, Microsoft"In recent years, the statistics literature has featured new developments in modeling and predictive analytics. Approaches such as cross-validation and statistical/machine learning techniques have become widespread. The author's previous book ("Applied Predictive Modeling", APM) provided a wide-ranging introduction and integration of these methods and suggested a workflow in R to carry out exploratory and confirmation analyses. With this project, the authors have identified an important and interesting component of these methods that describes building better models by focusing on the predictors (feature engineering)…The authors focus on the variables that go into the model (and how they are represented) and argue that such issues are as important (or more important) than the particular methods that are applied to an analysis...The proposed book is likely to serve as a textbook (for a number of undergraduate and graduate courses in a variety of disciplines) and reference (for a large number of statisticians seeking principled and well-organized modeling)."~Nicholas Horton, Amherst College"I think this book is great and a joy to read…I like the pragmatic and practical approach taken in the book, and the examples given are very illustrative. The emphasis on how and when to use resampling is refreshing and something that the community needs to hear more." ~Andreas C. Muller, Columbia University"The book is timely and needed. The interest in all things 'data science' morphed into everybody pretending to do, or know, Machine Learning. Kuhn and Johnson happen to actually know this—as evidenced by their earlier and still-popular tome entitled ‘Applied Predictive Modeling.’ The proposed ‘Feature Engineering and Selection’ builds on this and extends it. I expect it to become as popular with a wide reach as both a textbook, self-study material, and reference."~Dirk Eddelbuettel, University of Illinois at Urbana-Champaign"As a reviewer, it has been exciting and edifying to see this book develop into what is likely to become one of the foundational works on feature engineering. It is launching propitiously on the current tide of interest in both interpretable models and AutoML."~Robert Horton, Microsoft"In recent years, the statistics literature has featured new developments in modeling and predictive analytics. Approaches such as cross-validation and statistical/machine learning techniques have become widespread. The author's previous book ("Applied Predictive Modeling", APM) provided a wide-ranging introduction and integration of these methods and suggested a workflow in R to carry out exploratory and confirmation analyses. With this project, the authors have identified an important and interesting component of these methods that describes building better models by focusing on the predictors (feature engineering)…The authors focus on the variables that go into the model (and how they are represented) and argue that such issues are as important (or more important) than the particular methods that are applied to an analysis...The proposed book is likely to serve as a textbook (for a number of undergraduate and graduate courses in a variety of disciplines) and reference (for a large number of statisticians seeking principled and well-organized modeling)."~Nicholas Horton, Amherst College"I think this book is great and a joy to read…I like the pragmatic and practical approach taken in the book, and the examples given are very illustrative. The emphasis on how and when to use resampling is refreshing and something that the community needs to hear more." ~Andreas C. Muller, Columbia UniversityTable of Contents1. Introduction. 2. Illustrative Example: Predicting Risk of Ischemic Stroke. 3. A Review of the Predictive Modeling Process. 4. Exploratory Visualizations. 5. Encoding Categorical Predictors. 6. Engineering Numeric Predictors. 7. Detecting Interaction Effects. 8. Handling Missing Data. 9. Working with Profile Data. 10. Feature Selection Overview. 11. Greedy Search Methods. 12. Global Search Methods.

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Book SynopsisDerived from industry-training classes that the author teaches at the Embedded Systems Institute at Eindhoven, the Netherlands and at Buskerud University College at Kongsberg in Norway, Systems Architecting: A Business Perspective places the processes of systems architecting in a broader context by juxtaposing the relationship of the systems architect with enterprise and management. This practical, scenario-driven guide fills an important gap, providing systems architects insight into the business processes, and especially into the processes to which they actively contribute.The book uses a simple reference model to enable understanding of the inside of a system in relation to its context. It covers the impact of tool selection and brings balance to the application of the intellectual tools versus computer-aided tools. Stressing the importance of a clear strategy, the authors discuss methods and techniques that facilitate the architect's contributioTable of ContentsProcess and Organization. Role and Task of the Systems Architect. From Customer Understanding to Requirements. Systems Architect Methods and Means. Strategy.Harvesting Synergy, Product Families. Supporting Processes. Systems and Software. Boardroom Presentation. Human Side. Reflection and Wrap-Up. References. Pictorial Index.

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Book SynopsisDeepfakes is a synthetic media that leverage powerful Artificial Intelligence (AI) and machine learning (ML) techniques to generate fake visual and audio content that are extremely realistic, thus making it very hard for a human to distinguish from the original ones. Apart from technological introduction to the Deepfakes concept, the book details algorithms to detect Deepfakes, techniques for identifying manipulated content and identifying face swap, generative adversarial neural networks, media forensic techniques, deep learning architectures, forensic analysis of DeepFakes and so forth. Provides a technical introduction to DeepFakes, its benefits, and the potential harms Presents practical approaches of creation and detection of DeepFakes using Deep Learning (DL) Techniques Draws attention towards various challenging issues and societal impact of DeepFakes with their existing solutions Includes research analysis in the domain of DL fakes for assisting the creation and detection of DeepFakes applications Discusses future research directions with emergence of DeepFakes technology This book is aimed at graduate students, researchers and professionals in data science, artificial intelligence, computer vision, and machine learning.Table of Contents1. Introduction to DeepFake technologies 2. DeepFakes: A Systematic Review and Bibliometric Analysis 3. Deep Learning Techniques for Creation of DeepFakes 4. Analyzing DeepFakes Videos by face warping artifacts 5. Development of image translating model to counter Adversarial attacks 6. Detection of DeepFakes using local features and Convolutional Neural Network 7. DeepFakes: Positive Cases 8. Threats and challenges by DeepFake Technology 9. DeepFakes, media, and societal impacts 10. Fake News Detection using Machine Learning 11. Future of DeepFakes & Ectypes

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