Automatic control engineering Books

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 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 SynopsisThe SIMATIC S7-1500 programmable logic controller (PLC) sets standards in productivity and efficiency. By its system performance and with PROFINET as the standard interface, it ensures short system response times and a maximum of flexibility and networkability for demanding automation tasks in the entire production industry and in applications for medium-sized to high-end machines. The engineering software STEP 7 Professional operates inside TIA Portal, a user interface that is designed for intuitive operation. Functionality includes all aspects of automation: from the configuration of the controllers via programming in the IEC languages LAD, FBD, STL, and SCL up to the program test. In the book, the hardware components of the automation system S7-1500 are presented including the description of their configuration and parameterization. A comprehensive introduction into STEP 7 Professional V14 illustrates the basics of programming and troubleshooting. Beginners learn the basics of automation with Simatic S7-1500, users switching from other controllers will receive the relevant knowledge.Table of ContentsIntroduction to STEP 7 Professional V14 and to SIMATIC projects Hardware components of SIMATIC S7-1500 Configuration of devices and networks Tags, addressing and data types Operating states and execution of the user program Programming in LAD, FBD, SCL, and STL S7-GRAPH sequential control Online mode, diagnostics and program test Distributed I/O Communication via Industrial Ethernet Appendix: Web server, Technology functions, Data logging, Simulation

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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 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 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 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 SynopsisBeginning with the basics and moving gradually to greater challenges, this book takes you step-by-step through experiments and projects that show you how to make your Arduino or Raspberry Pi create and control movement, light, and sound. In other words: action!

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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 SynopsisIn this book, Tewari emphasizes the physical principles and engineering applications of modern control system design. Instead of detailing the mathematical theory, MATLAB examples are used throughout.Table of ContentsPreface xiii 1. Introduction 1 1.1 What is Control? 1 1.2 Open-Loop and Closed-Loop Control Systems 2 1.3 Other Classifications of Control Systems 6 1.4 On the Road to Control System Analysis and Design 10 1.5 MATLAB, SIMULINK, and the Control System Toolbox 11 References 12 2. Linear Systems and Classical Control 13 2.1 How Valid is the Assumption of Linearity? 13 2.2 Singularity Functions 22 2.3 Frequency Response 26 2.4 Laplace Transform and the Transfer Function 36 2.5 Response to Singularity Functions 51 2.6 Response to Arbitrary Inputs 58 2.7 Performance 62 2.8 Stability 71 2.9 Root-Locus Method 73 2.10 Nyquist Stability Criterion 77 2.11 Robustness 81 2.12 Closed-Loop Compensation Techniques for Single-Input, Single-Output Systems 87 2.12.1 Proportional-integral-derivative compensation 88 2.12.2 Lag, lead, and lead-lag compensation 96 2.13 Multivariable Systems 105 Exercises 115 References 124 3. State-Space Representation 125 3.1 The State-Space: Why Do I Need lt? 125 3.2 Linear Transformation of State-Space Representations 140 3.3 System Characteristics from State-Space Representation 146 3.4 Special State-Space Representations: The Canonical Forms 152 3.5 Block Building in Linear, Time-Invariant State-Space 160 Exercises 168 References 170 4. Solving the State-Equations 171 4.1 Solution of the Linear Time Invariant State Equations 171 4.2 Calculation of the State-Transition Matrix 176 4.3 Understanding the Stability Criteria through the State-Transition Matrix 183 4.4 Numerical Solution of Linear Time-Invariant State-Equations 184 4.5 Numerical Solution of Linear Time-Varying State-Equations 196 4.6 Numerical Solution of Nonlinear State-Equations 204 4.7 Simulating Control System Response with SIMULINK 213 Exercises 216 References 218 5. Control System Design in State-Space 219 5.1 Design: Classical vs. Modern 219 5.2 Controllability 222 5.3 Pole-Placement Design Using Full-State Feedback 228 5.3.1 Pole-placement regulator design for single-input plants 230 5.3.2 Pole-placement regulator design for multi-input plants 245 5.3.3 Pole-placement regulator design for plants with noise 247 5.3.4 Pole-placement design of tracking systems 251 5.4 Observers, Observability, and Compensators 256 5.4.1 Pole-placement design of full-order observers and compensators 258 5.4.2 Pole-placement design of reduced-order observers and compensators 269 5.4.3 Noise and robustness issues 276 Exercises 277 References 282 6. Linear Optimal Control 283 6.1 The Optimal Control Problem 283 6.1.l The general optimal control formulation for regulators 284 6.1.2 Optimal regulator gain matrix and the riccati equation 286 6.2 Infinite-Time Linear Optimal Regulator Design 288 6.3 Optimal Control of Tracking Systems 298 6.4 Output Weighted Linear Optimal Control 308 6.5 Terminal Time Weighting: Solving the Matrix Riccati Equation 312 Exercises 318 References 321 7. Kalman Filters 323 7.1 Stochastic Systems 323 7.2 Filtering of Random Signals 329 7.3 White Noise, and White Noise Filters 334 7.4 The Kalman Filter 339 7.5 Optimal (Linear, Quadratic, Gaussian) Compensators 351 7.6 Robust Multivariable LQG Control: Loop Transfer Recovery 356 Exercises 370 References 371 8. Digital Control Systems 373 8. l What are Digital Systems? 373 8.2 A/D Conversion and the z-Transform 375 8.3 Pulse Transfer Functions of Single-Input, Single-Output Systems 379 8.4 Frequency Response of Single-Input, Single-Output Digital Systems 384 8.5 Stability of Single-Input, Single-Output Digital Systems 386 8.6 Performance of Single-Input, Single-Output Digital Systems 390 8.7 Closed-Loop Compensation Techniques for Single-Input, Single-Output Digital Systems 393 8.8 State-Space Modeling of Multivariable Digital Systems 396 8.9 Solution of Linear Digital State-Equations 402 8.10 Design of Multivariable, Digital Control Systems Using Pole-Placement: Regulators, Observers, and Compensators 406 8.11 Linear Optimal Control of Digital Systems 415 8.12 Stochastic Digital Systems, Digital Kalman Filters, and Optimal Digital Compensators 424 Exercises 432 References 436 9. Advanced Topics in Modern Control 437 9.1 Introduction 437 9.2 H∞ Robust, Optimal Control 437 9.3 Structured Singular Value Synthesis for Robust Control 442 9.4 Time-Optimal Control with Pre-shaped Inputs 446 9.5 Output-Rate Weighted Linear Optimal Control 453 9.6 Nonlinear Optimal Control 455 Exercises 463 References 465 Appendix A: Introduction to MATLAB, SIMULINK and the Control System Toolbox 467 Appendix B: Review of Matrices and Linear Algebra 481 Appendix C: Mass, Stiffness, and Control Influence Matrices of the Flexible Spacecraft 487 Answers to Selected, Exercises 489 Index 495

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Book SynopsisControl theory, an interdisciplinary topic within the study of dynamical systems, is an important but often overlooked part of a physicist's education. This is the first broad and complete treatment of the subject specifically tailored for physicists, spanning the basics to the most recent advances.Trade Review'Exceptionally well written, organized and presented, Control Theory for Physicists is an ideal and comprehensive volume that is unreservedly recommended as a curriculum textbook. While a core addition to college and university library Mathematical Physics & Calculus collections, it should be noted for students, academia, physicists, and non-specialist general readers with an interest in the subject …' Midwest Book Review'… will enhance appreciation of the limits of practical applications of physics, especially those associated with thermodynamics and information theory … Highly recommended.' E. Kincanon, Choice Connect'This is a rare example of a textbook that is concise yet clear, math dense yet very accessible, and rigorous yet beautifully written. The treatment throughout prioritizes first-principles descriptions, with an emphasis on not only when control works but also when it fails. It includes well-contextualized examples and well-formulated problems. It is ready for classroom use, with additional resources for instructors-such as a solution manual and associated Mathematica notebooks-available from the publisher. A 100-page supplement on background mathematics is also available on the publisher's website, which provides a comprehensive review of key mathematical topics. As already noted by Hugo Touchette in his back cover endorsement, this book may indeed lead more departments to include control theory in their curriculum.' Adilson E. Motter, Professor of Physics and Astronomy at Northwestern University, Illinois, IEEE Control Systems'… Together with information theory, control theory is the area of engineering that has the most fundamental lessons to teach physicists, and John Bechhoefer's recent textbook, Control Theory for Physicists, is an excellent place to start learning them … the pedagogical presentation of the material in the book perfectly complements its engaging subject matter.' Michael Hinczewski, The BiophysicistTable of ContentsPart I. Core Material: 1. Historical introduction; 2. Dynamical systems; 3. Frequency-domain control; 4. Time-domain control; 5. Discrete-time systems; 6. System identification; Part II. Advanced Ideas: 7. Optimal control; 8. Stochastic systems; 9. Robust control; 10. Adaptive control; 11. Nonlinear control; Part III. Special Topics: 12. Discrete-state systems; 13. Quantum control; 14. Networks and complex systems; 15. Limits to control.

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Book SynopsisNew York Times bestselling author Deepak Chopra delivers a visionary and unprecedented exploration of how artificial intelligence can revolutionize well-being and open new horizons for personal development.“AI has the potential to help us create a more peaceful, just, sustainable, healthy, and joyful world. Digital Dharma shows you a path.”—Sam Altman, CEO of OpenAIIn a world captivated yet bewildered by artificial intelligence, spiritual icon Deepak Chopra, MD, illuminates AI’s untapped potential to unravel the enigma of consciousness, positioning AI not as a threat but as a catalyst for personal and collective growth. In Digital Dharma, Chopra navigates the balance between technology and expanded awareness, explaining that while AI cannot duplicate human intelligence, it can vastly enhance personal and spiritual growth.Chopra shows readers how the most popular, freely available chatbots can serve as guides through every level of human potential—survival and safety, emotional connection, self-worth, abundance, creativity, wisdom, and the infinite possibilities of cosmic consciousness. AI chatbots offer information, advice, and exploratory avenues of untapped potential about any aspect of human awareness. In practical terms, making AI your ally and guide depends on the art of the prompt, the questions a user poses to a chatbot. As Chopra shows in detail, by asking the right questions, you can bring AI into your inner world, which is where personal growth happens. Chopra provides a personal assessment for you to better understand yourself and exercises to help you expand your awareness in any part of your life.Digital Dharma masterfully helps readers to harness AI, not merely as a technological tool but as a partner in crafting a future where human potential solves the urgent problems facing the planet and each of us as individuals. Deepak Chopra invites us to transcend our limitations and explore a relationship with AI that elevates collective consciousness and personal evolution at the same time.

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Book SynopsisDiscover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of â1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix dTrade Review"The authors study and analyze methods using the sparsity property of some statistical models in order to recover the underlying signal in a dataset. They focus on the Lasso technique as an alternative to the standard least-squares method."—Zentralblatt MATH 1319Table of ContentsIntroduction. The Lasso for Linear Models. Generalized Linear Models. Generalizations of the Lasso Penalty. Optimization Methods. Statistical Inference. Matrix Decompositions, Approximations, and Completion. Sparse Multivariate Methods. Graphs and Model Selection. Signal Approximation and Compressed Sensing. Theoretical Results for the Lasso. Bibliography. Author Index. Index.

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Book SynopsisTrade Review"Wall Street Journal technology columnist Mims chronicles a product’s journey from manufacturer to doorstep in his timely debut. . . . Readers will be hooked by Mims’s ability to turn what could’ve been a dry supply-chain explainer into a legitimate page-turner. For those interested in what goes on before packages arrive at their door, this is a no-brainer." — Publishers Weekly (starred review) "Mims writes in a digestible style that conveys a pleasing you-are-there quality, and he does not shy away from describing the vast economic inequalities involved in the movement of commodities and the indifference of many managers toward their workers . . . A surprisingly absorbing foray into the optimization of product flow." — Kirkus Reviews "Mims will have readers enthralled with the minutiae of what he calls a 'sophisticated field of human endeavor.' This book will appeal to general audiences and those in any part of the industry." — Booklist "A detailed and dedicated explainer about the state of the logistics industry, Arriving Today by Wall Street Journal tech columnist Christopher Mims offers a snapshot of a logistics industry in flux. The world described in the book is a marvel of human ingenuity . . . a world that, because of the pace of change in the industry, is likely to be unrecognizable in five years’ time." — strategy+business Our global economy runs on logistics. Mims expertly demystifies this secretive science as he vividly portrays the ways in which it often robs the most vulnerable workers of their health and humanity. — Brad Stone, author of The Everything Store and Amazon Unbound With the elegance and efficiency of a first-rate tech journalist, Mims leads us into the nooks and crannies, robots, AI, warehouses, and ships that are highly complex so as to make our daily life simple. A must-read. — Scott Galloway, professor of marketing at NYU Stern School of Business and author of Four and The Algebra of Happiness A meticulously and presciently rendered account of the surprising journey of a USB charger from the factory to my home. It's nice to get your stuff fast. But Mims asks us to ponder, Was it worth it? — Steve LeVine, author of The Powerhouse Adeptly draws us into the container ships, fulfillment centers, and algorithms that deliver us what we want, when we want it. A balanced, much-needed account. — Robert Kanigel, author of The One Best Way and Hearing Homer's Song Mims elegantly explores the micro and the macro of how our modern world of stuff works, in a way that illuminates, dazzles, and sometimes terrifies. — Rose George, author of Ninety Percent of Everything, Nine Pints, and The Big Necessity A backstage pass into the twenty-first-century global economy, Arriving Today is the resource for understanding how modern supply chains really work—and why they sometimes fail. — Ryan Petersen, CEO of Flexport Finally, a book that sheds light on automation, logistics, and their impact on our everyday life today, and in the future. An engaging and insightful narrative. — Oren Etzioni, professor emeritus at the University of Washington and CEO of the Allen Institute for Artificial Intelligence Arriving Today is the essential key to understanding how our world is getting smaller and more interconnected by the day. — Gary Tan, cofounder of Initialized Capital

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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.A practical guide to industrial automation concepts,terminology, and applicationsIndustrial Automation: Hands-On is a single source of essential information for those involved in the design and use of automated machinery. The book emphasizes control systems and offers full coverage of other relevant topics, including machine building, mechanical engineering and devices, manufacturing business systems, and job functions in an industrial environment. Detailed charts and tables serve as handy design aids. This is an invaluable reference for novices and seasoned automation professionals alike.COVERAGE INCLUDES:* Automation and manufacturing * Key concepts used inautomation, controls, machinery design, and docum

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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 Synopsis Integrates MATLAB throughout the text.

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Book SynopsisEd Carryer is the Director of the Smart Product Design Laboratory (SPDL) in the Design Division of Mechanical Engineering at Stanford University. He is currently a Consulting Professor in the Design Division of Mechanical Engineering. He received his Ph.D. degree in Mechanical Engineering from Stanford University in 1992. Prior to that, he received an M.S. in Bio-Medical Engineering from the University of Wisconsin, Madison in 1978. His B.S.E. was awarded from the Illinois Institute of Technology in 1975, where he was a member (1/3) of the first graduating class of the Education and Experience in Engineering (E 3)program. Dr. Carryer's industrial experience varies wildly, from designing water treatment facilities for coal and nuclear power plants for Sargent & Lundy to designing the electronic controller for an Arctic Heated Glove under contract to NASA. He spent eight years in the Detroit area working in and about the auto industry. During that time he workTrade Review“Very comprehensive…Well written with good HW problems.” — Larry Banta, West Virginia University “What I love about this book is that it puts much of what we teach in one text allowing the students to study in more depth the details their projects require." — Daniel J. Block, University of Illinois “I expect this to become the gold standard for Mechatronics classes for years to come.” — David Fisher, Rose-Hulman “I was very impressed with the organization of the material and the level of knowledge the authors bring to each topic. I was also impressed with the concise and clear way topics are introduced and explained.” — David Fisher, Rose-Hulman “I think that’s great! My students get an introduction to Mechatronics then have a textbook to take with them after the course that they can continue to use and learn from.” — David Fisher, Rose-Hulman “The authors really know their stuff and offer good guidelines, rules of thumb, and advice on dealing with real electronics, actuators, and sensors. I also really enjoyed the project discussion and trying to put into words what needs to happen in a good design process!” — David Fisher, Rose-Hulman “The best features of the proposed text are its breadth and its detailed coverage of practical electronics.” — William R. Murray, California Polytechnic State University “The textbook is overflowing with information. There is traditional analysis, an extensive survey of current hardware (sensors, actuators, computer hardware), practical advice (do’s & don’t’s). There is a lot to assimilate with many useful chapters that contain pedagogical examples and a wealth of practical information.” — Mark Nagurka, Marquette University “The textbook is applied and not just a theoretical product. It reflects years of hardware experience from the authors.” — Mark Nagurka, Marquette University “This one volume includes many subjects that are part of the enterprise of mechatronics. The book has exceptionally strong coverage of microcontrollers.” — Mark Nagurka, Marquette University Table of ContentsPart 1: Introduction Preface Chapter 1 Introduction 1.1 Philosophy 1.3 Who Should Study Mechatronics? 1.3 How to Use this Book 1.4 Summary Part 2: Software Chapter 2 What’s a Micro? 2.1 Introduction 2.2 What IS a “Micro”? 2.3 Microprocessors, Microcontrollers, Digital Signal Processors (DSP’s) and More 2.4 Microcontroller Architecture 2.5 The Central Processing Unit (CPU) 2.5.1 Representing Numbers in the Digital Domain 2.5.2 The Arithmetic Logic Unit (ALU) 2.6 The Data Bus and the Address Bus 2.7 Memory 2.8 Subsystems and Peripherals 2.9 Von Neumann Architecture 2.10 The Harvard Architecture 2.11 Real World Examples 2.11.1 The Freescale MC9S12C32 Microcontroller 2.11.2 The Microchip PIC12F609 Microcontroller 2.12 Where to Find More Information 2.13 Homework Problems Chapter 3 Microcontroller Math and Number Manipulation 3.1 Introduction 3.2 Number Bases and Counting 3.3 Representing Negative Numbers 3.4 Data Types 3.5 Sizes of Common Data Types 3.6 Arithmetic on Fixed Size Variables 3.7 Modulo Arithmetic 3.8 Math Shortcuts 3.8 Boolean Algebra 3.9 Manipulating Individual Bits 3.10 Testing Individual Bits 3.11 Homework Problems Chapter 4: Programming Languages 4.1 Introduction 4.2 Machine Language 4.3 Assembly Language 4.4 High-Level Languages 4.5 Interpreters 4.6 Compilers 4.7 Hybrid Compiler/Interpreters 4.8 Integrated Development Environments (IDEs) 4.9 Choosing a Programming Language 4.10 Homework Problems Chapter 5: Program Structures for Embedded Systems 5.1 Background 5.2 Event Driven Programming 5.3 Event Checkers 5.4 Services 5.5 Building an Event Driven Program 5.6 An Example 5.7 Summary of Event Driven Programming 5.8 State Machines 5.9 A State Machine in Software 5.10 The Cockroach Example as a State Machine 5.11 Summary Homework Problems Chapter 6 Software Design 6.1 Introduction 6.2 Building as a Metaphor for Creating Software 6.3 Introducing Some Software Design Techniques 6.3.1 Decomposition 6.3.2 Abstraction and Information Hiding 6.3.3 Pseudo-Code 6.4 Software Design Process 6.4.1 Generating Requirements 6.4.2 Defining the Program Architecture 6.4.3 The Performance Specification 6.4.4 The Interface Specification 6.4.5 Detail Design 6.4.6 Implementation 6.4.6.1 Intra-Module Organization 6.4.6.2 Writing the Code 6.4.7 Unit Testing 6.4.8 Integration 6.5 The Sample Problem 6.5.1 Requirements for the Morse Code Receiver 6.5.2 The Morse Code Receiver System Architecture 6.5.3 The Morse Code Receiver Software Architecture 6.5.4 The Morse Code Receiver Performance Specifications 6.5.5 The Morse Code Receiver Interface Specification 6.5.5.1 The Button Module Interface Specification 6.5.5.2 The Morse Elements Module Interface Specification 6.5.5.3 The Morse Decode Module Interface Specification 6.5.5.4 The LCD Display Module Interface Specification 6.5.6 The Morse Code Receiver Detail Design 6.5.6.1 Button Module Detail Design 6.5.6.2 Morse Elements Detail Design 6.5.6.3 Morse Decode Detail Design 6.5.6.4 Display Detail Design 6.5.6.5 Main Detail Design 6.5.7 The Morse Code Receiver Implementation 6.5.8 The Morse Code Receiver Unit Testing. 6-28 6.5.9 The Morse Code Receiver Integration 6.6 Homework Problems Chapter 7 Communications 7.1: Introduction 7.2: Without a Medium, there is no Message 7.3: Bit-Parallel and Bit-Serial Communications 7.3.1: Bit-Serial Communications 7.3.1.1: Synchronous Serial Communications 7.3.1.2: Asynchronous Serial Communications 7.3.2: Bit Parallel Communications 7.4: Signaling Levels 7.4.1: TTL/CMOS Levels 7.4.2: RS-232 7.4.3: RS-485 7.5: Communicating Over Limited Bandwidth Channels 7.5.1: Telephones and Modems 7.5.1.1: Modulation Techniques 7.5.1.2: Amplitude Modulation (AM) 7.5.1.3: Frequency Modulation (FM) 7.5.1.4: Phase Modulation (PM) 7.5.1.5: Quadrature Amplitude Modulation (QAM) 7.6: Communicating with Light 7.7: Communicating over a Radio 7.7.1: RF Remote Controls 7.7.2: RF Data Links 7.7.3: RF Networks 7.8: Homework Problems Chapter 8 : Microcontroller Peripherals 8.1 : Accessing the Control Registers 8.2 : The Parallel Input/Output Subsystem 8.2.1 : The Data Direction Register 8.2.2 : The Input/Output Register(s) 8.2.3 : Shared Function Pins 8.3 : Timer Subsystems 8.3.1 : Timer Basics 8.3.2 : Timer Overflow 8.3.3 : Output Compare 8.3.4 : Input Capture 8.3.5 : Combining Input Capture and Output Compare to Control an Engine 8.4 : Pulse Width Modulation (PWM) 8.5 : PWM Using the Output Compare System 8.6 : The Analog-to-Digital (A/D) Converter Subsystem 8.6.1 : The Process for Converting an Analog Input to a Digital Value 8.6.2 : The A/D Converter Clock 8.6.3 : Multiplexer Switching Transients and DC Effects 8.6.4 : Automating the A/D Conversion Process 8.7 : Homework Problems Part 3: Electronics Chapter 9 Basic Circuit Analysis and Passive Components 9.1 Voltage, Current and Power 9.2 Circuits and Ground 9.3 Laying Down the Laws 9.4 Resistance 9.4.1 Resistors in Series and Parallel 9.4.2 The Voltage Divider 9.5 Thevenin Equivalents 9.6 Capacitors 9.6.1 Capacitors in Series and Parallel 9.6.2 Capacitors and Time-Varying Signals 9.7 Inductors 9.7.1 Inductors and Time-Varying Signals 9.8 The Time and Frequency Domains 9.9 Circuit Analysis with Multiple Component Types 9.9.1 Basic RC Circuit Configurations 9.9.2 Low-Pass RC Filter Behavior in the Time Domain 9.9.3 High-Pass RC Filter Behavior in the Time Domain 9.9.4 RL Circuit Behavior in the Time Domain 9.9.5 Low-Pass RC Filter Behavior in the Frequency Domain 9.9.6 High-Pass RC Filter Behavior in the Frequency Domain 9.9.7 High-Pass RC Filter with a DC Bias 9.10 Simulation Tools 9.10.1 Limitations of Simulation Tools 9.11 Real Voltage Sources 9.12 Real Measurements 9.12.1 Measuring Voltage 9.12.2 Measuring Current 9.13 Real Resistors 9.13.1 A Model for a Real Resistor 9.13.2 Resistor Construction Basics 9.13.3 Carbon Film Resistors 9.13.4 Metal Film Resistors 9.13.5 Power Dissipation in Resistors 9.13.6 Potentiometers 9.13.7 Multi-Resistor Packages 9.13.8 Choosing Resistors 9.14 Real Capacitors 9.14.1 A Model for a Real Capacitor 9.14.2 Capacitor Construction Basics 9.14.3 Polar vs. Non-Polar Capacitors 9.14.4 Ceramic Disk Capacitors 9.14.5 Monolithic Ceramic Capacitors 9.14.6 Aluminum Electrolytic Capacitors 9.14.7 Tantalum Capacitors 9.14.8 Film Capacitors 9.14.9 Electric Double Layer Capacitors / Super capacitors 9.14.10 Capacitor Labeling 9.14.10.1 Ceramic Capacitor (Disc and MLC) Labeling 9.14.10.2 Aluminum Electrolytic Capacitor Labeling 9.14.10.3 Tantalum Capacitor Labeling 9.14.10.4 Film Capacitor Labeling 9.14.11 Choosing a Capacitor 9.15 Homework Problems Chapter 10 Semiconductors 10.1 Doping, Holes and Electrons 10.2 Diodes 10.2.1 The VI Characteristic for Diodes 10.2.2 The Magnitude of Vf 10.2.3 Reverse Recovery 10.2.4 Schottky Diodes 10.2.5 Zener Diodes 10.2.6 Light Emitting Diodes 10.2.7 Photo-Diodes 10.3 Bipolar Junction Transistors 10.3.1 The Darlington Pair 10.3.2 The Photo-Transistor 10.4 MOSFETs 10.5 hoosing Between BJTs and MOSFETs 10.5.1 When Will a BJT be the Best (or Only) Choice? 10.5.2 When Will a MOSFET be the Best (or Only) Choice? 10.5.3 How Do You Choose When Either a MOSFET or a BJT Could Work? 10.6 Multi-Transistor Circuits 10.7 Reading Transistor Data Sheets 10.7.1 Reading a BJT Data Sheet 10.7.2 Reading a MOSFET Data Sheet 10.7.3 A Sample Application 10.7.4 A Potpourri of Transistor Circuits 10.8 Homework Problems Chapter 11 : Operational Amplifiers 11.1 : Operational Amplifier Behavior 11.2 : Negative Feedback 11.3 : The Ideal Op-Amp 11.4 : Analyzing Op-Amp Circuits 11.4.1 : The Golden Rules 11.4.2 : The Non-Inverting Op-Amp Configuration 11.4.3 : The Inverting Op-Amp Configuration 11.4.3.1 : The Virtual Ground 11.4.3.2 : There is Nothing Magic About Ground 11.4.4 : The Unity Gain Buffer 11.4.5 : The Difference Amplifier Configuration 11.4.6 : The Summer Configuration 11.4.7 : The Trans-Resistive Configuration 11.4.8 : Computation with Op-Amps 11.5 : The Comparator 11.5.1 : Comparator Circuits 11.6 : Homework Problems Chapter 12 : Real Operational Amplifiers and Comparators 12.1 : Real Op-Amp Characteristics — How the Ideal Assumptions Fail 12.1.1 : Non-Infinite Gain 12.1.2 : Variation in Open Loop Gain with Frequency 12.1.3 : Input Current is Not Zero 12.1.3.1 : Input Bias Current and Input Offset Current 12.1.3.2 : Input Impedance 12.1.4 : The Output Voltage Source is Not Ideal 12.1.5 : Other Non-Idealities 12.1.5.1 : Input Offset Voltage 12.1.5.2 : Power Supplies 12.1.5.3 : Input Common Mode Voltage Range 12.1.5.5 : Input Common Mode Rejection Ratio 12.1.5.6 : Temperature Effects 12.2 : Reading an Op-Amp Data Sheet 12.2.1 : Maxima, Minima and Typical Values 12.2.2 : The Front Page 12.2.3 : The Absolute Maximum Ratings Section 12.2.4 : The Electrical Characteristics Section 12.2.5 : The Packaging Section 12.2.6 : The Typical Applications Section 12.3 : Reading a Comparator Data Sheet 12.3.1 : Comparator Packaging 12.4 : Comparing Op-Amps 12.5 : Homework Problems Chapter 13 Sensors 13.1 Introduction 13.2 Sensor Output & Microcontroller Inputs 13.3 Sensor Design 13.3.1 Measuring Temperature with a Thermistor 13.3.2 Measuring Acceleration 13.3.3 Definitions of Sensor Performance Characteristics 13.4 Fundamental Sensors and Interface Circuits 13.4.1 Switches as Sensors 13.4.2 Interfacing to Switches 13.4.3 Resistive Sensors 13.4.4 Interfacing to Resistive Sensors 13.4.4.1 Using a Resistive Sensor in a Voltage Divider 13.4.4.2 Measuring Resistance Using a Current Source 13.4.4.3 The Constant Current Circuit 13.4.4.4 The Wheatstone Bridge 13.4.5 Capacitive Sensors 13.4.6 Interfacing to Capacitive Sensors 13.4.6.1 Measuring Capacitance with a Step Input 13.4.6.2 Measuring Capacitance with an Oscillator 13.4.6.3 Measuring Capacitance with a Wheatstone Bridge 13.5 A Survey of Sensors 13.5.1 Light Sensors 13.5.1.1 Photodiodes 13.5.1.2 Phototransistors 13.5.1.3 Emitter-Detector Pair Modules 13.5.1.4 Photocells 13.5.2 Strain Sensors 13.5.2.1 Metal Foil Strain Gages 13.5.2.2 Piezoresistive Strain Gages 13.5.2.3 Load Cells 13.5.3 Temperature Sensors 13.5.3.1 Thermocouples 13.5.3.2 Thermistors 13.5.4 Magnetic Field Sensors 13.5.4.1 Hall Effect Sensors 13.5.4.3 Reed Switches 13.5.5 Proximity Sensors 13.5.5.1 Capacitive Proximity Sensors 13.5.5.2 Inductive Proximity Sensors 13.5.5.3 Ultrasonic Proximity Sensors 13.5.6 Position Sensors 13.5.6.1 Potentiometers 13.5.6.2 Optical Encoders 13.5.6.3 Inductive Pickups / Gear Tooth Sensors 13.5.6.4 Reflective Infrared Sensors 13.5.6.5 Capacitive Displacement Sensors 13.5.6.6 Ultrasonic Displacement Sensors 13.5.6.7 Flex Sensors 13.5.7 Acceleration Sensors

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Book SynopsisApplied Mechatronics synthesizes the disciplines of Mechanical and Electrical Engineering to provide a comprehensive overview of the various technologies and tools used to develop mechatronic devices.Co-written by Mechanical Engineering and Electrical Engineering lecturers who co-teach this interdisciplinary course, this text highlights the information each discipline might have considered prerequisite so students can focus on material new to them. Designed for a first course in mechatronics, it contains numerous practical, classroom-tested examples, experiments, and simulations using SIMULINK, MATLAB, and LabVIEW, and presents material in a format that lends itself to collaborative, project-based learning.Table of ContentsAPPENDICES; A. DC POWER SUPPLY; B. PINOUT OF SELECTED ICS; C. INSTRUCTION SET, ADDRESSING MODES AND EXECUTION TIMES FOR THE MC9S12C; D. MC9S12C REGISTERS AND CONTROL BIT ASSIGNMENTS; E. USING THE CODEWARRIOR INTEGRATED DEVELOPMENT ENVIRONMENT (IDE); F. ASCII CODE TABLE; G. NUMBER SYSTEMS; H. MECHANISMS FOR MECHATRONICS

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Book Synopsis

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Book SynopsisJACQUELINE WILKIE (Senior Lecturer), MICHAEL JOHNSON (Professor), and REZA KATEBI (Senior Lecturer) are academic staff at the Industrial Control Centre, at the Department of Electronic and Electrical Engineering, at the University of Strathclyde. They have many years experience, teaching control and measurement across all years of the Undergraduate programme, and have been dedicated to revitalising teaching practice in this subject.

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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 SynopsisMultiagent systems (MAS) are one of the most exciting and the fastest growing domains in the intelligent resource management and agent-oriented technology, which deals with modeling of autonomous decisions making entities. Recent developments have produced very encouraging results in the novel approach of handling multiplayer interactive systems. In particular, the multiagent system approach is adapted to model, control, manage or test the operations and management of several system applications including multi-vehicles, microgrids, multi-robots, where agents represent individual entities in the network. Each participant is modeled as an autonomous participant with independent strategies and responses to outcomes. They are able to operate autonomously and interact pro-actively with their environment. In recent works, the problem of information consensus is addressed, where a team of vehicles communicate with each other to agree on key pieces of information that enable them to work tTable of ContentsPreface. Introduction. Theoretical Background. Distributed Intelligence in Power Systems. Consensus for Heterogeneous Systems with Delays. Secure Control of Distributed Multiagent Systems. Advanced Consensus Algorithms. Cooperative Control of Networked Power Systems. Dynamic Graphical Games. References. Index.

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Book SynopsisDespite the range of studies into grief and mourning in relation to the digital, research to date largely focuses on the cultural practices and meanings that are played out in and through digital environments. Digital Afterlife brings together experts from diverse fields who share an interest in Digital Afterlife and the wide-ranging issues that relate to this. The book covers a variety of matters that have been neglected in other research texts, for example:The legal, ethical, and philosophical conundrums of Digital AfterlifeThe ways digital media are currently being used to expand the possibilities of commemorating the dead and managing the grief of those left behindOur lives are shaped by and shape the creation of our Digital Afterlife as the digital has become a taken for granted aspect of human experience. This book will be of interest to undergraduates from computing, theology, business studies, philosophy, pTable of ContentsAcknowledgements. Editors. Contributors. Introduction. Chapter 1 ◾ Perspectives on Digital Afterlife. Chapter 2 ◾ Social Media and Digital Afterlife. Chapter 3 ◾ Posthumous Digital Material: Does It ‘Live On’ in Survivors’ Accounts of Their Dead? Chapter 4 ◾ The Transition from Life to the Digital Afterlife: Thanatechnology and Its Impact on Grief. Chapter 5 ◾ Profit and Loss: The Mortality of the Digital Immortality Platforms. Chapter 6 ◾ The ‘New(ish)’ Property, Informational Bodies, and Postmortality. Chapter 7 ◾ Digital Remains: The Users’ Perspectives. Chapter 8 ◾ Legal Issues in Digital Afterlife. Chapter 9 ◾ Building a Digital Immortal. Chapter 10 ◾ Philosophical Investigations into Digital Afterlife. Chapter 11 ◾ Postdigital Afterlife: A Philosophical Framework. Chapter 12 ◾ Digital Afterlife Matters. GLOSSARY. INDEX.

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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 SynopsisTree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level, which lie at the cutting edge of modern statistical and machine learning methodology.The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example, users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit), and more. The core chapters also end with a detailed section on relevant softwTrade ReviewTree-based algorithms have been a workhorse for data science teams for decades, but the data science field has lacked an all-encompassing review of trees - and their modern variants like XGBoost - until now. Greenwell has written the ultimate guide for tree-based methods: how they work, their pitfalls, and alternative solutions. He puts it all together in a readable and immediately usable book. You're guaranteed to learn new tips and tricks to help your data science team. -Alex Gutman, Director of Data Science, Author: Becoming a Data Head"Here’s a new title that is a “must have” for any data scientist who uses the R language. It’s a wonderful learning resource for tree-based techniques in statistical learning, one that’s become my go-to text when I find the need to do a deep dive into various ML topic areas for my work."Daniel D. Gutierrez, Editor-in-Chief for insideBIGDATA, USA, insideBIGDATA, February 2023Table of Contents1 Introduction 2 Binary recursive partitioning with CART 3 Conditional inference trees 4 "The hitchhiker’s GUIDE to modern decision trees" 5 Ensemble algorithms 6 Peeking inside the “black box”: post-hoc interpretability 7 Random forests 8 Gradient boosting machines

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Book SynopsisFully updated with the latest developments in the eigenvalue Monte Carlo calculations and automatic variance reduction techniques and containing an entirely new chapter on fission matrix and alternative hybrid techniques. This second edition explores the uses of the Monte Carlo method for real-world applications, explaining its concepts and limitations. Featuring illustrative examples, mathematical derivations, computer algorithms, and homework problems, it is an ideal textbook and practical guide for nuclear engineers and scientists looking into the applications of the Monte Carlo method, in addition to students in physics and engineering, and those engaged in the advancement of the Monte Carlo methods. Describes general and particle-transport-specific automated variance reduction techniques Presents Monte Carlo particle transport eigenvalue issues and methodologies to address these issues Presents detailed derivation of exisTrade ReviewWhen we decided to offer a new senior undergraduate level course on Monte Carlo in the nuclear engineering department at North Carolina State University, we easily decided to choose the Monte Carlo book by Dr. Haghighat since it came so highly recommended. One of things we liked most about this book is the way the topics are structured. The first seven chapters mainly cover the fundamental theories of Monte Carlo methods for particle transport. While the last five chapters present more advanced theories, they can be seamlessly coupled with hands-on training with industry standard Monte Carlo codes (in our course we selected MCNP6.2). We easily designed our course following the general outline of this book. Furthermore, the course materials have been refined and improved many times based on Dr. Haghighat's many years of teaching experience, as well as comments from students, practitioners, and reviewers. They are well explained and cover almost all the essential concepts in Monte Carlo. For the first half of the semester that focused on Monte Carlo methods, the course work assignments were mainly programming-based homework problems (mostly taken from the exercises in the book). Dr. Haghighat designed these exercises so well that they can not only strengthen the students' understanding, but also inspire their critical thinking. We highly recommend this textbook for senior undergraduate students, graduate students and researchers who are interested in Monte Carlo applications on radiation transport. - Professors Xu Wu and John Zino, North Carolina State University, USA. Table of Contents1 Introduction. 2 Random Variables and Sampling. 3 Random Number Generator (RNG). 4 Fundamentals of Probability and Statistics. 5 Integrals and Associated Variance Reduction Techniques. 6 Fixed-Source Monte Carlo Particle Transport. 7 Variance Reducation Techniques or Fixed-Source Particles. 8 Scoring/ Tallying. 9 Geometry and Particle Tracking. 10 Eigenvalue (Criticality) Monte Carlo Method for Particle Transport. 11 Fission Matrix Methods for Eigenvalue Monte Carlo Simulation. 12 Vector and Parallel Processing of Monte Carlo Particle Transport

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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 SynopsisArtificial Intelligence (AI) is undoubtedly playing an increasingly significant role in automobile technology. In fact, cars inhabit one of just a few domains where you will find many AI innovations packed into a single product.AI for Cars provides a brief guided tour through many different AI landscapes including robotics, image and speech processing, recommender systems and onto deep learning, all within the automobile world. From pedestrian detection to driver monitoring to recommendation engines, the book discusses the background, research and progress thousands of talented engineers and researchers have achieved thus far, and their plans to deploy this life-saving technology all over the world.Table of ContentsForeword Preface AI for Advanced Driver Assistance Systems Automatic Parking Traffic Sign Recognition Driver Monitoring System Summary AI for Autonomous Driving Perception Planning Motion Control Summary AI for In-Vehicle Infotainment Systems Gesture Control Voice Assistant User Action Prediction Summary AI for Research & Development Automated Rules Generation Virtual Testing Platform Synthetic Scenario Generation Summary AI for Services Predictive Diagnostics Predictive Maintenance Driver Behavior Analysis Summary The Future of AI in Cars A Tale Of Two Paradigms AI & Car Safety AI & Car Security Summary Further Reading References

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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 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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Book SynopsisA Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source.Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn progrTable of ContentsAssumptions about the reader’s backgroundBook overview Introduction to R/Python Programming Calculator Variable and TypeFunctions Control flowsSome built-in data structures Revisit of variables Object-oriented programming (OOP) in R/Python Miscellaneous More on R/Python Programming Work with R/Python scripts Debugging in R/Python Benchmarking Vectorization Embarrassingly parallelism in R/Python Evaluation strategySpeed up with C/C++ in R/PythonA first impression of functional programming Miscellaneous data.table and pandasSQL Get started with data.table and pandas Indexing & selecting data Add/Remove/UpdateGroup by Join Random Variables, Distributions & Linear Regression A refresher on distributions Inversion sampling & rejection sampling Joint distribution & copula Fit a distribution Confidence intervalHypothesis testing Basics of linear regression Ridge regression Optimization in PracticeConvexity Gradient descent Root-finding General purpose minimization tools in R/Python Linear programming Miscellaneous Machine Learning - A gentle introduction Supervised learning Gradient boosting machine Unsupervised learning Reinforcement learning Deep Q-Networks Computational differentiation Miscellaneous

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Book SynopsisThe 11th International Workshop on Dynamics and Control brought together scientists and engineers from diverse fields and gave them a venue to develop a greater understanding of this discipline and how it relates to many areas in science, engineering, economics, and biology. The event gave researchers an opportunity to investigate ideas and techniques from outside their own fields of expertise, enabling a cross-pollination of dynamics and control perspectives. Now there is a book that documents the major presentations of the workshop, providing a foundation for further research.The range and diversity of papers in Dynamical Systems and Control demonstrate the remarkable reach of the subject. All of these contributed papers shed light on a multiplicity of physical, biological, and economic phenomena through lines of reasoning that originate and grow from this discipline.The editors divide the book into three parts. The first covers fundamental advances in dynamics, dyTable of ContentsList of contributors, Preface, Part I, Part II, Part III, Subject Index

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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 ContentsPreface xi 1 Introduction 1 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 95 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 uncertainly 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 an 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 disturbances 238 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 exorcises 287 7.8 Conclusion 288 8 Robust Stability and Performance Analysis For Mimo Systems 289 8.1 General control configuration with uncertainly 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 uncertainly: motivation 305 8.8 The structured singular value 306 8.9 Robust stability with structured uncertainly 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 419 10.6 Decentralized feedback control 428 10.7 Conclusion 453 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 problems 473 12.2 Types of LMI problems 476 12.3 Tricks in LMT 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 51 8 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 SynopsisA NEW EDITION OF THE CLASSIC TEXT ON OPTIMAL CONTROL THEORY As a superb introductory text and an indispensable reference, this new edition of Optimal Control will serve the needs of both the professional engineer and the advanced student in mechanical, electrical, and aerospace engineering. Its coverage encompasses all the fundamental topics as well as the major changes that have occurred in recent years. An abundance of computer simulations using MATLAB and relevant Toolboxes is included to give the reader the actual experience of applying the theory to real-world situations. Major topics covered include: Static Optimization Optimal Control of Discrete-Time Systems Optimal Control of Continuous-Time Systems The Tracking Problem and Other LQR Extensions Final-Time-Free and Constrained Input Control Dynamic Programming Optimal Control for Polynomial Systems Output Feedback and Structured ControTable of ContentsPREFACE xi 1 STATIC OPTIMIZATION 1 1.1 Optimization without Constraints / 1 1.2 Optimization with Equality Constraints / 4 1.3 Numerical Solution Methods / 15 Problems / 15 2 OPTIMAL CONTROL OF DISCRETE-TIME SYSTEMS 19 2.1 Solution of the General Discrete-Time Optimization Problem / 19 2.2 Discrete-Time Linear Quadratic Regulator / 32 2.3 Digital Control of Continuous-Time Systems / 53 2.4 Steady-State Closed-Loop Control and Suboptimal Feedback / 65 2.5 Frequency-Domain Results / 96 Problems / 102 3 OPTIMAL CONTROL OF CONTINUOUS-TIME SYSTEMS 110 3.1 The Calculus of Variations / 110 3.2 Solution of the General Continuous-Time Optimization Problem / 112 3.3 Continuous-Time Linear Quadratic Regulator / 135 3.4 Steady-State Closed-Loop Control and Suboptimal Feedback / 154 3.5 Frequency-Domain Results / 164 Problems / 167 4 THE TRACKING PROBLEM AND OTHER LQR EXTENSIONS 177 4.1 The Tracking Problem / 177 4.2 Regulator with Function of Final State Fixed / 183 4.3 Second-Order Variations in the Performance Index / 185 4.4 The Discrete-Time Tracking Problem / 190 4.5 Discrete Regulator with Function of Final State Fixed / 199 4.6 Discrete Second-Order Variations in the Performance Index / 206 Problems / 211 5 FINAL-TIME-FREE AND CONSTRAINED INPUT CONTROL 213 5.1 Final-Time-Free Problems / 213 5.2 Constrained Input Problems / 232 Problems / 257 6 DYNAMIC PROGRAMMING 260 6.1 Bellman’s Principle of Optimality / 260 6.2 Discrete-Time Systems / 263 6.3 Continuous-Time Systems / 271 Problems / 283 7 OPTIMAL CONTROL FOR POLYNOMIAL SYSTEMS 287 7.1 Discrete Linear Quadratic Regulator / 287 7.2 Digital Control of Continuous-Time Systems / 292 Problems / 295 8 OUTPUT FEEDBACK AND STRUCTURED CONTROL 297 8.1 Linear Quadratic Regulator with Output Feedback / 297 8.2 Tracking a Reference Input / 313 8.3 Tracking by Regulator Redesign / 327 8.4 Command-Generator Tracker / 331 8.5 Explicit Model-Following Design / 338 8.6 Output Feedback in Game Theory and Decentralized Control / 343 Problems / 351 9 ROBUSTNESS AND MULTIVARIABLE FREQUENCY-DOMAIN TECHNIQUES 355 9.1 Introduction / 355 9.2 Multivariable Frequency-Domain Analysis / 357 9.3 Robust Output-Feedback Design / 380 9.4 Observers and the Kalman Filter / 383 9.5 LQG/Loop-Transfer Recovery / 408 9.6 H∞ DESIGN / 430 Problems / 435 10 DIFFERENTIAL GAMES 438 10.1 Optimal Control Derived Using Pontryagin’s Minimum Principle and the Bellman Equation / 439 10.2 Two-player Zero-sum Games / 444 10.3 Application of Zero-sum Games to H∞ Control / 450 10.4 Multiplayer Non-zero-sum Games / 453 11 REINFORCEMENT LEARNING AND OPTIMAL ADAPTIVE CONTROL 461 11.1 Reinforcement Learning / 462 11.2 Markov Decision Processes / 464 11.3 Policy Evaluation and Policy Improvement / 474 11.4 Temporal Difference Learning and Optimal Adaptive Control / 489 11.5 Optimal Adaptive Control for Discrete-time Systems / 490 11.6 Integral Reinforcement Learning for Optimal Adaptive Control of Continuous-time Systems / 503 11.7 Synchronous Optimal Adaptive Control for Continuous-time Systems / 513 APPENDIX A REVIEW OF MATRIX ALGEBRA 518 A.1 Basic Definitions and Facts / 518 A.2 Partitioned Matrices / 519 A.3 Quadratic Forms and Definiteness / 521 A.4 Matrix Calculus / 523 A.5 The Generalized Eigenvalue Problem / 525 REFERENCES 527 INDEX 535

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Book SynopsisThis book is a collection of projects based around various microcontrollers from the PIC family. The reader is carefully guided through the book, from very simple to more complex projects in order to gradually build their knowledge about PIC microcontrollers and digital electronics in general.Table of ContentsAbout the Authors vii Preface ix Acknowledgements xiii 1 PREPARING TO DO A PIC PROJECT 1 1.1 Introduction 1 1.2 Overview of PIC Microcontroller 2 1.3 Basics of PIC Assembly Language 9 1.4 Introduction to C Programming for PIC Microcontroller 16 1.5 MPLAB Integrated Development Environment (IDE) 28 1.6 Advanced Debugger Features – Stimulus 48 2 SIMPLE INTERFACES 55 2.1 Introduction 55 2.2 PIC12F629 Circuit Design 56 2.3 The PIC12F629 Strip Board Design 57 2.4 The PIC12F629 PCB Board Design 58 2.5 The PIC12F629 – Flashing LED Application 59 2.6 PIC16F627A Circuit Design 68 2.7 PIC16F629 Strip Board Design 69 2.8 PIC16F627A PCB Board Design 70 2.9 PIC16F627A – Display Segments 71 3 DISPLAY INTERFACES 83 3.1 Introduction 83 3.2 PIC16F627A Four-Digit, Seven-Segment LED Display Circuit Design 84 3.3 PIC16F627A Four-Digit, Seven-Segment LED Display Circuit Strip Board Design 84 3.4 PIC12F629 PCB Board Design 86 3.5 PIC16F627A Four-Digit, Seven-Segment LED Display Circuit Application 86 3.6 PIC16F627A LCD Display Circuit Design 93 3.7 PIC16F627A Four-Digit, Seven-Segment LED Display Circuit Strip Board and PCB Design 95 3.8 PIC16F627A LCD Display Circuit Application 96 4 RS232 INTERFACES 105 4.1 Introduction 105 4.2 RS232 Interface Circuit Design 106 4.3 PIC16F627A MCU – Transmit – C Program 109 4.4 PIC16F627A MCU – Transmit – Assembly Program 115 4.5 PIC16F627A MCU – Receive – C Program 119 4.6 PIC16F627A MCU – Receive – Assembly Program 121 4.7 PIC16F627A MCU – Transmit-Receive – C Program 124 4.8 PIC16F627A MCU – Transmit-Receive – Assembly Program 126 5 INTERFACING PICS WITH THE ANALOG WORLD 129 5.1 Introduction 129 5.2 Hardware Description 132 5.3 Level Indicator Program and Advanced Simulator Features 133 5.4 Level Indicator with Timing 147 5.5 Level Indicator with Better Timing – Timer Interrupts 149 5.6 Talkthrough Program with Adjustable Sampling Rate 156 6 OTHER PIC PROJECTS 159 6.1 Introduction 159 6.2 Stepper Motor Controller using PIC12F675 159 6.3 DC Motor Controller using a PIC12F675 164 6.4 An Ultrasonic Measuring System using the PIC16F627A 167 6.5 Function Generator 173 6.6 Digital Filtering 178 Appendix 189 Index 191

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Book SynopsisPresents key cutting edge research into the use of iterative learning control The book discusses the main methods of iterative learning control (ILC) and its interactions, as well as comparator performance that is so crucial to the end user.Table of ContentsPreface vii 1 Iterative Learning Control: Origins and General Overview 1 1.1 The Origins of ILC 2 1.2 A Synopsis of the Literature 5 1.3 Linear Models and Control Structures 6 1.3.1 Differential Linear Dynamics 7 1.4 ILC for Time-Varying Linear Systems 9 1.5 Discrete Linear Dynamics 11 1.6 ILC in a 2D Linear Systems/Repetitive Processes Setting 16 1.6.1 2D Discrete Linear Systems and ILC 16 1.6.2 ILC in a Repetitive Process Setting 17 1.7 ILC for Nonlinear Dynamics 18 1.8 Robust, Stochastic, and Adaptive ILC 19 1.9 Other ILC Problem Formulations 21 1.10 Concluding Remarks 22 2 Iterative Learning Control: Experimental Benchmarking 23 2.1 Robotic Systems 23 2.1.1 Gantry Robot 23 2.1.2 Anthromorphic Robot Arm 25 2.2 Electro-Mechanical Systems 26 2.2.1 Nonminimum Phase System 26 2.2.2 Multivariable Testbed 29 2.2.3 Rack Feeder System 30 2.3 Free Electron Laser Facility 32 2.4 ILC in Healthcare 37 2.5 Concluding Remarks 38 3 An Overview of Analysis and Design for Performance 39 3.1 ILC Stability and Convergence for Discrete Linear Dynamics 39 3.1.1 Transient Learning 41 3.1.2 Robustness 42 3.2 Repetitive Process/2D Linear Systems Analysis 43 3.2.1 Discrete Dynamics 43 3.2.2 Repetitive Process Stability Theory 46 3.2.3 Error Convergence Versus Along the Trial Performance 51 3.3 Concluding Remarks 55 4 Tuning and Frequency Domain Design of Simple Structure ILC Laws 57 4.1 Tuning Guidelines 57 4.2 Phase-Lead and Adjoint ILC Laws for Robotic-Assisted Stroke Rehabilitation 58 4.2.1 Phase-Lead ILC 61 4.2.2 Adjoint ILC 63 4.2.3 Experimental Results 63 4.3 ILC for Nonminimum Phase Systems Using a Reference Shift Algorithm 68 4.3.1 Filtering 74 4.3.2 Numerical Simulations 75 4.3.3 Experimental Results 75 4.4 Concluding Remarks 81 5 Optimal ILC 83 5.1 NOILC 83 5.1.1 Theory 83 5.1.2 NOILC Computation 86 5.2 Experimental NOILC Performance 89 5.2.1 Test Parameters 90 5.3 NOILC Applied to Free Electron Lasers 93 5.4 Parameter Optimal ILC 96 5.4.1 An Extension to Adaptive ILC 98 5.5 Predictive NOILC 99 5.5.1 Controlled System Analysis 104 5.5.2 Experimental Validation 106 5.6 Concluding Remarks 116 6 Robust ILC 117 6.1 Robust Inverse Model-Based ILC 117 6.2 Robust Gradient-Based ILC 123 6.2.1 Model Uncertainty –Case (i) 127 6.2.2 Model Uncertainty –Cases (ii) and (iii) 128 6.3 H∞ Robust ILC 132 6.3.1 Background and Early Results 132 6.3.2 H∞ Based Robust ILC Synthesis 137 6.3.3 A Design Example 142 6.3.4 Robust ILC Analysis Revisited 151 6.4 Concluding Remarks 153 7 Repetitive Process-Based ILC Design 155 7.1 Design with Experimental Validation 155 7.1.1 Discrete Nominal Model Design 155 7.1.2 Robust Design –Norm-Bounded Uncertainty 160 7.1.3 Robust Design – Polytopic Uncertainty and Simplified Implementation 165 7.1.4 Design for Differential Dynamics 170 7.2 Repetitive Process-Based ILC Design Using Relaxed Stability Theory 170 7.3 Finite Frequency Range Design and Experimental Validation 178 7.3.1 Stability Analysis 178 7.4 HOILC Design 194 7.5 Inferential ILC Design 196 7.6 Concluding Remarks 202 8 Constrained ILC Design 203 8.1 ILC with Saturating Inputs Design 203 8.1.1 Observer-Based State Control Law Design 203 8.1.2 ILC Design with Full State Feedback 209 8.1.3 Comparison with an Alternative Design 210 8.1.4 Experimental Results 215 8.2 Constrained ILC Design for LTV Systems 219 8.2.1 Problem Specification 219 8.2.2 Implementation of Constrained Algorithm 1 – a Receding Horizon Approach 223 8.2.3 Constrained ILC Algorithm 3 224 8.3 Experimental Validation on a High-Speed Rack Feeder System 226 8.3.1 Simulation Case Studies 226 8.3.2 Other Performance Issues 230 8.3.3 Experimental Results 236 8.3.4 Algorithm 1: QP-Based Constrained ILC 236 8.3.5 Algorithm 2: Receding Horizon Approach-Based Constrained ILC 237 8.4 Concluding Remarks 238 9 ILC for Distributed Parameter Systems 241 9.1 Gust Load Management for Wind Turbines 241 9.1.1 Oscillatory Flow 246 9.1.2 Flow with Vortical Disturbances 251 9.1.3 Blade Conditioning Measures 253 9.1.4 Actuator Dynamics and Trial-Varying ILC 254 9.1.5 Proper Orthogonal Decomposition-Based Reduced Order Model Design 257 9.2 Design Based on Finite-Dimensional Approximate Models with Experimental Validation 266 9.3 Finite Element and Sequential Experimental Design-based ILC 280 9.3.1 Finite Element Discretization 281 9.3.2 Application of ILC 283 9.3.3 Optimal Measurement Data Selection 284 9.4 Concluding Remarks 288 10 Nonlinear ILC 289 10.1 Feedback Linearized ILC for Center-Articulated Industrial Vehicles 289 10.2 Input–Output Linearization-based ILC Applied to Stroke Rehabilitation 293 10.2.1 System Configuration and Modeling 293 10.2.2 Input–Output Linearization 296 10.2.3 Experimental Results 299 10.3 Gap Metric ILC with Application to Stroke Rehabilitation 302 10.4 Nonlinear ILC – an Adaptive Lyapunov Approach 310 10.4.1 Motivation and Background Results 311 10.5 Extremum-Seeking ILC 320 10.6 Concluding Remarks 322 11 Newton Method Based ILC 323 11.1 Background 323 11.2 Algorithm Development 324 11.2.1 Computation of Newton-Based ILC 326 11.2.2 Convergence Analysis 327 11.3 Monotonic Trial-to-Trial Error Convergence 328 11.3.1 Monotonic Convergence with Parameter Optimization 329 11.3.2 Parameter Optimization for Monotonic and Fast Trial-to-Trial Error Convergence 330 11.4 Newton ILC for 3D Stroke Rehabilitation 331 11.4.1 Experimental Results 336 11.5 Constrained Newton ILC Design 337 11.6 Concluding Remarks 347 12 Stochastic ILC 349 12.1 Background and Early Results 349 12.2 Frequency Domain-Based Stochastic ILC Design 356 12.3 Experimental Comparison of ILC Laws 364 12.4 Repetitive Process-Based Analysis and Design 378 12.5 Concluding Remarks 387 13 Some Emerging Topics in Iterative Learning Control 389 13.1 ILC for Spatial Path Tracking 389 13.2 ILC in Agriculture and Food Production 394 13.2.1 The Broiler Production Process 395 13.2.2 ILC for FCR Minimization 400 13.2.3 Design Validation 404 13.3 ILC for Quantum Control 406 13.4 ILC in the Utility Industries 410 13.4.1 ILC Design 413 13.5 Concluding Remarks 415 Appendix A 417 A.1 The Entries in the Transfer-Function Matrix (2.2) 417 A.2 Entries in the Transfer-Function Matrix (2.4) 418 A.3 Matrices E1, E2, H1, and H2 for the Designs of (7.36) and (7.37) 419 References 421 Index 437

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Book SynopsisUsing a pedagogical style along with detailed proofs and illustrative examples, this book opens a view to the largely unexplored area of nonlinear systems with uncertainties. The focus is on adaptive nonlinear control results introduced with the new recursive design methodology--adaptive backstepping.Table of ContentsSTATE FEEDBACK. Design Tools for Stabilization. Adaptive Backstepping Design. Tuning Functions Design. Modular Design with Passive Identifiers. Modular Design with Swapping Identifiers. OUTPUT FEEDBACK. Output-Feedback Design Tools. Tuning Functions Designs. Modular Designs. Linear Systems. Appendices. Bibliography. Index.

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Book SynopsisThis text presents an approach developed by the author, to handle some of the most common types of component imperfections encountered in industrial automation, consumer electronics, and defence and transportation systems.Table of ContentsDead-Zone, Backlash, and Hysteresis. Inverse Models. Fixed Inverse Compensation. Adaptive Inverse Examples. Continuous-Time Adaptive Inverse Control. Discrete-Time Adaptive Inverse Control. Fixed Inverse Control for Output Nonlinearities. Adaptive Inverse Control for Output Nonlinearities. Adaptive Control of Partially Known Systems. Adaptive Control with Input and Output Nonlinearities. Appendices. Bibliography. Index.

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Book SynopsisThe complete control system engineering solution for continuous and batch manufacturing plants. This book presents a complete methodology of control system design for continuous and batch manufacturing in such diverse areas as pulp and paper, petrochemical, chemical, food, pharmaceutical, and biochemical production.Table of ContentsIntroduction to Plant-wide Process Control. Control Engineering on Capital Projects. A Practitioner's Model for Automation and Control. Process Modeling. Single Loop Regulatory Control. Enhancements to Single Loop Regulatory Control. Multivariable Regulatory Control. Discrete Control. Batch Control. Case Study - Pulp and Paper Mill. Appendices. Glossary.

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Book SynopsisThrough its presentation of the essentials, this text is briefer than some and has been carefully edited and designed to meet the specific needs of a one-semester course at the appropriate level for a Senior.Following extensive student testing for readability and understandability, examples have been intermixed with the theory throughout the book to introduce, motivate, and extend the main text. Although readability is emphasized, proofs are provided to promote logical thinking. Finally, the author''s conversational style holds the reader''s interest while exploring several important topics that traditionally have been reserved for graduate courses. The result is that students can apply theory that is sometimes a sterile subject in other courses, and can hit the ground running in advanced courses in feedback control design, dynamics of power systems, communications, and signal processing.Trade Review"...an excellent collection of examples...a good addition to the field of linear systems..." (Int Jnl of Robust & Nonlinear Control, May 2002)Table of ContentsSolution of State-Space Equations. Transform Methods. Writing State-Space Equations. Matrices Over a Field. Vector Spaces. Similarity Transformations. Stability. Minimality via Similarity Transformations. Poles and Zeros. References. Appendix. Index.

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