Topics include:The pros and cons of braced initialization, noexcept specifications, perfect forwarding, and smart pointer make functionsThe relationships among std::move, std::forward, rvalue references, and universal referencesTechniques for writing clear, correct, effective lambda expressionsHow std::atomic differs from volatile, how each should be used, and how they relate to C++'s concurrency APIHow best practices in "old" C++ programming (i.e., C++98) require revision for software development in modern C++
Effective Modern C++ follows the proven guideline-based, example-driven format of Scott Meyers' earlier books, but covers entirely new material.
"After I learned the C++ basics, I then learned how to use C++ in production code from Meyer's series of Effective C++ books. Effective Modern C++ is the most important how-to book for advice on key guidelines, styles, and idioms to use modern C++ effectively and well. Don't own it yet? Buy this one. Now".
-- Herb Sutter, Chair of ISO C++ Standards Committee and C++ Software Architect at Microsoft
Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one stroke, with a new understanding of intelligence itself.
Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines.
The brain is not a computer, but a memory system that stores experiences in a way that reflects the true structure of the world, remembering sequences of events and their nested relationships and making predictions based on those memories. It is this memory-prediction system that forms the basis of intelligence, perception, creativity, and even consciousness.
In an engaging style that will captivate audiences from the merely curious to the professional scientist, Hawkins shows how a clear understanding of how the brain works will make it possible for us to build intelligent machines, in silicon, that will exceed our human ability in surprising ways.
Written with acclaimed science writer Sandra Blakeslee, On Intelligence promises to completely transfigure the possibilities of the technology age. It is a landmark book in its scope and clarity.
Inside, you'll learn about:
Interaction design and physical computingThe Arduino hardware and software development environmentBasics of electricity and electronicsPrototyping on a solderless breadboardDrawing a schematic diagram
And more. With inexpensive hardware and open-source software components that you can download free, getting started with Arduino is a snap. To use the introductory examples in this book, all you need is a USB Arduino, USB A-B cable, and an LED.
Join the tens of thousands of hobbyists who have discovered this incredible (and educational) platform. Written by the co-founder of the Arduino project, with illustrations by Elisa Canducci, Getting Started with Arduino gets you in on the fun! This 128-page book is a greatly expanded follow-up to the author's original short PDF that's available on the Arduino website.
Artificial Intelligence helps choose what books you buy, what movies you see, and even who you date. It puts the "smart" in your smartphone and soon it will drive your car. It makes most of the trades on Wall Street, and controls vital energy, water, and transportation infrastructure. But Artificial Intelligence can also threaten our existence.
In as little as a decade, AI could match and then surpass human intelligence. Corporations and government agencies are pouring billions into achieving AI's Holy Grail—human-level intelligence. Once AI has attained it, scientists argue, it will have survival drives much like our own. We may be forced to compete with a rival more cunning, more powerful, and more alien than we can imagine.
Through profiles of tech visionaries, industry watchdogs, and groundbreaking AI systems, Our Final Invention explores the perils of the heedless pursuit of advanced AI. Until now, human intelligence has had no rival. Can we coexist with beings whose intelligence dwarfs our own? And will they allow us to?
Catherine Matthews, nineteen years old, has a unique gift: the ability to manipulate the net with her neural implant. Yanked out of her perfectly ordinary life, Catherine becomes the last firewall standing between Adam and his quest for world domination.
PRAISE FOR THE LAST FIREWALL
“Awesome near-term science fiction.” – Brad Feld, Foundry Group managing director
“An insightful and adrenaline-inducing tale of what humanity could become and the machines we could spawn.” – Ben Huh, CEO of Cheezburger
“A fun read and tantalizing study of the future of technology: both inviting and alarming.” – Harper Reed, former CTO of Obama for America, Threadless
"A fascinating and prescient take on what the world will look like once computers become smarter than people. Highly recommended." – Mat Ellis, Founder & CEO Cloudability
“A phenomenal ride through a post-scarcity world where humans are caught between rogue AIs. If you like having your mind blown, read this book!” – Gene Kim, author of The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win
Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or mediaMethods of clustering to detect groups of similar items in a large datasetSearch engine features -- crawlers, indexers, query engines, and the PageRank algorithmOptimization algorithms that search millions of possible solutions to a problem and choose the best oneBayesian filtering, used in spam filters for classifying documents based on word types and other featuresUsing decision trees not only to make predictions, but to model the way decisions are madePredicting numerical values rather than classifications to build price modelsSupport vector machines to match people in online dating sitesNon-negative matrix factorization to find the independent features in a datasetEvolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a gameEach chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you.
"Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."
-- Dan Russell, Google
"Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."
-- Tim Wolters, CTO, Collective Intellect
Hayles relates three interwoven stories: how information lost its body, that is, how it came to be conceptualized as an entity separate from the material forms that carry it; the cultural and technological construction of the cyborg; and the dismantling of the liberal humanist "subject" in cybernetic discourse, along with the emergence of the "posthuman."
Ranging widely across the history of technology, cultural studies, and literary criticism, Hayles shows what had to be erased, forgotten, and elided to conceive of information as a disembodied entity. Thus she moves from the post-World War II Macy Conferences on cybernetics to the 1952 novel Limbo by cybernetics aficionado Bernard Wolfe; from the concept of self-making to Philip K. Dick's literary explorations of hallucination and reality; and from artificial life to postmodern novels exploring the implications of seeing humans as cybernetic systems.
Although becoming posthuman can be nightmarish, Hayles shows how it can also be liberating. From the birth of cybernetics to artificial life, How We Became Posthuman provides an indispensable account of how we arrived in our virtual age, and of where we might go from here.
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This new edition is an ideal resource for professional digital system designers, programmers, application developers, and system software developers. It will also be of interest to undergraduate students in Computer Science, Computer Engineering and Electrical Engineering courses in Computer Organization, Computer Design, ranging from Sophomore required courses to Senior Electives.Winner of a 2014 Texty Award from the Text and Academic Authors AssociationIncludes new examples, exercises, and material highlighting the emergence of mobile computing and the cloudCovers parallelism in depth with examples and content highlighting parallel hardware and software topics Features the Intel Core i7, ARM Cortex-A8 and NVIDIA Fermi GPU as real-world examples throughout the book Adds a new concrete example, "Going Faster," to demonstrate how understanding hardware can inspire software optimizations that improve performance by 200 timesDiscusses and highlights the "Eight Great Ideas" of computer architecture: Performance via Parallelism; Performance via Pipelining; Performance via Prediction; Design for Moore's Law; Hierarchy of Memories; Abstraction to Simplify Design; Make the Common Case Fast; and Dependability via RedundancyIncludes a full set of updated and improved exercises
Inside, you’ll learn about:Interaction design and physical computing The Arduino hardware and software development environment Basics of electricity and electronics Prototyping on a solderless breadboard Drawing a schematic diagram
Getting started with Arduino is a snap. To use the introductory examples in this guide, all you need an Arduino Uno or earlier model, along with USB A-B cable and an LED. The easy-to-use Arduino development environment is free to download.
Join hundreds of thousands of hobbyists who have discovered this incredible (and educational) platform. Written by the co-founder of the Arduino project, Getting Started with Arduino gets you in on all the fun!
You don’t need to have mastered Arduino or programming to get started. Updated for the Arduino 1.0 release, the recipes in this second edition include practical examples and guidance to help you begin, expand, and enhance your projects right away—whether you’re an artist, designer, hobbyist, student, or engineer.Get up to speed on the Arduino board and essential software concepts quicklyLearn basic techniques for reading digital and analog signalsUse Arduino with a variety of popular input devices and sensorsDrive visual displays, generate sound, and control several types of motorsInteract with devices that use remote controls, including TVs and appliancesLearn techniques for handling time delays and time measurementApply advanced coding and memory handling techniques
But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.
Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.
Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype.
But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.
Each chapter will cover a different technique in a spreadsheet so you can follow along:Mathematical optimization, including non-linear programming and genetic algorithms Clustering via k-means, spherical k-means, and graph modularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, and bag-of-words models Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation Moving from spreadsheets into the R programming language
You get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.
In science fiction, artificial intelligence takes the shape of computers that can speak like people, think for themselves, and sometimes act against us. Sometimes the machines seem to know everything, and symbolize implacable and unknowable power, as in The Matrix. Such machines can also embody the limits of logic, and by extension our own powers of reason. In Arthur C. Clarke's 2001: A Space Odyssey, HAL was a computer of vast capability driven insane by the demands of his programming – to honestly and completely report information – when those instructions conflicted with orders to keep state secrets. Star Trek has given us the android, Lieutenant Commander Data, who strives to be more human. None of these visions came true in quite the way science fiction writers imagined, even though in many ways computers surpass their fictional counterparts. This eBook reviews work in the field and covers topics from chess-playing to quantum computing. The writers tackle how to make computers more powerful, how we define consciousness, what the hard problems are and even how computers might be built once the limits of silicon chips have been reached. Artificial intelligence also raises some thorny ethical questions, such as whether morality can be programmed. These are kinds of issues that make artificial intelligence and computing fascinating. Building an intelligent machine brings together the human desire to create and the question of what makes us what we are. If anyone ever builds a true thinking machine, that last question becomes much more complicated, not less. Data and HAL would probably agree.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
Implementations, as well as interesting, real-world examples of each data structure and algorithm, are included.
Using both a programming style and a writing style that are exceptionally clean, Kyle Loudon shows you how to use such essential data structures as lists, stacks, queues, sets, trees, heaps, priority queues, and graphs. He explains how to use algorithms for sorting, searching, numerical analysis, data compression, data encryption, common graph problems, and computational geometry. And he describes the relative efficiency of all implementations. The compression and encryption chapters not only give you working code for reasonably efficient solutions, they offer explanations of concepts in an approachable manner for people who never have had the time or expertise to study them in depth.
Anyone with a basic understanding of the C language can use this book. In order to provide maintainable and extendible code, an extra level of abstraction (such as pointers to functions) is used in examples where appropriate. Understanding that these techniques may be unfamiliar to some programmers, Loudon explains them clearly in the introductory chapters.
Contents include:PointersRecursionAnalysis of algorithmsData structures (lists, stacks, queues, sets, hash tables, trees, heaps, priority queues, graphs)Sorting and searchingNumerical methodsData compressionData encryptionGraph algorithmsGeometric algorithms
Presenting material refined over more than a decade of teaching parallel computing, author Gerassimos Barlas minimizes the challenge with multiple examples, extensive case studies, and full source code. Using this book, you can develop programs that run over distributed memory machines using MPI, create multi-threaded applications with either libraries or directives, write optimized applications that balance the workload between available computing resources, and profile and debug programs targeting multicore machines.Comprehensive coverage of all major multicore programming tools, including threads, OpenMP, MPI, and CUDADemonstrates parallel programming design patterns and examples of how different tools and paradigms can be integrated for superior performanceParticular focus on the emerging area of divisible load theory and its impact on load balancing and distributed systemsDownload source code, examples, and instructor support materials on the book's companion website
The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods.The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling.Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied.MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projectsAddresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fieldsProvides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
Hello, Startup is a practical, how-to guide that consists of three parts: Products, Technologies, and Teams. Although at its core, this is a book for programmers, by programmers, only Part II (Technologies) is significantly technical, while the rest should be accessible to technical and non-technical audiences alike.
If you’re at all interested in startups—whether you’re a programmer at the beginning of your career, a seasoned developer bored with large company politics, or a manager looking to motivate your engineers—this book is for you.
Semantic Web for the Working Ontologist transforms this information into the practical knowledge that programmers and subject domain experts need. Authors Allemang and Hendler begin with solutions to the basic problems, but don’t stop there: they demonstrate how to develop your own solutions to problems of increasing complexity and ensure that your skills will keep pace with the continued evolution of the Semantic Web.
• Provides practical information for all programmers and subject matter experts engaged in modeling data to fit the requirements of the Semantic Web.
• De-emphasizes algorithms and proofs, focusing instead on real-world problems, creative solutions, and highly illustrative examples.
• Presents detailed, ready-to-apply “recipes” for use in many specific situations.
• Shows how to create new recipes from RDF, RDFS, and OWL constructs.
Author Bob DuCharme has you writing simple queries right away before providing background on how SPARQL fits into RDF technologies. Using short examples that you can run yourself with open source software, you’ll learn how to update, add to, and delete data in RDF datasets.Get the big picture on RDF, linked data, and the semantic webUse SPARQL to find bad data and create new data from existing dataUse datatype metadata and functions in your queriesLearn techniques and tools to help your queries run more efficientlyUse RDF Schemas and OWL ontologies to extend the power of your queriesDiscover the roles that SPARQL can play in your applications
COVERS ALL SIX EXAM DOMAINS:
Legal and ethical principles
Hybrid and emerging technologies
ELECTRONIC CONTENT INCLUDES:250 practice exam questions Test engine that provides full-length practice exams and customized quizzes by chapter or by exam domain
Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.comDemystifies data mining concepts with easy to understand languageShows how to get up and running fast with 20 commonly used powerful techniques for predictive analysisExplains the process of using open source RapidMiner toolsDiscusses a simple 5 step process for implementing algorithms that can be used for performing predictive analyticsIncludes practical use cases and examples
Fully revised for the latest North American and ITU standards, Microwave Transmission Networks, Second Edition covers all stages of terrestrial point-to-point microwave network build-out, from planning and feasibility studies to system deployment and testing. This definitive volume is thoroughly updated with new information, including details on the impact of Ethernet and IP communications on microwave links. Useful formulas for solving microwave design-related problems are contained in this practical resource.
Find out how to:Plan, design, and build microwave point-to-point networks Determine network capacity, dimensions, architecture, budget, schedules, and work force requirements Understand microwave link engineering Calculate loss/attention, fading and fade margins, and link quality and availability Perform interference analysis Determine, procure, and install required hardware and power systems Manage the microwave project and its regulatory issues, ethical dilemmas, logistical concerns, and organizational challenges Test the microwave system throughout every stage of development and deployment Handle maintenance, troubleshooting, and upgrades
In Constraint Processing, Rina Dechter, synthesizes these contributions, along with her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. Throughout, she focuses on fundamental tools and principles, emphasizing the representation and analysis of algorithms.Examines the basic practical aspects of each topic and then tackles more advanced issues, including current research challengesBuilds the reader's understanding with definitions, examples, theory, algorithms and complexity analysisSynthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics
Key features include:
Thorough treatment of the MSP430’s architecture and functionality along with detailed application-specific guidance Programming and the use of sensor technology to build an embedded system A learn-by-doing experience
With this book you will learn:
The basic theory for electronics design
- Analog circuits
- Digital logic
- Computer arithmetic
- Microcontroller programming
How to design and build a working robotAssembly language and C programming How to develop your own high-performance embedded systems application using an on-going robotics applicationTeaches how to develop your own high-performance embedded systems application using an on-going robotics applicationThorough treatment of the MSP430’s architecture and functionality along with detailed application-specific guidanceFocuses on electronics, programming and the use of sensor technology to build an embedded systemCovers assembly language and C programming
Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today.By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.
Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text.
The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
Beginning with digital logic gates and progressing to the design of combinational and sequential circuits, the book uses these fundamental building blocks as the basis for the design of an actual MIPS processor. It provides practical examples of how to interface with peripherals using RS232, SPI, motor control, interrupts, wireless, and analog-to-digital conversion. SystemVerilog and VHDL are integrated throughout the text in examples illustrating the methods and techniques for CAD-based circuit design. There are also additional exercises and new examples of parallel and advanced architectures, practical I/O applications, embedded systems, and heterogeneous computing, plus a new appendix on C programming to strengthen the connection between programming and processor architecture.
This new edition will appeal to professional computer engineers and to students taking a course that combines digital logic and computer architecture.
Updated based on instructor feedback with more exercises and new examples of parallel and advanced architectures, practical I/O applications, embedded systems, and heterogeneous computingPresents digital system design examples in both VHDL and SystemVerilog (updated for the second edition from Verilog), shown side-by-side to compare and contrast their strengthsIncludes a new chapter on C programming to provide necessary prerequisites and strengthen the connection between programming and processor architectureCompanion Web site includes links to Xilinx CAD tools for FPGA design, lecture slides, laboratory projects, and solutions to exercises.
Instructors can also register at textbooks.elsevier.com for access to:Solutions to all exercises (PDF)Lab materials with solutionsHDL for textbook examples and exercise solutions Lecture slides (PPT)Sample exams\Sample course syllabusFigures from the text (JPG, PPT)
Chapters (958 topics): - Introduction, Electronics, Basic Electronics, DC Current Flow, Resistor Value Test, Simple DC Circuits, Types of Switching, Variable Voltages, Ohm's Law, DC Voltage, DC Current, Series/Parallel Resistors, AC Measurements, AC Voltage and Current, AC Theory, RCL Series Circuits, RCL Parallel Circuits, Capacitance, Capacitors, Inductance, Inductors, Impedance, Radio and Communication, Tuned Circuits, Attenuators, Passive Filters, Active Filters, Oscillators, Circuit Theorems, Complex Numbers, DC Power, AC Power, Silicon Controlled Rectifier, Power Supplies, Voltage Regulation, Magnetism, Electric Machines, Transformers, Three Phase Systems, Energy Transfer and Cost, Atomic Structures, Diode Theory, Diode Applications, Transistor Theory, Bipolar Transistor, Transistor Configurations, Active Transistor Circuits, Field Effect Transistors, Basic Operational Amplifier, Op-Amp Theory, Op-Amp Applications, Sum and Difference Amp, Analogue Multi-meter, Measurement, Component Testing, PIC Micro, PICa(R) Microcontroller, PICa(R) Architecture, PICa(R) Analogue to Digital, PICa(R) Byte Orientated Instructions, PICa(R) Bit Orientated Instructions, PICa(R) Literal and Control Instructions, Mechanics, Area, Surface Area and Symmetry, Volume, Compound Measures, Geometry, Motion, Machines, Optics, Computing, Hardware Devices, Data Structures, Data Files, Computer Systems, Data Handling, System Development, Computer Programming, Data Analysis, Binary Numbers, Binary Arithmetic, Digital, Logic Gates 1., Logic Gates 2., Logic Families, Flip Flops, Combinational Logic, Counters, Counting, Shift Registers, 555 Timer, Logic Interfacing, Boolean and DeMorgan's, Microprocessor, Micro-Computer, Data/Address Bus, Memory Addressing, Arithmetic and Logic Unit, Clock and Reset, Instructions and Control, Memory Cells, Microprocessor Memory, Addressing Modes, Instructions Set 1., Instructions Set 2., Instructions Set 3., Mathematics, Number Systems, Number Conversion, Number Types, Compound Measures, Roots, Angles and Parallels, Triangle Ratios, Triangle Angles, Percentages, Ratios, Fractions, Vectors, Circle Angles, Laws, Algebra 0., Algebra 1., Algebra 2., Mathematical Rules, Powers and Indices, Simplifying, Equations, Graphing, Slope and Translation, Curves and Angle Conversion, Personal Finance, Additional Notes.
Chapters in Part A explain the significant influence of automation on our life, on individuals, organizations, and society, in economic terms and context, and impacts of precision, accuracy and reliability with automatic and automated equipment and operations. The theoretical and scientific knowledge about the human role in automation is covered in Part B from the human-oriented and human-centered aspects of automation to be applied and operated by humans, to the human role as supervisor and intelligent controller of automation systems and platforms. This part concludes with analysis and discussion on the limits of automation to the best of our current understanding. Covering automation design from theory to building automation machines, systems, and systems-of-systems , Part C explains the fundamental elements of mechatronics, sensors, robots, and other components useful for automation, and how they are combined with control and automation software, including models and techniques for automation software engineering, and the automation of the design process itself. Chapters in Part D cover the basic design requirements for the automation and illustrate examples of how the challenging issues can be solved for the deign and integration of automation with respect to its main purpose: Continuous and discrete processes and industries, design techniques, criteria and algorithms for flow lines, and integrated automation. Concluding this part is the design for safety of automation, and of automation for safety. The main aspects of automation management are covered by the chapters in Part E: Cost effectiveness and economic reasons for the design, feasibility analysis, implementation, rationalization, use, and maintenance of particular automation; performance and functionality measures and criteria. Related also are the issues of how to manage automatically and control maintenance, replacement, and upgrading. Part F, industrial automation, begins with explanation of machine tool automation, including various types of numerical control (NC), flexible, and precision machinery for production, manufacturing, and assembly, digital and virtual industrial production, to detailed design, guidelines and application of automation in the principal industries, from aerospace and automotive to semi-conductor, mining, food, paper and wood industries. Chapters are also devoted to the design, control and operation of functions common to all industrial automation. Infrastructures and service automation are covered in Part G and it is explained how automation is designed, selected, integrated, justified and applied, its challenges and emerging trends in those areas and in the construction of structures, roads and bridges; of smart buildings, smart roads and intelligent vehicles; cleaning of surfaces, tunnels and sewers; land, air, and space transportation; information, knowledge, learning, training, and library services; and in sports and entertainment. Automation in medical and healthcare systems is covered in Part H and shows the exponential penetration and main contributions of automation to the health and medical well being of individuals and societies. First, the scientific and theoretical foundations of control and automation in biological and biomedical systems and mechanisms are explained, then specific areas are described and analyzed. Available, proven, and emerging automation techniques in healthcare delivery and elimination of hospital and other medical errors are also addressed. Finally, Part I, Home, Office, and Enterprise Automation is about functional automation areas at home, in the office, and in general enterprises, including multi-enterprise networks. Chapters also cover the automation theories, techniques and practice, design, operation, challenges and emerging trends in education and learning, banking, commerce. An important dimension of the material compiled for this part is that it is useful for all other functional areas of automation. The concluding part of this Springer Handbook contains figures and tables with statistical information and summaries about automation applications and impacts in four main areas: industrial automation, service automation, healthcare automation, and financial and e-commerce automation. A rich list of associations and of periodical publications around the world that focus on automation in its variety of related fields is also included for the benefit of readers worldwide.
Throughout the 94 chapters, divided into ten main parts, with 124 tables, 1005 figures, the 168 co-authors present proven knowledge, original analysis, best practices and authoritative expertise.
Plenty of case studies, creative examples and unique illustrations, covering topics of automation from the basics and fundamentals to advanced techniques, cases and theories will serve the readers and benefit the students and researchers, engineers and managers, inventors, investors and developers.
With 25 years of experience in designing computing equipment, the author stresses the practical design of state machines. He clearly delineates each step of the structured and rigorous design principles that can be applied to practical applications. The book begins by reviewing the analysis of combinatorial logic and Boolean algebra, and goes on to define sequential machines and discuss traditional and alternative methods for synthesizing synchronous sequential machines. The final chapters deal with asynchronous sequential machines and pulse-mode asynchronous sequential machines. Because this volume is technology-independent, these techniques can be used in a variety of fields, such as electrical and computer engineering as well as nanotechnology.
By presenting each method in detail, expounding on several corresponding examples, and providing over 500 useful figures, Sequential Logic is an excellent tutorial on analysis and synthesis procedures.
See Additional Notes for instructions to download the highly interactive PC software for your school. Used in thousands of schools and colleges worldwide the software is designed to work as a traditional textbook on your PC screen.
Comprising hundreds of menu selected colourful topics where the graphic images (from your eBook) are brought to life for every value change along with many additional learning software features.
Full colour printed is available for student handouts (using your values and selections) or images and text pasted to make student assignments.
Various additional software editors are included to enable your own calculations to be explored and evaluated from simple algebraic equations to complex formulae.
A combined eBook and educational software package at a tiny fraction of the previously published price.
Table of Contents (350 software topics) Introduction, Hardware Devices, Data Structures, Data Files, Computer Systems, Data Handling, System Development, Computer Programming, Binary Numbers, Binary Arithmetic, Logic Gates 1., Logic Gates 2., Logic Families, Flip Flops, Combinational Logic, Counters, Counting, Shift Registers, Logic Interfacing, Boolean and DeMorgan's, Micro-Computer, Data/Address Bus, Memory Addressing, Arithmetic and Logic Unit, Clock and Reset, Instructions and Control, Memory Cells, Microprocessor Memory, Addressing Modes, Instructions Set 1., Instructions Set 2., Instructions Set 3., Additional Notes.