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Introduction to Syntax
This comprehensive introduction to syntax explains the basic concepts of syntax, and how the structures which are in place for describing the world can also be applied to a description of language structure. Edith Moravcsik presents a detailed introduction to syntactic description, including linear order, selection, categories, meaning, sound form, variation and change. The final selection provides a summary which looks at how we can explain syntax. The book includes student-friendly features, such as chapter summaries, suggestions for further reading, exercises, and a glossary of terms.
R 1,764.00
Introduction to Pragmatics
Introduction to Pragmatics guides students through traditional and new approaches in the field, focusing particularly on phenomena at the elusive semantics/pragmatics boundary to explore the role of context in linguistic communication. Offers students an accessible introduction and an up-to-date survey of the field, encompassing both established and new approaches to pragmaticsAddresses the traditional range of topics – such as implicature, reference, presupposition, and speech acts – as well as newer areas of research, including neo-Gricean theories, RelevanceTheory, information structure, inference, and dynamic approaches to meaningExplores the relationship and boundaries between semantics and pragmaticsIdeal for students coming to pragmatics for the first time
R 1,588.00
Introduction to Deep Learning Using R
Understand deep learning, the nuances of its different models, and where these models can be applied.The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools.What You'll LearnUnderstand the intuition and mathematics that power deep learning modelsUtilize various algorithms using the R programming language and its packagesUse best practices for experimental design and variable selectionPractice the methodology to approach and effectively solve problems as a data scientistEvaluate the effectiveness of algorithmic solutions and enhance their predictive powerWho This Book Is ForStudents, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
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Short Introduction to Intuitionistic Logic
Intuitionistic logic is presented here as part of familiar classical logic which allows mechanical extraction of programs from proofs. to make the material more accessible, basic techniques are presented first for propositional logic; Part II contains extensions to predicate logic. This material provides an introduction and a safe background for reading research literature in logic and computer science as well as advanced monographs. Readers are assumed to be familiar with basic notions of first order logic. One device for making this book short was inventing new proofs of several theorems. The presentation is based on natural deduction. The topics include programming interpretation of intuitionistic logic by simply typed lambda-calculus (Curry-Howard isomorphism), negative translation of classical into intuitionistic logic, normalization of natural deductions, applications to category theory, Kripke models, algebraic and topological semantics, proof-search methods, interpolation theorem. The text developed from materal for several courses taught at Stanford University in 1992-1999.
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R 773.00
Introduction to Reconfigurable Supercomputing
This book covers technologies, applications, tools, languages, procedures, advantages, and disadvantages of reconfigurable supercomputing using Field Programmable Gate Arrays (FPGAs). The target audience is the community of users of High Performance Computers (HPC) who may benefit from porting their applications into a reconfigurable environment. As such, this book is intended to guide the HPC user through the many algorithmic considerations, hardware alternatives, usability issues, programming languages, and design tools that need to be understood before embarking on the creation of reconfigurable parallel codes. We hope to show that FPGA acceleration, based on the exploitation of the data parallelism, pipelining and concurrency remains promising in view of the diminishing improvements in traditional processor and system design.Table of Contents: FPGA Technology / Reconfigurable Supercomputing / Algorithmic Considerations / FPGA Programming Languages / Case Study: Sorting / Alternative Technologies and Concluding Remarks
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Introduction to Software Design
This textbook provides a balanced coverage on software design, including design principles, methodologies, and techniques in contexts of designing modules of small and larger sizes with a multi-paradigm viewpoint emphasizing design trade-off. Commonly used design diagrams and patterns are also covered in terms of the design ideas behind and their effective use. The book also explores what (software) design thinking is, the role it plays in a design process, and ways to promote it. Despite object orientation being still the center of attention, there is a strong promotion throughout the book that software design should consider all appropriate design paradigms and methodologies.The book is organized in 10 chapters. Chapter 1 is dedicated to an exploration of what software design may be and entail. Next, chapters 2 and 3 are designed to help readers better understand object orientation, and the essentials of object-oriented design. Chapter 4 is then dedicated to the design of methods, while chapters 5, 6, and 8 respectively include topics about the design of objects, modeling with the Unified Modeling Language, and the use of design patterns. Larger software elements are often directly responsible for the formation of software architecture, thus chapter 7 covers four kinds of larger software elements: libraries, components, frameworks, and microservices, and their architectural implications. Chapter 9 finally brings the presentation of software design to an end with a coverage on software architecture focusing on software architectural views. Case studies are important in learning how to design software, hence chapter 10 then gathers some small design case studies that can be used in earlier chapters as appropriate. These case studies can be extended in different ways to provide additional design opportunities.This textbook book is intended for a junior level undergraduate course on software design, yet the depth of the book makes it also appropriate for a design course at beginning graduate level. Professionals may also find the book useful in their professional development.
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Introduction to Embedded Systems
This textbook serves as an introduction to the subject of embedded systems design, using microcontrollers as core components. It develops concepts from the ground up, covering the development of embedded systems technology, architectural and organizational aspects of controllers and systems, processor models, and peripheral devices. Since microprocessor-based embedded systems tightly blend hardware and software components in a single application, the book also introduces the subjects of data representation formats, data operations, and programming styles. The practical component of the book is tailored around the architecture of a widely used Texas Instrument’s microcontroller, the MSP430 and a companion web site offers for download an experimenter’s kit and lab manual, along with Powerpoint slides and solutions for instructors.
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R 228.00
Computational Physics - A Practical Introduction to Computational Physics and Scientific Computing (Using C++), Vol. II
This book is an introduction to the computational methods used in physics and other related scientific fields. It is addressed to an audience that has already been exposed to the introductory level of college physics, usually taught during the first two years of an undergraduate program in science and engineering. It assumes no prior knowledge of numerical analysis, programming or computers and teaches whatever is necessary for the solution of the problems addressed in the text. C++ is used for programming the core programs and data analysis is performed using the powerful tools of the GNU/Linux environment. All the necessary software is open source and freely available. The book starts with very simple problems in particle motion and ends with an in-depth discussion of advanced techniques used in Monte Carlo simulations in statistical mechanics. The level of instruction rises slowly, while discussing problems like the diffusion equation, electrostatics on the plane, quantum mechanics and random walks.
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Introduction to Cyberdeception
This book is an introduction to both offensive and defensive techniques of cyberdeception. Unlike most books on cyberdeception, this book focuses on methods rather than detection. It treats cyberdeception techniques that are current, novel, and practical, and that go well beyond traditional honeypots. It contains features friendly for classroom use: (1) minimal use of programming details and mathematics, (2) modular chapters that can be covered in many orders, (3) exercises with each chapter, and (4) an extensive reference list.Cyberattacks have grown serious enough that understanding and using deception is essential to safe operation in cyberspace. The deception techniques covered are impersonation, delays, fakes, camouflage, false excuses, and social engineering. Special attention is devoted to cyberdeception in industrial control systems and within operating systems. This material is supported by a detailed discussion of how to plan deceptions and calculate their detectability and effectiveness. Some of the chapters provide further technical details of specific deception techniques and their application. Cyberdeception can be conducted ethically and efficiently when necessary by following a few basic principles. This book is intended for advanced undergraduate students and graduate students, as well as computer professionals learning on their own. It will be especially useful for anyone who helps run important and essential computer systems such as critical-infrastructure and military systems.
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Introduction to Computing with Fuzzy Sets
This book provides concise yet thorough coverage of the fundamentals and technology of fuzzy sets. Readers will find a lucid and systematic introduction to the essential concepts of fuzzy set-based information granules, their processing and detailed algorithms. Timely topics and recent advances in fuzzy modeling and its principles, neurocomputing, fuzzy set estimation, granulation–degranulation, and fuzzy sets of higher type and order are discussed. In turn, a wealth of examples, case studies, problems and motivating arguments, spread throughout the text and linked with various areas of artificial intelligence, will help readers acquire a solid working knowledge. Given the book’s well-balanced combination of the theory and applied facets of fuzzy sets, it will appeal to a broad readership in both academe and industry. It is also ideally suited as a textbook for graduate and undergraduate students in science, engineering, and operations research.
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Introduction to Modern Mathematical Computing
Thirty years ago mathematical, as opposed to applied numerical, computation was difficult to perform and so relatively little used. Three threads changed that: the emergence of the personal computer; the discovery of fiber-optics and the consequent development of the modern internet; and the building of the Three “M’s” Maple, Mathematica and Matlab. We intend to persuade that Maple and other like tools are worth knowing assuming only that one wishes to be a mathematician, a mathematics educator, a computer scientist, an engineer or scientist, or anyone else who wishes/needs to use mathematics better. We also hope to explain how to become an `experimental mathematician' while learning to be better at proving things. To accomplish this our material is divided into three main chapters followed by a postscript. These cover elementary number theory, calculus of one and several variables, introductory linear algebra, and visualization and interactive geometric computation.
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Introduction to Statistics Through Resampling Methods and Microsoft Office Excel
Learn statistical methods quickly and easily with the discovery method With its emphasis on the discovery method, this publication encourages readers to discover solutions on their own rather than simply copy answers or apply a formula by rote. Readers quickly master and learn to apply statistical methods, such as bootstrap, decision trees, t-test, and permutations to better characterize, report, test, and classify their research findings. In addition to traditional methods, specialized methods are covered, allowing readers to select and apply the most effective method for their research, including: * Tests and estimation procedures for one, two, and multiple samples * Model building * Multivariate analysis * Complex experimental design Throughout the text, Microsoft Office Excel(r) is used to illustrate new concepts and assist readers in completing exercises. An Excel Primer is included as an Appendix for readers who need to learn or brush up on their Excel skills. Written in an informal, highly accessible style, this text is an excellent guide to descriptive statistics, estimation, testing hypotheses, and model building. All the pedagogical tools needed to facilitate quick learning are provided: * More than 100 exercises scattered throughout the text stimulate readers' thinking and actively engage them in applying their newfound skills * Companion FTP site provides access to all data sets discussed in the text * An Instructor's Manual is available upon request from the publisher * Dozens of thought-provoking questions in the final chapter assist readers in applying statistics to solve real-life problems * Helpful appendices include an index to Excel and Excel add-in functions This text serves as an excellent introduction to statistics for students in all disciplines. The accessible style and focus on real-life problem solving are perfectly suited to both students and practitioners.
R 4,232.00
Introduction to Industrial Automation
This 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.
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R 414.50
Introduction to Stochastic Processes Using R
This textbook presents some basic stochastic processes, mainly Markov processes. It begins with a brief introduction to the framework of stochastic processes followed by the thorough discussion on Markov chains, which is the simplest and the most important class of stochastic processes. The book then elaborates the theory of Markov chains in detail including classification of states, the first passage distribution, the concept of periodicity and the limiting behaviour of a Markov chain in terms of associated stationary and long run distributions. The book first illustrates the theory for some typical Markov chains, such as random walk, gambler's ruin problem, Ehrenfest model and Bienayme-Galton-Watson branching process; and then extends the discussion when time parameter is continuous. It presents some important examples of a continuous time Markov chain, which include Poisson process, birth process, death process, birth and death processes and their variations. These processes play a fundamental role in the theory and applications in queuing and inventory models, population growth, epidemiology and engineering systems. The book studies in detail the Poisson process, which is the most frequently applied stochastic process in a variety of fields, with its extension to a renewal process.The book also presents important basic concepts on Brownian motion process, a stochastic process of historic importance. It covers its few extensions and variations, such as Brownian bridge, geometric Brownian motion process, which have applications in finance, stock markets, inventory etc. The book is designed primarily to serve as a textbook for a one semester introductory course in stochastic processes, in a post-graduate program, such as Statistics, Mathematics, Data Science and Finance. It can also be used for relevant courses in other disciplines. Additionally, it provides sufficient background material for studying inference in stochastic processes. The book thus fulfils the need of a concise but clear and student-friendly introduction to various types of stochastic processes.
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Introduction to Aerospace Engineering
Provides a broad and accessible introduction to the field of aerospace engineering, ideal for semester-long courses Aerospace engineering, the field of engineering focused on the development of aircraft and spacecraft, is taught at universities in both dedicated aerospace engineering programs as well as in wider mechanical engineering curriculums around the world-yet accessible introductory textbooks covering all essential areas of the subject are rare. Filling this significant gap in the market, Introduction to Aerospace Engineering: Basic Principles of Flight provides beginning students with a strong foundational knowledge of the key concepts they will further explore as they advance through their studies. Designed to align with the curriculum of a single-semester course, this comprehensive textbook offers a student-friendly presentation that combines the theoretical and practical aspects of aerospace engineering. Clear and concise chapters cover the laws of aerodynamics, pressure, and atmospheric modeling, aircraft configurations, the forces of flight, stability and control, rockets, propulsion, and more. Detailed illustrations, well-defined equations, end-of-chapter summaries, and ample review questions throughout the text ensure students understand the core topics of aerodynamics, propulsion, flight mechanics, and aircraft performance. Drawn from the author’s thirty years’ experience teaching the subject to countless numbers of university students, this much-needed textbook: Explains basic vocabulary and fundamental aerodynamic concepts Describes aircraft configurations, low-speed aerofoils, high-lift devices, and rockets Covers essential topics including thrust, propulsion, performance, maneuvers, and stability and control Introduces each topic in a concise and straightforward manner as students are guided through progressively more advanced material Includes access to companion website containing a solutions manual and lecture slides for instructors Introduction to Aerospace Engineering: Basic Principles of Flight is the perfect "one stop" textbook for instructors, undergraduates, and graduate students in Introduction to Aerospace Engineering or Introduction to Flight courses in Aerospace Engineering or Mechanical Engineering programs.
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First Introduction to Quantum Computing and Information
This textbook addresses and introduces new developments in the field of quantum information and computing (QIC) for a primary audience of undergraduate students.Developments over the past few decades have spurred the need for QIC courseware at major research institutions. To address this, the present 2nd edition of a highly accessible textbook/reference broadens the exposure of QIC science for the undergraduate market. The subject matter is introduced in such a way so that it is accessible to students with only a first-year calculus background. Greater accessibility allows a broader range of academic offerings.Topics and features:Introduces beginning undergraduate students to quantum theory and developments in QIC, without exposure to upper-level physics and mathematicsContains a new chapter on Adiabatic Quantum ComputingIntegrates Mathematica-based software examples and projects, which offers a “hands-on" experience and facilitates navigation of difficult abstract conceptsOffers helpful links to additional exercises, problems, and solution manualsFacilitates a more holistic approach to the teaching of difficult concepts, incorporating symbolic manipulation softwareProvides new material on Quantum Error CorrectionAllows a broad-range of course offerings spanning physics, engineering, math and computer scienceThis unique introductory textbook can serve courses offered in university physics, engineering, math, and definitely computer science departments. Use of Mathematica software allows a fast learning curve for students who have limited experience with scientific programming.
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Introduction to Numerical Analysis
Numerical analysis is an increasingly important link between pure mathematics and its application in science and technology. This textbook provides an introduction to the justification and development of constructive methods that provide sufficiently accurate approximations to the solution of numerical problems, and the analysis of the influence that errors in data, finite-precision calculations, and approximation formulas have on results, problem formulation and the choice of method. It also serves as an introduction to scientific programming in MATLAB, including many simple and difficult, theoretical and computational exercises. A unique feature of this book is the consequent development of interval analysis as a tool for rigorous computation and computer assisted proofs, along with the traditional material.
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Introduction to Databases
Introduced forty years ago, relational databases proved unusually succe- ful and durable. However, relational database systems were not designed for modern applications and computers. As a result, specialized database systems now proliferate trying to capture various pieces of the database market. Database research is pulled into di?erent directions, and speci- ized database conferences are created. Yet the current chaos in databases is likely only temporary because every technology, including databases, becomes standardized over time. The history of databases shows periods of chaos followed by periods of dominant technologies. For example, in the early days of computing, users stored their data in text ?les in any format and organization they wanted. These early days were followed by information retrieval systems, which required some structure for text documents, such as a title, authors, and a publisher. The information retrieval systems were followed by database systems, which added even more structure to the data and made querying easier. In the late 1990s, the emergence of the Internet brought a period of relative chaos and interest in unstructured and “semistructured data” as it wasenvisionedthateverywebpagewouldbelikeapageinabook.However, with the growing maturity of the Internet, the interest in structured data was regained because the most popular websites are, in fact, based on databases. The question is not whether future data stores need structure but what structure they need.
R 4,360.00
Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning
This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated material can serve as inspiration for a variety of projects and hands-on assignments in a classroom setting. I will certainly keep this book as a personal resource for the courses I teach, and strongly recommend it to my students. --Dr. Carlotta Domeniconi, Associate Professor, Computer Science Department, GMUThis book offers a curriculum for introducing interpretability to machine learning at every stage. The authors provide compelling examples that a core teaching practice like leading interpretive discussions can be taught and learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning.--Anusha Dandapani, Chief Data and Analytics Officer, UNICC and Adjunct Faculty, NYUThis is a wonderful book! I’m pleased that the next generation of scientists will finally be able to learn this important topic. This is the first book I’ve seen that has up-to-date and well-rounded coverage. Thank you to the authors!--Dr. Cynthia Rudin, Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, and Biostatistics & Bioinformatics Literature on Explainable AI has up until now been relatively scarce and featured mainly mainstream algorithms like SHAP and LIME. This book has closed this gap by providing an extremely broad review of various algorithms proposed in the scientific circles over the previous 5-10 years. This book is a great guide to anyone who is new to the field of XAI or is already familiar with the field and is willing to expand their knowledge. A comprehensive review of the state-of-the-art Explainable AI methods starting from visualization, interpretable methods, local and global explanations, time series methods, and finishing with deep learning provides an unparalleled source of information currently unavailable anywhere else. Additionally, notebooks with vivid examples are a great supplement that makes the book even more attractive for practitioners of any level.Overall, the authors provide readers with an enormous breadth of coverage without losing sight of practical aspects, which makes this book truly unique and a great addition to the library of any data scientist.Dr. Andrey Sharapov, Product Data Scientist, Explainable AI Expert and Speaker, Founder of Explainable AI-XAI Group
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Introduction to Logic
This is a comprehensive introduction to the fundamentals of logic (both formal logic and critical reasoning), with exceptionally clear yet conversational explanations and a multitude of engaging examples and exercises. Herrick's examples are on-point and fun, often bringing in real-life situations and popular culture. And more so than other logic textbooks, Introduction to Logic brings in the history of philosophy and logic through interesting boxes/sidebars anddiscussions, showing logic's relation to philosophy. The book is especially suited for use in the "Open Course Library," a comprehensive online logic course that is open and free. Jointly funded by the state of Washington and the Bill & Melinda Gates Foundation, the Logic Course is one of 80 hybrid courses available online at no charge to the more than 400,000 students in the Washington system. The course is also available for logic courses throughout the US and the world. The Logic Course is available online now. Herrick was chosen todevelop the Logic Course for the online Open Course Library. Here is a link for information on the Open Library Course Logic Course: The most affordable logic text available, Introduction to Logic is offered in both paperback and eBook formats and is ideal for any introduction to logic course.Ancillaries: An Instructor's Manual features a brief summary of each chapter of the text, answers to all the questions in the text, additional questions and exercises for instructors to use on quizzes and exams, and a PowerPoint presentation that covers the entire book. The Instructor's Manual is available on CD or online at www.oup.com/us/herrick. Also online at www.oup.com/us/herrick, students will find self-quizzes with a limited number of questions taken from the test bank. As part of the Open CourseLibrary, instructors and students can access the entire online logic course, which features 115 video demonstrations that follow the book chapter-by-chapter, over 20 detailed PowerPoint presentations covering the most technical parts of the course, and over 40 online lectures featuring a self-testwith answers for each chapter. Additionally, Herrick's website, www.manyworldsoflogic.com, exists as a general online resource for teachers and students of logic and features extra credit assignments and further study materials.Message: Untangling the complexities of logical theory using clear explanations and many examples drawn right out of everyday life
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Introduction to Web Development
An Introduction to Web Development: A Conceptual Approach introduces students to essential concepts of web development like design, copyright, netiquette, accessibility, and privacy and to essential content types like text, image, animation, sound, and video.Students explore topics such as: Internet cookies and how these may help or harm; avoiding Internet-based threats, like viruses, ransomware, and phishing attacks; ethics of image editing and selection; how web filtering provides different results of searches, newsfeeds, and other recommendations based on user characteristics; and the role of web-based applications and fake news.The second edition features new content focused on recent web-based threats to democratic societies. Events involving ransomware attacks, fake news, Russian dysinformatyea campaigns, United States voter manipulation, social media-based harassment of individuals, as well as the consequences of phishing certain high-profile individuals are discussed. Additionally, concepts central to media literacy are covered to help students counteract the influence of fake news and voter manipulation. Each chapter concludes with a self-quiz that contains topics for further discussion. The second edition contains an added chapter that introduces students to coding and web interactivity by encouraging students to adapt simple examples.By making use of free software in the embedded examples, An Introduction to Web Development allows students to learn important web development tools without incurring excessive expenses. The text effectively blends technical information and practical instruction. The book can be used in courses on introductory web development, digital literacy, and computer literacy with an internet focus.
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Introduction to Software Process Improvement
This textbook is a systematic guide to the steps in setting up a Capability Maturity Model Integration (CMMI) improvement initiative. Readers will learn the project management practices necessary to deliver high-quality software solutions to the customer on time and on budget. The text also highlights how software process improvement can achieve specific business goals to provide a tangible return on investment. Topics and features: supplies review questions, summaries and key topics for each chapter, as well as a glossary of acronyms; describes the CMMI model thoroughly, detailing the five maturity levels; provides a broad overview of software engineering; reviews the activities and teams required to set up a CMMI improvement initiative; examines in detail the implementation of CMMI in a typical organization at each of the maturity levels; investigates the various tools that support organizations in improving their software engineering maturity; discusses the SCAMPI appraisal methodology.
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R 600.80
Introduction To Computational Mathematics (2nd Edition)
This unique book provides a comprehensive introduction to computational mathematics, which forms an essential part of contemporary numerical algorithms, scientific computing and optimization. It uses a theorem-free approach with just the right balance between mathematics and numerical algorithms. This edition covers all major topics in computational mathematics with a wide range of carefully selected numerical algorithms, ranging from the root-finding algorithm, numerical integration, numerical methods of partial differential equations, finite element methods, optimization algorithms, stochastic models, nonlinear curve-fitting to data modelling, bio-inspired algorithms and swarm intelligence. This book is especially suitable for both undergraduates and graduates in computational mathematics, numerical algorithms, scientific computing, mathematical programming, artificial intelligence and engineering optimization. Thus, it can be used as a textbook and/or reference book.
R 2,142.49
Introduction to Object Recognition
Rapid development of computer hardware has enabled usage of automatic object recognition in an increasing number of applications, ranging from industrial image processing to medical applications, as well as tasks triggered by the widespread use of the internet. Each area of application has its specific requirements, and consequently these cannot all be tackled appropriately by a single, general-purpose algorithm.This easy-to-read text/reference provides a comprehensive introduction to the field of object recognition (OR). The book presents an overview of the diverse applications for OR and highlights important algorithm classes, presenting representative example algorithms for each class. The presentation of each algorithm describes the basic algorithm flow in detail, complete with graphical illustrations. Pseudocode implementations are also included for many of the methods, and definitions are supplied for terms which may be unfamiliar to the novice reader. Supporting a clear and intuitive tutorial style, the usage of mathematics is kept to a minimum.Topics and features: presents example algorithms covering global approaches, transformation-search-based methods, geometrical model driven methods, 3D object recognition schemes, flexible contour fitting algorithms, and descriptor-based methods; explores each method in its entirety, rather than focusing on individual steps in isolation, with a detailed description of the flow of each algorithm, including graphical illustrations; explains the important concepts at length in a simple-to-understand style, with a minimum usage of mathematics; discusses a broad spectrum of applications, including some examples from commercial products; contains appendices discussing topics related to OR and widely used in the algorithms, (but not at the core of the methods described in the chapters).Practitioners of industrial image processing will find this simple introduction and overview to OR a valuable reference, as will graduate students in computer vision courses.Marco Treiber is a software developer at Siemens Electronics Assembly Systems, Munich, Germany, where he is Technical Lead in Image Processing for the Vision System of SiPlace placement machines, used in SMT assembly.
R 2,008.65
Introduction to F5 Networks LTM iRules
For network professionals everywhere this feature of LTM(t) is probably the most challenging. This book aims to help those faced with writing iRules and getting the best out of them. Anyone with an interest in iRules, particularly those new to them or with no programming knowledge will find this book invaluable. With over 100 rule examples there's plenty of material included to learn from and get you started. The book approaches iRules from the same standpoint as a network engineer and is for those in the networking field with little or no programming knowledge. This is an introductory beginners reference. By the time you've read the entire book you'll certainly understand a lot more about programming and you'll be able to write and understand iRules and fulfil most requirements demanded of you. The second edition more than doubles the amount of content and adds more detail, further examples, command and event references, glob and regular expression guides and more.
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R 486.00
Introduction to MATLAB for Engineers and Scientists
Familiarize yourself with MATLAB using this concise, practical tutorial that is focused on writing code to learn concepts. Starting from the basics, this book covers array-based computing, plotting and working with files, numerical computation formalism, and the primary concepts of approximations. Introduction to MATLAB is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation.In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you’ll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts. What You'll LearnApply sample code to your engineering or science problemsWork with MATLAB arrays, functions, and loops Use MATLAB’s plotting functions for data visualizationSolve numerical computing and computational engineering problems with a MATLAB case studyWho This Book Is ForEngineers, scientists, researchers, and students who are new to MATLAB. Some prior programming experience would be helpful but not required.
R 866.00
