- Table View
- List View
Introduction to Biological Networks
by Alpan Raval Animesh RayThe new research area of genomics-inspired network biology lacks an introductory book that enables both physical/computational scientists and biologists to obtain a general yet sufficiently rigorous perspective of current thinking. Filling this gap, Introduction to Biological Networks provides a thorough introduction to genomics-inspired network bi
Introduction to Biometrics (Texts in Computer Science)
by Anil K. Jain Arun A. Ross Karthik Nandakumar Thomas SwearingenThis textbook introduces the fundamental concepts and techniques used in biometric recognition to students, practitioners, and non-experts in the field. Specifically, the book describes key methodologies used for sensing, feature extraction, and matching of commonly used biometric modalities such as fingerprint, face, iris, and voice. In addition, it presents techniques for fusion of biometric information to meet stringent accuracy requirements, also discussing various security issues and associated remedies involved in the deployment of biometric systems. Furthermore, this second edition captures the progress made in the field of biometric recognition, with highlights including: Lucid explanation of core biometric concepts (e.g., individuality and persistence), which builds a strong foundation for more in-depth study and research on biometrics A new chapter on deep neural networks that provides a primer to recent advancements in machine learning and computer vision Illustrative examples of how deep neural network models have contributed to the rapid evolution of biometrics in areas such as robust feature representation and synthetic biometric data generation A new chapter on speaker recognition, which introduces the readers to person recognition based on the human voice characteristics Presentation of emerging security threats such as deepfakes and adversarial attacks and sophisticated countermeasures such as presentation attack detection and template security While this textbook has been designed for senior undergraduate students and first-year graduate students studying a course on biometrics, it is also a useful reference guide for biometric system designers, developers, and integrators.
Introduction to Blender 3.0: Learn Organic and Architectural Modeling, Lighting, Materials, Painting, Rendering, and Compositing with Blender
by Gianpiero MoioliMaster the basics of 3D modeling for art, architecture, and design by exploring Blender 3.0. This book explains modeling, materials, lighting, painting, and more with Blender and other external tools.You will configure a 3D architectural environment and set up the workflow of an art and design project within Blender. You will use Blender's main tools—mesh modeling and sculpting—to create virtual objects and environments. And, you will explore building materials and light scenes, followed by drawing and virtual painting. Chapters cover rendering scenes and transforming them into 2D images or videos. You will learn to use Blender 3.0 for video editing as a compositor and video sequence editor (VSE or sequencer) with a wide range of effects available through the nodal system.On completing this book, you will have the knowledge to create art, design, and architecture with this 3D modeler.What You Will LearnCreate objects and architectural buildings with different techniques of 3D modelingMaster creating an environment for your objects and how to light themDetermine how to create node materials and assign them to your Blender objectsPick up UV unwrapping and texture paintingGet closer to painting and drawing in BlenderRender your scenes and create stunning videosWho This Book Is ForArtists, designers, architects, and animation artists who want to learn Blender by tackling the challenges of building high-end computer graphics, art, design, and architecture. Ideal for readers with little-to-no experience with Blender as it starts with the basics and covers techniques to produce objects, materials, environments.
Introduction to Blockchain Technology (River Publishers Series in Rapids in Computing and Information Science and Technology)
by Ahmed BanafaThis book explores the fundamentals and applications of Blockchain technology. Readers will learn about the decentralized peer-to-peer network, distributed ledger, and the trust model that defines Blockchain technology. They will also be introduced to the basic components of Blockchain (transaction, block, block header, and the chain), its operations (hashing, verification, validation, and consensus model), underlying algorithms, and essentials of trust (hard fork and soft fork). Private and public Blockchain networks similar to Bitcoin and Ethereum will be introduced, as will concepts of Smart Contracts, Proof of Work and Proof of Stack. Blockchain is an emerging technology that can radically improve transaction security at banking, supply chain, and other transaction networks. It’s estimated that Blockchain will generate $3.1 trillion in new business value by 2030. Essentially, it provides the basis for a dynamic distributed ledger that can be applied to save time when recording transactions between parties, remove costs associated with intermediaries, and reduce risks of fraud and tampering.
Introduction to Blockchain and Ethereum: Use distributed ledgers to validate digital transactions in a decentralized and trustless manner
by Fatima Castiglione MaldonadoBuild distributed applications that resolve data ownership issues when working with transactions between multiple partiesKey FeaturesExplore a perfect balance between theories and hands-on activitiesDiscover popular Blockchain use cases such as BitcoinCreate your first smart contract in Solidity for EthereumBook DescriptionBlockchain applications provide a single-shared ledger to eliminate trust issues involving multiple stakeholders. With the help of Introduction to Blockchain and Ethereum, you'll learn how to create distributed Blockchain applications which do not depend on a central server or datacenter. The course begins by explaining Bitcoin, Altcoins, and Ethereum, followed by taking you through distributed programming using the Solidity language on the Ethereum Blockchain. By the end of this course, you'll be able to write, compile, and deploy your own smart contracts to the Ethereum Blockchain.What you will learnGrasp Blockchain concepts such as private and public keys, addresses, wallets, and hashesSend and analyze transactions in the Ethereum Rinkeby test networkCompile and deploy your own ERC20-compliant smart contracts and tokensTest your smart contracts using MyEtherWalletCreate a distributed web interface for your contractCombine Solidity and JavaScript to create your very own decentralized applicationWho this book is forIntroduction to Blockchain and Ethereum is ideal for you if you want to get to grips with Blockchain technology and develop your own distributed applications with smart contracts written in Solidity. Prior exposure to an object-oriented programming language such as JavaScript is needed, as you'll cover the basics before getting straight to work.
Introduction to Broadband Communication Systems
by Cajetan M. Akujuobi Matthew N.O. SadikuBroadband networks, such as asynchronous transfer mode (ATM), frame relay, and leased lines, allow us to easily access multimedia services (data, voice, and video) in one package. Exploring why broadband networks are important in modern-day telecommunications, Introduction to Broadband Communication Systems covers the concepts and components of bot
Introduction to C++
by George S. TselikisThis book is primarily for students who are taking a course on the C++ language, for those who wish to self-study the C++ language, and for programmers who have experience with C and want to advance to C++. It could also prove useful to instructors of the C++ course who are looking for explanatory programming examples to add in their lectures. The focus of this book is to provide a solid introduction to the C++ language and programming knowledge through a large number of practical examples and meaningful advice. It includes more than 500 exercises and examples of progressive difficulty to aid the reader in understanding the C++ principles and to see how concepts can materialize in code. The examples are designed to be short, concrete, and substantial, quickly giving the reader the ability to understand how to apply correctly and efficiently the features of the C++ language and to get a solid programming know-how. Rest assured that if you are able to understand this book’s examples and solve the exercises, you can safely go on to edit larger programs, you will be able to develop your own applications, and you will have certainly established a solid fundamental conceptual and practical background to expand your knowledge and skills.
Introduction to Certificateless Cryptography
by Athanasios V. Vasilakos Hu Xiong Zhen QinAs an intermediate model between conventional PKC and ID-PKC, CL-PKC can avoid the heavy overhead of certificate management in traditional PKC as well as the key escrow problem in ID-PKC altogether. Since the introduction of CL-PKC, many concrete constructions, security models, and applications have been proposed during the last decade. Differing from the other books on the market, this one provides rigorous treatment of CL-PKC.Definitions, precise assumptions, and rigorous proofs of security are provided in a manner that makes them easy to understand.
Introduction to Classifier Performance Analysis with R (Chapman & Hall/CRC Data Science Series)
by Sutaip L.C. SawClassification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and machine learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An important aspect involved in this task is classifier performance analysis (CPA). Introduction to Classifier Performance Analysis with R provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Coverage draws on the extensive literature available on the subject, including descriptive and inferential approaches to CPA. Exercises are included at the end of each chapter to reinforce learning.Key Features: An introduction to binary and multiclass classification problems is provided, including some classifiers based on statistical, machine and ensemble learning. Commonly used techniques for binary and multiclass CPA are covered, some from less well-known but useful points of view. Coverage also includes important topics that have not received much attention in textbook accounts of CPA. Limitations of some commonly used performance measures are highlighted. Coverage includes performance parameters and inferential techniques for them. Also covered are techniques for comparative analysis of competing classifiers. A key contribution involves the use of key R meta-packages like tidyverse and tidymodels for CPA, particularly the very useful yardstick package. This is a useful resource for upper level undergraduate and masters level students in data science, machine learning and related disciplines. Practitioners interested in learning how to use R to evaluate classifier performance can also potentially benefit from the book. The material and references in the book can also serve the needs of researchers in CPA.
Introduction to Coding Theory (Discrete Mathematics and Its Applications #5)
by Jurgen BierbrauerAlthough its roots lie in information theory, the applications of coding theory now extend to statistics, cryptography, and many areas of pure mathematics, as well as pervading large parts of theoretical computer science, from universal hashing to numerical integration.Introduction to Coding Theory introduces the theory of error-correcting codes in a thorough but gentle presentation. Part I begins with basic concepts, then builds from binary linear codes and Reed-Solomon codes to universal hashing, asymptotic results, and 3-dimensional codes. Part II emphasizes cyclic codes, applications, and the geometric desciption of codes. The author takes a unique, more natural approach to cyclic codes that is not couched in ring theory but by virtue of its simplicity, leads to far-reaching generalizations. Throughout the book, his discussions are packed with applications that include, but reach well beyond, data transmission, with each one introduced as soon as the codes are developed.Although designed as an undergraduate text with myriad exercises, lists of key topics, and chapter summaries, Introduction to Coding Theory explores enough advanced topics to hold equal value as a graduate text and professional reference. Mastering the contents of this book brings a complete understanding of the theory of cyclic codes, including their various applications and the Euclidean algorithm decoding of BCH-codes, and carries readers to the level of the most recent research.
Introduction to Coding Theory (Discrete Mathematics and Its Applications)
by Jurgen BierbrauerThis book is designed to be usable as a textbook for an undergraduate course or for an advanced graduate course in coding theory as well as a reference for researchers in discrete mathematics, engineering and theoretical computer science. This second edition has three parts: an elementary introduction to coding, theory and applications of codes, and algebraic curves. The latter part presents a brief introduction to the theory of algebraic curves and its most important applications to coding theory.
Introduction to Cognitive Radio Networks and Applications
by GEETAM TOMAR, ASHISH BAGWARI AND JYOTSHANA KANTICognitive radio is 5-G technology, comes under IEEE 802.22 WRAN (Wireless Regional Area Network) standards. It is currently experiencing rapid growth due to its potential to solve many of the problems affecting present-day wireless systems. The foremost objective of "Introduction to Cognitive Radio Networks and Applications" is to educate wireless communication generalists about cognitive radio communication networks. Written by international leading experts in the field, this book caters to the needs of researchers in the field who require a basis in the principles and the challenges of cognitive radio networks.
Introduction to Combinatorial Designs (Discrete Mathematics and Its Applications)
by W.D. WallisCombinatorial theory is one of the fastest growing areas of modern mathematics. Focusing on a major part of this subject, Introduction to Combinatorial Designs, Second Edition provides a solid foundation in the classical areas of design theory as well as in more contemporary designs based on applications in a variety of fields.After an o
Introduction to Combinatorial Testing (Chapman & Hall/CRC Innovations in Software Engineering and Software Development Series)
by Yu Lei D. Richard Kuhn Raghu N. KackerCombinatorial testing of software analyzes interactions among variables using a very small number of tests. This advanced approach has demonstrated success in providing strong, low-cost testing in real-world situations. Introduction to Combinatorial Testing presents a complete self-contained tutorial on advanced combinatorial testing methods for re
Introduction to Communications Technologies: A Guide for Non-Engineers, Third Edition
by Stephan Jones Ronald J. Kovac Frank M. GroomThanks to the advancement of faster processors within communication devices, there has been a rapid change in how information is modulated, multiplexed, managed, and moved. While formulas and functions are critical in creating the granular components and operations of individual technologies, understanding the applications and their purposes in the
Introduction to Compiler Construction in a Java World
by Bill Campbell Swami Iyer Bahar Akbal-DelibasImmersing students in Java and the JVM, this text enables a deep understanding of the Java programming language and its implementation. It focuses on design, organization, and testing, helping students learn good software engineering skills and become better programmers. By working with and extending a real, functional compiler, students develop a hands-on appreciation of how compilers work, how to write compilers, and how the Java language behaves. Fully documented Java code for the compiler is accessible on a supplementary website.
Introduction to Compiler Design
by Torben Ægidius MogensenThis textbook is intended for an introductory course on Compiler Design, suitable for use in an undergraduate programme in computer science or related fields. Introduction to Compiler Design presents techniques for making realistic, though non-optimizing compilers for simple programming languages using methods that are close to those used in "real" compilers, albeit slightly simplified in places for presentation purposes. All phases required for translating a high-level language to machine language is covered, including lexing, parsing, intermediate-code generation, machine-code generation and register allocation. Interpretation is covered briefly. Aiming to be neutral with respect to implementation languages, algorithms are presented in pseudo-code rather than in any specific programming language, and suggestions for implementation in several different language flavors are in many cases given. The techniques are illustrated with examples and exercises. The author has taught Compiler Design at the University of Copenhagen for over a decade, and the book is based on material used in the undergraduate Compiler Design course there. Additional material for use with this book, including solutions to selected exercises, is available at http://www.diku.dk/~torbenm/ICD
Introduction to Compiler Design
by Torben Ægidius MogensenThis textbook is intended for an introductory course on Compiler Design, suitable for use in an undergraduate programme in computer science or related fields. Introduction to Compiler Design presents techniques for making realistic, though non-optimizing compilers for simple programming languages using methods that are close to those used in "real" compilers, albeit slightly simplified in places for presentation purposes. All phases required for translating a high-level language to machine language is covered, including lexing, parsing, intermediate-code generation, machine-code generation and register allocation. Interpretation is covered briefly. Aiming to be neutral with respect to implementation languages, algorithms are presented in pseudo-code rather than in any specific programming language, and suggestions for implementation in several different language flavors are in many cases given. The techniques are illustrated with examples and exercises. The author has taught Compiler Design at the University of Copenhagen for over a decade, and the book is based on material used in the undergraduate Compiler Design course there. Additional material for use with this book, including solutions to selected exercises, is available at http://www. diku. dk/~torbenm/ICD
Introduction to Compiler Design (Undergraduate Topics in Computer Science)
by Torben Ægidius MogensenThe third edition of this textbook has been fully revised and adds material about the SSA form, polymorphism, garbage collection, and pattern matching. It presents techniques for making realistic compilers for simple to intermediate-complexity programming languages. The techniques presented in the book are close to those used in professional compilers, albeit in places slightly simplified for presentation purposes. "Further reading" sections point to material about the full versions of the techniques.All phases required for translating a high-level language to symbolic machine language are covered, and some techniques for optimising code are presented. Type checking and interpretation are also included.Aiming to be neutral with respect to implementation languages, algorithms are mostly presented in pseudo code rather than in any specific language, but suggestions are in many places given for how these can be realised in different language paradigms.Depending on how much of the material from the book is used, it is suitable for both undergraduate and graduate courses for introducing compiler design and implementation.
Introduction to Computation and Programming Using Python
by John V. GuttagThis book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MITs OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (or MOOC) offered by the pioneering MIT--Harvard collaboration edX. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. "Introduction to Computation and Programming Using Python" can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.
Introduction to Computation and Programming Using Python
by John V. GuttagThis book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in a massive open online course (or MOOC) offered by the pioneering MIT-Harvard collaboration edX.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.
Introduction to Computation and Programming Using Python, revised and expanded edition: With Application To Understanding Data
by John V. GuttagAn introductory text that teaches students the art of computational problem solving, covering topics that range from simple algorithms to information visualization.This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of “data science” for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in a massive open online course (or MOOC) offered by the pioneering MIT-Harvard collaboration edX.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.
Introduction to Computation and Programming Using Python, second edition: With Application to Understanding Data (The\mit Press Ser.)
by John V. GuttagThe new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization.This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.
Introduction to Computation and Programming Using Python, third edition: With Application to Computational Modeling and Understanding Data
by John V. GuttagThe new edition of an introduction to the art of computational problem solving using Python.This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including numpy, matplotlib, random, pandas, and sklearn. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data as well as substantial material on machine learning. All of the code in the book and an errata sheet are available on the book&’s web page on the MIT Press website.
Introduction to Computation and Programming Using Python: With Application to Understanding Data
by John V. GuttagThis book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.