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Fundamentals of Linux: Explore the essentials of the Linux command line
by Oliver PelzDevelop a solid understanding of the important command-line tools and utilities in LinuxKey FeaturesDelve into the fundamentals of LinuxExplore and work with virtualization, command lines, and Bash shell scriptsUse special file permission flags such as setuid and setgidBook DescriptionLinux is a Unix-like operating system assembled under the model of free and open source software development and distribution. Fundamentals of Linux will help you learn all the essentials of the Linux command line required to get you started. The book will start by teaching you how to work with virtualization software and install CentOS 7 Linux as a VM. Then, you will get to grips with the workings of various command line operations, such as cursor movement, commands, options, and arguments. As you make your way through the chapters, the book will not only focus on the most essential Linux commands but also give an introduction to Bash shell scripting. Finally, you will explore advanced topics, such as networking and troubleshooting your system, and you will get familiar with the advanced file permissions: ACL, setuid, and setgid. Fundamentals of Linux includes real-world tasks, use cases, and problems that, as a system administrator, you might encounter in your day-to-day activities.What you will learnExplore basic and advanced command-line conceptsInstall Linux, work with VirtualBox, and install CentOS 7 in VirtualBox Work with the command line efficiently and learn how to navigate through the Linux filesystemCreate file and user group permissions and edit filesUse Sticky bit to secure your Linux filesystemDefine and remove ACL from Linux filesWho this book is forFundamentals of Linux is for individuals looking to work as a Linux system administrator.
Fundamentals of Machine Learning and Deep Learning in Medicine
by Reza Borhani Soheila Borhani Aggelos K. KatsaggelosThis book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid pace. Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader’s learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge. This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional prerequisites.
Fundamentals of Machine Learning for Predictive Data Analytics
by John D. Kelleher Aoife D'Arcy Brian Mac NameeMachine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The\mit Press Ser.)
by John D. Kelleher Brian Mac Namee Aoife D'ArcyA comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies
by John D. Kelleher Brian Mac Namee Aoife D'ArcyThe second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
Fundamentals of Machine Theory and Mechanisms
by Antonio Simón Mata Alex Bataller Torras Juan Antonio Cabrera Carrillo Francisco Ezquerro Juanco Antonio Jesús Guerra Fernández Fernando Nadal Martínez Antonio Ortiz FernándezThis book develops the basic content for an introductory course in Mechanism and Machine Theory. The text is clear and simple, supported by more than 350 figures. More than 60 solved exercises have been included to mark the translation of this book from Spanish into English. Topics treated include: dynamic analysis of machines; introduction to vibratory behavi∨ rotor and piston balanced; critical speed for shafts; gears and train gears; synthesis for planar mechanisms; and kinematic and dynamic analysis for robots. The chapters in relation to kinematics and dynamics for planar mechanisms can be studied with the help of WinMecc software, which allows the reader to study in an easy and intuitive way, but exhaustive at the same time. This computer program analyzes planar mechanisms of one-degree of freedom and whatever number of links. The program allows users to build a complex mechanism. They can modify any input data in real time changing values in a numeric way or using the computer mouse to manipulate links and vectors while mechanism is moving and showing the results. This powerful tool does not only show the results in a numeric way by means of tables and diagrams but also in a visual way with scalable vectors and curves.
Fundamentals of Magnonics (Lecture Notes in Physics #969)
by Sergio M. RezendeFundamentals of Magnonics is a textbook for beginning graduate students in the areas of magnetism and spintronics. The level of presentation assumes only basic knowledge of the origin of magnetism and electromagnetism, and quantum mechanics. The book utilizes elementary mathematical derivations, aimed mainly at explaining the physical concepts involved in the phenomena studied and enabling a deeper understanding of the experiments presented. Key topics include the basic phenomena of ferromagnetic resonance in bulk materials and thin films, semi-classical theory of spin waves, quantum theory of spin waves and magnons, magnons in antiferromagnets, parametric excitation of magnons, nonlinear and chaotic phenomena, Bose-Einstein condensation of magnons, and magnon spintronics. Featuring end-of-chapter problem sets accompanied by extensive contemporary and historical references, this book provides the essential tools for any graduate or advanced undergraduate-level course of studies on the emerging field of magnonics.
Fundamentals of Matrix-Analytic Methods
by Qi-Ming HeFundamentals of Matrix-Analytic Methods targets advanced-level students in mathematics, engineering and computer science. It focuses on the fundamental parts of Matrix-Analytic Methods, Phase-Type Distributions, Markovian arrival processes and Structured Markov chains and matrix geometric solutions. New materials and techniques are presented for the first time in research and engineering design. This book emphasizes stochastic modeling by offering probabilistic interpretation and constructive proofs for Matrix-Analytic Methods. Such an approach is especially useful for engineering analysis and design. Exercises and examples are provided throughout the book.
Fundamentals of Modern Electric Circuit Analysis and Filter Synthesis: A Transfer Function Approach
by Afshin IzadianThis textbook explains the fundamentals of electric circuits and uses the transfer function as a tool to analyze circuits, systems, and filters. The author avoids the Fourier transform and three phase circuits, since these topics are often not taught in circuits courses. General transfer functions for low pass, high pass, band pass and band reject filters are demonstrated, with first order and higher order filters explained in plain language. The author’s presentation is designed to be accessible to a broad audience, with the concepts of circuit analysis explained in basic language, reinforced by numerous, solved examples.
Fundamentals of Modern Electric Circuit Analysis and Filter Synthesis: A Transfer Function Approach
by Afshin IzadianThis textbook explains the fundamentals of electric circuits and uses the transfer function as a tool to analyze circuits, systems, and filters. The author avoids the Fourier transform, since this topic is often not taught in circuits courses. General transfer functions for low pass, high pass, band pass and band reject filters are demonstrated, with first order and higher order filters explained in plain language. The author’s presentation is designed to be accessible to a broad audience, with the concepts of circuit analysis explained in basic language, reinforced by numerous, solved examples.
The Fundamentals of Modern Statistical Genetics
by Nan M. Laird Christoph LangeThis book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendel's first experiments to genome-wide association studies, the book describes how genetic information can be incorporated into statistical models to discover disease genes. All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation, linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis. Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders. The intended audience is statisticians, biostatisticians, epidemiologists and quantitatively- oriented geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or to pursue research in methodology. A background in intermediate level statistical methods is required. The authors include few mathematical derivations, and the exercises provide problems for students with a broad range of skill levels. No background in genetics is assumed.
Fundamentals of Multimedia
by Ze-Nian Li Mark S. Drew Jiangchuan LiuThis textbook introduces the "Fundamentals of Multimedia", addressing real issues commonly faced in the workplace. The essential concepts are explained in a practical way to enable students to apply their existing skills to address problems in multimedia. Fully revised and updated, this new edition now includes coverage of such topics as 3D TV, social networks, high-efficiency video compression and conferencing, wireless and mobile networks, and their attendant technologies. Features: presents an overview of the key concepts in multimedia, including color science; reviews lossless and lossy compression methods for image, video and audio data; examines the demands placed by multimedia communications on wired and wireless networks; discusses the impact of social media and cloud computing on information sharing and on multimedia content search and retrieval; includes study exercises at the end of each chapter; provides supplementary resources for both students and instructors at an associated website.
Fundamentals of Multimedia (Texts in Computer Science)
by Ze-Nian Li Mark S. Drew Jiangchuan LiuPREVIOUS EDITIONThis textbook introduces the “Fundamentals of Multimedia”, addressing real issues commonly faced in the workplace. The essential concepts are explained in a practical way to enable students to apply their existing skills to address problems in multimedia. Fully revised and updated, this new edition now includes coverage of such topics as 3D TV, social networks, high-efficiency video compression and conferencing, wireless and mobile networks, and their attendant technologies. Features: presents an overview of the key concepts in multimedia, including color science; reviews lossless and lossy compression methods for image, video and audio data; examines the demands placed by multimedia communications on wired and wireless networks; discusses the impact of social media and cloud computing on information sharing and on multimedia content search and retrieval; includes study exercises at the end of each chapter; provides supplementary resources for both students and instructors at an associated website.
Fundamentals of Network Forensics: A Research Perspective (Computer Communications and Networks)
by R.C. Joshi Emmanuel S. PilliThis timely text/reference presents a detailed introduction to the essential aspects of computer network forensics. The book considers not only how to uncover information hidden in email messages, web pages and web servers, but also what this reveals about the functioning of the Internet and its core protocols. This, in turn, enables the identification of shortcomings and highlights where improvements can be made for a more secure network. Topics and features: provides learning objectives in every chapter, and review questions throughout the book to test understanding; introduces the basic concepts of network process models, network forensics frameworks and network forensics tools; discusses various techniques for the acquisition of packets in a network forensics system, network forensics analysis, and attribution in network forensics; examines a range of advanced topics, including botnet, smartphone, and cloud forensics; reviews a number of freely available tools for performing forensic activities.
Fundamentals of Optical Computing Technology: Forward The Next Generation Supercomputer
by Junbo Yang Mengjun Zhu Zhengzheng Shao Xiujian LiThis book presents the principles, experimental technologies, up-to-date research findings and applications of various optical-computing technologies and devices. It also discusses semiconductor multiple quantum well (MQW) photoelectronic devices, vertical-cavity surface-emitting lasers (VCSELs), lasers, micro optical elements and diffractive optical elements, optical storage, optical parallel interconnections, and optical-buffer technology as the main technologies for optical computing. Furthermore, it explores the potential of optical-computing technology. It offers those involved in optical design, photonics, and photoelectronic research and related industries insights into the fundamentals and theories of optical computing, enabling them and to extend and develop the functions of fundamental elements to meet the requirement of optical-computing systems.
Fundamentals of Optical Networks and Components
by Partha Pratim SahuThis book is intended as an undergraduate/postgraduate level textbook for courses on high-speed optical networks as well as computer networks. Nine chapters cover the basic principles of the technology and different devices for optical networks, as well as processing of integrated waveguide devices of optical networks using different technologies. It provides students, researchers and practicing engineers with an expert guide to the fundamental concepts, issues and state-of-the-art developments in optical networks. It includes examples throughout all the chapters of the book to aid understanding of basic problems and solutions. Presents basics of the optical network devices and discusses latest developments Includes examples and exercises throughout all the chapters of the book to aid understanding of basic problems and solutions for undergraduate and postgraduate students Discusses different optical network node architectures and their components Includes basic theories and latest developments of hardware devices with their fabrication technologies (such as optical switch, wavelength router, wavelength division multiplexer/demultiplexer and add/drop multiplexer), helpful for researchers to initiate research on this field and to develop research problem-solving capability Reviews fiber-optic networks without WDM and single-hop and multi-hop WDM optical networks P. P. Sahu received his M.Tech. degree from the Indian Institute of Technology Delhi and his Ph.D. degree in engineering from Jadavpur University, India. In 1991, he joined Haryana State Electronics Development Corporation Limited, where he has been engaged in R&D works related to optical fiber components and telecommunication instruments. In 1996, he joined Northeastern Regional Institute of Science and Technology as a faculty member. At present, he is working as a professor in the Department of Electronics and Communication Engineering, Tezpur Central University, India. His field of interest is integrated optic and electronic circuits, wireless and optical communication, clinical instrumentation, green energy, etc. He has received an INSA teacher award (instituted by the highest academic body Indian National Science Academy) for high level of teaching and research. He has published more than 90 papers in peer-reviewed international journals, 60 papers in international conference, and has written five books published by Springer Nature, McGraw-Hill. Dr Sahu is a Fellow of the Optical Society of India, Life Member of Indian Society for Technical Education and Senior Member of the IEEE.
Fundamentals of Parallel Multicore Architecture: Multichip And Multicore Systems (Chapman And Hall/crc Computational Science Ser.)
by Yan SolihinAlthough multicore is now a mainstream architecture, there are few textbooks that cover parallel multicore architectures. Filling this gap, Fundamentals of Parallel Multicore Architecture provides all the material for a graduate or senior undergraduate course that focuses on the architecture of multicore processors. The book is also useful as a ref
Fundamentals of Parameterized Complexity
by Rodney G. Downey Michael R. FellowsThis comprehensive and self-contained textbook presents an accessible overview of the state of the art of multivariate algorithmics and complexity. Increasingly, multivariate algorithmics is having significant practical impact in many application domains, with even more developments on the horizon. The text describes how the multivariate framework allows an extended dialog with a problem, enabling the reader who masters the complexity issues under discussion to use the positive and negative toolkits in their own research. Features: describes many of the standard algorithmic techniques available for establishing parametric tractability; reviews the classical hardness classes; explores the various limitations and relaxations of the methods; showcases the powerful new lower bound techniques; examines various different algorithmic solutions to the same problems, highlighting the insights to be gained from each approach; demonstrates how complexity methods and ideas have evolved over the past 25 years.
Fundamentals of Predictive Text Mining
by Tong Zhang Nitin Indurkhya Sholom M. WeissOne consequence of the pervasive use of computers is that most documents originate in digital form. Widespread use of the Internet makes them readily available. Text mining - the process of analyzing unstructured natural-language text - is concerned with how to extract information from these documents. Developed from the authors' highly successful Springer reference on text mining, Fundamentals of Predictive Text Mining is an introductory textbook and guide to this rapidly evolving field. Integrating topics spanning the varied disciplines of data mining, machine learning, databases, and computational linguistics, this uniquely useful book also provides practical advice for text mining. In-depth discussions are presented on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Background on data mining is beneficial, but not essential. Where advanced concepts are discussed that require mathematical maturity for a proper understanding, intuitive explanations are also provided for less advanced readers. Topics and features: presents a comprehensive, practical and easy-to-read introduction to text mining; includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter; explores the application and utility of each method, as well as the optimum techniques for specific scenarios; provides several descriptive case studies that take readers from problem description to systems deployment in the real world; includes access to industrial-strength text-mining software that runs on any computer; describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English); contains links to free downloadable software and other supplementary instruction material. Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students. Dr. Sholom M. Weiss is a Research Staff Member with the IBM Predictive Modeling group, in Yorktown Heights, New York, and Professor Emeritus of Computer Science at Rutgers University. Dr. Nitin Indurkhya is Professor at the School of Computer Science and Engineering, University of New South Wales, Australia, as well as founder and president of data-mining consulting company Data-Miner Pty Ltd. Dr. Tong Zhang is Associate Professor at the Department of Statistics and Biostatistics at Rutgers University, New Jersey.
Fundamentals of Quantum Computing: Theory and Practice
by Venkateswaran KasirajanThis introductory book on quantum computing includes an emphasis on the development of algorithms. Appropriate for both university students as well as software developers interested in programming a quantum computer, this practical approach to modern quantum computing takes the reader through the required background and up to the latest developments. Beginning with introductory chapters on the required math and quantum mechanics, Fundamentals of Quantum Computing proceeds to describe four leading qubit modalities and explains the core principles of quantum computing in detail. Providing a step-by-step derivation of math and source code, some of the well-known quantum algorithms are explained in simple ways so the reader can try them either on IBM Q or Microsoft QDK. The book also includes a chapter on adiabatic quantum computing and modern concepts such as topological quantum computing and surface codes.Features:o Foundational chapters that build the necessary background on math and quantum mechanics.o Examples and illustrations throughout provide a practical approach to quantum programming with end-of-chapter exercises.o Detailed treatment on four leading qubit modalities -- trapped-ion, superconducting transmons, topological qubits, and quantum dots -- teaches how qubits work so that readers can understand how quantum computers work under the hood and devise efficient algorithms and error correction codes. Also introduces protected qubits - 0-π qubits, fluxon parity protected qubits, and charge-parity protected qubits. o Principles of quantum computing, such as quantum superposition principle, quantum entanglement, quantum teleportation, no-cloning theorem, quantum parallelism, and quantum interference are explained in detail. A dedicated chapter on quantum algorithm explores both oracle-based, and Quantum Fourier Transform-based algorithms in detail with step-by-step math and working code that runs on IBM QisKit and Microsoft QDK. Topics on EPR Paradox, Quantum Key Distribution protocols, Density Matrix formalism, and Stabilizer formalism are intriguing. While focusing on the universal gate model of quantum computing, this book also introduces adiabatic quantum computing and quantum annealing.This book includes a section on fault-tolerant quantum computing to make the discussions complete. The topics on Quantum Error Correction, Surface codes such as Toric code and Planar code, and protected qubits help explain how fault tolerance can be built at the system level.
Fundamentals of Quantum Programming in IBM's Quantum Computers (Studies in Big Data #81)
by Weng-Long Chang Athanasios V. VasilakosThis textbook introduces major topics that include quantum bits, superposition, entanglement, logic gates, quantum search algorithm, quantum Fourier transform, inverse quantum Fourier transform, Shor’s order-finding algorithm and phase estimation. Everyone can write algorithms and programs in the cloud making using IBM’s quantum computers that support IBM Q Experience which contains the composer, open quantum assembly language, simulators and real quantum devices. Furthermore, this book teaches you how to use open quantum assembly language to write quantum programs for dealing with complex problems. Through numerous examples and exercises, readers will learn how to write a quantum program with open quantum assembly language for solving any problem from start to complete. This book includes six main chapters: ·Quantum Bits and Quantum Gates—learn what quantum bits are, how to declare and measure them, what quantum gates are and how they work on a simulator or a real device in the cloud. ·Boolean Algebra and its Applications—learn how to decompose CCNOT gate into six CNOT gates and nine gates of one bit and how to use NOT gates, CNOT gates and CCNOT gates to implement logic operations including NOT, OR, AND, NOR, NAND, Exclusive-OR (XOR) and Exclusive-NOR (XNOR). ·Quantum Search Algorithm and its Applications—learn core concepts of quantum search algorithm and how to write quantum programs to implement core concepts of quantum search algorithm for solving two famous NP-complete problems that are the satisfiability problem in n Boolean variables and m clauses and the clique problem in a graph with n vertices and q edges. ·Quantum Fourier Transform and its Applications—learn core concepts of quantum Fourier transform and inverse quantum Fourier transform and how to write quantum programs to implement them for solving two real applications that are to compute the period and the frequency of two given oracular functions. ·Order-Finding and Factoring—learn core concepts of Shor’s order-finding algorithm and how to write quantum programs to implement Shor’s order-finding algorithm for completing the prime factorization to 15. Phase Estimation and its Applications—learn core concepts of phase estimation and quantum counting and how to write quantum programs to implement them to compute the number of solution(s) in the independent set problem in a graph with two vertices and one edge.
Fundamentals of Reinforcement Learning
by Rafael Ris-AlaArtificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization.This book provides an introduction to AI, specifies machine learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner. It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges.Understanding the Fundamentals of Reinforcement Learning will allow you to:Understand essential AI conceptsGain professional experienceInterpret sequential decision problems and solve them with reinforcement learningLearn how the Q-Learning algorithm worksPractice with commented Python codeFind advantageous directions
Fundamentals of Reliability Engineering: Applications in Multistage Interconnection Networks
by Indra GunawanThis book presents fundamentals of reliability engineering with its applications in evaluating reliability of multistage interconnection networks. In the first part of the book, it introduces the concept of reliability engineering, elements of probability theory, probability distributions, availability and data analysis. The second part of the book provides an overview of parallel/distributed computing, network design considerations, and more. The book covers a comprehensive reliability engineering methods and its practical aspects in the interconnection network systems. Students, engineers, researchers, managers will find this book as a valuable reference source.
Fundamentals of Scientific Computing
by Bertil GustafssonThe book of nature is written in the language of mathematics -- Galileo Galilei How is it possible to predict weather patterns for tomorrow, with access solely to today's weather data? And how is it possible to predict the aerodynamic behavior of an aircraft that has yet to be built? The answer is computer simulations based on mathematical models - sets of equations - that describe the underlying physical properties. However, these equations are usually much too complicated to solve, either by the smartest mathematician or the largest supercomputer. This problem is overcome by constructing an approximation: a numerical model with a simpler structure can be translated into a program that tells the computer how to carry out the simulation. This book conveys the fundamentals of mathematical models, numerical methods and algorithms. Opening with a tutorial on mathematical models and analysis, it proceeds to introduce the most important classes of numerical methods, with finite element, finite difference and spectral methods as central tools. The concluding section describes applications in physics and engineering, including wave propagation, heat conduction and fluid dynamics. Also covered are the principles of computers and programming, including MATLAB®.
Fundamentals of Sketch-Based Passwords
by Benjamin S. Riggan Wesley E. Snyder Cliff WangThis SpringerBrief explores graphical password systems and examines novel drawing-based methods in terms of security, usability, and human computer-interactions. It provides a systematic approach for recognizing, comparing, and matching sketch-based passwords in the context of modern computing systems. The book offers both a security and usability analysis of the accumulative framework used for incorporating handwriting biometrics and a human computer-interaction performance analysis. The chapters offer new perspectives and experimental results regarding model uniqueness, recognition tolerance, and the human-computer interaction. The results demonstrate that biometrics reduce the equal error rate (EER) by more than 10%, and show that people are capable of accurately reproducing a sketch-based password. Fundamentals of Sketch-based Passwords: A General Framework targets computer scientists and engineers focused on computer security, biometrics, and human factors. Advanced-level students in computer science and electrical engineering will find this material useful as a study guide for their classes.