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Reviews the new High Efficiency Video Coding (HEVC) standard and advancements in adaptive streaming technologies for use in broadband networks and the Internet This book describes next-generation video coding and streaming technologies with a comparative assessment of the strengths and weaknesses. Specific emphasis is placed on the H.265/HEVC video coding standard and adaptive bit rate video streaming. In addition to evaluating the impact of different types of video content and powerful feature sets on HEVC coding efficiency, the text provides an in-depth study on the practical performance of popular adaptive streaming platforms and useful tips for streaming optimization. Readers will learn of new over-the-top (OTT) online TV advancements, the direction of the broadband telecommunications industry, and the latest developments that will help keep implementation costs down and maximize return on infrastructure investment. Reviews the emerging High Efficiency Video Coding (HEVC) standard and compares its coding performance with the MPEG-4 Advanced Video Coding (AVC) and MPEG-2 standards Provides invaluable insights into the intra and inter coding efficiencies of HEVC, such as the impact of hierarchical block partitioning and new prediction modes Evaluates the performance of the Apple and Microsoft adaptive streaming platforms and presents innovative techniques related to aggregate stream bandwidth prediction, duplicate chunk Includes end-of-chapter homework problems and access to instructor slides Next-Generation Video Coding and Streaming is written for students, researchers, and industry professionals working in the field of video communications. Benny Bing has worked in academia for over 20 years. He has published over 80 research papers and 12 books, and has 6 video patents licensed to industry. He has served as a technical editor for several IEEE journals and an IEEE Communications Society Distinguished lecturer. He also received the National Association of Broadcasters (NAB) Technology Innovation Award for demonstrations of advanced media technologies.

Music is much more than listening to audio encoded in some unreadable binary format. It is, instead, an adventure similar to reading a book and entering its world, complete with a story, plot, sound, images, texts, and plenty of related data with, for instance, historical, scientific, literary, and musicological contents. Navigation of this world, such as that of an opera, a jazz suite and jam session, a symphony, a piece from non-Western culture, is possible thanks to the specifications of new standard IEEE 1599, IEEE Recommended Practice for Defining a Commonly Acceptable Musical Application Using XML, which uses symbols in language XML and music layers to express all its multimedia characteristics. Because of its encompassing features, this standard allows the use of existing audio and video standards, as well as recuperation of material in some old format, the events of which are managed by a single XML file, which is human and machine readable – musical symbols have been read by humans for at least forty centuries. Anyone wanting to realize a computer application using IEEE 1599 – music and computer science departments, computer generated music research laboratories (e.g. CCRMA at Stanford, CNMAT at Berkeley, and IRCAM in Paris), music library conservationists, music industry frontrunners (Apple, TDK, Yamaha, Sony), etc. – will need this first book-length explanation of the new standard as a reference. The book will include a manual teaching how to encode music with IEEE 1599 as an appendix, plus a CD-R with a video demonstrating the applications described in the text and actual sample applications that the user can load onto his or her PC and experiment with.

The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations in areas such as virtual reality, video games, animation films, and sport simulations

Programming multi-core and many-core computing systems Sabri Pllana, Linnaeus University, Sweden Fatos Xhafa, Technical University of Catalonia, Spain Provides state-of-the-art methods for programming multi-core and many-core systems The book comprises a selection of twenty two chapters covering: fundamental techniques and algorithms; programming approaches; methodologies and frameworks; scheduling and management; testing and evaluation methodologies; and case studies for programming multi-core and many-core systems. Program development for multi-core processors, especially for heterogeneous multi-core processors, is significantly more complex than for single-core processors. However, programmers have been traditionally trained for the development of sequential programs, and only a small percentage of them have experience with parallel programming. In the past, only a relatively small group of programmers interested in High Performance Computing (HPC) was concerned with the parallel programming issues, but the situation has changed dramatically with the appearance of multi-core processors on commonly used computing systems. It is expected that with the pervasiveness of multi-core processors, parallel programming will become mainstream. The pervasiveness of multi-core processors affects a large spectrum of systems, from embedded and general-purpose, to high-end computing systems. This book assists programmers in mastering the efficient programming of multi-core systems, which is of paramount importance for the software-intensive industry towards a more effective product-development cycle. Key features: Lessons, challenges, and roadmaps ahead. Contains real world examples and case studies. Helps programmers in mastering the efficient programming of multi-core and many-core systems. The book serves as a reference for a larger audience of practitioners, young researchers and graduate level students. A basic level of programming knowledge is required to use this book.

Acoustics deals with the production, control, transmission, reception, and effects of sound. Owing to acoustics being an interdisciplinary field, this book is intended to be equally accessible to readers from a range of backgrounds including electrical engineering, physics and mechanical engineering. This book introduces the fundamentals of acoustic wave motion. It addresses in a clear and systematic way some of the most difficult parts of acoustics for beginners, such as the widely different approximations due to the wide frequency range, the apparently arbitrary choice between the use of analytical solutions to the wave equation with boundary conditions, and the fundamentally different energy-based considerations used in noise control. As a result, it provides readers with a self-contained source of information on acoustics which can be used for self-study or as a graduate course text. Key features: Places an emphasis on detailed derivations based on the fundamental laws of physics and interpretations of the resulting formulas. Avoids, where possible, electrical and mechanical equivalent circuits, so as to make it accessible to readers with different backgrounds. Introduces duct acoustics, sound in enclosures, and sound radiation and scattering. Contains a set of appendices which includes material on signal analysis and processing as these tools are essential for the modern acoustician.

For both students and engineers in R&D, this book explains machine vision in a concise, hands-on way, using the Vision Development Module of the LabView software by National Instruments. Following a short introduction to the basics of machine vision and the technical procedures of image acquisition, the book goes on to guide readers in the use of the various software functions of LabView's machine vision module. It covers typical machine vision tasks, including particle analysis, edge detection, pattern and shape matching, dimension measurements as well as optical character recognition, enabling readers to quickly and efficiently use these functions for their own machine vision applications. A discussion of the concepts involved in programming the Vision Development Module rounds off the book, while example problems and exercises are included for training purposes as well as to further explain the concept of machine vision. With its step-by-step guide and clear structure, this is an essential reference for beginners and experienced researchers alike.

Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets

Embedded systems are everywhere in contemporary life and are supposed to make our lives more comfortable. In industry, embedded systems are used to manage and control complex systems (e.g. nuclear power plants, telecommunications and flight control) and they are also taking an important place in our daily activities (e.g. smartphones, security alarms and traffic lights). In the design of embedded systems, memory allocation and data assignment are among the main challenges that electronic designers have to face. In fact, they impact heavily on the main cost metrics (power consumption, performance and area) in electronic devices. Thus designers of embedded systems have to pay careful attention in order to minimize memory requirements, thus improving memory throughput and limiting the power consumption by the system’s memory. Electronic designers attempt to minimize memory requirements with the aim of lowering the overall system costs. A state of the art of optimization techniques for memory management and data assignment is presented in this book.

Widely used across industrial and manufacturing automation, Programmable Logic Controllers (PLCs) perform a broad range of electromechanical tasks with multiple input and output arrangements, designed specifically to cope in severe environmental conditions such as automotive and chemical plants. Programmable Logic Controllers: A Practical Approach using CoDeSys is a hands-on guide to rapidly gain proficiency in the development and operation of PLCs based on the IEC 61131-3 standard. Using the freely-available* software tool CoDeSys, which is widely used in industrial design automation projects, the author takes a highly practical approach to PLC design using real-world examples. The design tool, CoDeSys, also features a built in simulator/soft PLC enabling the reader to undertake exercises and test the examples. Key features: Introduces to programming techniques using IEC 61131-3 guidelines in the five PLC-recognised programming languages. Focuses on a methodical approach to programming, based on Boolean algebra, flowcharts, sequence diagrams and state-diagrams. Contains a useful methodology to solve problems, develop a structured code and document the programming code. Covers I/O like typical sensors, signals, signal formats, noise and cabling. Features Power Point slides covering all topics, example programs and solutions to end-of-chapter exercises via companion website. No prior knowledge of programming PLCs is assumed making this text ideally suited to electronics engineering students pursuing a career in electronic design automation. Experienced PLC users in all fields of manufacturing will discover new possibilities and gain useful tips for more efficient and structured programming. * Register at www.codesys.com www.wiley.com/go/hanssen/logiccontrollers

A comprehensive new edition on mobile computing—covering both mobile and sensor data The new paradigm of pervasive computing was born from the needs of highly mobile workers to access and transfer data while on the go. Significant advances in the technology have lent and will continue to lend prevalence to its use—especially in m-commerce. Covering both mobile data and sensor data, this comprehensive text offers updated research on sensor technology, data stream processing, mobile database security, and contextual processing. Packed with cases studies, exercises, and examples, Fundamentals of Pervasive Information Management Systems covers essential aspects of wireless communication and provides a thorough discussion about managing information on mobile database systems (MDS). It addresses the integration of web and workflow with mobile computing and looks at the current state of research. Fundamentals of Pervasive Information Management Systems presents chapters on: Mobile Database System Mobile and Wireless Communication Location and Handoff Management Fundamentals of Database Processing Introduction to Concurrency Control Mechanisms Effect of Mobility on Data Processing Transaction Management in Mobile Database Systems Mobile Database Recovery Wireless Information Dissemination Introduction to Sensor Technology Sensor Technology and Data Streams Management Sensor Network Deployment: Case Studies Fundamentals of Pervasive Information Management Systems is an ideal book for researchers, teachers, and graduate students of mobile computing. The book may also be used as a reference text for researchers or managers.

Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixtures adapted to different types of data. The algorithms used are described and related works with different classical methods are presented and commented upon. This chapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted to the latent block model proposed in the mixture approach context. The authors discuss this model in detail and present its interest regarding co-clustering. Various algorithms are presented in a general context. Chapter 3 focuses on binary and categorical data. It presents, in detail, the appropriated latent block mixture models. Variants of these models and algorithms are presented and illustrated using examples. Chapter 4 focuses on contingency data. Mutual information, phi-squared and model-based co-clustering are studied. Models, algorithms and connections among different approaches are described and illustrated. Chapter 5 presents the case of continuous data. In the same way, the different approaches used in the previous chapters are extended to this situation. Contents 1. Cluster Analysis. 2. Model-Based Co-Clustering. 3. Co-Clustering of Binary and Categorical Data. 4. Co-Clustering of Contingency Tables. 5. Co-Clustering of Continuous Data. About the Authors Gérard Govaert is Professor at the University of Technology of Compiègne, France. He is also a member of the CNRS Laboratory Heudiasyc (Heuristic and diagnostic of complex systems). His research interests include latent structure modeling, model selection, model-based cluster analysis, block clustering and statistical pattern recognition. He is one of the authors of the MIXMOD (MIXtureMODelling) software. Mohamed Nadif is Professor at the University of Paris-Descartes, France, where he is a member of LIPADE (Paris Descartes computer science laboratory) in the Mathematics and Computer Science department. His research interests include machine learning, data mining, model-based cluster analysis, co-clustering, factorization and data analysis. Cluster Analysis is an important tool in a variety of scientific areas. Chapter 1 briefly presents a state of the art of already well-established as well more recent methods. The hierarchical, partitioning and fuzzy approaches will be discussed amongst others. The authors review the difficulty of these classical methods in tackling the high dimensionality, sparsity and scalability. Chapter 2 discusses the interests of coclustering, presenting different approaches and defining a co-cluster. The authors focus on co-clustering as a simultaneous clustering and discuss the cases of binary, continuous and co-occurrence data. The criteria and algorithms are described and illustrated on simulated and real data. Chapter 3 considers co-clustering as a model-based co-clustering. A latent block model is defined for different kinds of data. The estimation of parameters and co-clustering is tackled under two approaches: maximum likelihood and classification maximum likelihood. Hard and soft algorithms are described and applied on simulated and real data. Chapter 4 considers co-clustering as a matrix approximation. The trifactorization approach is considered and algorithms based on update rules are described. Links with numerical and probabi

An overview on the challenging new topic of phase-aware signal processing Speech communication technology is a key factor in human-machine interaction, digital hearing aids, mobile telephony, and automatic speech/speaker recognition. With the proliferation of these applications, there is a growing requirement for advanced methodologies that can push the limits of the conventional solutions relying on processing the signal magnitude spectrum. Single-Channel Phase-Aware Signal Processing in Speech Communication provides a comprehensive guide to phase signal processing and reviews the history of phase importance in the literature, basic problems in phase processing, fundamentals of phase estimation together with several applications to demonstrate the usefulness of phase processing. Key features: Analysis of recent advances demonstrating the positive impact of phase-based processing in pushing the limits of conventional methods. Offers unique coverage of the historical context, fundamentals of phase processing and provides several examples in speech communication. Provides a detailed review of many references and discusses the existing signal processing techniques required to deal with phase information in different applications involved with speech. The book supplies various examples and MATLAB® implementations delivered within the PhaseLab toolbox. Single-Channel Phase-Aware Signal Processing in Speech Communication is a valuable single-source for students, non-expert DSP engineers, academics and graduate students.

This book covers both the fundamentals, as well as practical techniques in designing and building microcontrollers in industrial and commercial applications. Examples included in this book have been compiled, built, and tested Includes Both ARM® assembly and C codes Direct Register Access (DRA) model and the Software Driver (SD) model programming techniques and discussed If you are an instructor and adopted this book for your course, please email ieeeproposals@wiley.com to get access to the instructor files for this book.

How Geographic Redundancy Can Improve Service Availability and Reliability of Computer-Based Systems Enterprises make significant investments in geographically redundant systems to mitigate the very unlikely risk of a natural or man-made disaster rendering their primary site inaccessible or destroying it completely. While geographic redundancy has obvious benefits for disaster recovery, it is far less obvious what benefit georedundancy offers for more common hardware, software, and human failures. Beyond Redundancy provides both a theoretical and practical treatment of the feasible and likely benefits from geographic redundancy for both service availability and service reliability. The book is organized into three sections: Basics provides the necessary background on georedundancy and service availability Modeling and Analysis of Redundancy gives the technical and mathematical details of service availability modeling of georedundant configurations Recommendations offers specific recommendations on architecture, requirements, design, testing, and analysis of georedundant configurations A complete georedundant case study is included to illustrate the recommendations. The book considers both georedundant systems and georedundant solutions. The text also provides a general discussion about the capital expense/operating expense tradeoff that frames system redundancy and georedundancy. These added features make Beyond Redundancy an invaluable resource for network/system planners, IS/IT personnel, system architects, system engineers, developers, testers, and disaster recovery/business continuity consultants and planners.

A concise, engineering-oriented resource that provides practical support to IT professionals and those responsible for the quality of the software or systems they develop Software quality stems from two distinctive, but associated, topics in software engineering: software functional quality and software structural quality. This book studies the tenets of both of these notions, which focus on the efficiency and value of a design, respectively. It addresses engineering quality on both the application and system levels with attention to information systems (IS) and embedded systems (ES) as well as recent developments. Software Quality Engineering introduces the basic concepts of quality engineering like the nature of the engineering process, quality models and measurements, and evaluation quality, and provides a step-by-step overview of the application of software quality engineering in commonly recognized phases of the software development process. It also discusses management of software quality engineering processes, with special attention to budget, planning, conflict resolution, and traceability of quality requirements. Targeted at graduate engineering students and software quality specialists, Software Quality Engineering: Provides an analysis of interdependence between software functionality and its quality Includes a list of software quality engineering “to-dos” and models of software quality requirements traceability Covers the practical use of related ISO/IEC JTCI/SC7 standards

A comprehensive, practical book on software management that dispels real-world issues through relevant case studies Software managers inevitably will meet obstacles while trying to deliver quality products and provide value to customers, often with tight time restrictions. The result: Software War Stories. This book provides readers with practical advice on how to handle the many issues that can arise as a software project unfolds. It utilizes case studies that focus on what can be done to establish and meet reasonable expectations as they occur in government, industrial, and academic settings. The book also offers important discussions on both traditional and agile methods as well as lean development concepts. Software War Stories: Covers the basics of management as applied to situations ranging from agile projects to large IT projects with infrastructure problems Includes coverage of topics ranging from planning, estimating, and organizing to risk and opportunity management Uses twelve case studies to communicate lessons learned by the author in practice Offers end-of-chapter exercises, sample solutions, and a blog for providing updates and answers to readers' questions Software War Stories: Case Studies in Software Management mentors practitioners, software engineers, students and more, providing relevant situational examples encountered when managing software projects and organizations.

Big Data is a new field, with many technological challenges to be understood in order to use it to its full potential. These challenges arise at all stages of working with Big Data, beginning with data generation and acquisition. The storage and management phase presents two critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires complex analysis. Here the authors propose using metaheuristics as a solution to these challenges; they are first able to deal with large size problems and secondly flexible and therefore easily adaptable to different types of data and different contexts. The use of metaheuristics to overcome some of these data mining challenges is introduced and justified in the first part of the book, alongside a specific protocol for the performance evaluation of algorithms. An introduction to metaheuristics follows. The second part of the book details a number of data mining tasks, including clustering, association rules, supervised classification and feature selection, before explaining how metaheuristics can be used to deal with them. This book is designed to be self-contained, so that readers can understand all of the concepts discussed within it, and to provide an overview of recent applications of metaheuristics to knowledge discovery problems in the context of Big Data.

This book provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data. Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency. The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares approach. This book is a popularized reference for students looking to work with spatialized data, but who do not have the advanced statistical theoretical basics.

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

The only book to offer special coverage of the fundamentals of multicore DSP for implementation on the TMS320C66xx SoC This unique book provides readers with an understanding of the TMS320C66xx SoC as well as its constraints. It offers critical analysis of each element, which not only broadens their knowledge of the subject, but aids them in gaining a better understanding of how these elements work so well together. Written by Texas Instruments’ First DSP Educator Award winner, Naim Dahnoun, the book teaches readers how to use the development tools, take advantage of the maximum performance and functionality of this processor and have an understanding of the rich content which spans from architecture, development tools and programming models, such as OpenCL and OpenMP, to debugging tools. It also covers various multicore audio and image applications in detail. Additionally, this one-of-a-kind book is supplemented with: A rich set of tested laboratory exercises and solutions Audio and Image processing applications source code for the Code Composer Studio (integrated development environment from Texas Instruments) Multiple tables and illustrations With no other book on the market offering any coverage at all on the subject and its rich content with twenty chapters, Multicore DSP: From Algorithms to Real-time Implementation on the TMS320C66x SoC is a rare and much-needed source of information for undergraduates and postgraduates in the field that allows them to make real-time applications work in a relatively short period of time. It is also incredibly beneficial to hardware and software engineers involved in programming real-time embedded systems.