PIC microcontrollers are used worldwide in commercial and industrial devices. The 8-bit PIC which this book focuses on is a versatile work horse that completes many designs. An engineer working with applications that include a microcontroller will no doubt come across the PIC sooner rather than later. It is a must to have a working knowledge of this 8-bit technology. This book takes the novice from introduction of embedded systems through to advanced development techniques for utilizing and optimizing the PIC family of microcontrollers in your device. To truly understand the PIC, assembly and C programming language must be understood. The author explains both with sample code and examples, and makes the transition from the former to the latter an easy one. This is a solid building block for future PIC endeavors. New to the 2nd Edition: Include end of chapter questions/activities moving from introductory to advanced More worked examples Includes PowerPoint slides for instructors Includes all code snips on a companion web site for ease of use A survey of 16/32-bit PICs A project using ZigBee
Application of Big Data for National Security provides users with state-of-the-art concepts, methods, and technologies for Big Data analytics in the fight against terrorism and crime, including a wide range of case studies and application scenarios. This book combines expertise from an international team of experts in law enforcement, national security, and law, as well as computer sciences, criminology, linguistics, and psychology, creating a unique cross-disciplinary collection of knowledge and insights into this increasingly global issue. The strategic frameworks and critical factors presented in Application of Big Data for National Security consider technical, legal, ethical, and societal impacts, but also practical considerations of Big Data system design and deployment, illustrating how data and security concerns intersect. In identifying current and future technical and operational challenges it supports law enforcement and government agencies in their operational, tactical and strategic decisions when employing Big Data for national security •Contextualizes the Big Data concept and how it relates to national security and crime detection and prevention •Presents strategic approaches for the design, adoption, and deployment of Big Data technologies in preventing terrorism and reducing crime •Includes a series of case studies and scenarios to demonstrate the application of Big Data in a national security context •Indicates future directions for Big Data as an enabler of advanced crime prevention and detection
This book offers a comprehensive and balanced introduction to the design of small embedded systems. Important topics covered include microcontroller architectures, memory technologies, data conversion, serial protocols, program design, low power design, and design for the real time environment. The final chapter ingeniously applies systematic engineering design principles to embedded system design. While the Microchip PIC 16F84 is used extensively to illustrate the early material, examples elsewhere are drawn from a range of microcontroller families, leading to a broad view of device capabilities.
Zhu, Shao Ying; Hill, Richard; Trovati, Marcello (Springer, 2015)
This practical and didactic text/reference discusses the leading edge of secure cloud computing, exploring the essential concepts and principles, tools, techniques and deployment models in this field. Enlightening perspectives are presented by an international collection of pre-eminent authorities in cloud security assurance from both academia and industry.
Adapted from a modular undergraduate course on computational mathematics, Concise Computer Mathematics delivers an easily accessible, self-contained introduction to the basic notions of mathematics necessary for a computer science degree. The text reflects the need to quickly introduce students from a variety of educational backgrounds to a number of essential mathematical concepts. The material is divided into four units: discrete mathematics (sets, relations, functions), logic (Boolean types, truth tables, proofs), linear algebra (vectors, matrices and graphics), and special topics (graph theory, number theory, basic elements of calculus). The chapters contain a brief theoretical presentation of the topic, followed by a selection of problems (which are direct applications of the theory) and additional supplementary problems (which may require a bit more work). Each chapter ends with answers or worked solutions for all of the problems.
Starck, Jean-Luc; Murtagh, Fionn; Fadili, Jalal (Cambridge University Press, 2016-01)
This thoroughly updated new edition presents state-of-the-art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB® and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications.
Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools. The book is organized according to the traditional core approaches to cluster analysis, from the origins to recent developments. After an overview of approaches and a quick journey through the history of cluster analysis, the book focuses on the four major approaches to cluster analysis. These approaches include methods for optimizing an objective function that describes how well data is grouped around centroids, dissimilarity-based methods, mixture models and partitioning models, and clustering methods inspired by nonparametric density estimation. The book also describes additional approaches to cluster analysis, including constrained and semi-supervised clustering, and explores other relevant issues, such as evaluating the quality of a cluster. This handbook is accessible to readers from various disciplines, reflecting the interdisciplinary nature of cluster analysis. For those already experienced with cluster analysis, the book offers a broad and structured overview. For newcomers to the field, it presents an introduction to key issues. For researchers who are temporarily or marginally involved with cluster analysis problems, the book gives enough algorithmic and practical details to facilitate working knowledge of specific clustering areas.
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