For undergraduate electrical, electronic or communications engineering courses.

Now in its second edition, Digital Signal Processing offers modern coverage of the fundamentals, implementation and applications of digital signal processing techniques from a practical point of view.
The past ten years has seen a significant growth in DSP applications throughout all areas of technology and this growth is expected well into the next millennium. This successful textbook covers most aspects of DSP found in undergraduate electrical, electronic or communications engineering courses. Unlike many other texts, it also covers a number of DSP techniques which are of particular relevance to industry such as adaptive filtering and multirate processing. The emphasis throughout the book is on the practical aspects of DSP.

This book proposes a combination of cognitive modeling with model-based user interface development to tackle the problem of maintaining the usability of applications that target several device types at once (e.g., desktop PC, smart phone, smart TV). Model-based applications provide interesting meta-information about the elements of the user interface (UI) that are accessible through computational introspection. Cognitive user models can capitalize on this meta-information to provide improved predictions of the interaction behavior of future human users of applications under development. In order to achieve this, cognitive processes that link UI properties to usability aspects like effectiveness (user error) and efficiency (task completion time) are established empirically, are explained through cognitive modeling, and are validated in the course of this treatise. In the case of user error, the book develops an extended model of sequential action control based on the Memory for Goals theory and it is confirmed in different behavioral domains and experimental paradigms. This new model of user cognition and behavior is implemented using the MeMo workbench and integrated with the model-based application framework MASP in order to provide automated usability predictions from early software development stages on. Finally, the validity of the resulting integrated system is confirmed by empirical data from a new application, eliciting unexpected behavioral patterns.

Online learning from a signal processing perspective

There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. "Kernel Adaptive Filtering" is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters.

Covers the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, and the extended kernel recursive least squares algorithm

Presents a powerful model-selection method called maximum marginal likelihood

Addresses the principal bottleneck of kernel adaptive filters--their growing structure

Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site

Concludes each chapter with a summary of the state of the art and potential future directions for original research

"Kernel Adaptive Filtering" is ideal for engineers, computer scientists, and graduate students interested in nonlinear adaptive systems for online applications (applications where the data stream arrives one sample at a time and incremental optimal solutions are desirable). It is also a useful guide for those who look for nonlinear adaptive filtering methodologies to solve practical problems.

Developed by recognized experts in the field, this first-of-its-kind resource provides an overview of the basic principles of passive radar technology, real passive radar systems and new developments in the industry. It explains in-depth how passive radar works and how it differs from the active type, while demonstrating the benefits and drawbacks of the technology. The book also explores properties of ambiguity functions, digital vs. analog, digitally-coded waveforms, vertical-plane coverage, and satellite-borne and radar illuminators. The book functions as a practical guide on direct signal suppression, passive radar performance prediction and detection and tracking. It contains concrete examples of systems and results, including analog TV, FM radio, cell phone base stations, DVB-T and DAB, HF skywave transmissions, indoor WiFi and low-cost scientific remote sensing.

This book is a collection of selected peer-reviewed papers presented at the International Conference on Signal Processing and Communication (ICSC 2018). It covers current research and developments in the fields of communications, signal processing, VLSI circuits and systems, and embedded systems. The book offers in-depth discussions and analyses of latest problems across different sub-fields of signal processing and communications. The contents of this book will prove to be useful for students, researchers, and professionals working in electronics and electrical engineering, as well as other allied fields.

For those involved with the design and analysis of electro-optical systems, the book outlines current and future ground, air and spacebourne applications of electro-optical systems. It describes their performance requirements and practical methods of achieving design objectives.

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting information from remotely sensed waveforms or time series data. This book combines both, providing a unique balance between the role of signal processing and image processing.

Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches. Not limited to satellite data, it also considers signals and images from hydroacoustic, seismic, microwave, and other sensors. Chapters cover important topics in signal and image processing and discuss techniques for dealing with remote sensing problems. Each chapter offers an introduction to the topic before delving into research results, making the book accessible to a broad audience.

This second edition reflects the considerable advances that have occurred in the field, with 23 of 27 chapters being new or entirely rewritten. Coverage includes new mathematical developments such as compressive sensing, empirical mode decomposition, and sparse representation, as well as new component analysis methods such as non-negative matrix and tensor factorization. The book also presents new experimental results on SAR and hyperspectral image processing.

The emphasis is on mathematical techniques that will far outlast the rapidly changing sensor, software, and hardware technologies. Written for industrial and academic researchers and graduate students alike, this book helps readers connect the "dots" in image and signal processing.

New in This Edition

The second edition includes four chapters from the first edition, plus 23 new or entirely rewritten chapters, and 190 new figures. New topics covered include:

• Compressive sensing
• The mixed pixel problem with hyperspectral images
• Hyperspectral image (HSI) target detection and classification based on sparse representation
• An ISAR technique for refocusing moving targets in SAR images
• Empirical mode decomposition for signal processing
• Feature extraction for classification of remote sensing signals and images
• Active learning methods in classification of remote sensing images
• Signal subspace identification of hyperspectral data
• Wavelet-based multi/hyperspectral image restoration and fusion

The second edition is not intended to replace the first edition entirely and readers are encouraged to read both editions of the book for a more complete picture of signal and image processing in remote sensing. See Signal and Image Processing for Remote Sensing (CRC Press 2006).

This book comprises a collection of papers by international experts, presented at the International Conference on NextGen Electronic Technologies (ICNETS2-2017). ICNETS2 encompassed six symposia covering all aspects of electronics and communications engineering domains, including relevant nano/micro materials and devices. Featuring the latest research on computational signal processing and analysis, the book is useful to researchers, professionals, and students working in the core areas of electronics and their applications, especially signal processing, embedded systems, and networking.

Modern-day radar problems can't be solved by conventional radar technology -- but they can be solved with the ground-breaking signal processing and analysis methods presented in this unique, expertly authored book. Illustrated with nearly 400 figures and including over 70 equations.

This book gives a concise introduction to both image and video processing, providing a balanced coverage between theory, applications and standards. It gives an introduction to both 2-D and 3-D signal processing theory, supported by an introduction to random processes and some essential results from information theory, providing the necessary foundation for a full understanding of the image and video processing concepts that follow. A significant new feature is the explanation of practical network coding methods for image and video transmission. There is also coverage of new approaches such as: super-resolution methods, non-local processing, and directional transforms.

This book also has on-line support that contains many short MATLAB programs that complement examples and exercises on multidimensional signal, image, and video processing. There are numerous short video clips showing applications in video processing and coding, plus a copy of the vidview video player for playing .yuv video files on a Windows PC and an illustration of the effect of packet loss on H.264/AVC coded bitstreams.

New to this edition:

New appendices on random processes, information theory

New coverage of image analysis - edge detection, linking, clustering, and segmentation

Expanded coverage on image sensing and perception, including color spaces.

Now summarizes the new MPEG coding standards: scalable video coding (SVC) and multiview video coding (MVC), in addition to coverage of H.264/AVC.

Updated video processing material including new example on scalable video coding and more material on object- and region-based video coding.

More on video coding for networks including practical network coding (PNC), highlighting the significant advantages of PNC for both video downloading and streaming.

New coverage of super-resolution methods for image and video.
Only R&D level tutorial that gives an integrated treatment of image and video processing - topics that are interconnected.
New chapters on introductory random processes, information theory, and image enhancement and analysis
Coverage and discussion of the latest standards in video coding: H.264/AVC and the new scalable video standard (SVC)

A common complaint heard in today's business office is, There is paper everywhere but I cannot find the document that I'm looking for It is estimated that 90 percent of all data and information currently being processed and distributed within offices and between organizations resides on paper. Finding the physical space to store this paper can be a key problem. To overcome this problem and others, there is a need for a dramatic new approach to information processing found in business. Such an approach is found in this unique and useful volume.

Essentially, image processing systems in business use today's computer technology to solve paper processing and storage problems. Their main means of processing is performed electronically, that is, documents are captured initially on an electronic medium and forwarded to other users in the same mode. In this manner, their essential means of communicating with users is in an electronic format versus a paper one. Also, image processing systems in business are much more flexible in meeting changing user needs, especially when the data is stored on optical disk.

The main objective of this book is to provide a multidisciplinary overview of methodological approaches, architectures, platforms, and algorithms for the realization of an Internet of Things (IoT)-based Smart Urban Ecosystem (SUE). Moreover, the book details a set of real-world applications and case studies related to specific smart infrastructures and smart cities, including structural health monitoring, smart urban drainage networks, smart grids, power efficiency, healthcare, city security, and emergency management. A Smart Urban Ecosystem (SUE) is a people-centric system of systems that involves smart city environments, applications, and infrastructures. SUEs require the close integration of cyber and physical components for monitoring, understanding and controlling the urban environment. In this context, the Internet of Things (IoT) offers a valuable enabling technology, as it bridges the gap between physical things and software components, and empowers cooperation between distributed, pervasive, and heterogeneous entities.

Since its emergence as an important research area in the early 1980s, the topic of wavelets has undergone tremendous development on both theoretical and applied fronts. Myriad research and survey papers and monographs have been published on the subject, documenting different areas of applications such as sound and image processing, denoising, data compression, tomography, and medical imaging. The study of wavelets remains a very active field of research, and many of its central techniques and ideas have evolved into new and promising research areas.

This volume, a collection of invited contributions developed from talks at an international conference on wavelets, is divided into three parts: Part I is devoted to the mathematical theory of wavelets and features several papers on wavelet sets and the construction of wavelet bases in different settings. Part II looks at the use of multiscale harmonic analysis for understanding the geometry of large data sets and extracting information from them. Part III focuses on applications of wavelet theory to the study of several real-world problems.

Overall, the book is an excellent reference for graduate students, researchers, and practitioners in theoretical and applied mathematics, or in engineering.

This volume includes papers presented at IIH-MSP 2017, the 13th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, held on 12-15 August 2017 in Matsue, Shimane, Japan. The conference covered topics ranging from information hiding and security, and multimedia signal processing and networking, to bio-inspired multimedia technologies and systems. This volume focuses on subjects related to multimedia security and applications, wearable computing, Internet of Things (IoT) privacy and information security, biomedical system design and applications, emerging techniques and applications, soft computing and applications, applications of image encoding and rendering, and information hiding and its criteria. Updated with the latest research outcomes and findings, the papers presented appeal to researchers and students in the corresponding fields.

This work offers a practical, non-theoretical introduction to the principles, application and benefits of multi-sensor technology in smart weapon development, surveillance systems, and an increasing variety of non-military applications. It also provides an overview of MMW (Millimeter-Wave) and IR (infrared) sensor constraints, design principles, system alternatives and targeting applications.

There have been significant developments in the design and application of algorithms for both one-dimensional signal processing and multidimensional signal processing, namely image and video processing, with the recent focus changing from a step-by-step procedure of designing the algorithm first and following up with in-depth analysis and performance improvement to instead applying heuristic-based methods to solve signal-processing problems.

In this book the contributing authors demonstrate both general-purpose algorithms and those aimed at solving specialized application problems, with a special emphasis on heuristic iterative optimization methods employing modern evolutionary and swarm intelligence based techniques. The applications considered are in domains such as communications engineering, estimation and tracking, digital filter design, wireless sensor networks, bioelectric signal classification, image denoising, and image feature tracking.

The book presents interesting, state-of-the-art methodologies for solving real-world problems and it is a suitable reference for researchers and engineers in the areas of heuristics and signal processing."

This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving emerging problems in neuroscience, and which clearly presents new theory, algorithms, software and hardware tools that are specifically tailored to the nature of the neurobiological environment. It gives a broad overview of the basic principles, theories and methods in statistical signal processing for basic and applied neuroscience problems.
Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience.
A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing communityContains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience researchPresents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems

In three parts, this book contributes to the advancement of engineering education and that serves as a general reference on digital signal processing. Part I presents the basics of analog and digital signals and systems in the time and frequency domain. It covers the core topics: convolution, transforms, filters, and random signal analysis. It also treats important applications including signal detection in noise, radar range estimation for airborne targets, binary communication systems, channel estimation, banking and financial applications, and audio effects production. Part II considers selected signal processing systems and techniques. Core topics covered are the Hilbert transformer, binary signal transmission, phase-locked loops, sigma-delta modulation, noise shaping, quantization, adaptive filters, and non-stationary signal analysis. Part III presents some selected advanced DSP topics.

The limitations of satellites create a large gap in assistive directional technologies, especially indoors. The methods and advances in alternate directional technologies is allowing for new systems to fill the gaps caused by the limitations of GPS systems. Positioning and Navigation in Complex Environments is a critical scholarly resource that examines the methodologies and advances in technologies that allow for indoor navigation. Featuring insight on a broad scope of topics, such as multipath mitigation, Global Navigation Satellite System (GNSS), and multi-sensor integration, this book is directed toward data scientists, engineers, government agencies, researchers, and graduate-level students.

Tensor signal processing is an emerging field with important applications to computer vision and image processing.

This book presents the state of the art in this new branch of signal processing, offering a great deal of research and discussions by leading experts in the area. The wide-ranging volume offers an overview into cutting-edge research into the newest tensor processing techniques and their application to different domains related to computer vision and image processing.

This comprehensive text will prove to be an invaluable reference and resource for researchers, practitioners and advanced students working in the area of computer vision and image processing.

The most outstanding feature of this book is that it treats the design of filters that approximate a constant group delay, and both, the prescribed magnitude and group delay response of one-dimensional as well as two-dimensional digital filters. It so fills a void in the literature, that almost solely deals with the magnitude response of the filter transfer function. The volume contains many of the important results that have appeared in professional journals only recently.