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.

Advanced techniques in image processing have led to many innovations supporting the medical field, especially in the area of disease diagnosis. Biomedical imaging is an essential part of early disease detection and often considered a first step in the proper management of medical pathological conditions. Classification and Clustering in Biomedical Signal Processing focuses on existing and proposed methods for medical imaging, signal processing, and analysis for the purposes of diagnosing and monitoring patient conditions. Featuring the most recent empirical research findings in the areas of signal processing for biomedical applications with an emphasis on classification and clustering techniques, this essential publication is designed for use by medical professionals, IT developers, and advanced-level graduate students.

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.

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.

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.

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 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.

For courses in Probability and Random Processes. "Probability, Statistics, and Random Processes for Engineers, 4e "is a useful text for electrical and computer engineers. This book is a comprehensive treatment of probability and random processes that, more than any other available source, combines "rigor" with "accessibility." Beginning with the fundamentals of probability theory and requiring only college-level calculus, the book develops all the tools needed to understand more advanced topics such as random sequences, continuous-time random processes, and statistical signal processing. The book progresses at a leisurely pace, never assuming more knowledge than contained in the material already covered. Rigor is established by developing all results from the basic axioms and carefully defining and discussing such advanced notions as stochastic convergence, stochastic integrals and resolution of stochastic processes.

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.

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.

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)

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.

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."

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.

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

Low-power sensors and their applications in various fields ranging from military to civilian lives have made tremendous progress in the recent years. Low-power and extended battery life are the key focuses for long term, reliable and easy operation of these sensors. Sensors and Low Power Signal Processing provides a general overview of a sensor's working principle and a discussion of the emerging sensor technologies including chemical, electro-chemical and MEMS based sensors. Also included is a discussion on design challenges associated with low-power analog circuits and the schemes to overcome them. Finally, a short discussion of some of the simple wireless telemetry schemes best suited for low-power sensor applications and sensor packaging issues is discussed. Applications and sensor prototypes included are environmental monitoring, health care monitoring and issues related to the development of sensor prototypes and associated electronics to achieve high signal-to-noise ratio will also be presented.

This reference provides an in-depth discussion of the theory and application of lapped transforms (LTs). It explains how LTs can lead to a better complexity/performance trade-off than other transforms or filter bands used in signal processing. The text addresses the increased use LTs, especially with HDTV and how they may become the standard for high-quality audio coding.

DSP is utilized in just about every electronic system or device. DSP is taking one piece of information be it data, image, video, or audio, most likely compressing, sending, and filtering it to another location within your application to appear in the form of a document, picture or video.

Like Smith before it, this book is different to most on the market by following a popular applied approach to this tricky subject, and will be the perfect starting point for engineers who need to get into DSP from the ground floor. This book starts with the absolute basics of this integral process.

No experience is expected and with no prior knowledge taken for granted, a refresher chapter on complex numbers and trigonometry can be found at the very beginning of the material. Real-world worked examples, reference designs, and tools - including online applets that enable readers to visualize key principles - complete a package that will help engineers who that needs to learn anew or refresh their memory on this essential technology as they move to projects that require DSP familiarity.
Dismayed when presented with a mass of equations as an explanation of DSP? This is the book for you

Clear examples and a non-mathematical approach gets you up to speed with DSP

Includes an overview of the DSP functions and implementation used in typical DSP-intensive applications, including error correction, CDMA mobile communication, and radar systems

The latest research and developments in robust adaptive beamforming
Recent work has made great strides toward devising robust adaptive beamformers that vastly improve signal strength against background noise and directional interference. This dynamic technology has diverse applications, including radar, sonar, acoustics, astronomy, seismology, communications, and medical imaging. There are also exciting emerging applications such as smart antennas for wireless communications, handheld ultrasound imaging systems, and directional hearing aids.
Robust Adaptive Beamforming compiles the theories and work of leading researchers investigating various approaches in one comprehensive volume. Unlike previous efforts, these pioneering studies are based on theories that use an uncertainty set of the array steering vector. The researchers define their theories, explain their methodologies, and present their conclusions. Methods presented include:
* Coupling the standard Capon beamformers with a spherical or ellipsoidal uncertainty set of the array steering vector
* Diagonal loading for finite sample size beamforming
* Mean-squared error beamforming for signal estimation
* Constant modulus beamforming
* Robust wideband beamforming using a steered adaptive beamformer to adapt the weight vector within a generalized sidelobe canceller formulation
Robust Adaptive Beamforming provides a truly up-to-date resource and reference for engineers, researchers, and graduate students in this promising, rapidly expanding field.

This is an edited volume, written by well-recognized international researchers with extended chapter style versions of the best papers presented at the SITIS 2006 International Conference. This book presents the state-of-the-art and recent research results on the application of advanced signal processing techniques for improving the value of image and video data. It introduces new results on video coding on time-honored topic of securing image information. The book is designed for a professional audience composed of practitioners and researchers in industry. This book is also suitable for advanced-level students in computer science.

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.

This book presents watermarking algorithms derived from signal processing methods such as wavelet transform, matrix decomposition and cosine transform to address the limitations of current technologies. For each algorithm, mathematical foundations are explained with analysis conducted to evaluate performances on robotness and efficiency. Combining theories and practice, it is suitable for information security researchers and industrial engineers.