An authoritative exposition of the methods at the heart of modern non-stationary signal processing from a recognised leader in the field. Offering a global view that favours interpretations and historical perspectives, it explores the basic concepts of time-frequency analysis, and examines the most recent results and developments in the field in the context of existing, lesser-known approaches. Several example waveform families from bioacoustics, mathematics and physics are examined in detail, with the methods for their analysis explained using a wealth of illustrative examples. Methods are discussed in terms of analysis, geometry and statistics. This is an excellent resource for anyone wanting to understand the 'why and how' of important methodological developments in time-frequency analysis, including academics and graduate students in signal processing and applied mathematics, as well as application-oriented scientists.

Signal processing--the concept of frequency often referred to as spectral concepts--is the focal point of this collection of essays. Discussing parametric methods, with specific focus on time-series models, Capon's method, and notions of sub-spaces, as well as the popular and traditional analog methods, this text also addresses the quests for better frequency resolution in spectral concepts with advancements in digital tools.

This comprehensive and engaging textbook introduces the basic principles and techniques of signal processing, from the fundamental ideas of signals and systems theory to real-world applications. Students are introduced to the powerful foundations of modern signal processing, including the basic geometry of Hilbert space, the mathematics of Fourier transforms, and essentials of sampling, interpolation, approximation and compression The authors discuss real-world issues and hurdles to using these tools, and ways of adapting them to overcome problems of finiteness and localization, the limitations of uncertainty, and computational costs. It includes over 160 homework problems and over 220 worked examples, specifically designed to test and expand students' understanding of the fundamentals of signal processing, and is accompanied by extensive online materials designed to aid learning, including Mathematica (R) resources and interactive demonstrations.

Stream processing is a novel distributed computing paradigm that supports the gathering, processing, and analysis of high-volume, heterogeneous, continuous data streams, to extract insights and actionable results in real time. This comprehensive, hands-on guide combining the fundamental building blocks and emerging research in stream processing is ideal for application designers, system builders, analytic developers, as well as students and researchers in the field. This book introduces the key components of the stream computing paradigm, including the distributed system infrastructure, the programming model, design patterns, and streaming analytics. The explanation of the underlying theoretical principles, illustrative examples and implementations using the IBM InfoSphere Streams SPL language, and real-world case studies provide students and practitioners with a comprehensive understanding of such applications and the middleware that supports them.

Dieses Buch gibt eine Einfuhrung in die Nachrichtentechnik bis hin zu modernen Verfahren der Datenubertragung und Datensicherheit. Zu Beginn wird ein Abriss der Geschichte sowie ausgewahlte Modelle der Nachrichtentechnik vorgestellt. Die von Claude E. Shannon begrundete Informationstheorie lost sich von der Bedeutung der Daten und benutzt, vereinfacht ausgedruckt, ausschliesslich deren statistische Eigenschaften. Auf Basis der Informationstheorie werden Einfuhrungen in die Gebiete Quellen- und Kanalcodierung, Ubertragungskanale, Entscheidungstheorie, Modulationsverfahren sowie elementare Kommunikationsprotokolle und Datensicherheit gegeben. Exemplarisch werden zu diesen Gebieten ausgewahlte praktische Verfahren, Methoden und Algorithmen beschrieben. Ein ausfuhrlicher Anhang stellt Grundlagen der Wahrscheinlichkeitsrechnung, der Fourier-Analyse und der Signal- und Systemtheorie bereit."

This monograph deals with principal component analysis (PCA), kernel component analysis (KPCA), and independent component analysis (ICA), highlighting their applications to streaming-data implementations. The basic concepts related to PCA, KPCA, and ICA are widely available in the literature; however, very few texts deal with their practical implementation in computationally limited resources. This monograph discusses the state-of-the-art online PCA and KPCA techniques in a unified and principled manner, presenting solutions that achieve a higher convergence speed and accuracy in many applications, particularly image processing. Besides, this work also explains how to remove various artifacts from data records based on blind source separation by independent component analysis implemented with ICA, splitting feature identification from feature separation. Herein, three FastICA online hardware architectures and implementation for biomedical signal processing are addressed. The main features are summarized as follows: 1) energy-efficient FastICA using the proposed early determination scheme; 2) cost-effective variable-channel FastICA using the Gram-Schmidt-based whitening algorithm; and 3) moving-window-based online FastICA algorithm with limited memory. The post-layout simulation results with artificial and EEG data validate the design concepts.

Many new DCT-like transforms have been proposed since the first edition of this book. For example, the integer DCT that yields integer transform coefficients, the directional DCT to take advantage of several directions of the image and the steerable DCT. The advent of higher dimensional frames such as UHDTV and 4K-TV demand for small and large transform blocks to encode small or large similar areas respectively in an efficient way. Therefore, a new updated book on DCT, adapted to the modern days, considering the new advances in this area and targeted for students, researchers and the industry is a necessity.

Understand the theory and function of wireless antennas with this comprehensive guide As wireless technology continues to develop, understanding of antenna properties and performance will only become more critical. Since antennas can be understood as junctions of waveguides, eigenmode analysis--the foundation of waveguide theory, concerned with the unexcited states of systems and their natural resonant characteristics--promises to be a crucial frontier in the study of antenna theory. Foundations of Antenna Radiation Theory incorporates the modal analysis, generic antenna properties and design methods discovered or developed in the last few decades, not being reflected in most antenna books, into a comprehensive introduction to the theory of antennas. This book puts readers into conversation with the latest research and situates students and researchers at the cutting edge of an important field of wireless technology. The book also includes: Detailed discussions of the solution methods for Maxwell equations and wave equations to provide a theoretical foundation for electromagnetic analysis of antennas Recent developments for antenna radiation in closed and open space, modal analysis and field expansions, dyadic Green's functions, time-domain theory, state-of-the-art antenna array synthesis methods, wireless power transmission systems, and more Innovative material derived from the author's own research Foundations of Antenna Radiation Theory is ideal for graduate or advanced undergraduate students studying antenna theory, as well as for reference by researchers, engineers, and industry professionals in the areas of wireless technology.

Have you ever wanted to know how modern digital communications systems work? Find out with this step-by-step guide to building a complete digital radio that includes every element of a typical, real-world communication system. Chapter by chapter, you will create a MATLAB realization of the various pieces of the system, exploring the key ideas along the way, as well as analyzing and assessing the performance of each component. Then, in the final chapters, you will discover how all the parts fit together and interact as you build the complete receiver. In addition to coverage of crucial issues, such as timing, carrier recovery and equalization, the text contains over 400 practical exercises, providing invaluable preparation for industry, where wireless communications and software radio are becoming increasingly important. A variety of extra resources are also provided online, including lecture slides and a solutions manual for instructors.

This title sets out to show that 2-D signal analysis has its own role to play alongside signal processing and image processing.
Concentrating its coverage on those 2-D signals coming from physical sensors (such as radars and sonars), the discussion explores a 2-D spectral approach but develops the modeling of 2-D signals and proposes several data-oriented analysis techniques for dealing with them. Coverage is also given to potential future developments in this area.

A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. * Presents the necessary basic ideas from both digital signal processing and machine learning concepts * Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing * Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.

This monograph covers the topic of Wireless for Machine Learning (ML). Although the general intersection of ML and wireless communications is currently a prolific field of research that has already generated multiple publications, there is little review work on Wireless for ML. As data generation increasingly takes place on devices without a wired connection, ML related traffic will be ubiquitous in wireless networks. Research has shown that traditional wireless protocols are highly inefficient or unsustainable to support ML, which creates the need for new wireless communication methods. This monograph gives an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support ML services over distributed datasets. Currently, there are two clear themes within the literature, analog over-the-air computation and digital radio resource management optimized for ML. A comprehensive introduction to these methods is presented, reviews are made of the most important works, open problems are highlighted and application scenarios are discussed.

Methods for image recovery and reconstruction aim to estimate a good-quality image from noisy, incomplete, or indirect measurements. Such methods are also known as computational imaging. New methods for image reconstruction attempt to lower complexity, decrease data requirements, or improve image quality for a given input data quality.Image reconstruction typically involves optimizing a cost function to recover a vector of unknown variables that agrees with collected measurements and prior assumptions. State-of-the-art image reconstruction methods learn these prior assumptions from training data using various machine learning techniques, such as bilevel methods. This review discusses methods for learning parameters for image reconstruction problems using bilevel formulations, and it lies at the intersection of a specific machine learning method, bilevel, and a specific application, filter learning for image reconstruction.The review discusses multiple perspectives to motivate the use of bilevel methods and to make them more easily accessible to different audiences. Various ways to optimize the bilevel problem are covered, providing pros and cons of the variety of proposed approaches. Finally, an overview of bilevel applications in image reconstruction is provided.

Arduino 101 houses an Intel Curie module which offers a better performance at a lower power footprint. The module has two 32-bit MCUs - an x86 Intel Quark processor and an ARC EM4 processor along with 384kB flash memory and 80kB SRAM. These onboard MCUs combine a variety of new technologies including wireless communication via Bluetooth Low Energy, 6 axis motion sensor with an accelerometer, and a gyroscope. With this book, you will: Explore neural net pattern matching Have the Arduino learn gesture recognition Perfect for students, teachers, and hobbyists who need just enough information to get started with the Arduino 101.

Intelligent Computing for Interactive System Design provides a comprehensive resource on what has become the dominant paradigm in designing novel interaction methods, involving gestures, speech, text, touch and brain-controlled interaction, embedded in innovative and emerging human-computer interfaces. These interfaces support ubiquitous interaction with applications and services running on smartphones, wearables, in-vehicle systems, virtual and augmented reality, robotic systems, the Internet of Things (IoT), and many other domains that are now highly competitive, both in commercial and in research contexts. This book presents the crucial theoretical foundations needed by any student, researcher, or practitioner working on novel interface design, with chapters on statistical methods, digital signal processing (DSP), and machine learning (ML). These foundations are followed by chapters that discuss case studies on smart cities, brain-computer interfaces, probabilistic mobile text entry, secure gestures, personal context from mobile phones, adaptive touch interfaces, and automotive user interfaces. The case studies chapters also highlight an in-depth look at the practical application of DSP and ML methods used for processing of touch, gesture, biometric, or embedded sensor inputs. A common theme throughout the case studies is ubiquitous support for humans in their daily professional or personal activities. In addition, the book provides walk-through examples of different DSP and ML techniques and their use in interactive systems. Common terms are defined, and information on practical resources is provided (e.g., software tools, data resources) for hands-on project work to develop and evaluate multimodal and multi-sensor systems. In a series of in-chapter commentary boxes, an expert on the legal and ethical issues explores the emergent deep concerns of the professional community, on how DSP and ML should be adopted and used in socially appropriate ways, to most effectively advance human performance during ubiquitous interaction with omnipresent computers. This carefully edited collection is written by international experts and pioneers in the fields of DSP and ML. It provides a textbook for students and a reference and technology roadmap for developers and professionals working on interaction design on emerging platforms.

Binary decisions guide our everyday lives in situations both critical and trivial. The choices made by politicians and physicians may have consequential implications on a global or individual scale. Perhaps less consequential is whether or not we choose to carry an umbrella on a cloudy day. Any choice made inherently involves a conscious, subconscious, or formal tradeoff between benefits and detriments.This monograph develops and presents a framework for binary hypothesis testing as it applies to both the classical and quantum mechanical environments. The authors set the scene by first describing separately the operating characteristics associated with classical binary hypothesis testing and those within quantum mechanics. They proceed to describe in detail in subsequent chapters how quantum measurements that employ redundant, or overcomplete, representations of the state of the system being measured can be used.Written in a tutorial style, readers from both classical and quantum backgrounds will find this an enlightening treatise on the topic. Examples and problems are used throughout to enable the reader to readily grasp the new concepts and to further their own understanding. This monograph is a comprehensive and accessible overview of a complex problem for students and researchers in signal processing.

In view of the extensive development of CCS 7 and fast-paced growth of ISDN in telecommunication networks throughout the world, this valuable resource serves as a timely reference and guide. Practical and up-to-date, ENGINEERING NETWORKS FOR SYNCHRONIZATION, CCS 7, AND ISDN provides in-depth instruction on three important and closely related elements of the modern digital network: network synchronization, CCITT Common Channel Signaling System No. 7 (CCS 7), and Narrowband ISDN. Sponsored by: IEEE Communications Society.