This easy-to-follow textbook presents an engaging introduction to the fascinating world of medical image analysis. Avoiding an overly mathematical treatment, the text focuses on intuitive explanations, illustrating the key algorithms and concepts in a way which will make sense to students from a broad range of different backgrounds. Topics and features: explains what light is, and how it can be captured by a camera and converted into an image, as well as how images can be compressed and stored; describes basic image manipulation methods for understanding and improving image quality, and a useful segmentation algorithm; reviews the basic image processing methods for segmenting or enhancing certain features in an image, with a focus on morphology methods for binary images; examines how to detect, describe, and recognize objects in an image, and how the nature of color can be used for segmenting objects; introduces a statistical method to determine what class of object the pixels in an image represent; describes how to change the geometry within an image, how to align two images so that they are as similar as possible, and how to detect lines and paths in images; provides further exercises and other supplementary material at an associated website. This concise and accessible textbook will be invaluable to undergraduate students of computer science, engineering, medicine, and any multi-disciplinary courses that combine topics on health with data science. Medical practitioners working with medical imaging devices will also appreciate this easy-to-understand explanation of the technology.

Experience a guided tour of the key information-theoretic principles that underpin the design of next-generation cellular systems with this invaluable reference. Written by experts in the field, the text encompasses principled theoretical guidelines for the design and performance analysis of network architectures, coding and modulation schemes, and communication protocols. Presenting an extensive overview of the most important ideas and topics necessary for the development of future wireless systems, as well as providing a detailed introduction to network information theory, this is the perfect tool for researchers and graduate students in the fields of information theory and wireless communications, as well as for practitioners in the telecommunications industry.

In der hochbitratigen optischen Nachrichtentechnik ist es wichtig, parasitare induktive und kapazitive Einflusse auf die Funktion von Laser- und Fotodioden zu kompensieren. Wegen des nichtlinearen Charakters der u-i-Relationen der Induktivitaten, Kapazitaten und Widerstande ist es moeglich, Kompensationsverfahren gegen parasitare Effekte zu entwickeln oder die Nichtlinearitaten gezielt zur Signalubertragung einzusetzen. Reiner Thiele beweist, dass bei Applikation der vorgestellten Kompensationsverfahren kapazitive und induktive Influenzen auf die Grundfunktion der optoelektronischen Bauelemente vermeidbar sind, das Klemmenverhalten durch die u-i-Kennlinien von Laser- oder Fotodioden komplett erfasst wird und ungunstige Einflusse der Systemumgebung auf die optoelektronischen Schaltungen vermieden werden. Ausserdem stellt er Definitionen fur optoelektronische Grundstromkreise sowie ihre Berechnung fur die Applikation gleichartiger Laser- oder Fotodioden als Sende- bzw. Empfangsbauelemente der optischen Nachrichtentechnik vor. Der Autor: Prof. Dr.-Ing. Reiner Thiele lehrte an der Hochschule Zittau/Goerlitz und unterrichtet derzeit an der Staatlichen Studienakademie Bautzen.

An understanding of random processes is crucial to many engineering fields–including communication theory, computer vision, and digital signal processing in electrical and computer engineering, and vibrational theory and stress analysis in mechanical engineering. The filtering, estimation, and detection of random processes in noisy environments are critical tasks necessary in the analysis and design of new communications systems and useful signal processing algorithms. Random Processes: Filtering, Estimation, and Detection clearly explains the basics of probability and random processes and details modern detection and estimation theory to accomplish these tasks.

In this book, Lonnie Ludeman, an award-winning authority in digital signal processing, joins the fundamentals of random processes with the standard techniques of linear and nonlinear systems analysis and hypothesis testing to give signal estimation techniques, specify optimum estimation procedures, provide optimum decision rules for classification purposes, and describe performance evaluation definitions and procedures for the resulting methods. The text covers four main, interrelated topics:

• Probability and characterizations of random variables and random processes
• Linear and nonlinear systems with random excitations
• Optimum estimation theory including both the Wiener and Kalman Filters
• Detection theory for both discrete and continuous time measurements

Lucid, thorough, and well-stocked with numerous examples and practice problems that emphasize the concepts discussed, Random Processes: Filtering, Estimation, and Detection is an understandable and useful text ideal as both a self-study guide for professionals in the field and as a core text for graduate students.

Using easy-to-follow mathematics, this textbook provides comprehensive coverage of block codes and techniques for reliable communications and data storage. It covers major code designs and constructions from geometric, algebraic, and graph-theoretic points of view, decoding algorithms, error control additive white Gaussian noise (AWGN) and erasure, and dataless recovery. It simplifies a highly mathematical subject to a level that can be understood and applied with a minimum background in mathematics, provides step-by-step explanation of all covered topics, both fundamental and advanced, and includes plenty of practical illustrative examples to assist understanding. Numerous homework problems are included to strengthen student comprehension of new and abstract concepts, and a solutions manual is available online for instructors. Modern developments, including polar codes, are also covered. An essential textbook for senior undergraduates and graduates taking introductory coding courses, students taking advanced full-year graduate coding courses, and professionals working on coding for communications and data storage.

This Springerbreif introduces a threshold-based channel sparsification approach, and then, the sparsity is exploited for scalable channel training. Last but not least, this brief introduces two scalable cooperative signal detection algorithms in C-RANs. The authors wish to spur new research activities in the following important question: how to leverage the revolutionary architecture of C-RAN to attain unprecedented system capacity at an affordable cost and complexity. Cloud radio access network (C-RAN) is a novel mobile network architecture that has a lot of significance in future wireless networks like 5G. the high density of remote radio heads in C-RANs leads to severe scalability issues in terms of computational and implementation complexities. This Springerbrief undertakes a comprehensive study on scalable signal processing for C-RANs, where 'scalable' means that the computational and implementation complexities do not grow rapidly with the network size. This Springerbrief will be target researchers and professionals working in the Cloud Radio Access Network (C-Ran) field, as well as advanced-level students studying electrical engineering.

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.

Provides a modern mathematical approach to the design of communication networks for graduate students, blending control, optimization, and stochastic network theories. A broad range of performance analysis tools are discussed, including important advanced topics that have been made accessible to students for the first time. Taking a top-down approach to network protocol design, the authors begin with the deterministic model and progress to more sophisticated models. Network algorithms and protocols are tied closely to the theory, illustrating the practical engineering applications of each topic. The background behind the mathematical analyses is given before the formal proofs and is supported by worked examples, enabling students to understand the big picture before going into the detailed theory. End-of-chapter problems cover a range of difficulties, with complex problems broken into several parts, and hints to many problems are provided to guide students. Full solutions are available online for instructors.

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."

"Once again, Harry Van Trees has written the definitive textbook and research reference."
–Norman L. Owsley
Office of Naval Research, IPA University of Rhode Island

A comprehensive treatment of optimum array processing

Array processing plays an important role in many diverse application areas, including radar, sonar, communications, seismology, radio astronomy, tomography, and cellular communications.

Optimum Array Processing gives an integrated presentation of classical and statistical array processing. Classical analysis and synthesis techniques for linear and planar arrays are developed. A statistical characterization of space-time random processes is provided. Many different aspects of optimum array processing are covered, including waveform estimation, adaptive beamforming, parameter estimation, and signal detection. Both plane-wave signals and spatially spread signals are studied, and all results are developed in a pedagogically sound manner.

This book provides a fundamental understanding of array processing that is ample preparation for research or implementation of actual array processing systems. It provides a comprehensive synthesis of the array processing literature and includes more than 2,000 references. Readers will find an extensive variety of models and criteria for study and comparison, realistic examples and practical applications of optimum algorithms, challenging problems that expand the book’s material, and detailed derivations of important results. A supplemental Web site is available that contains MATLAB scripts for most of the figures used in the book so readers can explore diverse scenarios.

The book uses results from Parts I and III of Detection, Estimation, and Modulation Theory. These two books have been reprinted in paperback for availability.

For students in signal processing or professionals looking for thorough understanding of array processing theory, Optimum Array Processing provides authoritative, comprehensive coverage in the same clear manner as the earlier parts of Detection, Estimation, and Modulation Theory.

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.

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. The first volume, Foundations, establishes core topics in inference and learning, and prepares readers for studying their practical application. The second volume, Inference, introduces readers to cutting-edge techniques for inferring unknown variables and quantities. The final volume, Learning, provides a rigorous introduction to state-of-the-art learning methods. A consistent structure and pedagogy is employed throughout all three volumes to reinforce student understanding, with over 1280 end-of-chapter problems (including solutions for instructors), over 600 figures, over 470 solved examples, datasets and downloadable Matlab code. Unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.

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.

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.

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.