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.

"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 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.

Compressive sensing is a new signal processing paradigm that aims to encode sparse signals by using far lower sampling rates than those in the traditional Nyquist approach. It helps acquire, store, fuse and process large data sets efficiently and accurately. This method, which links data acquisition, compression, dimensionality reduction and optimization, has attracted significant attention from researchers and engineers in various areas. This comprehensive reference develops a unified view on how to incorporate efficiently the idea of compressive sensing over assorted wireless network scenarios, interweaving concepts from signal processing, optimization, information theory, communications and networking to address the issues in question from an engineering perspective. It enables students, researchers and communications engineers to develop a working knowledge of compressive sensing, including background on the basics of compressive sensing theory, an understanding of its benefits and limitations, and the skills needed to take advantage of compressive sensing in wireless networks.

Elucidating fundamental design principles by means of accurate trade-off analysis of relevant design options using suitable mathematical tools, this is the first book to provide a coherent treatment of transmission technologies essential to current and future wireless systems. Develop in-depth knowledge of the capabilities and limitations of wireless transmission technologies in supporting high-quality wireless transmission services, and foster a thorough understanding of various design trade-offs, to help identify an ideal choice for your own application requirements. Key technologies such as advanced diversity combining, multi-user scheduling, multi-user multi-antenna transmission, relay transmission, and cognitive radio are examined, making this an essential resource for senior graduate students, researchers, and engineers working in wireless communications.

Bringing together experts in multimodal signal processing, this book provides a detailed introduction to the area, with a focus on the analysis, recognition and interpretation of human communication. The technology described has powerful applications. For instance, automatic analysis of the outputs of cameras and microphones in a meeting can make sense of what is happening - who spoke, what they said, whether there was an active discussion and who was dominant in it. These analyses are layered to move from basic interpretations of the signals to richer semantic information. The book covers the necessary analyses in a tutorial manner, going from basic ideas to recent research results. It includes chapters on advanced speech processing and computer vision technologies, language understanding, interaction modeling and abstraction, as well as meeting support technology. This guide connects fundamental research with a wide range of prototype applications to support and analyze group interactions in meetings.

Das Buch vermittelt grundlegende Kenntnisse zur Synthese kombinatorischer (Schaltnetze) und sequentieller Schaltungen (Schaltwerke/Automaten) und wendet sich dabei vor allem an Studierende der Ingenieurwissenschaften.

Written using clear and accessible language, this text provides detailed coverage of the core mathematical concepts underpinning signal processing. All the core areas of mathematics are covered, including generalized inverses, singular value decomposition, function representation, and optimization, with detailed explanations of how basic concepts in these areas underpin the methods used to perform signal processing tasks. A particular emphasis is placed on the practical applications of signal processing, with numerous in-text practice questions and real-world examples illustrating key concepts, and MATLAB programs with accompanying graphical representations providing all the necessary computational background. This is an ideal text for graduate students taking courses in signal processing and mathematical methods, or those who want to establish a firm foundation in these areas before progressing to more advanced study.

This book provides a rigorous treatment of deterministic and random signals. It offers detailed information on topics including random signals, system modelling and system analysis. System analysis in frequency domain using Fourier transform and Laplace transform is explained with theory and numerical problems. The advanced techniques used for signal processing, especially for speech and image processing, are discussed. The properties of continuous time and discrete time signals are explained with a number of numerical problems. The physical significance of different properties is explained using real-life examples. To aid understanding, concept check questions, review questions, a summary of important concepts, and frequently asked questions are included. MATLAB programs, with output plots and simulation examples, are provided for each concept. Students can execute these simulations and verify the outputs.

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.

Advanced Methods in Biomedical Signal Processing and Analysis presents state-of-the-art methods in biosignal processing, including recurrence quantification analysis, heart rate variability, analysis of the RRI time-series signals, joint time-frequency analyses, wavelet transforms and wavelet packet decomposition, empirical mode decomposition, modeling of biosignals, Gabor Transform, empirical mode decomposition. The book also gives an understanding of feature extraction, feature ranking, and feature selection methods, while also demonstrating how to apply artificial intelligence and machine learning to biosignal techniques.

This book is intended to be a little different from other books in its coverage. There are a great many digital signal processing (DSP) books and signals and systems books on the market. Since most undergraduate courses begin with signals and systems and then move on in later years to DSP, I felt a need to combine the two into one book that was concise yet not too overburdening. This means that students need only purchase one book instead of two and at the same time see the flow of knowledge from one subject into the next. Like the rudiments of music, it starts at the very beginning with some elementary knowledge and builds on it chapter by chapter to advanced work by chapter 15. I have been teaching now for 38 years and always think it necessary to credit the pioneers of the subjects we teach and ask the question "How did we get to this present stage in technological achievement"? Therefore, in Chapter 1 I have given a concise history trying to not sway too much away from the subject area. This is followed by the rudimentary theory in increasing complexity. It has already been taught successfully to a class at Auckland University of Technology New Zealand.