- Table View
- List View
Signal Polarization Selection for Aircraft Radar Control: Models and Methods (Springer Aerospace Technology)
by Nikolay Kondratyevich Yurkov Alexey Yevgenyevich Bukharov Dmitry Alexandrovich ZatuchnyThis book highlights the synthesis of polarization selection system in the background of passive noise formed by reflections from space-distributed targets. This synthesis is fulfilled as close as possible to its ideal configuration in terms of maximal signal-to-noise ratio for the matched load of radar station antenna system. It presents a new approach to radar system resolution enhancement based on the development of mathematical model for radiometric receivers with mono-pulse antenna systems, as well as creation of a new algorithm that allows increasing angular resolution during the object’s search and tracking due to special signal processing.
Signal Processing Algorithms for Communication and Radar Systems
by Kung YaoBased on time-tested course material, this authoritative text examines the key topics, advanced mathematical concepts, and novel analytical tools needed to understand modern communication and radar systems. It covers computational linear algebra theory, VLSI systolic algorithms and designs, practical aspects of chaos theory, and applications in beamforming and array processing, and uses a variety of CDMA codes, as well as acoustic sensing and beamforming algorithms to illustrate key concepts. Classical topics such as spectral analysis are also covered, and each chapter includes a wealth of homework problems. This is an invaluable text for graduate students in electrical and computer engineering, and an essential reference for practitioners in communications and radar engineering.
Signal Processing and Analysis Techniques for Nuclear Quadrupole Resonance Spectroscopy (Signals and Communication Technology)
by Cristian Monea Nicu BizonThis book is about improving prohibited substances detection using the nuclear quadrupole resonance (NQR) technique at security checkpoints. The book proposes multiple signal processing and analysis techniques for improving detection of dangerous or contraband substances, such as explosives, narcotics, or toxic substances. Also, several hardware solutions are described and implemented in a custom-designed NQR spectrometer. A new approach to NQR signal detection is introduced using artificial intelligence/deep learning techniques. The book will be useful for for researchers and practitioners in the areas of electrical engineering, signal processing and analysis, applied spectroscopy, as well as for security or laboratory equipment manufacturers.
Signal Processing and Image Processing for Acoustical Imaging
by Woon Siong GanThis book discusses the applications of signal and image processing in acoustical imaging. It first describes the basic tools involved – the 2D transform, fast Fourier transform (FFT) and applications, and deconvolution – before introducing readers to higher-order statistics, wavelets, and neural networks. It also addresses the important topic of digital signal processing, focusing on the example of homomorphic signal processing. The book then details the design of digital filters and array signal processing, and lastly examines applications in image processing: image enhancement and optimization, image restoration, and image compression.
Signal Processing and Integrated Circuits
by Hussein BaherThis book provides a balanced account of analog, digital and mixed-mode signal processing with applications in telecommunications. Part I Perspective gives an overview of the areas of Systems on a Chip (Soc) and mobile communication which are used to demonstrate the complementary relationship between analog and digital systems. Part II Analog (continuous-time) and Digital Signal Processing contains both fundamental and advanced analysis, and design techniques, of analog and digital systems. This includes analog and digital filter design; fast Fourier transform (FFT) algorithms; stochastic signals; linear estimation and adaptive filters. Part III Analog MOS Integrated Circuits for Signal Processing covers basic MOS transistor operation and fabrication through to the design of complex integrated circuits such as high performance Op Amps, Operational Transconductance Amplifiers (OTA's) and Gm-C circuits. Part IV Switched-capacitor and Mixed-mode Signal Processing outlines the design of switched-capacitor filters, and concludes with sigma-delta data converters as an extensive application of analog and digital signal processing Contains the fundamentals and advanced techniques of continuous-time and discrete-time signal processing.Presents in detail the design of analog MOS integrated circuits for signal processing, with application to the design of switched-capacitor filters.Uses the comprehensive design of integrated sigma-delta data converters to illustrate and unify the techniques of signal processing.Includes solved examples, end of chapter problems and MATLAB® throughout the book, to help readers understand the mathematical complexities of signal processing.The treatment of the topic is at the senior undergraduate to graduate and professional levels, with sufficient introductory material for the book to be used as a self-contained reference.
Signal Processing and Machine Learning for Biomedical Big Data
by Ervin Sejdic Tiago H. FalkWithin the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.
Signal Processing and Machine Learning with Applications
by Michael M. Richter Sheuli Paul Veton Këpuska Marius SilaghiSignal processing captures, interprets, describes and manipulates physical phenomena. Mathematics, statistics, probability, and stochastic processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information. This book presents different kinds of signals humans use and applies them for human machine interaction to communicate. Signal Processing and Machine Learning with Applications presents methods that are used to perform various Machine Learning and Artificial Intelligence tasks in conjunction with their applications. It is organized in three parts: Realms of Signal Processing; Machine Learning and Recognition; and Advanced Applications and Artificial Intelligence. The comprehensive coverage is accompanied by numerous examples, questions with solutions, with historical notes. The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engaged with signal processing, machine learning and the applications.
Signal Processing and Networking for Big Data Applications
by Zhu Han Mingyi Hong Dan WangThis unique text helps make sense of big data in engineering applications using tools and techniques from signal processing. It presents fundamental signal processing theories and software implementations, reviews current research trends and challenges, and describes the techniques used for analysis, design and optimization. Readers will learn about key theoretical issues such as data modelling and representation, scalable and low-complexity information processing and optimization, tensor and sublinear algorithms, and deep learning and software architecture, and their application to a wide range of engineering scenarios. Applications discussed in detail include wireless networking, smart grid systems, and sensor networks and cloud computing. This is the ideal text for researchers and practising engineers wanting to solve practical problems involving large amounts of data, and for students looking to grasp the fundamentals of big data analytics.
Signal Processing Applications Using Multidimensional Polynomial Splines (SpringerBriefs in Applied Sciences and Technology)
by Dhananjay Singh Madhusudan Singh Zaynidinov HakimjonThis book highlights new methods, algorithms and software for the digital processing and recovery of signals. In addition, it describes a new method for modeling one dimensional and multidimensional signals as successions of local polynomial splines and their spectral characteristics. It provides examples of how the proposed methods can be applied in specific cases, together with signal processing software examples in the MATLAB environment, and models of special processes in the Simulink environment. The book’s goal is to make it easier for beginners to understand the subject matter; it is intended for engineers, undergraduate and graduate students engaged in research or the evaluation and design of hardware and software for the digital processing and recovery of signals.
Signal Processing First
by James H. Mcclellan Ronald W. Schafer Mark A. YoderThis class-tested learning package provides an introduction to fundamental concepts, specifically discrete-time systems, for beginning engineering students and is widely used as a self-teaching tool to discover more about USP applications, multimedia signals, and MATLAB. Unique features like visual learning demonstrations, MATLAB laboratories, and solved homework problems make this an essential learning tool to master fundamental concepts in today's electrical and computer engineering curricula.
Signal Processing for 5G: Algorithms and Implementations
by Fa-Long Luo Charlie Jianzhong ZhangFa-Long Luo, Ph.D., IEEE Fellow Charlie (Jianzhong) Zhang, Ph.D., IEEE Fellow A comprehensive and invaluable guide to 5G technology, implementation and practice in one single volume. For all things 5G, this book is a must-read. Signal processing techniques have played the most important role in wireless communications since the second generation of cellular systems. It is anticipated that new techniques employed in 5G wireless networks will not only improve peak service rates significantly, but also enhance capacity, coverage, reliability , low-latency, efficiency, flexibility, compatibility and convergence to meet the increasing demands imposed by applications such as big data, cloud service, machine-to-machine (M2M) and mission-critical communications. This book is a comprehensive and detailed guide to all signal processing techniques employed in 5G wireless networks. Uniquely organized into four categories, New Modulation and Coding, New Spatial Processing, New Spectrum Opportunities and New System-level Enabling Technologies, it covers everything from network architecture, physical-layer (down-link and up-link), protocols and air interface, to cell acquisition, scheduling and rate adaption, access procedures and relaying to spectrum allocations. All technology aspects and major roadmaps of global 5G standard development and deployments are included in the book. Key Features: Offers step-by-step guidance on bringing 5G technology into practice, by applying algorithms and design methodology to real-time circuit implementation, taking into account rapidly growing applications that have multi-standards and multi-systems. Addresses spatial signal processing for 5G, in particular massive multiple-input multiple-output (massive-MIMO), FD-MIMO and 3D-MIMO along with orbital angular momentum multiplexing, 3D beamforming and diversity. Provides detailed algorithms and implementations, and compares all multicarrier modulation and multiple access schemes that offer superior data transmission performance including FBMC, GFDM, F-OFDM, UFMC, SEFDM, FTN, MUSA, SCMA and NOMA. Demonstrates the translation of signal processing theories into practical solutions for new spectrum opportunities in terms of millimeter wave, full-duplex transmission and license assisted access. Presents well-designed implementation examples, from individual function block to system level for effective and accurate learning. Covers signal processing aspects of emerging system and network architectures, including ultra-dense networks (UDN), software-defined networks (SDN), device-to-device (D2D) communications and cloud radio access network (C-RAN).
Signal Processing for Cognitive Radios
by Sudharman K. JayaweeraThis book examines signal processing techniques for cognitive radios. The book is divided into three parts:Part I, is an introduction to cognitive radios and presents a history of the cognitive radio (CR), and introduce their architecture, functionalities, ideal aspects, hardware platforms, and state-of-the-art developments. Dr. Jayaweera also introduces the specific type of CR that has gained the most research attention in recent years: the CR for Dynamic Spectrum Access (DSA).Part II of the book, Theoretical Foundations, guides the reader from classical to modern theories on statistical signal processing and inference. The author addresses detection and estimation theory, power spectrum estimation, classification, adaptive algorithms (machine learning), and inference and decision processes. Applications to the signal processing, inference and learning problems encountered in cognitive radios are interspersed throughout with concrete and accessible examples.Part III of the book, Signal Processing in Radios, identifies the key signal processing, inference, and learning tasks to be performed by wideband autonomous cognitive radios. The author provides signal processing solutions to each task by relating the tasks to materials covered in Part II. Specialized chapters then discuss specific signal processing algorithms required for DSA and DSS cognitive radios.
Signal Processing for Intelligent Sensor Systems with MATLAB
by David C. SwansonSignal Processing for Intelligent Sensors with MATLAB, Second Edition once again presents the key topics and salient information required for sensor design and application. Organized to make it accessible to engineers in school as well as those practicing in the field, this reference explores a broad array of subjects and is divided into sections:
Signal Processing for Joint Radar Communications (IEEE Press)
by Kumar Vijay Mishra M. R. Bhavani Shankar Björn Ottersten A. Lee SwindlehurstSignal Processing for Joint Radar Communications A one-stop, comprehensive source for the latest research in joint radar communications In Signal Processing for Joint Radar Communications, four eminent electrical engineers deliver a practical and informative contribution to the diffusion of newly developed joint radar communications (JRC) tools into the sensing and communications communities. This book illustrates recent successes in applying modern signal processing theories to core problems in JRC. The book offers new results on algorithms and applications of JRC from diverse perspectives, including waveform design, physical layer processing, privacy, security, hardware prototyping, resource allocation, and sampling theory. The distinguished editors bring together contributions from more than 40 leading JRC researchers working on remote sensing, electromagnetics, optimization, signal processing, and beyond 5G wireless networks. The included resources provide an in-depth mathematical treatment of relevant signal processing tools and computational methods allowing readers to take full advantage of JRC systems. Readers will also find: Thorough introductions to fundamental limits and background on JRC theory and applications, including dual-function radar communications, cooperative JRC, distributed JRC, and passive JRCComprehensive explorations of JRC processing via waveform analyses, interference mitigation, and modeling with jamming and clutterPractical discussions of information-theoretic, optimization, and networking aspects of JRCIn-depth examinations of JRC applications in cutting-edge scenarios including automotive systems, intelligent reflecting surfaces, and secure parameter estimation Perfect for researchers and professionals in the fields of radar, signal processing, communications, information theory, networking, and electronic warfare, Signal Processing for Joint Radar Communications will also earn a place in the libraries of engineers working in the defense, aerospace, wireless communications, and automotive industries.
Signal Processing for Radiation Detectors
by Mohammad NakhostinPresents the fundamental concepts of signal processing for all application areas of ionizing radiation This book provides a clear understanding of the principles of signal processing of radiation detectors. It puts great emphasis on the characteristics of pulses from various types of detectors and offers a full overview on the basic concepts required to understand detector signal processing systems and pulse processing techniques. Signal Processing for Radiation Detectors covers all of the important aspects of signal processing, including energy spectroscopy, timing measurements, position-sensing, pulse-shape discrimination, and radiation intensity measurement. The book encompasses a wide range of applications so that readers from different disciplines can benefit from all of the information. In addition, this resource: Describes both analog and digital techniques of signal processing Presents a complete compilation of digital pulse processing algorithms Extrapolates content from more than 700 references covering classic papers as well as those of today Demonstrates concepts with more than 340 original illustrations Signal Processing for Radiation Detectors provides researchers, engineers, and graduate students working in disciplines such as nuclear physics and engineering, environmental and biomedical engineering, and medical physics and radiological science, the knowledge to design their own systems, optimize available systems or to set up new experiments.
Signal Processing in Magnetic Resonance Spectroscopy with Biomedical Applications
by Dzevad Belkic Karen BelkicAddressing the critical need in clinical oncology for robust and stable signal processing in magnetic resonance spectroscopy (MRS), Signal Processing in Magnetic Resonance Spectroscopy with Biomedical Applications explores cutting-edge theory-based innovations for obtaining reliable quantitative information from MR signals for cancer diagnostics. By defining the natural framework of signal processing using the well-established theory of quantum physics, the book illustrates how advances in signal processing can optimize MRS. <p> The authors employ the fast Padé transform (FPT) as the unique polynomial quotient for the spectral analysis of MR time signals. They prove that residual spectra are necessary but not sufficient criteria to estimate the error invoked in quantification. Instead, they provide a more comprehensive strategy that monitors constancy of spectral parameters as one of the most reliable signatures of stability and robustness of quantification. The authors also use Froissart doublets to unequivocally distinguish between genuine and spurious resonances in both noise-free and noise-corrupted time signals, enabling the exact reconstruction of all the genuine spectral parameters. They show how the FPT resolves and quantifies tightly overlapped resonances that are abundantly seen in MR spectra generated using data from encoded time signals from the brain, breast, ovary, and prostate. <p> Written by a mathematical physicist and a clinical scientist, this book captures the multidisciplinary nature of biomedicine. It examines the remarkable ability of the FPT to unambiguously quantify isolated, tightly overlapped, and nearly confluent resonances.
Signal Processing in Medicine and Biology: Innovations in Big Data Processing
by Iyad Obeid Joseph Picone Ivan SelesnickSignal Processing in Medicine and Biology: Innovations in Big Data Processing provides an interdisciplinary look at state-of-the-art innovations in biomedical signal processing, especially as it applies to large data sets and machine learning. Chapters are presented with detailed mathematics and complete implementation specifics so that readers can completely master these techniques. The book presents tutorials and examples of successful applications and will appeal to a wide range of professionals, researchers, and students interested in applications of signal processing, medicine, and biology at the intersection between healthcare, engineering, and computer science.
Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications
by Iyad Obeid Ivan Selesnick Joseph PiconeThis book covers emerging trends in signal processing research and biomedical engineering, exploring the ways in which signal processing plays a vital role in applications ranging from medical electronics to data mining of electronic medical records. Topics covered include statistical modeling of electroencephalograph data for predicting or detecting seizure, stroke, or Parkinson’s; machine learning methods and their application to biomedical problems, which is often poorly understood, even within the scientific community; signal analysis; medical imaging; and machine learning, data mining, and classification. The book features tutorials and examples of successful applications that will appeal to a wide range of professionals and researchers interested in applications of signal processing, medicine, and biology.
Signal Processing in Neuroscience
by Xiaoli LiThis book reviews cutting-edge developments in neural signalling processing (NSP), systematically introducing readers to various models and methods in the context of NSP. Neuronal Signal Processing is a comparatively new field in computer sciences and neuroscience, and is rapidly establishing itself as an important tool, one that offers an ideal opportunity to forge stronger links between experimentalists and computer scientists. This new signal-processing tool can be used in conjunction with existing computational tools to analyse neural activity, which is monitored through different sensors such as spike trains, local filed potentials and EEG. The analysis of neural activity can yield vital insights into the function of the brain. This book highlights the contribution of signal processing in the area of computational neuroscience by providing a forum for researchers in this field to share their experiences to date.
Signal Processing in Radar Systems
by Vyacheslav TuzlukovAn essential task in radar systems is to find an appropriate solution to the problems related to robust signal processing and the definition of signal parameters. Signal Processing in Radar Systems addresses robust signal processing problems in complex radar systems and digital signal processing subsystems. It also tackles the important issue of defining signal parameters. The book presents problems related to traditional methods of synthesis and analysis of the main digital signal processing operations. It also examines problems related to modern methods of robust signal processing in noise, with a focus on the generalized approach to signal processing in noise under coherent filtering. In addition, the book puts forth a new problem statement and new methods to solve problems of adaptation and control by functioning processes. Taking a systems approach to designing complex radar systems, it offers readers guidance in solving optimization problems. Organized into three parts, the book first discusses the main design principles of the modern robust digital signal processing algorithms used in complex radar systems. The second part covers the main principles of computer system design for these algorithms and provides real-world examples of systems. The third part deals with experimental measurements of the main statistical parameters of stochastic processes. It also defines their estimations for robust signal processing in complex radar systems. Written by an internationally recognized professor and expert in signal processing, this book summarizes investigations carried out over the past 30 years. It supplies practitioners, researchers, and students with general principles for designing the robust digital signal processing algorithms employed by complex radar systems.
Signal Processing in X-ray Pulsar-Based Navigation
by Hua Zhang Luping Xu Jingrong Sun Bo YanThis book highlights key technologies of signal processing in pulsar-based navigation. It discusses the modeling, simulation, acquisition, and correction of relativistic effects of signals from X-ray pulsars. It demonstrates the methods of contour reconstruction and denoising, and introduces the concept and methods of the average contour. The performance of the phase measurement methods using signal contour is analyzed. The role of wavelets and bispectral methods in the denoising of pulsar signals is discussed. The measurements of pulsar signals’ arriving time are looked into from the perspective of time series. The book is intended for researchers and engineers interested in pulsar-based navigation. It is also a good reference source for senior undergraduates and postgraduate students majoring in navigation and signal processing.
Signal Processing of Airborne Radar Stations: Plane Flight Control in Difficult Meteoconditions (Springer Aerospace Technology)
by Vereshchagin A.V. Zatuchny D.A. Sinitsyn V.A. Sinitsyn E.A. Shatrakov Y.G.This book highlights new methods and parametric algorithms for the digital coherent processing of signals in airborne radar systems located on air vehicles. Using the autoregressive (AR) model, it delivers more accurate danger assessments for flight in wind shear and atmospheric turbulence, while also suggesting how they could be implemented. Given its scope, the book is intended for technical experts whose work involves the development, production and operation of airborne radio-electronic systems.
Signal Processing Techniques for Computational Health Informatics (Intelligent Systems Reference Library #192)
by Md Atiqur Rahman Ahad Mosabber Uddin AhmedThis book focuses on signal processing techniques used in computational health informatics. As computational health informatics is the interdisciplinary study of the design, development, adoption and application of information and technology-based innovations, specifically, computational techniques that are relevant in health care, the book covers a comprehensive and representative range of signal processing techniques used in biomedical applications, including: bio-signal origin and dynamics, sensors used for data acquisition, artefact and noise removal techniques, feature extraction techniques in the time, frequency, time–frequency and complexity domain, and image processing techniques in different image modalities.Moreover, it includes an extensive discussion of security and privacy challenges, opportunities and future directions for computational health informatics in the big data age, and addresses the incorporation of recent techniques from the areas of artificial intelligence, deep learning and human–computer interaction. The systematic analysis of the state-of-the-art techniques covered here helps to further our understanding of the physiological processes involved and expandour capabilities in medical diagnosis and prognosis.In closing, the book, the first of its kind, blends state-of-the-art theory and practices of signal processing techniques inthe health informatics domain with real-world case studies building on those theories. As a result, it can be used as a text for health informatics courses to provide medics with cutting-edge signal processing techniques, or to introducehealth professionals who are already serving in this sector to some of the most exciting computational ideas that paved the way for the development of computational health informatics.
Signal Processing Techniques for Power Efficient Wireless Communication Systems: Practical Approaches for RF Impairments Reduction (Signals and Communication Technology)
by Christian Schmidt Fernando Gregorio Gustavo González Juan CousseauThis book presents a synthesis of the research carried out in the Laboratory of Signal Processing and Communications (LaPSyC), CONICET, Universidad Nacional del Sur, Argentina, since 2003. It presents models and techniques widely used by the signal processing community, focusing on low-complexity methodologies that are scalable to different applications. It also highlights measures of the performance and impact of each compensation technique. The book is divided into three parts: 1) basic models 2) compensation techniques and 3) applications in advanced technologies. The first part addresses basic architectures of transceivers, their component blocks and modulation techniques. It also describes the performance to be taken into account, regardless of the distortions that need to be compensated. In the second part, several schemes of compensation and/or reduction of imperfections are explored, including linearization of power amplifiers, compensation of the characteristics of analog-to- digital converters and CFO compensation for OFDM modulation. The third and last part demonstrates the use of some of these techniques in modern wireless-communication systems, such as full-duplex transmission, massive MIMO schemes and Internet of Things applications.
Signal Traffic: Critical Studies of Media Infrastructures (The Geopolitics of Information)
by Nicole Starosielski Lisa ParksThe contributors to Signal Traffic investigate how the material artifacts of media infrastructure--transoceanic cables, mobile telephone towers, Internet data centers, and the like--intersect with everyday life. Essayists confront the multiple and hybrid forms networks take, the different ways networks are imagined and engaged with by publics around the world, their local effects, and what human beings experience when a network fails. Some contributors explore the physical objects and industrial relations that make up an infrastructure. Others venture into the marginalized communities orphaned from the knowledge economies, technological literacies, and epistemological questions linked to infrastructural formation and use. The wide-ranging insights delineate the oft-ignored contrasts between industrialized and developing regions, rich and poor areas, and urban and rural settings, bringing technological differences into focus. Contributors include Charles R. Acland, Paul Dourish, Sarah Harris, Jennifer Holt and Patrick Vonderau, Shannon Mattern, Toby Miller, Lisa Parks, Christian Sandvig, Nicole Starosielski, Jonathan Sterne, and Helga Tawil-Souri.