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DIGITAL SPEECH TRANSMISSION AND ENHANCEMENT Enables readers to understand the latest developments in speech enhancement/transmission due to advances in computational power and device miniaturization The Second Edition of Digital Speech Transmission and Enhancement has been updated throughout to provide all the necessary details on the latest advances in the theory and practice in speech signal processing and its applications, including many new research results, standards, algorithms, and developments which have recently appeared and are on their way into state-of-the-art applications. Besides mobile communications, which constituted the main application domain of the first edition, speech enhancement for hearing instruments and man-machine interfaces has gained significantly more prominence in the past decade, and as such receives greater focus in this updated and expanded second edition. Readers can expect to find information and novel methods on: Low-latency spectral analysis-synthesis, single-channel and dual-channel algorithms for noise reduction and dereverberation Multi-microphone processing methods, which are now widely used in applications such as mobile phones, hearing aids, and man-computer interfaces Algorithms for near-end listening enhancement, which provide a significantly increased speech intelligibility for users at the noisy receiving side of their mobile phone Fundamentals of speech signal processing, estimation and machine learning, speech coding, error concealment by soft decoding, and artificial bandwidth extension of speech signals Digital Speech Transmission and Enhancement is a single-source, comprehensive guide to the fundamental issues, algorithms, standards, and trends in speech signal processing and speech communication technology, and as such is an invaluable resource for engineers, researchers, academics, and graduate students in the areas of communications, electrical engineering, and information technology.

DIGITAL SIGNAL PROCESSING Understand the future of signal processing with the latest edition of this groundbreaking text Signal processing is a key aspect of virtually all engineering fields. Digital techniques enormously expand the possible applications of signal processing, forming a part of not only conventional engineering projects but also data analysis and artificial intelligence. There are considerable challenges raised by these techniques, however, as the gulf between theory and practice can be wide; the successful integration of digital signal processing techniques requires engineers capable of bridging this gulf. For years, Digital Signal Processing has met this need with a comprehensive guide that consistently connects abstract theory with practical applications. Now fully updated to reflect the most recent developments in this crucial field, the tenth• edition of this seminal text promises to foster a broader understanding of signal processing among a new generation of engineers and researchers. Readers of the new edition of Digital Signal Processing will also find: Exercises at the end of each chapter to reinforce key conceptsA new chapter covering digital signal processing for neural networksHandy structure beginning with undergraduate-level material before moving to more advanced concepts in the second half Digital Signal Processing is a must-own for students, researchers, and industry professionals in any of the hundreds of fields and subfields that make use of signal processing algorithms. •This is the English language translation of the French original Traitement Numérique du Signal 10th edition by Maurice Bellanger © Dunod 2022 and is the 4th edition in English.

Over 70 recipes to help you develop smart applications on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano using the power of machine learning Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesOver 20+ new recipes, including recognizing music genres and detecting objects in a sceneCreate practical examples using TensorFlow Lite for Microcontrollers, Edge Impulse, and moreExplore cutting-edge technologies, such as on-device training for updating models without data leaving the deviceBook DescriptionDiscover the incredible world of tiny Machine Learning (tinyML) and create smart projects using real-world data sensors with the Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano. TinyML Cookbook, Second Edition, will show you how to build unique end-to-end ML applications using temperature, humidity, vision, audio, and accelerometer sensors in different scenarios. These projects will equip you with the knowledge and skills to bring intelligence to microcontrollers. You'll train custom models from weather prediction to real-time speech recognition using TensorFlow and Edge Impulse.Expert tips will help you squeeze ML models into tight memory budgets and accelerate performance using CMSIS-DSP. This improved edition includes new recipes featuring an LSTM neural network to recognize music genres and the Faster-Objects-More-Objects (FOMO) algorithm for detecting objects in a scene. Furthermore, you'll work on scikit-learn model deployment on microcontrollers, implement on-device training, and deploy a model using microTVM, including on a microNPU. This beginner-friendly and comprehensive book will help you stay up to date with the latest developments in the tinyML community and give you the knowledge to build unique projects with microcontrollers!What you will learnUnderstand the microcontroller programming fundamentalsWork with real-world sensors, such as the microphone, camera, and accelerometerImplement an app that responds to human voice or recognizes music genresLeverage transfer learning with FOMO and KerasLearn best practices on how to use the CMSIS-DSP libraryCreate a gesture-recognition app to build a remote controlDesign a CIFAR-10 model for memory-constrained microcontrollersTrain a neural network on microcontrollersWho this book is forThis book is ideal for machine learning engineers or data scientists looking to build embedded/edge ML applications and IoT developers who want to add machine learning capabilities to their devices. If you're an engineer, student, or hobbyist interested in exploring tinyML, then this book is your perfect companion. Basic familiarity with C/C++ and Python programming is a prerequisite; however, no prior knowledge of microcontrollers is necessary to get started with this book.

Brain Computer Interface: EEG Signal Processing discusses electroencephalogram (EEG) signal processing using effective methodology and algorithms. This book provides a basic introduction to EEG and a classification of different components present in EEG. It also helps the reader to understand the scope of processing EEG signals and their associated applications. Further, it covers specific aspects such as epilepsy detection; exploitation of P300 for various applications; design of an EEG acquisition system; and detection of saccade, fix, and blink from EEG and EOG data.Key Features: Explains the basis of brain computer interface and how it can be established using different EEG signal characteristics Covers the detailed classification of different types of EEG signals with respect to their physical characteristics Explains detection and diagnosis of epileptic seizures from the EEG data of a subject Reviews the design and development of a low-cost and robust EEG acquisition system Provides mathematical analysis of EEGs, including MATLAB® codes for students to experiment with EEG data This book is aimed at graduate students and researchers in biomedical, electrical, electronics, communication engineering, healthcare, and cyber physical systems.

Where most introductory texts to the field of digital signal processing assume a degree of technical knowledge, this class-tested textbook provides a comprehensive introduction to the fundamentals of digital signal processing in a way that is accessible to all. Beginning from the first principles, readers will learn how signals are acquired, represented, analyzed and transformed by digital computers. Specific attention is given to digital sampling, discrete Fourier analysis and linear filtering in the time and frequency domains. All concepts are introduced practically and theoretically, combining intuitive illustrations, mathematical derivations and software implementations written in the Python programming language. Practical exercises are included at the end of each chapter to test reader knowledge. Written in a clear and accessible style, Digital Signals Theory is particularly aimed at students and general readers interested in audio and digital signal processing, but who may not have extensive mathematical or engineering training.

This book is a broad-based introduction to an increasingly important topic. The overview is easy because each chapter is structured uniformly: theory, methods, realization alternatives, methodical and practical advantages and disadvantages, and examples from industrial technology and medicine as well as research and development practice.Compared to the first edition “Biosignal Processing”, the content of this book has been expanded by a sensor chapter (galvanic and capacitive sensors) and exemplary experimental data. Chapter 7: “Stochastic Processes” is also new with selected topics such as statistical analysis of time series, signal detection, and signal decomposition. Furthermore, various electronic measuring circuits and calculations have been adapted to the changed legal regulations and standards and some calculation errors have been corrected.The content is oriented to the sequence of the metrological and signal-analytical chain: neuron as a signal source–sensor technology–signal amplification and conditioning–signal sampling and digitization–methods of biosignal processing–biostatistics and stochastic processes.The target groupsThe book is suitable for medical technology studies, research, and practice. You can inform yourself compactly across your professional boundaries about the neighbouring fields and topics at the interdisciplinary interface between medicine and technology.

As the definitive resource on position sensing technology, Understanding Position Sensors encompasses all aspects necessary for a full understanding of the field, with topics of background, operational theory, design, and application.While grasping the theory of technologies used in the measurement of linear and angular/rotary position sensors, the reader will also learn about terminology, interfacing, testing, and other valuable concepts that are useful in the understanding of sensors in general.The first three chapters provide readers with the necessary background information on sensors. These chapters review the working definitions and conventions used in sensing technology; specification of position sensors and the effect on performance; and sensor output types, plus an extensive section covering communication protocols. The remaining chapters describe each separate sensor technology in detail. These include resistive sensors, cable extension transducers, capacitive sensors, inductive sensors, LVDT and RVDT sensors, distributed impedance sensors, Hall effect sensors, magnetoresistive sensors, magnetostrictive sensors, linear and rotary encoders, optical triangulation position sensors, and ultrasonic position sensors. Presents sensor specification, theory of operation, sensor design, and application criteria Reviews the background history of position sensors as well as the underlying engineering techniques Includes end-of-chapter exercises Understanding Position Sensors is written for electrical, mechanical, and material engineers, as well as for engineering students who are interested in understanding sensor technologies, and can be used as a textbook for an engineering course on sensor technology.

In diesem Open-Access-Tagungsband sind die besten Beiträge des 8. Jahreskolloquiums'Bildverarbeitung in der Automation'(BVAu 2022) enthalten. Das Kolloquium fand am 02. November 2022 auf dem Innovation Campus Lemgo statt.Die vorgestellten neuesten Forschungsergebnisse auf den Gebieten der industriellen Bildverarbeitung erweitern den aktuellen Stand der Forschung und Technik. Die in den Beiträgen enthaltenen anschaulichen Anwendungsbeispiele aus dem Bereich der Automation setzen die Ergebnisse in den direkten Anwendungsbezug.

Machine Learning Algorithms for Signal and Image Processing Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as: Speech recognition, image reconstruction, object classification and detection, and text processing Healthcare monitoring, biomedical systems, and green energy How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.

This comprehensive compendium highlights the research results of nonlinear channel modeling and simulation. Nonlinear channels include nonlinear satellite channels, nonlinear Volterra channels, molecular MIMO channels, etc.This volume involves wavelet theory, neural network, echo state network, machine learning, support vector machine, chaos calculation, principal component analysis, Markov chain model, correlation entropy, fuzzy theory and other theories for nonlinear channel modeling and equalization.The useful reference text enriches the theoretical system of nonlinear channel modeling and improving the means of establishing nonlinear channel model. It is suitable for engineering technicians, researchers and graduate students in information and communication engineering, and control science and engineering, intelligent science and technology.

NONLINEAR FILTERS Discover the utility of using deep learning and (deep) reinforcement learning in deriving filtering algorithms with this insightful and powerful new resource Nonlinear Filters: Theory and Applications delivers an insightful view on state and parameter estimation by merging ideas from control theory, statistical signal processing, and machine learning. Taking an algorithmic approach, the book covers both classic and machine learning-based filtering algorithms. Readers of Nonlinear Filters will greatly benefit from the wide spectrum of presented topics including stability, robustness, computability, and algorithmic sufficiency. Readers will also enjoy: Organization that allows the book to act as a stand-alone, self-contained reference A thorough exploration of the notion of observability, nonlinear observers, and the theory of optimal nonlinear filtering that bridges the gap between different science and engineering disciplines A profound account of Bayesian filters including Kalman filter and its variants as well as particle filter A rigorous derivation of the smooth variable structure filter as a predictor-corrector estimator formulated based on a stability theorem, used to confine the estimated states within a neighborhood of their true values A concise tutorial on deep learning and reinforcement learning A detailed presentation of the expectation maximization algorithm and its machine learning-based variants, used for joint state and parameter estimation Guidelines for constructing nonparametric Bayesian models from parametric ones Perfect for researchers, professors, and graduate students in engineering, computer science, applied mathematics, and artificial intelligence, Nonlinear Filters: Theory and Applications will also earn a place in the libraries of those studying or practicing in fields involving pandemic diseases, cybersecurity, information fusion, augmented reality, autonomous driving, urban traffic network, navigation and tracking, robotics, power systems, hybrid technologies, and finance.

Pythonで音作りをはじめよう！ ・音のプログラミングが音響楽の基本からわかる！ ・音の信号処理もていねいに解説！ ・打楽器・管楽器・弦楽器・鍵盤楽器の音が手もとで作れる！ ・ソースコードはWebからダウンロード可能！ 本書は、コンピュータで音作りをしてみたい方に向けた、サウンドプログラミングの入門書です。音作りに興味があるけど何からはじめたらという初心者のために音響の基本から解説をはじめ、コンピュータでの音の考え方、音を加工するディジタル信号処理の基礎をていねいに説明し、シンセサイザ、エフェクタの音作りなどを解説します。さらに、さまざまな音響合成のテクニックとともに、その具体例として、ゼロから楽器音をつくり出すフルスクラッチ合成のレシピを紹介します。サウンドプログラミングの言語には、音データの読み書きはもちろん、波形、周波数特性、そしてスペクトログラムの描画も簡単に行える、Pythonを採用しています。 Pythonを使ってサウンドプログラミングの第一歩を踏み出しましょう！

Spatial audio is a dynamic and rapidly evolving field, as it is closely linked to advances in computer technology and digital signal processing. The democratization of virtual reality hardware available as consumer devices has moved the field further out of traditional laboratory research, and directly into applied research targeting a wide range of consumers. Advances in Fundamental and Applied Research on Spatial Audio presents a collection of eight peer-reviewed chapters on this exciting area of research. The contributions are organized into three sections:'Acoustic Methodology”,'Perception”, and'Applications”, and cover a range of topics, addressing both headphone- and loudspeaker-based reproductions, offering both methodological overviews and specific case studies.

Technological advancements have enhanced all functions of society and revolutionized the healthcare field. Smart healthcare applications and practices have grown within the past decade, strengthening overall care. Biomedical signals observe physiological activities, which provide essential information to healthcare professionals. Biomedical signal processing can be optimized through artificial intelligence (AI) and machine learning (ML), presenting the next step towards smart healthcare. AI-Enabled Smart Healthcare Using Biomedical Signals will not only cover the mathematical description of the AI- and ML-based methods, but also analyze and demonstrate the usability of different AI methods for a range of biomedical signals. The book covers all types of biomedical signals helpful for smart healthcare applications. Covering topics such as automated diagnosis, emotion identification, and frequency discrimination techniques, this premier reference source is an excellent resource for healthcare administration, biomedical engineers, medical laboratory technicians, medical technology assistants, computer scientists, libraries, students and faculty of higher education, researchers, and academicians.

Positron Emission Tomography (PET) is a key technique in the medical imaging area, which allows to diagnose the organism functions and to track the tumor changes. In PET measurement the patient is injected with radiotracer, containing a large number of metastable atoms of radionuclide, that emmits positrons. As the result of positron annihilation, the two photons travelling off with nearly opposite directions are produced and registered by detection system positioned so that it surrounds the patient body. State-of-the-art PET scanners use scintillation crystals which are characterized by high detection efficiency of annihilation photons. In this context, it is worth to mention that the Jagiellonian PET (J-PET) Collaboration developed a novel whole-body PET scanner based on plastic scintillators. They are much cheaper than crystal scintillators, which gives the opportunity to reduce the high cost of PET scanners and make them more affordable. However, plastic scintillators have much lower detection efficiency of gamma quanta compared to inorganic scintillation crystals. This can be compensated by increasing the scanner field of view and improving the time resolution in the measurement of the time of flight of gamma quanta. The J-PET scanner consists of plastic scintillator strips read out at both ends by a pair of photomultipliers and arranged axially around a cylindrical tomograph tunnel. The axial coordinate of the annihilation photon interaction point in the scintillator strip is derived from the difference of the light propagation time measured with the pair of photomultipliers. The operational principles of the J-PET scanner are similar to conventional tomographs, except that the highly accurate time information is of paramount importance. Therefore, the J-PET scanner demands a preparation of novel methods on each step of the data processing. The goal of the work presented in this dissertation is a development of the signal and image processing algorithms taking into account uniqueness of the J-PET detector. The proposed methods include: signal recovery based on samples of a waveform registered on photomultiplier output, reconstruction of position and time of interaction of annihilation photon in the scintillator strip, classification of PET events types and image reconstruction that operates exclusively in the image space. Due to the dissimilarity from the conventional PET scanners, majority of the methods presented in this dissertation are innovative solutions in digital signal and image processing in tomography.

Designed to cover the fundamental concepts of digital signal processing, the book introduces topics such as discrete-time signals, the z-transform, frequency analysis, discrete and fast Fourier transforms, digital filters, FIR, statistical DSP, applications, and more. DSP has been applied in most disciplines ranging from engineering to telecommunications, and from astronomy to medical imaging. This book focuses on the fundamentals of DSP, namely on the representation of signals by mathematical models and on the processing of signals by discrete-time systems. FEATURES: Designed to cover the fundamental concepts of DSPIntroduces topics such as discrete-time signals, the z-transform, frequency analysis, discrete and fast Fourier transforms, digital filters, FIR, statistical DSP, applications, and moreFeatures a variety of exercises and a glossary

信号処理は，電気回路などの知識をベースとして，音声・画像処理のために大切な知識として位置づけられています。 本書では，従来の信号処理のテキストにおける内容をできるだけ平易化し，必要と思われる数学的なアプローチも随所に記載をして，理解しやすさに重きをおいています。数学III程度の内容がある程度理解できるようであれば充分に読み進められるように配慮し，その補完として第2章において数学的な扱いをあえて示しています。 読者には，本書で学習し，既存の良書によりさらに高度な知識や教養を身につけて，社会に羽ばたいてほしいと願っています。

This book is the result of an exhaustive review of the general algorithms used for noise reduction using two general application criteria: one-input, one-output systems, and two-input, one-output systems. The text describes theoretically and experimentally the processes related to high-order statistical analysis algorithms, as well as its practical combination with the convolutional analysis. On the other hand, the results of applications in real telecommunications signals, sensing of variables and biosignals such as human tremor are also shown. The reader will benefit from both the theoretical foundations described in the book, and the practical examples including generic codes of all the functions described and modifiable for use in different applications. This book is an ideal text for engineers, graduate students and scientists involved with noise reduction and signal processing. Key Features: Emphasis on noise reduction techniquesAccompanying codesInteractive elementsPractical approach

This book provides a comprehensive introduction to all major topics in digital signal processing (DSP). The book is designed to serve as a textbook for courses offered to undergraduate students enrolled in electrical, electronics, and communication engineering disciplines. The text is augmented with many illustrative examples for easy understanding of the topics covered. Every chapter contains several numerical problems with answers followed by question-and-answer type assignments. The detailed coverage and pedagogical tools make this an ideal textbook for students and researchers enrolled in electrical engineering and related programs.

This book focuses on artifi cial intelligence in the field of digital signal processing and wireless communication. The implementation of machine learning and deep learning in audio, image, and video processing is presented, while adaptive signal processing and biomedical signal processing are also explored through DL algorithms, as well as 5G and green communication. Finally, metaheuristic algorithms of related mathematical problems are explored.

This textbook is unique because of its in-depth treatment of the applications of wavelets and wavelet transforms to many areas, across many disciplines. The book is written to serve the needs of a one or two semester course at either the undergraduate or graduate level. The author uses a very simplified, accessible approach that de-emphasizes mathematical rigor. The presentation includes many diagrams to illustrate points being discussed and uses MATLAB for all of application code. The author reinforces concepts introduced in the book with easy to grasp review questions and problems, tailored to each specific chapter for better mastery of the subject matter. This book enables students to understand the fundamental concepts of wavelets and wavelet transforms, as well as how to use them for problem solutions in digital signal and image processing, mixed-signal testing, space applications, aerospace applications, biomedical, cyber security, homeland security and many other application areas.

Signal 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 engagedwith signal processing, machine learning and the applications.

En traitement du signal, les techniques numériques apportent des possibilités prodigieuses. Cependant, ces techniques présentent un certain degré d'abstraction et leur application aux cas concrets requiert des connaissances théoriques qui peuvent représenter un obstacle.L'ambition de cet ouvrage est de vaincre cet obstacle en faisant le lien entrela théorie et la pratique, et en mettant à la portée du plus grand nombre les résultats les plus utiles dans ce domaine. Des applications pratiques et des exercices corrigés sont proposés à la fin de chaque chapitre afin que le lecteurpuisse valider de manière concrète ses acquis.Cette nouvelle édition, complétée et mise à jour, comporte un nouveau chapitreconsacré aux réseaux de neurones.

The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long-standing problem domains like speech and Image analysis. As well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering).This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests etc. This book focuses on AI utilization in the speech, image, communications and yirtual reality domains.

This 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.

This book presents novel RIS-Based Smart Radio techniques, targeting at achieving high-quality channel links in cellular communications via design and optimization of the RIS construction. Unlike traditional antenna arrays, three unique characteristics of the RIS will be revealed in this book. First, the built-in programmable configuration of the RIS enables analog beamforming inherently without extra hardware or signal processing. Second, the incident signals can be controlled to partly reflect and partly transmit through the RIS simultaneously, adding more flexibility to signal transmission. Third, the RIS has no digital processing capability to actively send signals nor any radio frequency (RF) components. As such, it is necessary to develop novel channel estimation and communication protocols, design joint digital and RIS-based analog beamforming schemes and perform interference control via mixed reflection and transmission. This book also investigates how to integrate the RIS to legacy communication systems. RIS techniques are further investigated in this book (benefited from its ability to actively shape the propagation environment) to achieve two types of wireless applications, i.e., RF sensing and localization. The influence of the sensing objectives on the wireless signal propagation can be potentially recognized by the receivers, which are then utilized to identify the objectives in RF sensing. Unlike traditional sensing techniques, RIS-aided sensing can actively customize the wireless channels and generate a favorable massive number of independent paths interacting with the sensing objectives. It is desirable to design RIS-based sensing algorithms, and optimize RIS configurations. For the second application, i.e., RIS aided localization, an RIS is deployed between the access point (AP) and users. The AP can then analyze reflected signals from users via different RIS configurations to obtain accurate locations of users. However, this is a challenging task due to the dynamic user topology, as well as the mutual influence between multiple users and the RIS. Therefore, the operations of the RIS, the AP, and multiple users need to be carefully coordinated. A new RIS-based localization protocol for device cooperation and an RIS configuration optimization algorithm are also required. This book targets researchers and graduate-level students focusing on communications and networks. Signal processing engineers, computer and information scientists, applied mathematicians and statisticians, who work in RIS research and development will also find this book useful.

ディジタル信号処理システムの最適化技術がわかる！ ディジタル信号処理システムの最適化技術について、実際の研究成果に基づき、現在の基礎技術から産業応用までをまとめました。 ディジタル信号処理はディジタル信号を扱う分野ならどこで も使われる基盤的な技術ですが、ますます高度化・複雑化する情報システムに対応した信号処理システムの設計にはシステム最適化が必須となっています。 本書は、基礎信号処理、広く用いられる音声・音響信号処理、産業応用と情報システムに用いられるシステム最適化技術についてオムニバス的に紹介します。あわせて信号処理が用いられる幅広い分野において横断的に適用可能なシステム最適化技術の可能性についても解説します。 実際の研究・事例ベースで説明するため、読者の現在の実務・研究に展開し、応用できる内容となっています。信号処理分野で現在、どのような研究や産業応用がなされているかがわかる貴重な一冊となっています。

To cope with the new running conditions in the ALICE experiment at the Large Hadron Collider at CERN, a new integrated circuit named SAMPA has been created that can process 32 analogue channels, convert them to digital, perform filtering and compression, and transmit the data on high speed links to the data acquisition system. The main purpose of this work is to specify, design, test and verify the digital signal processing part of the SAMPA device to accommodate the requirements of the detectors involved. Innovative solutions have been employed to reduce the bandwidth required by the detectors, as well as adaptations to ease data handling later in the processing chain. The new SAMPA device was built to replace two existing circuits, in addition to reducing the current consumption, and doubling the amount of processing channels. About 50000 of the devices will be installed in the Time Projection Chamber and Muon Chamber detectors in the ALICE experiment.

Channelizers and reconstructors find use in a wide variety of signal processing systems across many industries, including aerospace and telecommunications. This book represents a comprehensive collection of techniques, tips and tricks for the practicing digital signal processing professional that designs and implements wideband communications and sensing systems. Collected from a wide range of sources from across the industry and academia, rich in text, graphics, and examples, this book brings together a comprehensive assortment of techniques that are needed to design and field channelizers and reconstructors in real-world systems. Through a wide range of analysis and worked examples in the MatlabTM programming language, the reader is guided step-by-step through the process of designing and implementing these systems in the most efficient way possible.

This textbook provides an introduction to the study of digital signal processing, employing a top-to-bottom structure to motivate the reader, a graphical approach to the solution of the signal processing mathematics, and extensive use of MATLAB.In contrast to the conventional teaching approach, the book offers a top-down approach which first introduces students to digital filter design, provoking questions about the mathematical tools required. The following chapters provide answers to these questions, introducing signals in the discrete domain, Fourier analysis, filters in the time domain and the Z-transform. The author introduces the mathematics in a conceptual manner with figures to illustrate the physical meaning of the equations involved. Chapter six builds on these concepts and discusses advanced filter design, and chapter seven discusses matters of practical implementation. This book introduces the corresponding MATLAB functions and programs in everychapter with examples, and the final chapter introduces the actual real-time filter from MATLAB.Aimed primarily at undergraduate students in electrical and electronic engineering, this book enables the reader to implement a digital filter using MATLAB.Deliver the conceptual knowledge of digital signal processing with extensive use of the illustrations from practical viewpoint. Also, the digital signal processing is initiated from the digital not from the continuous domain.

This textbook for a one semester introductory course in digital signal processing for senior undergraduate and first year graduate students in electrical and computer engineering departments is concise, highly readable, and yet provides comprehensive coverage of the topic. Each new topic is presented with examples and figures. The highly mathematical content of the topic is presented lucidly to make the learning the subject easier. Practical aspects of the subject are clearly indicated so that the student can apply the principles in real applications. Matlab programs for FIR filter design are provided as supplementary material online.

An Introduction to the Digital Analysis of Stationary Signals: A Computer Illustrated Text directly illustrates the various techniques required to make accurate measurements of the properties of fluctuating signals. Emphasis is on qualitative ideas rather than detailed mathematical analysis for which the computer illustrated text format is ideally suited. The author reinforces normal figures and diagrams with computer-generated graphical displays produced dynamically by the student. This package of text and accompanying software is not specific to any particular microcomputer.

This 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.

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.

Predictive Filtering for Microsatellite Control Systems introduces technological design, modeling, stability analysis, predictive filtering, state estimation problem and real-time operation of spacecraft control systems in aerospace engineering. The book gives a systematically and almost self-contained description of the many facets of envisaging, designing, implementing or experimentally exploring predictive filtering for spacecraft control systems, along with the adequate designs of integrated modeling, dynamics, state estimation, and signal processing of spacecrafts and nonlinear systems. - Unifies existing and emerging concepts concerning predictive filtering theory, state estimation, and signal processing for spacecraft control systems - Provides a series of latest results in, including but not limited to, nonlinear filtering, attitude determination, and state estimation towards spacecraft control systems - Gives numerical and simulation results in each chapter in order to reflect the engineering practice and demonstrate the main focus of the developed analysis and synthesis approach - Covers advanced topics in nonlinear filtering with aerospace application

This hands-on, laboratory driven textbook helps readers understand principles of digital signal processing (DSP) and basics of software-based digital communication, particularly software-defined networks (SDN) and software-defined radio (SDR). In the book only the most important concepts are presented. Each book chapter is an introduction to computer laboratory and is accompanied by complete laboratory exercises and ready-to-go Matlab programs with figures and comments (available at the book webpage and running also in GNU Octave 5.2 with free software packages), showing all or most details of relevant algorithms. Students are tasked to understand programs, modify them, and apply presented concepts to recorded real RF signal or simulated received signals, with modelled transmission condition and hardware imperfections. Teaching is done by showing examples and their modifications to different real-world telecommunication-like applications. The book consists of three parts: introduction to DSP (spectral analysis and digital filtering), introduction to DSP advanced topics (multi-rate, adaptive, model-based and multimedia - speech, audio, video - signal analysis and processing) and introduction to software-defined modern telecommunication systems (SDR technology, analog and digital modulations, single- and multi-carrier systems, channel estimation and correction as well as synchronization issues).Many real signals are processed in the book, in the first part – mainly speech and audio, while in the second part – mainly RF recordings taken from RTL-SDR USB stick and ADALM-PLUTO module, for example captured IQ data of VOR avionics signal, classical FM radio with RDS, digital DAB/DAB+ radio and 4G-LTE digital telephony. Additionally, modelling and simulation of some transmission scenarios are tested in software in the book, in particular TETRA, ADSL and 5G signals.​Provides an introduction to digital signal processing and software-based digital communication;Presents a transition from digital signal processing to software-defined telecommunication;Features a suite of pedagogical materials including a laboratory test-bed and computer exercises/experiments​​.

This book presents advanced forensic detective control and obfuscation techniques for securing hardware IP cores by exploring beyond conventional technologies. The theme is important to researchers in various areas of specialization, because it encompasses the overlapping topics of EDA-CAD, hardware design security, VLSI design, IP core protection, optimization using evolutionary computing, system-on-chip design and finally application specific processor/hardware accelerator design for consumer electronics applications.

This book presents works from world-class experts from academia, industry, and national agencies representing countries from across the world focused on automotive fields for in-vehicle signal processing and safety. These include cutting-edge studies on safety, driver behavior, infrastructure, and human-to-vehicle interfaces. Vehicle Systems, Driver Modeling and Safety is appropriate for researchers, engineers, and professionals working in signal processing for vehicle systems, next generation system design from driver-assisted through fully autonomous vehicles.

This multimedia eBook establishes a solid foundation in the essential principles of how signals interact with transmission lines, how the physical design of interconnects affects transmission line properties, and how to interpret single-ended and differential time domain reflection (TDR) measurements to extract important figures of merits and avoid common mistakes. This book presents an intuitive understanding of transmission lines. Instructional videos are provided in every chapter that cover important aspects of the interconnect design and characterization process. This video eBook helps establish foundations for designing and characterizing the electrical properties of interconnects to explain in a simplified way how signals propagate and interact with interconnects and how the physical design of transmission structures will impact performance. Never be intimidated by impedance or differential pairs again.

Sound, devoid of meaning, would not matter to us. It is the information sound conveys that helps the brain to understand its environment. Sound and its underlying meaning are always associated with time and space. There is no sound without spatial properties, and the brain always organizes this information within a temporal–spatial framework. This book is devoted to understanding the importance of meaning for spatial and related further aspects of hearing, including cross-modal inference.People, when exposed to acoustic stimuli, do not react directly to what they hear but rather to what they hear means to them.This semiotic maxim may not always apply, for instance, when the reactions are reflexive. But, where it does apply, it poses a major challenge to the builders of models of the auditory system. Take, for example, an auditory model that is meant to be implemented on a robotic agent for autonomous search-&-rescue actions. Or think of a system that can performjudgments on the sound quality of multimedia-reproduction systems. It becomes immediately clear that such a system needs • Cognitive capabilities, including substantial inherent knowledge • The ability to integrate information across different sensory modalities To realize these functions, the auditory system provides a pair of sensory organs, the two ears, and the means to perform adequate preprocessing of the signals provided by the ears. This is realized in the subcortical parts of the auditory system. In the title of a prior book, the term Binaural Listening is used to indicate a focus on sub-cortical functions. Psychoacoustics and auditory signal processing contribute substantially to this area. The preprocessed signals are then forwarded to the cortical parts of the auditory system where, among other things, recognition, classification, localization, scene analysis, assignment of meaning, quality assessment, and action planning take place. Also, information from different sensory modalities is integrated at this level. Between sub-cortical and cortical regions of the auditory system, numerous feedback loops exist that ultimately support the high complexity and plasticity of the auditory system. The current book concentrates on these cognitive functions. Instead of processing signals, processing symbols is now the predominant modeling task. Substantial contributions to the field draw upon the knowledge acquired by cognitive psychology. The keyword Binaural Understanding in the book title characterizes this shift. Both books, The Technology of Binaural Listening and the current one, have been stimulated and supported by AABBA, an open research group devoted to the development and application of models of binaural hearing. The current book is dedicated to technologies that help explain, facilitate, apply, and support various aspects of binaural understanding. It is organized into five parts, each containing three to six chapters in order to provide a comprehensive overview of this emerging area. Each chapter was thoroughly reviewed by at least two anonymous, external experts. The first part deals with the psychophysical and physiological effects of Forming and Interpreting Aural Objects as well as the underlying models. The fundamental concepts of reflexive and reflective auditory feedback are introduced. Mechanisms of binaural attention and attention switching are covered—as well as how auditory Gestalt rules facilitate binaural understanding. A general blackboard architecture is introduced as an example of how machines can learn to form and interpret aural objects to simulate human cognitive listening. The second part, Configuring and Understanding Aural Space, focuses on the human understanding of complex three-dimensional environments—covering the psychological and biological fundamentals of auditory space formation. This part further addresses the human mechani

This book highlights recent advances and emerging technologies that utilize computational intelligence in signal processing, computing, imaging science, artificial intelligence, and their applications. It covers all branches of artificial intelligence and machine learning that are based on computation at some level, e.g. artificial neural networks, evolutionary algorithms, fuzzy systems, and automatic medical identification systems. Exploring recent trends in research and applications, the book offers a valuable resource for professors, researchers, and engineers alike.

Seismic data must be interpreted using digital signal processing techniques in order to create accurate representations of petroleum reservoirs and the interior structure of the Earth. This book provides an advanced overview of digital signal processing (DSP) and its applications to exploration seismology using real-world examples. The book begins by introducing seismic theory, describing how to identify seismic events in terms of signals and noise, and how to convert seismic data into the language of DSP. Deterministic DSP is then covered, together with non-conventional sampling techniques. The final part covers statistical seismic signal processing via Wiener optimum filtering, deconvolution, linear-prediction filtering and seismic wavelet processing. With over sixty end-of-chapter exercises, seismic data sets and data processing MATLAB codes included, this is an ideal resource for electrical engineering students unfamiliar with seismic data, and for Earth Scientists and petroleum professionals interested in DSP techniques.

Digital Audio Theory: A Practical Guide bridges the fundamental concepts and equations of digital audio with their real-world implementation in an accessible introduction, with dozens of programming examples and projects.Starting with digital audio conversion, then segueing into filtering, and finally real-time spectral processing, Digital Audio Theory introduces the uninitiated reader to signal processing principles and techniques used in audio effects and virtual instruments that are found in digital audio workstations. Every chapter includes programming snippets for the reader to hear, explore, and experiment with digital audio concepts. Practical projects challenge the reader, providing hands-on experience in designing real-time audio effects, building FIR and IIR filters, applying noise reduction and feedback control, measuring impulse responses, software synthesis, and much more. Music technologists, recording engineers, and students of these fields will welcome Bennett's approach, which targets readers with a background in music, sound, and recording. This guide is suitable for all levels of knowledge in mathematics, signals and systems, and linear circuits. Code for the programming examples and accompanying videos made by the author can be found on the companion website, DigitalAudioTheory.com.

This book describes for readers the entire, interconnected complex of theoretical and practical aspects of designing and organizing the production of various electronic devices, the general and main distinguishing feature of which is the high speed of processing and transmitting of digital signals. The authors discuss all the main stages of design - from the upper system level of the hierarchy (telecommunications system, 5G mobile communications) to the lower level of basic semiconductor elements, printed circuit boards. Since the developers of these devices in practice deal with distorted digital signals that are transmitted against a background of interference, the authors not only explain the physical nature of such effects, but also offer specific solutions as to how to avoid such parasitic effects, even at the design stage of high-speed devices.

This book highlights recent research on computer recognition systems, one of the most promising directions in artificial intelligence. Offering the most comprehensive study on this field to date, it gathers 36 carefully selected articles contributed by experts on pattern recognition. Presenting recent research on methodology and applications, the book offers a valuable reference tool for scientists whose work involves designing computer pattern recognition systems. Its target audience also includes researchers and students in computer science, artificial intelligence, and robotics.

This 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 ofthe 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.

Considerable amount of effort has been devoted, over the recent years, towards the development of electronic skin (e-skin) for many application domains such as prosthetics, robotics, and industrial automation.Electronic Skin: Sensors and Systems focuses on the main components constituting the e-skin system. The e-skin system is based on: i) sensing materials composing the tactile sensor array, ii) the front end electronics for data acquisition and signal conditioning, iii) the embedded processing unit performing tactile data decoding, and iv) the communication interface in charge of transmitting the sensors data for further computing.Technical topics discussed in the book include: • Tactile sensing material; • Electronic Skin systems;• Embedded computing and tactile data decoding; • Communication systems for tactile data transmission; • Relevant applications of e-skin system;The book takes into account not only sensing materials but it also provides a thorough assessment of the current state of the art at system level. The book addresses embedded electronics and tactile data processing and decoding, techniques for low power embedded computing, and the communication interface.Electronic Skin: Sensors and Systems is ideal for researchers, Ph.D. students, academic staff and Masters/research students in sensors/sensing systems, embedded systems, data processing and decoding, and communication systems.

The book addresses the current demand for a scientific approach to advanced wireless technology and its future developments. It gives a clear presentation of both antennas and adaptive signal processing which is what makes antennas powerful, maneuverable and necessary for advanced wireless technology. The book presents electromagnetic signal processing techniques that both control the antenna beam and track the moving station, which is required for effective, fast, dynamic beamforming.The first part of the book presents a comprehensive description and analysis of basic antenna theory, starting from short dipole antennas to array antennas. This section also includes important concepts related to antenna parameters, electromagnetic wave propagation, the Friis equation, the radar equation and wave reflection and transmission through media.The second part of the book focuses on smart antennas, commencing from a look at the traditional approach to beamforming before getting into the details of smart antennas. Complete derivation and description of the techniques for electromagnetic field signal processing techniques for adaptive beamforming are also presented. Artificial Intelligence (AI) driven beamforming is presented using computationally fast and low-memory demanding technique for AI beamforming is presented with the different excitation functions available. A novel method for fast, low memory and accurate, maneuverable single beam generation is presented, as well as other methods for beamforming with fewer elements along with a simple method for tracking the mobile antenna and station. In this section, for completeness, the use of antenna signal processing for synthetic aperture techniques for imaging is also presented, specifically the Inverse Synthetic Aperture Imaging technique.The third part of the book presents technological aspects of advanced wireless technology, including the 5G wireless system and the various devices needed to construct it. While the books'main emphasis is theoretical understanding and design, it includes applications, and legal matters are also presented.

This book is a collection of specific research problems in signal processing and their solutions. It touches upon most core topics, including active and passive processing, discrete-time and continuous signals, and design of filters and networks for specific applications. This unique collection of design problems and conceptual insights will be useful to graduate students, researchers, and professionals working on signal processing problems. In addition, the book can also be used as a supplementary text for graduate courses in advanced signal processing, and for professional development courses for practicing engineers.