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This book provides a compact but comprehensive treatment that guides the reader through the important applications of operational amplifiers. The author uses his extensive classroom experience to guide readers toward a deeper understanding of key concepts of operational amplifier circuits. The NI® Multisim™ is used throughout the book to analyze and design the circuits. The book is designed to serve as a textbook for courses offered to undergraduate and postgraduate students enrolled in electrical and computer engineering. The prerequisite for this book is a first course in electric circuits.
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The book focuses on sparse multi-sensor array systems and design approaches. Both principles and engineering practice have been addressed, with more weight placed on algorithm development. This is achieved by providing an in-depth study on sparse sensing for several major multi-sensor array applications such as beam-pattern synthesis, adaptive beamforming, target detection, arrival angle estimation, and dual-functional radar communications. Sparsity sensed in multi-sensor arrays refers to the sparse property of the spatial spectrum sensed. The exploitation of the sparsity in the sensed can significantly enhance the performance of signal processing systems. The comprehensive and systematic treatment of theory and practice in different array applications is one of the major features of the book, which is particularly suited for readers who are interested to learn practical solutions in array signal processing. The book benefits researchers, engineers, and graduate students in the fields of signal processing, electrical engineering, telecommunications, etc.
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This textbook provides an introduction to common concepts in biosignal acquisition and processing, rooted in a Project-Based Learning (PBL). A series of exercises and corresponding solutions is described, with a focus on common problems that new entrants encounter in the undergraduate and graduate curriculums in biomedical engineering, psychophysiology, human-computer interaction, physiotherapy, and others developing work in the boundary between biosignal acquisition and other disciplines. Solutions include both hardware and software components, encompassing sensor design, biosignals acquisition using the Arduino and BITalino platforms, Python for signal processing, analysis, and knowledge extraction, including also HTML/CSS/JavaScript for user interface design. All the content is supported with rich graphics, source code, and other useful resources made available to the interested reader. This is an ideal book for undergraduate students and practitioners (across disciplines) who are studying or working in the field of biomedical signal acquisition, processing, and analysis.
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This book features the proceedings of the 6th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (BDCC 2023). The papers feature detail on cognitive computing and its self-learning systems that use data mining, pattern recognition and natural language processing (NLP) to mirror the way the human brain works. This international conference focuses on technologies from knowledge representation techniques and natural language processing algorithms to dynamic learning approaches. Topics covered include Data Science for Cognitive Analysis, Real-Time Ubiquitous Data Science, Platform for Privacy Preserving Data Science, and Internet-Based Cognitive Platform. Contains proceedings from 6th EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing; Features topics ranging from data science for cognitive analysis to Internet-based cognitive platforms; Relevant for researchers, professionals, academics, and students in big data for sustainable cognitive computing.
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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 and IIR filter design are provided as supplementary material online. Written to be accessible to students of varying backgrounds, this textbook explains digital signal processing from both a theoretical and practical point of view Presents concepts in a clear, concise and comprehensive manner, so that students can learn easily this highly mathematical topic Provides detailed coverage of various types of filter design, including an introduction to the discrete wavelet transform Includes worked examples throughout every chapter, with an emphasis on real applications Includes numerous exercises at the end of each chapter Provides Matlab programs for FIR filter design, as supplementary material online.
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This book constitutes a review of recent developments in the theory and practical exploitation of the elliptical model for measured data in both classical and emerging areas of signal processing. It develops techniques usable in (among other areas): graph learning, robust clustering, linear shrinkage, information geometry, subspace-based algorithm design, and semiparametric and misspecified estimation. The various contributions combine to show how the goal of inferring information from a set of acquired data, recurrent in statistical signal processing, can be achieved, even when the common practical assumption of Gaussian distribution in the data is not valid. The elliptical model propounded maintains the performance of its inference procedures even when that assumption fails. The elliptical distribution, being fully characterized by its location vector, its scatter/covariance matrix and its so-called density generator, used to describe the impulsiveness of the data, is sufficiently flexible to model heterogeneous applications. This book is of interest to any graduate students and academic researchers wishing to acquaint themselves with the latest research in an area of rising consequence. It is also of assistance to practitioners working in data analysis, wireless communications, radar, and image processing.
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This book explains the motivation for using microphone arrays as opposed to using a single sensor for sound acquisition. The book then goes on to summarize the most useful ideas, concepts, results, and new algorithms therein. The material presented in this work includes analysis of the advantages of using microphone arrays, including dimensionality reduction to remove the redundancy while preserving the variability of the array signals using the principal component analysis (PCA). The authors also discuss benefits such as beamforming with low-rank approximations, fixed, adaptive, and robust distortionless beamforming, differential beamforming, and a new form of binaural beamforming that takes advantage of both beamforming and human binaural hearing properties to improve speech intelligibility. The book makes the microphone array signal processing theory and applications available in a complete and self-contained text. The authors attempt to explain the main ideas in a clear and rigorous way so that the reader can easily capture the potentials, opportunities, challenges, and limitations of microphone array signal processing. This book is written for those who work on the topics of microphone arrays, noise reduction, speech enhancement, speech communication, and human-machine speech interfaces. Presents the benefits of microphone arrays over single sensors from a signal enhancement / spatial filtering perspective; Includes algorithms for dimensionality reduction and fixed and adaptive beamforming with microphone arrays; Posits a framework that takes advantage of microphone array and human binaural hearing to improve speech intelligibility.
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This book is mostly devoted to amplification of analogue signals. It covers different technologies (bipolar, MOS, and MES), and different frequency ranges but it always deals with small signals. Analogue signals processed in electronic system may have a wide variety of origins. Among them we have the signals coming from sensors (electro-mechanical, electro-magnetic, electro-chemical, electro-acoustic, electro-optical, etc.), the signals coming from antennas being produced by another electronic system or are simply cosmic produced, and signals that are generated within the electronic systems. The common property of most of the signals is their small amplitude. In many cases it is below a micro-volt. Since at the output of the system we most frequently need a high amplitude signal the main action undertaken in the electronic system before any further processing is to amplify.
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This book focuses on both theoretical and practical aspects of the “Device-Edge-Cloud continuum”, a development approach aimed at the seamless provision of next-generation cyber-physical services through the dynamic orchestration of heterogeneous computing resources, located at different distances to the user and featured by different peculiarities (high responsiveness, high computing power, etc.). The book specifically explores recent advances in paradigms, architectures, models, and applications for the “Device-Edge-Cloud continuum”, which raises many 'in-the-small' and 'in-the-large' issues involving device programming, system architectures and methods for the development of IoT ecosystem. In this direction, the contributions presented in the book propose original solutions and aim at relevant domains spanning from healthcare to industry, agriculture and transportation. Presents approaches, paradigms and architectures for the development of the Device-Edge-Cloud continuum; Describes applications of the Device-Edge-Cloud continuum in smart cities, smart agriculture, and autonomous vehicles; Proposes original solutions that aim at relevant domains spanning from healthcare to industry.
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This book covers virtually all aspects of image formation in medical imaging, including systems based on ionizing radiation (x-rays, gamma rays) and non-ionizing techniques (ultrasound, optical, thermal, magnetic resonance, and magnetic particle imaging) alike. In addition, it discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging. Given its coverage, the book provides both a forum and valuable resource for researchers involved in image formation, experimental methods, image performance, segmentation, pattern recognition, feature extraction, classifier design, machine learning / deep learning, radiomics, CAD workstation design, human–computer interaction, databases, and performance evaluation. .
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