The book presents fundamentals of communication electronic circuits, including structure, principle, analyzing methodology, design and design software. Radio frequency amplifier, sinusoidal oscillator, amplitude modulation and demodulation, angular modulation and demodulation are described in detail. The book serves for learning and teaching but can also help researchers and professionals as reference.

This book gathers high-quality research papers presented at the Global AI Congress 2019, which was organized by the Institute of Engineering and Management, Kolkata, India, on 12-14 September 2019. Sharing contributions prepared by researchers, practitioners, developers and experts in the areas of artificial intelligence, the book covers the areas of AI for E-commerce and web applications, AI and sensors, augmented reality, big data, brain computing interfaces, computer vision, cognitive radio networks, data mining, deep learning, expert systems, fuzzy sets and systems, image processing, knowledge representation, nature-inspired computing, quantum machine learning, reasoning, robotics and autonomous systems, robotics and the IoT, social network analysis, speech processing, video processing, and virtual reality.

Nowadays, tensors play a central role for the representation, mining, analysis, and fusion of multidimensional, multimodal, and heterogeneous big data in numerous fields. This set on Matrices and Tensors in Signal Processing aims at giving a self-contained and comprehensive presentation of various concepts and methods, starting from fundamental algebraic structures to advanced tensor-based applications, including recently developed tensor models and efficient algorithms for dimensionality reduction and parameter estimation. Although its title suggests an orientation towards signal processing, the results presented in this set will also be of use to readers interested in other disciplines. This first book provides an introduction to matrices and tensors of higher-order based on the structures of vector space and tensor space. Some standard algebraic structures are first described, with a focus on the hilbertian approach for signal representation, and function approximation based on Fourier series and orthogonal polynomial series. Matrices and hypermatrices associated with linear, bilinear and multilinear maps are more particularly studied. Some basic results are presented for block matrices. The notions of decomposition, rank, eigenvalue, singular value, and unfolding of a tensor are introduced, by emphasizing similarities and differences between matrices and tensors of higher-order.

This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website.

This book offers an in-depth discussion related to metrological aspects of automated tests. The accuracy of experimental estimates of test object performance is examined from the standpoint of their statistical variance and systematic biases. The proposed metrological model of automated tests allows to determine the metrological characteristics of measurement means using data from their static and dynamic calibrations. Knowledge of these characteristics provides an ability to examine their impact on the accuracy of test results for the purposes of estimating statistical uncertainties caused by instrumentation errors and eliminating biases that occur as a consequence of inertial properties of measurement means. Optimization of requirements for measurement errors to ensure a given accuracy of test results is discussed as well. Proposed approaches and described methods are illustrated by test examples of turbomachinery products.

This book is about recent research in the area of profiling humans from their voice, which seeks to deduce and describe the speaker's entire persona and their surroundings from voice alone. It covers several key aspects of this technology, describing how the human voice is unique in its ability to both capture and influence the human persona -- how, in some ways, voice is more potent and valuable then DNA and fingerprints as a metric, since it not only carries information about the speaker, but also about their current state and their surroundings at the time of speaking. It provides a comprehensive review of advances made in multiple scientific fields that now contribute to its foundations. It describes how artificial intelligence enables mechanisms of discovery that were not possible before in this context, driving the field forward in unprecedented ways. It also touches upon related and relevant challenges posed by voice disguise and other mechanisms of voice manipulation. The book acts as a good resource for academic researchers, and for professional agencies in many areas such as law enforcement, healthcare, social services, entertainment etc.

This book provides the basic concepts and fundamental principles of dynamic systems including experimental methods, calibration, signal conditioning, data acquisition and processing as well as the results presentation. How to select suitable sensors to measure is also introduced. It is an essential reference to students, lecturers, professionals and any interested lay readers in measurement technology.

This book reports the latest results in the study of Bistatic/Multistatic SAR system and signal processing techniques. Novel research ideas and experimental verification have been collected on all kinds of configurations of Bistatic/Multistatic SAR system, including the preliminary construction of system model, imaging algorithm design, mission design and the corresponding application representations etc. Handy well-prepared tables are provided for readers' quick-reference, and the practical design of an interferometric SAR system is illustrated step by step. The book will be of interest to university researchers, R&D engineers and graduate students in Remote Sensing who wish to learn the core principles, methods, algorithms, and applications of Bistatic/Multistatic SAR system.

Describes the operating principles of analog MOS integrated circuits and how to design and use such circuits. The initial section explores general properties of analog MOS integrated circuits and the math and physics background required. The remainder of the book is devoted to the design of circuits. Includes such devices as switched-capacitor filters, analog-to-digital and digital-to-analog converters, amplifiers, modulators, oscillators, and others. Tables and numerical design examples clarify the step-by-step processes involved.

The volume is dedicated to fields related to design, modeling, fundamentals and application of communication systems. Focusing on wireless technology it covers content based video retrieval, B4G wierless communication systems and their application, data fusion and pattern recognition. It also discusses the influences of coding compression and e-mobility on information and communication technology.

The book presents selected, extended and peer reviewed papers from the International Multiconference on System, Automation and Control held Leipzig in 2016. These are complemented with solicited contributions by international experts. This volume is devoted to power electronics in renewable energy systems as well as to hybrid renewable energy systems.

A self-contained introduction to adaptive inverse control
Now featuring a revised preface that emphasizes the coverage of both control systems and signal processing, this reissued edition of Adaptive Inverse Control takes a novel approach that is not available in any other book.
Written by two pioneers in the field, Adaptive Inverse Control presents methods of adaptive signal processing that are borrowed from the field of digital signal processing to solve problems in dynamic systems control. This unique approach allows engineers in both fields to share tools and techniques. Clearly and intuitively written, Adaptive Inverse Control illuminates theory with an emphasis on practical applications and commonsense understanding. It covers: the adaptive inverse control concept; Weiner filters; adaptive LMS filters; adaptive modeling; inverse plant modeling; adaptive inverse control; other configurations for adaptive inverse control; plant disturbance canceling; system integration; Multiple-Input Multiple-Output (MIMO) adaptive inverse control systems; nonlinear adaptive inverse control systems; and more.
Complete with a glossary, an index, and chapter summaries that consolidate the information presented, Adaptive Inverse Control is appropriate as a textbook for advanced undergraduate- and graduate-level courses on adaptive control and also serves as a valuable resource for practitioners in the fields of control systems and signal processing.

The book presents selected, extended and peer reviewed papers from the International Multiconference on System, Automation and Control held Leipzig in 2018. These are complemented with solicited contributions by international experts. Main topics are automatic control, robotics, synthesis of automation systems. Application examples range from man-machine interaction, mechatronics, on to biological and economical models.

The book presents selected, extended and peer reviewed papers from the International Multiconference on System, Automation and Control held Leipzig in 2016. These are complemented with solicited contributions by international experts. Main topics are: mechanical and thermal sensors, nano sensors, optical, chemical and biomedical sensors. They are applied in energy harvesting, biomedical and environmental measurments and more.

This thesis proposes machine learning methods for understanding scenes via behaviour analysis and online anomaly detection in video. The book introduces novel Bayesian topic models for detection of events that are different from typical activities and a novel framework for change point detection for identifying sudden behavioural changes. Behaviour analysis and anomaly detection are key components of intelligent vision systems. Anomaly detection can be considered from two perspectives: abnormal events can be defined as those that violate typical activities or as a sudden change in behaviour. Topic modelling and change-point detection methodologies, respectively, are employed to achieve these objectives. The thesis starts with the development of learning algorithms for a dynamic topic model, which extract topics that represent typical activities of a scene. These typical activities are used in a normality measure in anomaly detection decision-making. The book also proposes a novel anomaly localisation procedure. In the first topic model presented, a number of topics should be specified in advance. A novel dynamic nonparametric hierarchical Dirichlet process topic model is then developed where the number of topics is determined from data. Batch and online inference algorithms are developed. The latter part of the thesis considers behaviour analysis and anomaly detection within the change-point detection methodology. A novel general framework for change-point detection is introduced. Gaussian process time series data is considered. Statistical hypothesis tests are proposed for both offline and online data processing and multiple change point detection are proposed and theoretical properties of the tests are derived. The thesis is accompanied by open-source toolboxes that can be used by researchers and engineers.

Design and MATLAB concepts have been integrated in text.
* Integrates applications as it relates signals to a remote sensing system, a controls system, radio astronomy, a biomedical system and seismology.

The book covers the most recent developments in machine learning, signal analysis, and their applications. It covers the topics of machine intelligence such as: deep learning, soft computing approaches, support vector machines (SVMs), least square SVMs (LSSVMs) and their variants; and covers the topics of signal analysis such as: biomedical signals including electroencephalogram (EEG), magnetoencephalography (MEG), electrocardiogram (ECG) and electromyogram (EMG) as well as other signals such as speech signals, communication signals, vibration signals, image, and video. Further, it analyzes normal and abnormal categories of real-world signals, for example normal and epileptic EEG signals using numerous classification techniques. The book is envisioned for researchers and graduate students in Computer Science and Engineering, Electrical Engineering, Applied Mathematics, and Biomedical Signal Processing.

For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals — radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.

A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms.

Power Electrical Systems are an indespensable feature of the exploitation and diagnostics of elecrical machines and energy resources. The Volume presents extended and peer reviewed papers from the international conference on PES in Barcelona, 2014. Among the topics dealt with are: electrical machines design, voltage and control, automotive power drives, electromagnetic compatibility, monitoring and diagnostics, renewable energy systems. The International Conference on Power Electrical Systems (PES) is a forum for researchers and specialists in different fields of electrical engineering related to Hybrid Renewable Energy Systems (HRES); Power Electronics in Renewable Energy Systems; Topologies and Control of Power Electronics Converters Used in Renewable Energy Systems; Electric machines modelling and control; Automotive electrical systems; Electric machine design; Monitoring and diagnostics; Special machines; Power systems; Power electronic converters; Renewable energy systems; Variable speed drives; Electromagnetic compatibility; Variable speed generating systems; Transformers.

A comprehensive collection of fundamental principles and applications of analog electronic circuits, including semiconductor diodes, bipolar junction transistors, field-effect transistors (FETs), operational amplifiers, power amplifiers, and feedback circuits. With abundant practical examples, it is an essential reference for researchers, students and engineers in electronical engineering and information processing.

This book explores the fundamentals of computer music and functional programming through the Haskell programming language. Functional programming is typically considered difficult to learn. This introduction in the context of creating music will allow students and professionals with a musical inclination to leverage their experience to help understand concepts that might be intimidating in more traditional computer science settings. Conversely, the book opens the door for programmers to interact with music by using a medium that is familiar to them. Readers will learn how to use the Euterpea library for Haskell (http://www.euterpea.com) to represent and create their own music with code, without the need for other music software. The book explores common paradigms used in algorithmic music composition, such as stochastic generation, musical grammars, self-similarity, and real-time interactive systems. Other topics covered include the basics of signal-based systems in Haskell, sound synthesis, and virtual instrument design.

An Introduction to Audio Content Analysis Enables readers to understand the algorithmic analysis of musical audio signals with AI-driven approaches An Introduction to Audio Content Analysis serves as a comprehensive guide on audio content analysis explaining how signal processing and machine learning approaches can be utilized for the extraction of musical content from audio. It gives readers the algorithmic understanding to teach a computer to interpret music signals and thus allows for the design of tools for interacting with music. The work ties together topics from audio signal processing and machine learning, showing how to use audio content analysis to pick up musical characteristics automatically. A multitude of audio content analysis tasks related to the extraction of tonal, temporal, timbral, and intensity-related characteristics of the music signal are presented. Each task is introduced from both a musical and a technical perspective, detailing the algorithmic approach as well as providing practical guidance on implementation details and evaluation. To aid in reader comprehension, each task description begins with a short introduction to the most important musical and perceptual characteristics of the covered topic, followed by a detailed algorithmic model and its evaluation, and concluded with questions and exercises. For the interested reader, updated supplemental materials are provided via an accompanying website. Written by a well-known expert in the music industry, sample topics covered in Introduction to Audio Content Analysis include: Digital audio signals and their representation, common time-frequency transforms, audio features Pitch and fundamental frequency detection, key and chord Representation of dynamics in music and intensity-related features Beat histograms, onset and tempo detection, beat histograms, and detection of structure in music, and sequence alignment Audio fingerprinting, musical genre, mood, and instrument classification An invaluable guide for newcomers to audio signal processing and industry experts alike, An Introduction to Audio Content Analysis covers a wide range of introductory topics pertaining to music information retrieval and machine listening, allowing students and researchers to quickly gain core holistic knowledge in audio analysis and dig deeper into specific aspects of the field with the help of a large amount of references.

This comprehensive and engaging textbook introduces the basic principles and techniques of signal processing, from the fundamental ideas of signals and systems theory to real-world applications. Students are introduced to the powerful foundations of modern signal processing, including the basic geometry of Hilbert space, the mathematics of Fourier transforms, and essentials of sampling, interpolation, approximation and compression The authors discuss real-world issues and hurdles to using these tools, and ways of adapting them to overcome problems of finiteness and localization, the limitations of uncertainty, and computational costs. It includes over 160 homework problems and over 220 worked examples, specifically designed to test and expand students' understanding of the fundamentals of signal processing, and is accompanied by extensive online materials designed to aid learning, including Mathematica (R) resources and interactive demonstrations.

Signal Processing is one of the large specializations in electrical engineering, mechanical engineering and computer sciences. It derives input from physics, mathematics and is an indispensable feature of all natural- and life sciences in research and in application. The snew series "Advanced Issues on Signals, Systems and Devices" presents original publications mainly from speakers on the International Conferences on Signal Systems and Devices but also from other international authors. The Conference is a forum for researchers and specialists in different fields covering all types of sensors and measurement systems as for example: Biomedical and Environmental Measurements & Instrumentation; Optical, Chemical and Biomedical Sensors; Mechanical and Thermal Sensors; Micro-Sensors and MEMS-Technology; Nano Sensors, Nano Systems and Nano Technology; Spectroscopy Methods; Signal Processing and Modelling; Multi Sensor Data Fusion; Data Acquisition & Distributed Measurements; Medical and Environmental Applications; Circuit Test, Device Characterization and Modelling; Custom and Semi-Custom Circuits; Analog Circuit Design; Low-Voltage, Low-Power VLSI Design; Hardware Implementation; Materials, Devices and Interconnects; Packaging and Reliability; Battery Monitoring: Impedance Spectroscopy for Measurement and Sensor Solutions; Energy Harvesting and Wireless power Transfer Systems; Wireless Sensor Networks in Industrial Plants This first volume of the new series mainly devotes to the most recent research and implementation of sensors-, circuit systems in signal processing, energy harvesting, nano- and molecular electronics.