This book discusses autonomous spacecraft navigation based on X-ray pulsars, analyzing how to process X-ray pulsar signals, how to simulate them, and how to estimate the pulse's time of arrival based on epoch folding. In turn, the book presents a range of X-ray pulsar-based spacecraft positioning/time-keeping/attitude determination methods. It also describes the error transmission mechanism of the X-ray pulsar-based navigation system and its corresponding compensation methods. Further, the book introduces readers to navigation based on multiple measurement information fusion, such as X-ray pulsar/traditional celestial body integrated navigation and X-ray pulsar/INS integrated navigation. As such, it offers readers extensive information on both the theory and applications of X-ray pulsar-based navigation, and reflects the latest developments in China and abroad.

A bridge between the application of subspace-based methods for parameter estimation in signal processing and subspace-based system identification in control systems Model-Based Processing An Applied Subspace Identification Approach provides expert insight on developing models for designing model-based signal processors (MBSP) employing subspace identification techniques to achieve model-based identification (MBID) and enables readers to evaluate overall performance using validation and statistical analysis methods. Focusing on subspace approaches to system identification problems, this book teaches readers to identify models quickly and incorporate them into various processing problems including state estimation, tracking, detection, classification, controls, communications, and other applications that require reliable models that can be adapted to dynamic environments. The extraction of a model from data is vital to numerous applications, from the detection of submarines to determining the epicenter of an earthquake to controlling an autonomous vehicles--all requiring a fundamental understanding of their underlying processes and measurement instrumentation. Emphasizing real-world solutions to a variety of model development problems, this text demonstrates how model-based subspace identification system identification enables the extraction of a model from measured data sequences from simple time series polynomials to complex constructs of parametrically adaptive, nonlinear distributed systems. In addition, this resource features: Kalman filtering for linear, linearized, and nonlinear systems; modern unscented Kalman filters; as well as Bayesian particle filters Practical processor designs including comprehensive methods of performance analysis Provides a link between model development and practical applications in model-based signal processing Offers in-depth examination of the subspace approach that applies subspace algorithms to synthesized examples and actual applications Enables readers to bridge the gap from statistical signal processing to subspace identification Includes appendices, problem sets, case studies, examples, and notes for MATLAB Model-Based Processing: An Applied Subspace Identification Approach is essential reading for advanced undergraduate and graduate students of engineering and science as well as engineers working in industry and academia.

This book focuses on broadband power amplifier design for wireless communication. Nonlinear model embedding is described as a powerful tool for designing broadband continuous Class-J and continuous class F power amplifiers. The authors also discuss various techniques for extending bandwidth of load modulation based power amplifiers, such as Doherty power amplifier and Chireix outphasing amplifiers. The book also covers recent trends on digital as well as analog techniques to enhance bandwidth and linearity in wireless transmitters. Presents latest trends in designing broadband power amplifiers; Covers latest techniques for using nonlinear model embedding in designing power amplifiers based on waveform engineering; Describes the latest techniques for extending bandwidth of load modulation based power amplifiers such as Doherty power amplifier and Chireix outphasing amplifiers; Includes coverage of hybrid analog/digital predistortion as wideband solution for wireless transmitters; Discusses recent trends on on-chip power amplifier design with GaN /GaAs MMICs for high frequency applications.

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 of the characteristics of analog-to- digital converters and CFO compensation for OFDM modulation. The third and last part demonstrates the use of some of these techniques in modern wireless-communication systems, such as full-duplex transmission, massive MIMO schemes and Internet of Things applications.

This book presents the complex topic of using computational intelligence for pattern recognition in a straightforward and applicable way, using Matlab to illustrate topics and concepts. The author covers computational intelligence tools like particle swarm optimization, bacterial foraging, simulated annealing, genetic algorithm, and artificial neural networks. The Matlab based illustrations along with the code are given for every topic. Readers get a quick basic understanding of various pattern recognition techniques using only the required depth in math. The Matlab program and algorithm are given along with the running text, providing clarity and usefulness of the various techniques. Presents pattern recognition and the computational intelligence using Matlab; Includes mixtures of theory, math, and algorithms, letting readers understand the concepts quickly; Outlines an array of classifiers, various regression models, statistical tests and the techniques for pattern recognition using computational intelligence.

This book reports on the latest advances in the study of biomedical signal processing, and discusses in detail a number of open problems concerning clinical, biomedical and neural signals. It methodically collects and presents in a unified form the research findings previously scattered throughout various scientific journals and conference proceedings. In addition, the chapters are self-contained and can be read independently. Accordingly, the book will be of interest to university researchers, R&D engineers and graduate students who wish to learn the core principles of biomedical signal analysis, algorithms, and applications, while also offering a valuable reference work for biomedical engineers and clinicians who wish to learn more about the theory and recent applications of neural engineering and biomedical signal processing.

The book discusses subjective ratings of quality and preference of unknown voices and dialog partners - their likability, for example. Human natural and artificial voices are studied in passive listening and interactive scenarios. In this book, the background, state of research, and contributions to the assessment and prediction of talker quality that is constituted in voice perception and in dialog are presented. Starting from theories and empirical findings from human interaction, major results and approaches are transferred to the domain of human-computer interaction (HCI). The main objective of this book is to contribute to the evaluation of spoken interaction in humans and between humans and computers, and in particular to the quality subsequently attributed to the speaking system or person based on the listening and interactive experience. Provides a comprehensive overview of research in evaluation of speakers and dialog partners; Presents recent results on the relevance of a first passive and interactive impression; Includes human and HCI evaluation results from a communicative perspective.

This book enables readers to achieve ultra-low energy digital system performance. The author's main focus is the energy consumption of microcontroller architectures in digital (sub)-systems. The book covers a broad range of topics extensively: from circuits through design strategy to system architectures. The result is a set of techniques and a context to realize minimum energy digital systems. Several prototype silicon implementations are discussed, which put the proposed techniques to the test. The achieved results demonstrate an extraordinary combination of variation-resilience, high speed performance and ultra-low energy.

This book introduces readers to all the necessary components and knowledge to start being a vital part of the IoT revolution. The author discusses how to create smart-IoT solutions to help solve a variety of real problems. Coverage includes the most important aspects of IoT architecture, the various applications of IoT, and the enabling technologies for IoT. This book presents key IoT concepts and abstractions, while showcasing real case studies. The discussion also includes an analysis of IoT strengths, weaknesses, opportunities and threats. Readers will benefit from the in-depth introduction to internet of things concepts, along with discussion of IoT algorithms and architectures tradeoffs. Case studies include smart homes, smart agriculture, and smart automotive.

This book presents the state-of-the-art and breakthrough innovations in design automation for cyber-physical systems.The authors discuss various aspects of cyber-physical systems design, including modeling, co-design, optimization, tools, formal methods, validation, verification, and case studies. Coverage includes a survey of the various existing cyber-physical systems functional design methodologies and related tools will provide the reader unique insights into the conceptual design of cyber-physical systems.

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered.

This book explains speech enhancement in the Fractional Fourier Transform (FRFT) domain and investigates the use of different FRFT algorithms in both single channel and multi-channel enhancement systems, which has proven to be an ideal time frequency analysis tool in many speech signal processing applications. The authors discuss the complexities involved in the highly non- stationary signal processing and the concepts of FRFT for speech enhancement applications. The book explains the fundamentals of FRFT as well as its implementation in speech enhancement. Theories of different FRFT methods are also discussed. The book lets readers understand the new fractional domains to prepare them to develop new algorithms. A comprehensive literature survey regarding the topic is also made available to the reader.

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.

Drawing on 15 years of experience in the development and use of wearable sensors in sports science, this book bridges the gap between technical research and the widespread adoption of inertial sensors in biomechanical assessment and ambulatory studies of locomotion. It offers a 'no-nonsense' guide to using inertial sensors for readers from the sports science disciplines who may be unfamiliar with the terms, concepts and approaches that lead to these sensors' successful use. At the same time, the book introduces readers with a technical background, e.g. in engineering, to sport science methodologies that can provide valuable insights into the use of sensors in a practical environment that extends well beyond bench testing.

This book presents selected research papers on current developments in the fields of soft computing and signal processing from the Second International Conference on Soft Computing and Signal Processing (ICSCSP 2019). The respective contributions address topics such as soft sets, rough sets, fuzzy logic, neural networks, genetic algorithms and machine learning, and discuss various aspects of these topics, e.g. technological considerations, product implementation, and application issues.

This book describes a new way to design and utilize Instrumentation Amplifiers (IAs) by taking advantages of the current-mode (CM) approach. For the first time, all different topologies of CMIAs are discussed and compared, providing a single-source reference for instrumentation and measurement experts who want to choose a topology for a specific application. The authors also explain major challenges in designing CMIAs, so the book can be useful for anyone studying instrumentation amplifiers, and even other analog circuits. Coverage also includes various CM signal processing techniques employed in CMIAs, and applications of the CMIAs in biomedical and data acquisition are demonstrated.

Digital Signal Processing:A Primer with MATLAB (R) provides excellent coverage of discrete-time signals and systems. At the beginning of each chapter, an abstract states the chapter objectives. All principles are also presented in a lucid, logical, step-by-step approach. As much as possible, the authors avoid wordiness and detail overload that could hide concepts and impede understanding. In recognition of requirements by the Accreditation Board for Engineering and Technology (ABET) on integrating computer tools, the use of MATLAB (R) is encouraged in a student-friendly manner. MATLAB is introduced in Appendix C and applied gradually throughout the book. Each illustrative example is immediately followed by practice problems along with its answer. Students can follow the example step-by-step to solve the practice problems without flipping pages or looking at the end of the book for answers. These practice problems test students' comprehension and reinforce key concepts before moving onto the next section. Toward the end of each chapter, the authors discuss some application aspects of the concepts covered in the chapter. The material covered in the chapter is applied to at least one or two practical problems. It helps students see how the concepts are used in real-life situations. Also, thoroughly worked examples are given liberally at the end of every section. These examples give students a solid grasp of the solutions as well as the confidence to solve similar problems themselves. Some of hte problems are solved in two or three ways to facilitate a deeper understanding and comparison of different approaches. Designed for a three-hour semester course, Digital Signal Processing:A Primer with MATLAB (R) is intended as a textbook for a senior-level undergraduate student in electrical and computer engineering. The prerequisites for a course based on this book are knowledge of standard mathematics, including calculus and complex numbers.

This book highlights new methods, algorithms and software for the digital processing and recovery of signals. In addition, it describes a new method for modeling one dimensional and multidimensional signals as successions of local polynomial splines and their spectral characteristics. It provides examples of how the proposed methods can be applied in specific cases, together with signal processing software examples in the MATLAB environment, and models of special processes in the Simulink environment. The book's goal is to make it easier for beginners to understand the subject matter; it is intended for engineers, undergraduate and graduate students engaged in research or the evaluation and design of hardware and software for the digital processing and recovery of signals.

A compact overview on signals and systems, with emphasis on analysis of continuous and discrete systems in time domain. Frequency-domain analysis, transform analysis and state-space analysis are also discussed in detail. With abundant examples and exercises to facilitate learning, it is an ideal texts for graduate students and lecturers in signal processing, and communication engineering.

This book comprehensively describes high-resolution microwave imaging and super-resolution information processing technologies and discusses new theories, methods and achievements in the high-resolution microwave imaging fields. Its chapters, which include abundant research results and examples, systematically summarize the authors' main research findings in recent years. The book is intended for researchers, engineers and postgraduates in the fields of electronics systems, signal information processing and data analysis, microwave remote sensing and microwave imaging radar, as well as space technology, especially in the microwave remote sensing and airborne or space-borne microwave imaging radar fields.

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.

This book presents digital signal processing theories and methods and their applications in data analysis, error analysis and statistical signal processing. Algorithms and Matlab programming are included to guide readers step by step in dealing with practical difficulties. Designed in a self-contained way, the book is suitable for graduate students in electrical engineering, information science and engineering in general.

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.

Starting with essential maths, fundamentals of signals and systems, and classical concepts of DSP, this book presents, from an application-oriented perspective, modern concepts and methods of DSP including machine learning for audio acoustics and engineering. Content highlights include but are not limited to room acoustic parameter measurements, filter design, codecs, machine learning for audio pattern recognition and machine audition, spatial audio, array technologies and hearing aids. Some research outcomes are fed into book as worked examples. As a research informed text, the book attempts to present DSP and machine learning from a new and more relevant angle to acousticians and audio engineers. Some MATLAB (R) codes or frameworks of algorithms are given as downloads available on the CRC Press website. Suggested exploration and mini project ideas are given for "proof of concept" type of exercises and directions for further study and investigation. The book is intended for researchers, professionals, and senior year students in the field of audio acoustics.

A fundamental introduction to the delopment of random signal processing with an emphasis on analysis. Linear transformation, nonlinear transformation, spectral analysis of stationary and narrow band random process are discussed in detail. With abundant exercises, this book is an essential reference for graduate students, scientists and practitioners in electronical engineering and signal processing.