This textbook offers a tutorial introduction to robotics and Computer Vision which is light and easy to absorb. The practice of robotic vision involves the application of computational algorithms to data. Over the fairly recent history of the fields of robotics and computer vision a very large body of algorithms has been developed. However this body of knowledge is something of a barrier for anybody entering the field, or even looking to see if they want to enter the field - What is the right algorithm for a particular problem?, and importantly: How can I try it out without spending days coding and debugging it from the original research papers? The author has maintained two open-source MATLAB Toolboxes for more than 10 years: one for robotics and one for vision. The key strength of the Toolboxes provide a set of tools that allow the user to work with real problems, not trivial examples. For the student the book makes the algorithms accessible, the Toolbox code can be read to gain understanding, and the examples illustrate how it can be used -instant gratification in just a couple of lines of MATLAB code. The code can also be the starting point for new work, for researchers or students, by writing programs based on Toolbox functions, or modifying the Toolbox code itself. The purpose of this book is to expand on the tutorial material provided with the toolboxes, add many more examples, and to weave this into a narrative that covers robotics and computer vision separately and together. The author shows how complex problems can be decomposed and solved using just a few simple lines of code, and hopefully to inspire up and coming researchers. The topics covered are guided by the real problems observed over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes a lot of Matlab examples and figures. The book is a real walk through the fundamentals light and color, camera modelling, image processing, feature extraction and multi-view geometry, and bring it all together in a visual servo system. "An authoritative book, reaching across fields, thoughtfully conceived and brilliantly accomplished Oussama Khatib, Stanford

Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB (R), this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementary material - including lecture videos per section, homeworks, data, and code in MATLAB (R), Python, Julia, and R - available on databookuw.com.

"Noise and Vibration Analysis" is a complete and practical guide that combines both signal processing and modal analysis theory with their practical application in noise and vibration analysis. It provides an invaluable, integrated guide for practicing engineers as well as a suitable introduction for students new to the topic of noise and vibration. Taking a practical learning approach, Brandt includes exercises that allow the content to be developed in an academic course framework or as supplementary material for private and further study.Addresses the theory and application of signal analysis procedures as they are applied in modern instruments and software for noise and vibration analysisFeatures numerous line diagrams and illustrationsAccompanied by a web site at www.wiley.com/go/brandt with numerous MATLAB tools and examples.

"Noise and Vibration Analysis" provides an excellent resource for researchers and engineers from automotive, aerospace, mechanical, or electronics industries who work with experimental or analytical vibration analysis and/or acoustics. It will also appeal to graduate students enrolled in vibration analysis, experimental structural dynamics, or applied signal analysis courses.

The use of neural networks is permeating every area of signal processing. They can provide powerful means for solving many problems, especially in nonlinear, real-time, adaptive, and blind signal processing. The Handbook of Neural Network Signal Processing brings together applications that were previously scattered among various publications to provide an up-to-date, detailed treatment of the subject from an engineering point of view.

The authors cover basic principles, modeling, algorithms, architectures, implementation procedures, and well-designed simulation examples of audio, video, speech, communication, geophysical, sonar, radar, medical, and many other signals. The subject of neural networks and their application to signal processing is constantly improving. You need a handy reference that will inform you of current applications in this new area. The Handbook of Neural Network Signal Processing provides this much needed service for all engineers and scientists in the field.

Advances in Imaging and Electron Physics, Volume 208, merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. The series features extended articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science, digital image processing, electromagnetic wave propagation, electron microscopy and the computing methods used in all these domains.

Advances in Imaging and Electron Physics, Volume 207, merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. The series features extended articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science, digital image processing, electromagnetic wave propagation, electron microscopy and the computing methods used in all these domains.

The current popular and scientific interest in virtual environments has provided a new impetus for investigating binaural and spatial hearing. However, the many intriguing phenomena of spatial hearing have long made it an exciting area of scientific inquiry. Psychophysical and physiological investigations of spatial hearing seem to be converging on common explanations of underlying mechanisms. These understandings have in turn been incorporated into sophisticated yet mathematically tractable models of binaural interaction. Thus, binaural and spatial hearing is one of the few areas in which professionals are soon likely to find adequate physiological explanations of complex psychological phenomena that can be reasonably and usefully approximated by mathematical and physical models. This volume grew out of the Conference on Binaural and Spatial Hearing, a four-day event held at Wright-Patterson Air Force Base in response to rapid developments in binaural and spatial hearing research and technology. Meant to be more than just a proceedings, it presents chapters that are longer than typical proceedings papers and contain considerably more review material, including extensive bibliographies in many cases. Arranged into topical sections, the chapters represent major thrusts in the recent literature. The authors of the first chapter in each section have been encouraged to take a broad perspective and review the current state of literature. Subsequent chapters in each section tend to be somewhat more narrowly focused, and often emphasize the authors' own work. Thus, each section provides overview, background, and current research on a particular topic. This book is significant in that it reviews the important work during the past 10 to 15 years, and provides greater breadth and depth than most of the previous works.

Advances in Imaging and Electron Physics, Volume 205 is the latest release in this series that merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. The series features extended articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science, and digital image processing, electromagnetic wave propagation, electron microscopy, and the computing methods used in all these domains.

Case-based reasoning in design is becoming an important approach to computer-support for design as well as an important component in understanding the design process. Design has become a major focus for problem solving paradigms due to its complexity and open-ended nature. This book presents a clear description of how case-based reasoning can be applied to design problems, including the representation of design cases, indexing and retrieving design cases, and the range of paradigms for adapting design cases. With a focus on design, this book differs from others that provide a generalist view of case-based reasoning.
This volume provides two important contributions to the area:
* a general description of the issues and alternatives in applying case-based reasoning to design, and
* a description of specific implementations of case-based design.
Through this combination, the reader will learn about both the general issues and the practical problems in supporting design through case-based reasoning. This book was prepared to fill a gap in the literature on the unique problems that design introduces to computational paradigms developed in computer science. It also addresses the needs of computational support for design problem solving from both theoretical and practical perspectives.

Integrates rational approximation with adaptive filtering, providing viable, numerically reliable procedures for creating adaptive infinite impulse response (IIR) filters. The choice of filter structure to adapt, algorithm design and the approximation properties for each type of algorithm are also addressed. This work recasts the theory of adaptive IIR filters by concentrating on recursive lattice filters, freeing systems from the need for direct-form filters.;A solutions manual is available for instructors only. College or university bookstores may order five or more copies at a special student price which is available upon request.

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.

This book introduces the basic concepts of signal processing for scientists and students with no engineering background. The book presents the concepts with minimum use of mathematical formulations and more emphasis on visual illustrations. The idea is to present an intuitive approach to understanding the basics of signal processing and exemplify some practical applications of the concepts by which the readers achieve basic knowledge and skills in signal processing. Most of illustrations in the book have been created by computer programming in MATLAB (R); thus, the reader will learn the basics of using computers in signal processing applications.

Partial-update adaptive signal processing algorithms not only permit significant complexity reduction in adaptive filter implementations, but can also improve adaptive filter performance in telecommunications applications. This book gives state-of-the-art methods for the design and development of partial-update adaptive signal processing algorithms for use in systems development.
Partial-Update Adaptive Signal Processing provides a comprehensive coverage of key partial updating schemes, giving detailed information on the theory and applications of acoustic and network echo cancellation, channel equalization and multiuser detection. It also examines convergence and stability issues for partial update algorithms, providing detailed complexity analysis and a unifying treatment of partial-update techniques.
Features:
- Advanced analysis and design tools
- Application examples illustrating the use of partial-update adaptive signal processing
- MATLAB codes for developed algorithms
This unique reference will be of interest to signal processing and communications engineers, researchers, R&D engineers and graduate students.
"This is a very systematic and methodical treatment of an adaptive signal processing topic, of particular significance in power limited applications such as in wireless communication systems and smart ad hoc sensor networks. I am very happy to have this book on my shelf, not to gather dust, but to be consulted and used in my own research and teaching activities" - Professor A. G. Constantinides, Imperial College, London
About the author:
Kutluyil Dogancay is an associate professor of Electrical Engineering at the University of SouthAustralia. His research interests span statistical and adaptive signal processing and he serves as a consultant to defence and private industry. He was the Signal Processing and Communications Program Chair of IDC Conference 2007, and is currently chair of the IEEE South Australia Communications and Signal Processing Chapter.
* Advanced analysis and design tools
* Algorithm summaries in tabular format
* Case studies illustrate the application of partial update adaptive signal processing
* MATLAB code listings on an accompanying website

Accurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging - including compressed sensing, wavelets and optimization - in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the pitfalls of these latest approaches.

Nowadays, many aspects of electrical and electronic engineering are essentially applications of DSP. This is due to the focus on processing information in the form of digital signals, using certain DSP hardware designed to execute software. Fundamental topics in digital signal processing are introduced with theory, analytical tables, and applications with simulation tools. The book provides a collection of solved problems on digital signal processing and statistical signal processing. The solutions are based directly on the math-formulas given in extensive tables throughout the book, so the reader can solve practical problems on signal processing quickly and efficiently. FEATURES Explains how applications of DSP can be implemented in certain programming environments designed for real time systems, ex. biomedical signal analysis and medical image processing. Pairs theory with basic concepts and supporting analytical tables. Includes an extensive collection of solved problems throughout the text. Fosters the ability to solve practical problems on signal processing without focusing on extended theory. Covers the modeling process and addresses broader fundamental issues.

This book gives the fundamental principles and device design techniques for surface acoustic wave filters. It covers the devices in widespread use today: bandpass and pulse compression filters, correlators and non-linear convolvers and resonators. The newest technologies for low bandpass filters are fully covered such as unidirectional transducers, resonators in impedance element filters, resonators in double-mode surface acoustic wave filters and transverse-coupled resonators using waveguides.
The book covers the theory of acoustic wave physics, the piezoelectric effect, electrostatics at a surface, effective permittivity, piezoelectric SAW excitation and reception, and the SAW element factor. These are the main requirements for developing quasi-static theory, which gives a basis for the non-reflective transducers in transversal bandpass filters and interdigital pulse compression filters. It is also needed for the reflective transducers used in the newer devices.
* A thorough revision of a classic on surface acoustic wave filters first published in 1985 and still in print
* Uniquely combines easy -to -understand principles with practical design techniques for all the devices in widespread use today
* Complete coverage of all the latest devices which are key to mobile phones, TVs and radar systems
* Includes a new foreword by Sir Eric Albert Ash

As demand for applications working in extended frequency ranges increases, classical digital signal processing (DSP) techniques, not protected against aliasing, are becoming less effective. Digital alias-free signal processing (DASP) is a technique for overcoming the problems of aliasing at extended frequency ranges. Based on non-uniform or randomised sampling techniques and the development of novel algorithms, it creates the capacity to suppress potential aliasing crucial for high frequency applications and to reduce the complexity of designs.

This book provides practical and comprehensive coverage of the theory and techniques behind alias-free digital signal processing.

Digital Alias-free Signal Processing is ideal for practising engineers and researchers working on the development of digital signal processing applications at extended frequencies. It is also a valuable reference for electrical and computer engineering graduates taking courses in signal processing or digital signal processing.

Key features: Analyses issues of sampling, randomised and pseudo-randomised quantisation and direct and indirectly randomised sampling. Examines periodic and hybrid sampling, including information on processing algorithms and potential limitations imposed by signal dynamics. Sets out leading methods and techniques for complexity reduced designs, in particular designs of large aperture sensor arrays, massive data acquisition and compression from a number of signal sources, as well as complexity-reduced processing of non-uniform data. Presents examples of engineering applications using these techniques including spectrum analysis, waveform reconstruction and the estimation of variousparameters, emphasising the importance of the technique for developing new technologies. Links DASP and traditional technologies by mapping them into embedded systems with standard inputs and outputs.

Fully focused on addressing the missing connection between signal processing and ML. Provides one-stop guide reference for the readers. Oriented towards the material and flow with regard to general introduction, technical aspects. Comprehensively elaborates on the material with examples and.

A uniquely practical DSP text, this book gives a thorough understanding of the principles and applications of DSP with a minimum of mathematics, and provides the reader with an introduction to DSP applications in telecoms, control engineering and measurement and data analysis systems.
The new edition contains:
- Expanded coverage of the basic concepts to aid understanding
- New sections on filter sysnthesis, control theory and contemporary topics of speech and image recognition
- Full solutions to all questions and exercises in the book
- A complete on-line resource
The on-line resource offers instructors and students complete lecture notes, lecture videos, PowerPoint slides for presentations, final exams and solutions, project exercises, URLs to DSP applet experiment animations and e-meeting software for direct communication with the authors.
Assuming the reader already has some prior knowledge of signal theory, this textbook will be highly suitable for undergraduate and postgraduate students in electrical and electronic engineering taking introductory and advanced courses in DSP, as well as courses in communications and control systems engineering. It will also prove an invaluable introduction to DSP and its applications for the professional engineer.
- Expanded coverage of the basic concepts to aid understanding, along with a wide range of DSP applications
- New textbook features included throughout, including learning objectives, summary
sections, exercises and worked examples to increase accessibility of the text
- Full solutions to all questions and exercises included in the book, with extra resources on-line

Readers will use this knowledge to develop the required techniques for design, installation and maintenance of their own fiber optic systems.
* Ideal for those with some background in communications but without previous knowledge of fiber optics
* Provides a comprehensive treatment of the fundamentals of fiber optic systems and their individual components
* Places emphasis on practical techniques of component installation and system design

Because this is a book for engineers the practical coverage is reinforced by use of the latest interanational standards, in particular BICSI standards (USA and international) and EU requirements. This will make the book ideal for the large number of industry-based training courses. Coverage has also been matched to the requirements of the revised City & Guilds 3466-04 course.
*Covers the real-world issues of selection, design, installation, testing, safety, legislation... neglected by university texts
*An easy-to-read introduction that assumes no prior knowledge beyond basic concepts of voltage and current - ideal for non-specialists as well as practitioners
*Covers new BICSI (US / international) regulations and EU framework

Signal Processing for Active Control sets out the signal processing and automatic control techniques that are used in the analysis and implementation of active systems for the control of sound and vibration. After reviewing the performance limitations introduced by physical aspects of active control, Stephen Elliott presents the calculation of the optimal performance and the implementation of adaptive real time controllers for a wide variety of active control systems.
Active sound and vibration control are technologically important problems with many applications. 'Active control' means controlling disturbance by superimposing a second disturbance on the original source of disturbance. Put simply, initial noise + other specially-generated noise or vibration = silence or controlled noise].
This book presents a unified approach to techniques that are used in the analysis and implementation of different control systems. It includes practical examples at the end of each chapter to illustrate the use of various approaches.
This book is intended for researchers, engineers, and students in the field of acoustics, active control, signal processing, and electrical engineering.

'Introduction to Digital Signal Processing' covers the basic theory and practice of digital signal processing (DSP) at an introductory level. As with all volumes in the Essential Electronics Series, this book retains the unique formula of minimal mathematics and straightforward explanations. The author has included examples throughout of the standard software design package, MATLAB and screen dumps are used widely throughout to illustrate the text.
Ideal for students on degree and diploma level courses in electric and electronic engineering, 'Introduction to Digital Signal Processing' contains numerous worked examples throughout as well as further problems with solutions to enable students to work both independently and in conjunction with their course.
Assumes only minimum knowledge of mathematics and electronics.
Concise and written in a straightforward and accessible style.
Packed with worked examples, exercises and self-assesment questions.

For undergraduate-level courses in Signals and Systems. This comprehensive exploration of signals and systems develops continuous-time and discrete-time concepts/methods in parallel -- highlighting the similarities and differences -- and features introductory treatments of the applications of these basic methods in such areas as filtering, communication, sampling, discrete-time processing of continuous-time signals, and feedback. Relatively self-contained, the text assumes no prior experience with system analysis, convolution, Fourier analysis, or Laplace and z-transforms.

Written by international experts in this field, the book describes the principles of, and presents case studies for, the wide range of tomographic imaging techniques that can be used in the process industries. It includes sufficient introductory material
to this multi-disciplinary subject in order that readers from a variety of backgrounds will be able to fully understand the fundamental principles and features of the sensors and image reconstruction techniques needed for process tomography.