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Books in Electrical and electronic engineering

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121-130 of 1374 results in All results

Nature-Inspired Computation and Swarm Intelligence

  • 1st Edition
  • April 9, 2020
  • Xin-She Yang
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 9 7 1 4 - 1
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 2 6 0 9 - 4
Nature-inspired computation and swarm intelligence have become popular and effective tools for solving problems in optimization, computational intelligence, soft computing and data science. Recently, the literature in the field has expanded rapidly, with new algorithms and applications emerging. Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is a timely reference giving a comprehensive review of relevant state-of-the-art developments in algorithms, theory and applications of nature-inspired algorithms and swarm intelligence. It reviews and documents the new developments, focusing on nature-inspired algorithms and their theoretical analysis, as well as providing a guide to their implementation. The book includes case studies of diverse real-world applications, balancing explanation of the theory with practical implementation. Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications is suitable for researchers and graduate students in computer science, engineering, data science, and management science, who want a comprehensive review of algorithms, theory and implementation within the fields of nature inspired computation and swarm intelligence.

Nonlinear Optics

  • 4th Edition
  • March 30, 2020
  • Robert W. Boyd
  • English
  • Hardback
    9 7 8 - 0 - 3 2 3 - 8 5 0 5 7 - 5
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 1 0 0 2 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 1 0 0 3 - 4
Nonlinear Optics, Fourth Edition, is a tutorial-based introduction to nonlinear optics that is suitable for graduate-level courses in electrical and electronic engineering, and for electronic and computer engineering departments, physics departments, and as a reference for industry practitioners of nonlinear optics. It will appeal to a wide audience of optics, physics and electrical and electronic engineering students, as well as practitioners in related fields, such as materials science and chemistry.

Advances in Imaging and Electron Physics

  • 1st Edition
  • Volume 213
  • March 18, 2020
  • Martin Hÿtch + 1 more
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 8 2 0 9 9 7 - 4
  • eBook
    9 7 8 - 0 - 1 2 - 8 2 0 9 9 8 - 1
Advances in Imaging and Electron Physics, Volume 213, 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.

LPWAN Technologies for IoT and M2M Applications

  • 1st Edition
  • March 17, 2020
  • Bharat S Chaudhari + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 8 8 8 0 - 4
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 8 8 8 1 - 1
Low power wide area network (LPWAN) is a promising solution for long range and low power Internet of Things (IoT) and machine to machine (M2M) communication applications. The LPWANs are resource-constrained networks and have critical requirements for long battery life, extended coverage, high scalability, and low device and deployment costs. There are several design and deployment challenges such as media access control, spectrum management, link optimization and adaptability, energy harvesting, duty cycle restrictions, coexistence and interference, interoperability and heterogeneity, security and privacy, and others.LPWAN Technologies for IoT and M2M Applications is intended to provide a one-stop solution for study of LPWAN technologies as it covers a broad range of topics and multidisciplinary aspects of LPWAN and IoT. Primarily, the book focuses on design requirements and constraints, channel access, spectrum management, coexistence and interference issues, energy efficiency, technology candidates, use cases of different applications in smart city, healthcare, and transportation systems, security issues, hardware/software platforms, challenges, and future directions.

Acoustic Signals and Hearing

  • 1st Edition
  • February 29, 2020
  • Mikio Tohyama
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 6 3 9 1 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 6 6 1 1 - 6
Understanding acoustics – the science of sound -- is essential for audio and communications engineers working in media technology. It is also extremely important for engineers to understand what allows a sound to be heard in the way it is, what makes speech intelligible, and how a particular sound is recognized within a multitude of sounds. Acoustic Signals and Hearing: A Time-Envelope and Phase Spectral Approach is unique in presenting the principles of sound and sound fields from the perspective of hearing, particularly through the use of speech and musical sounds. Acoustic Signals and Hearing: A Time-Envelope and Phase Spectral Approach is an ideal resource for researchers and acoustic engineers working in today’s environment of media technology, and graduate students studying acoustics, audio engineering, and signal processing.

Electronics and Communications for Scientists and Engineers

  • 2nd Edition
  • February 25, 2020
  • Martin Plonus
  • English
  • Paperback
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  • eBook
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Electronics and Communications for Scientists and Engineers, Second Edition, offers a valuable and unique overview on the basics of electronic technology and the internet. Class-tested over many years with students at Northwestern University, this useful text covers the essential electronics and communications topics for students and practitioners in engineering, physics, chemistry, and other applied sciences. It describes the electronic underpinnings of the World Wide Web and explains the basics of digital technology, including computing and communications, circuits, analog and digital electronics, as well as special topics such as operational amplifiers, data compression, ultra high definition TV, artificial intelligence, and quantum computers.

Power System Small Signal Stability Analysis and Control

  • 2nd Edition
  • February 20, 2020
  • Debasish Mondal + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 7 7 6 8 - 6
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 7 7 6 9 - 3
Power System Small Signal Stability Analysis and Control, Second Edition analyzes severe outages due to the sustained growth of small signal oscillations in modern interconnected power systems. This fully revised edition addresses the continued expansion of power systems and the rapid upgrade to smart grid technologies that call for the implementation of robust and optimal controls. With a new chapter on MATLAB programs, this book describes how the application of power system damping controllers such as Power System Stabilizers and Flexible Alternating Current Transmission System controllers—namely Static Var Compensator and Thyristor Controlled Series Compensator —can guard against system disruptions. Detailed mathematical derivations, illustrated case studies, the application of soft computation techniques, designs of robust controllers, and end-of-chapter exercises make it a useful resource to researchers, practicing engineers, and post-graduates in electrical engineering.

Machine Learning

  • 2nd Edition
  • February 19, 2020
  • Sergios Theodoridis
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 8 1 8 8 0 3 - 3
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 8 8 0 4 - 0
Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python. The chapters are written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as courses on sparse modeling, deep learning, and probabilistic graphical models. New to this edition: Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition. The chapter, starting from the basic perceptron and feed-forward neural networks concepts, now presents an in depth treatment of deep networks, including recent optimization algorithms, batch normalization, regularization techniques such as the dropout method, convolutional neural networks, recurrent neural networks, attention mechanisms, adversarial examples and training, capsule networks and generative architectures, such as restricted Boltzman machines (RBMs), variational autoencoders and generative adversarial networks (GANs). Expanded treatment of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes.

Vehicle Collision Dynamics

  • 1st Edition
  • January 15, 2020
  • Dario Vangi
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 1 2 7 5 0 - 6
  • eBook
    9 7 8 - 0 - 1 2 - 8 1 2 7 5 1 - 3
Vehicle Collision Dynamics provides a unified framework and timely collection of up-to-date results on front crash, side crash and car to car crashes. The book is ideal as a reference, with an approach that's simple, clear, and easy to comprehend. As the mathematical and software-based modelling and analysis of vehicle crash scenarios have not been systematically investigated, this is an ideal source for further study. Numerous academic and industry studies have analyzed vehicle safety during physical crash scenarios, thus material responses during crashes serve as one of the most important performance indices for mechanical design problems. In addition to mathematical methodologies, this book provides thorough coverage of computer simulations, software-based modeling, and an analysis of methods capable of providing more flexibility.

Probabilistic Graphical Models for Computer Vision.

  • 1st Edition
  • December 12, 2019
  • Qiang Ji
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 8 0 3 4 6 7 - 5
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 4 9 5 - 8
Probabilistic Graphical Models for Computer Vision introduces probabilistic graphical models (PGMs) for computer vision problems and teaches how to develop the PGM model from training data. This book discusses PGMs and their significance in the context of solving computer vision problems, giving the basic concepts, definitions and properties. It also provides a comprehensive introduction to well-established theories for different types of PGMs, including both directed and undirected PGMs, such as Bayesian Networks, Markov Networks and their variants.

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