Using real-life examples to illustrate the performance of learning algorithms and instructing readers how to apply them to practical applications, this work offers a comprehensive treatment of subspace learning algorithms for neural networks. The authors summarize a decade of high quality research offering a host of practical applications. They demonstrate ways to extend the use of algorithms to fields such as encryption communication, data mining, computer vision, and signal and image processing to name just a few. The brilliance of the work lies with how it coherently builds a theoretical understanding of the convergence behavior of subspace learning algorithms through a summary of chaotic behaviors.

Using real-life examples to illustrate the performance of learning algorithms and instructing readers how to apply them to practical applications, this work offers a comprehensive treatment of subspace learning algorithms for neural networks. The authors summarize a decade of high quality research offering a host of practical applications. They demonstrate ways to extend the use of algorithms to fields such as encryption communication, data mining, computer vision, and signal and image processing to name just a few. The brilliance of the work lies with how it coherently builds a theoretical understanding of the convergence behavior of subspace learning algorithms through a summary of chaotic behaviors.

This book constitutes the refereed proceedings of the 15th International Symposium on Neural Networks, ISNN 2018, held in Minsk, Belarus in June 2018.The 98 revised regular papers presented in this volume were carefully reviewed and selected from 214 submissions. The papers cover many topics of neural network-related research including intelligent control, neurodynamic analysis, bio-signal, bioinformatics and biomedical engineering, clustering, classification, forecasting, models, algorithms, cognitive computation, machine learning, and optimization.

Traditionally, Computer Aided Design (CAD) tools have been used to create the nominal design of an integrated circuit (IC), such that the circuit nominal response meets the desired performance specifications. In reality, however, due to the disturbances ofthe IC manufacturing process, the actual performancesof the mass produced chips are different than those for the nominal design. Even if the manufacturing process were tightly controlled, so that there were little variations across the chips manufactured, the environmentalchanges (e. g. those oftemperature, supply voltages, etc. ) would alsomakethe circuit performances vary during the circuit life span. Process-related performance variations may lead to low manufacturing yield, and unacceptable product quality. For these reasons, statistical circuit design techniques are required to design the circuit parameters, taking the statistical process variations into account. This book deals with some theoretical and practical aspects of IC statistical design, and emphasizes how they differ from those for discrete circuits. It de scribes a spectrum of different statistical design problems, such as parametric yield optimization, generalized on-target design, variability minimization, per formance tunning, and worst-case design. The main emphasis of the presen tation is placed on the principles and practical solutions for performance vari ability minimization. It is hoped that the book may serve as an introductory reference material for various groups of IC designers, and the methodologies described will help them enhance the circuit quality and manufacturability. The book containsseven chapters.

Traditionally, Computer Aided Design (CAD) tools have been used to create the nominal design of an integrated circuit (IC), such that the circuit nominal response meets the desired performance specifications. In reality, however, due to the disturbances ofthe IC manufacturing process, the actual performancesof the mass produced chips are different than those for the nominal design. Even if the manufacturing process were tightly controlled, so that there were little variations across the chips manufactured, the environmentalchanges (e. g. those oftemperature, supply voltages, etc. ) would alsomakethe circuit performances vary during the circuit life span. Process-related performance variations may lead to low manufacturing yield, and unacceptable product quality. For these reasons, statistical circuit design techniques are required to design the circuit parameters, taking the statistical process variations into account. This book deals with some theoretical and practical aspects of IC statistical design, and emphasizes how they differ from those for discrete circuits. It de scribes a spectrum of different statistical design problems, such as parametric yield optimization, generalized on-target design, variability minimization, per formance tunning, and worst-case design. The main emphasis of the presen tation is placed on the principles and practical solutions for performance vari ability minimization. It is hoped that the book may serve as an introductory reference material for various groups of IC designers, and the methodologies described will help them enhance the circuit quality and manufacturability. The book containsseven chapters."

Substrate Integrated Antennas and Arrays provides a single source for cutting-edge information on substrate integrated circuits (SICs), substrate integrated waveguide (SIW) feeding networks, SIW slot array antennas, SIC traveling-wave antennas, SIW feeding antennas, SIW monopulse antennas, and SIW multibeam antennas. Inspired by the author's extensive research, this comprehensive book: Describes a revolutionary SIC-based antenna technique with the potential to replace existing antenna technologies Examines theoretical and experimental results connected to electrical and mechanical performance Explains how to overcome difficulties in meeting bandwidth, gain, and efficiency specifications Substrate Integrated Antennas and Arrays offers valuable insight into the state of the art of SIC and SIW antenna technologies, presenting research useful to the development of wireless communication base station antennas, portable microwave point-to-point systems, collision avoidance radars, conformal antennas, and satellite antennas.

This book constitutes the refereed proceedings of the 10th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2015, held in Beijing, China, in May 2015. The 36 papers presented in this volume were carefully reviewed and selected from 53 submissions. The accepted papers cover diverse issues of graph-based methods and applications, with 7 in graph representation, 15 in graph matching, 7 in graph clustering and classification, and 7 in graph-based applications.

Computer Vision for Multimedia Applications: Methods and Solutions includes the latest developments in computer vision methods applicable to various problems in multimedia computing. This publication presents discussions on new ideas, as well as problems in computer vision and multimedia computing. It will serve as an important reference in multimedia and computer vision for academicians, researchers, and academic libraries.