Weifeng Liu – författare

Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters. Covers the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, and the extended kernel recursive least squares algorithm Presents a powerful model-selection method called maximum marginal likelihood Addresses the principal bottleneck of kernel adaptive filters—their growing structure Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site Concludes each chapter with a summary of the state of the art and potential future directions for original research Kernel Adaptive Filtering is ideal for engineers, computer scientists, and graduate students interested in nonlinear adaptive systems for online applications (applications where the data stream arrives one sample at a time and incremental optimal solutions are desirable). It is also a useful guide for those who look for nonlinear adaptive filtering methodologies to solve practical problems.

Online learning from a signal processing perspective

There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters.

Covers the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, and the extended kernel recursive least squares algorithm

Presents a powerful model-selection method called maximum marginal likelihood

Addresses the principal bottleneck of kernel adaptive filters—their growing structure

Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors'' Web site

Concludes each chapter with a summary of the state of the art and potential future directions for original research

Kernel Adaptive Filtering is ideal for engineers, computer scientists, and graduate students interested in nonlinear adaptive systems for online applications (applications where the data stream arrives one sample at a time and incremental optimal solutions are desirable). It is also a useful guide for those who look for nonlinear adaptive filtering methodologies to solve practical problems.

A broad and practical reference to IC socket technology The first and only comprehensive resource on IC (Integrated Circuit) socket technology, IC Component Sockets offers a complete overview of socket technology and design in order to provide engineers and their managers with a good understanding of these specialized technologies and the processes for evaluating them. The authors, both acknowledged experts in the field, address all relevant aspects of the subject-including materials, design, performance characteristics, failure modes and mechanisms, and qualification and reliability assessment-with emphasis on the technology's inherent advantages and challenges. Topics of interest include:* Socket design and contact technologies* Performance characteristics and material properties* Contact failure modes and mechanisms* Qualification testing conditions* Qualification sequences and setup * IEEE prediction methodology* Theoretical calculation of contact reliability Including a list of standards and specifications, this book is an important and timely resource for today's electronics engineers concerned with evaluating and perfecting socket design, manufacture, and use.