Kategoria BISAC: Technology & Engineering >> Signals & Signal Processing

Digital Signal Processing with Kernel Methods will review the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. In addition it will offer an explanation of the fundamental concepts from both fields of machine learning and signal processing for it to be a self-contained text. Readers will quickly be brought up to speed on the topic through this approach so as to be in a position to develop the concepts and application software in their own research.

A practical guide to analog and mixed-signal electronics, with an emphasis on design problems and applications

This book provides an in-depth coverage of essential analog and mixed-signal topics such as power amplifiers, active filters, noise and dynamic range, analog-to-digital and digital-to-analog conversion techniques, phase-locked loops, and switching power supplies. Readers will learn the basics of linear systems, types of nonlinearities and their effects, op-amp circuits, the high-gain analog filter-amplifier, and signal generation. The author uses system...

Reviews the new High Efficiency Video Coding (HEVC) standard and advancements in adaptive streaming technologies for use in broadband networks and the Internet

This book describes next-generation video coding and streaming technologies with a comparative assessment of the strengths and weaknesses. Specific emphasis is placed on the H.265/HEVC video coding standard and adaptive bit rate video streaming. In addition to evaluating the impact of different types of video content and powerful feature sets on HEVC coding efficiency, the text provides an in-depth study on...

This book will focus on image and video techniques and algorithms for registration in addition to quality performance metrics. There is a significant number of registration methods operating in different levels and domains (e.g. block or feature based, pixel level, Fourier domain, etc.) focusing on specific applications. The book offers a state-of-the-art review of registration based on the targeted application providing an introduction to the particular problems and limitations of each application, an overview of the previous approaches and a detailed analysis of the most well-known...

This book fills the gap between the literature on nonlinear filters and nonlinear observers by presenting a new state estimation strategy, the smooth variable structure filter (SVSF). The book is a valuable resource to researchers outside of the control society, where literature on nonlinear observers is less well-known. SVSF is a predictor-corrector estimator that is formulated based on a stability theorem, to confine the estimated states within a neighborhood of their true values. It has the potential to improve performance in the presence of severe and changing modeling uncertainties...

Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimensionality reduction.