A unique treatment of signal processing using a model-based perspective Signal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels require using the underlying physics to correct the problem causing these low levels and extracting the desired information. Model-based signal processing incorporates the physical phenomena, measurements, and noise in the form of mathematical models to solve this problem. Not only does the approach enable signal processors to work directly in terms of the problem's physics, instrumentation, and uncertainties, but it provides far superior performance over the standard techniques. Model-based signal processing is both a modeler's as well as a signal processor's tool. Model-Based Signal Processing develops the model-based approach in a unified manner and follows it through the text in the algorithms, examples, applications, and case studies. The approach, coupled with the hierarchy of physics-based models that the author develops, including linear as well as nonlinear representations, makes it a unique contribution to the field of signal processing. The text includes parametric (e.g., autoregressive or all-pole), sinusoidal, wave-based, and state-space models as some of the model sets with its focus on how they may be used to solve signal processing problems. Special features are provided that assist readers in understanding the material and learning how to apply their new knowledge to solving real-life problems. * Unified treatment of well-known signal processing models including physics-based model sets * Simple applications demonstrate how the model-based approach works, while detailed case studies demonstrate problem solutions in their entirety from concept to model development, through simulation, application to real data, and detailed performance analysis * Summaries provided with each chapter ensure that readers understand the key points needed to move forward in the text as well as MATLAB(r) Notes that describe the key commands and toolboxes readily available to perform the algorithms discussed * References lead to more in-depth coverage of specialized topics * Problem sets test readers' knowledge and help them put their new skills into practice The author demonstrates how the basic idea of model-based signal processing is a highly effective and natural way to solve both basic as well as complex processing problems. Designed as a graduate-level text, this book is also essential reading for practicing signal-processing professionals and scientists, who will find the variety of case studies to be invaluable. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department

Impulse response measurements that are performed outdoors are highly susceptible to the uncertainties caused by the non-perfect measurement setup, the presence of background noise, and fluctuations in media such as wind and temperature drift. This work concentrates on two scenarios: the measurement of reflection coefficients of noise barriers and the influence of temperature variances in machinery cavities. Regarding the sound barrier measurement outdoors, a linear four-microphone array can be used to separate direct sound and reflected sound if the sound barrier does not include complicated scattering structures. With regard to the impulse response of an air-borne sound measurement for a machine monitoring system, a time-warping model for inter-period and intra-period temperature variances is investigated.

This monograph presents a unified approach to model-based processing for underwater acoustic arrays. The use of physical models in passive array processing is not a new idea, but it has been used on a case-by-case basis, and as such, lacks any unifying structure. This work views all such processing methods as estimation procedures, which then can be unified by treating them all as a form of joint estimation based on a Kalman-type recursive processor, which can be recursive either in space or time, depending on the application. This is done for three reasons. First, the Kalman filter provides a natural framework for the inclusion of physical models in a processing scheme. Second, it allows poorly known model parameters to be jointly estimated along with the quantities of interest. This is important, since in certain areas of array processing already in use, such as those based on matched-field processing, the so-called mismatch problem either degrades performance or, indeed, prevents any solution at all. Thirdly, such a unification provides a formal means of quantifying the performance improvement. The term model-based will be strictly defined as the use of physics-based models as a means of introducing a priori information. This leads naturally to viewing the method as a Bayesian processor. Short expositions of estimation theory and acoustic array theory are presented, followed by a presentation of the Kalman filter in its recursive estimator form. Examples of applications to localization, bearing estimation, range estimation and model parameter estimation are provided along with experimental results verifying the method. The book is sufficiently self-contained to serve as a guide for the application of model-based array processing for the practicing engineer.

With increasing availability of computation power, digital signal analysis algorithms have the potential of evolving from the common framewise operational method to samplewise operations which offer more precision in time. This thesis discusses a set of methods with samplewise operations: local signal approximation via Recursive Least Squares (RLS) where a mathematical model is fit to the signal within a sliding window at each sample. Thereby both the signal models and cost windows are generated by Autonomous Linear State Space Models (ALSSMs). The modeling capability of ALSSMs is vast, as they can model exponentials, polynomials and sinusoidal functions as well as any linear and multiplicative combination thereof. The fitting method offers efficient recursions, subsample precision by way of the signal model and additional goodness of fit measures based on the recursively computed fitting cost. Classical methods such as standard Savitzky-Golay (SG) smoothing filters and the Short-Time Fourier Transform (STFT) are united under a common framework. First, we complete the existing framework. The ALSSM parameterization and RLS recursions are provided for a general function. The solution of the fit parameters for different constraint problems are reviewed. Moreover, feature extraction from both the fit parameters and the cost is detailed as well as examples of their use. In particular, we introduce terminology to analyze the fitting problem from the perspective of projection to a local Hilbert space and as a linear filter. Analytical rules are given for computation of the equivalent filter response and the steady-state precision matrix of the cost. After establishing the local approximation framework, we further discuss two classes of signal models in particular, namely polynomial and sinusoidal functions. The signal models are complementary, as by nature, polynomials are suited for time-domain description of signals while sinusoids are suited for the frequency-domain. For local approximation of polynomials, we derive analytical expressions for the steady-state covariance matrix and the linear filter of the coefficients based on the theory of orthogonal polynomial bases. We then discuss the fundamental application of smoothing filters based on local polynomial approximation. We generalize standard SG filters to any ALSSM window and introduce a novel class of smoothing filters based on polynomial fitting to running sums.

Physiology is a set of processes that maintain homeostasis, and physiological measurement is a means of observing these processes. Systems theory and signal processing offer formal tools for the study of processes and measured quantities. This book shows that systems modeling can be used to develop simulations of physiological systems, which use formal relations between the underlying processes and the observed measurements. The inverse of such relations suggest signal processing tools that can be applied to interpret experimental data. Both signal processing and systems modeling are invaluable in the study of human physiology. Discussing signal processing techniques ranging from filtering and spectrum analysis to wavelet analysis, the book uses graphs and analogies to supplement the mathematics and make the book more accessible to physiologists and more interesting to engineers. Physiological systems modeling helps in both gaining insight and generating methods of analysis. This book shows how numerical computation with graphical display, haptics and multimedia can be used to simulate physiological systems. In this third edition the simulations are more closely related to clinical examination and experimental physiology than in previous editions. Detailed models of nerve and muscle at the cellular and systemic levels, and simplified models of cardiovascular blood flow provide examples for the mathematical methods and computer simulations. Several of the models are sufficiently sophisticated to be of value in understanding real world issues like neuromuscular disease. The book features expanded problem sets and a link to extra downloadable material containing simulation programs that are solutions to the theory developed in the text.

This book is part of a three-volume set that constitutes the refereed proceedings of the 11th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2007. Coverage in this first volume includes artificial neural networks and connectionists systems, fuzzy and neuro-fuzzy systems, evolutionary computation, machine learning and classical AI, agent systems, and information engineering and applications in ubiquitous computing environments.

A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.

This book narrates an assessment of numerous advanced power converters employed on primitive phase to enhance the efficiency of power translation pertaining to renewable energy systems. It presents the mathematical modelling, analysis, and control of recent power converters topologies, namely, AC/DC, DC/DC, and DC/AC converters. Numerous advanced DC-DC Converters, namely, multi-input DC-DC Converter, Cuk, SEPIC, Zeta and so forth have been assessed mathematically using state space analysis applied with an aim to enhance power efficiency of renewable energy systems. The book: Explains various power electronics converters for different types of renewable energy sources Provides a review of the major power conversion topologies in one book Focuses on experimental analysis rather than simulation work Recommends usage of MATLAB, PSCAD, and PSIM simulation software for detailed analysis Includes DC-DC converters with reasonable peculiar power rating This book is aimed at researchers, graduate students in electric power engineering, power and industrial electronics, and renewable energy.

This book presents an up-to-date tutorial and overview on learning technologies such as random forests, sparsity, and low-rank matrix estimation and cutting-edge visual/signal processing techniques, including face recognition, Kalman filtering, and multirate DSP. It discusses the applications that make use of deep learning, convolutional neural networks, random forests, etc.

This volume is the most comprehensive reference work on visual communications to date. An international group of well-known experts in the field provide up-to-date and in-depth contributions on topics such as fundamental theory, international standards for industrial applications, high definition television, optical communications networks, and VLSI design. The book includes information for learning about both the fundamentals of image/video compression as well as more advanced topics in visual communications research. In addition, the Handbook of Visual Communications explores the latest developments in the field, such as model-based image coding, and provides readers with insight into possible future developments. - Displays comprehensive coverage from fundamental theory to international standards and VLSI design - Includes 518 pages of contributions from well-known experts - Presents state-of-the-art knowledge--the most up-to-date and accurate information on various topics in the field - Provides an extensive overview of international standards for industrial applications