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Author: Jerry M. Mendel
Publisher: Springer Science & Business Media
ISBN: 1461233704
Category : Technology & Engineering
Languages : en
Pages : 233
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Book Description
Convolution is the most important operation that describes the behavior of a linear time-invariant dynamical system. Deconvolution is the unraveling of convolution. It is the inverse problem of generating the system's input from knowledge about the system's output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that handles both effects. It draws upon ideas from Maximum Likelihood, when unknown parameters are random. It leads to linear and nonlinear signal processors that provide high-resolution estimates of a system's input. All aspects of MLD are described, from first principles in this book. The purpose of this volume is to explain MLD as simply as possible. To do this, the entire theory of MLD is presented in terms of a convolutional signal generating model and some relatively simple ideas from optimization theory. Earlier approaches to MLD, which are couched in the language of state-variable models and estimation theory, are unnecessary to understand the essence of MLD. MLD is a model-based signal processing procedure, because it is based on a signal model, namely the convolutional model. The book focuses on three aspects of MLD: (1) specification of a probability model for the system's measured output; (2) determination of an appropriate likelihood function; and (3) maximization of that likelihood function. Many practical algorithms are obtained. Computational aspects of MLD are described in great detail. Extensive simulations are provided, including real data applications.
Author: Jerry M. Mendel
Publisher: Springer Science & Business Media
ISBN: 1461233704
Category : Technology & Engineering
Languages : en
Pages : 233
Get Book Here
Book Description
Convolution is the most important operation that describes the behavior of a linear time-invariant dynamical system. Deconvolution is the unraveling of convolution. It is the inverse problem of generating the system's input from knowledge about the system's output and dynamics. Deconvolution requires a careful balancing of bandwidth and signal-to-noise ratio effects. Maximum-likelihood deconvolution (MLD) is a design procedure that handles both effects. It draws upon ideas from Maximum Likelihood, when unknown parameters are random. It leads to linear and nonlinear signal processors that provide high-resolution estimates of a system's input. All aspects of MLD are described, from first principles in this book. The purpose of this volume is to explain MLD as simply as possible. To do this, the entire theory of MLD is presented in terms of a convolutional signal generating model and some relatively simple ideas from optimization theory. Earlier approaches to MLD, which are couched in the language of state-variable models and estimation theory, are unnecessary to understand the essence of MLD. MLD is a model-based signal processing procedure, because it is based on a signal model, namely the convolutional model. The book focuses on three aspects of MLD: (1) specification of a probability model for the system's measured output; (2) determination of an appropriate likelihood function; and (3) maximization of that likelihood function. Many practical algorithms are obtained. Computational aspects of MLD are described in great detail. Extensive simulations are provided, including real data applications.
Author: Jerry M. Mendel
Publisher:
ISBN: 9783540972082
Category : Estimation theory
Languages : en
Pages : 227
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Book Description
Author: P. Groeneboom
Publisher: Springer Science & Business Media
ISBN: 9783764327941
Category : Mathematics
Languages : en
Pages : 140
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Book Description
This book contains the lecture notes for a DMV course presented by the authors at Gunzburg, Germany, in September, 1990. In the course we sketched the theory of information bounds for non parametric and semiparametric models, and developed the theory of non parametric maximum likelihood estimation in several particular inverse problems: interval censoring and deconvolution models. Part I, based on Jon Wellner's lectures, gives a brief sketch of information lower bound theory: Hajek's convolution theorem and extensions, useful minimax bounds for parametric problems due to Ibragimov and Has'minskii, and a recent result characterizing differentiable functionals due to van der Vaart (1991). The differentiability theorem is illustrated with the examples of interval censoring and deconvolution (which are pursued from the estimation perspective in part II). The differentiability theorem gives a way of clearly distinguishing situations in which 1 2 the parameter of interest can be estimated at rate n / and situations in which this is not the case. However it says nothing about which rates to expect when the functional is not differentiable. Even the casual reader will notice that several models are introduced, but not pursued in any detail; many problems remain. Part II, based on Piet Groeneboom's lectures, focuses on non parametric maximum likelihood estimates (NPMLE's) for certain inverse problems. The first chapter deals with the interval censoring problem.
Author: Jerry M. Mendel
Publisher: Elsevier
ISBN: 148325819X
Category : Science
Languages : en
Pages : 269
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Book Description
Optimal Seismic Deconvolution: An Estimation-Based Approach presents an approach to the problem of seismic deconvolution. It is meant for two different audiences: practitioners of recursive estimation theory and geophysical signal processors. The book opens with a chapter on elements of minimum-variance estimation that are essential for all later developments. Included is a derivation of the Kaiman filter and discussions of prediction and smoothing. Separate chapters follow on minimum-variance deconvolution; maximum-likelihood and maximum a posteriori estimation methods; the philosophy of maximum-likelihood deconvolution (MLD); and two detection procedures for determining the location parameters in the input sequence product model. Subsequent chapters deal with the problem of estimating the parameters of the source wavelet when everything else is assumed known a priori; estimation of statistical parameters when the source wavelet is known a priori; and a different block component method for simultaneously estimating all wavelet and statistical parameters, detecting input signal occurrence times, and deconvolving a seismic signal. The final chapter shows how to incorporate the simplest of all models—the normal incidence model—into the maximum-likelihood deconvolution procedure.
Author: Patrizio Campisi
Publisher: CRC Press
ISBN: 1420007297
Category : Technology & Engineering
Languages : en
Pages : 474
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Book Description
Blind image deconvolution is constantly receiving increasing attention from the academic as well the industrial world due to both its theoretical and practical implications. The field of blind image deconvolution has several applications in different areas such as image restoration, microscopy, medical imaging, biological imaging, remote sensing, astronomy, nondestructive testing, geophysical prospecting, and many others. Blind Image Deconvolution: Theory and Applications surveys the current state of research and practice as presented by the most recognized experts in the field, thus filling a gap in the available literature on blind image deconvolution. Explore the gamut of blind image deconvolution approaches and algorithms that currently exist and follow the current research trends into the future. This comprehensive treatise discusses Bayesian techniques, single- and multi-channel methods, adaptive and multi-frame techniques, and a host of applications to multimedia processing, astronomy, remote sensing imagery, and medical and biological imaging at the whole-body, small-part, and cellular levels. Everything you need to step into this dynamic field is at your fingertips in this unique, self-contained masterwork. For image enhancement and restoration without a priori information, turn to Blind Image Deconvolution: Theory and Applications for the knowledge and techniques you need to tackle real-world problems.
Author: Steven A. Haney
Publisher: John Wiley & Sons
ISBN: 9780470229859
Category : Medical
Languages : en
Pages : 424
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Book Description
The authoritative reference on High Content Screening (HCS) in biological and pharmaceutical research, this guide covers: the basics of HCS: examples of HCS used in biological applications and early drug discovery, emphasizing oncology and neuroscience; the use of HCS across the drug development pipeline; and data management, data analysis, and systems biology, with guidelines for using large datasets. With an accompanying CD-ROM, this is the premier reference on HCS for researchers, lab managers, and graduate students.
Author: Laszlo Zaborszky
Publisher: Springer Science & Business Media
ISBN: 0387289429
Category : Medical
Languages : en
Pages : 703
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Book Description
The first two editions of this title had a tremendous impact in neuroscience. Between the Second edition in 1989 and today, there has been an explosion of information in the field, including advances in molecular techniques, such as genomics and proteomics, which have become increasing important in neuroscience. A renaissance in fluorescence has occurred, driven by the development of new probes, new microscopes, live imagers, and computer processing. The introduction of new markers has enormously stimulated the field, moving it from tissue culture to neurophysiology to functional MRI techniques.
Author: James Duncan
Publisher: Springer Science & Business Media
ISBN: 9783540630463
Category : Medical
Languages : en
Pages : 580
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Book Description
This book constitutes the refereed proceedings of the 15th International Conference on Information Processing in Medical Imaging, IPMI'97, held in Poultney, Vermont, USA, in June 1997. The 27 revised full papers presented were selected from a total of 96 submissions; also included are 31 poster presentations. The book is divided into topical sections on shape models and matching, novel imaging methods, segmentation, image quality and statistical character of measured data, registration/mapping, statistical models in functional neuroimaging, and MR analysis and processing.
Author: Valtere Evangelista
Publisher: Springer Science & Business Media
ISBN: 1402036167
Category : Science
Languages : en
Pages : 477
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Book Description
How deep we can see inside Nature's smallest secrets? Will it be possible some day in the near future to investigate living structures at atomic level? This area of study is very interdisciplinary, since it applies the principles and the techniques of biology, physics, chemistry, mathematics, and engineering to elucidate the structures of biological macromolecules, of supramolecular structures, organelles, and cells. This book offers updated information on how much information we are able to obtain in the exploration of the inner details of biological specimens in their native structure and composition. The book deals with the implementation of laser beam and stage scanning systems incorporating confocal optics or multiphoton microscopy; the advent of new electro-optical detectors with great sensitivity, linearity, and dynamic range; the possibility of 2D fast image enhancement, reconstruction, restoration, analysis and 3D display, and the application of luminescence techniques (FLIMT, FRET combined with the use of quantum dots), which gives the possibility to investigate the chemical and molecular spatio-temporal organization of life processes; Electron Microscopy and Scanning Force Microscopy (SFM), are also presented, which has opened completely new perspectives for analyzing the surface topography of biological matter in its aqueous environment at a resolution comparable to that achieved by EM.
Author: S. P. Maurya
Publisher: Springer Nature
ISBN: 3030456625
Category : Science
Languages : en
Pages : 221
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Book Description
This book introduces readers to seismic inversion methods and their application to both synthetic and real seismic data sets. Seismic inversion methods are routinely used to estimate attributes like P-impedance, S-impedance, density, the ratio of P-wave and S-wave velocities and elastic impedances from seismic and well log data. These attributes help to understand lithology and fluid contents in the subsurface. There are several seismic inversion methods available, but their application and results differ considerably, which can lead to confusion. This book explains all popular inversion methods, discusses their mathematical backgrounds, and demonstrates their capacity to extract information from seismic reflection data. The types covered include model-based inversion, colored inversion, sparse spike inversion, band-limited inversion, simultaneous inversion, elastic impedance inversion and geostatistical inversion, which includes single-attribute analysis, multi-attribute analysis, probabilistic neural networks and multi-layer feed-forward neural networks. In addition, the book describes local and global optimization methods and their application to seismic reflection data. Given its multidisciplinary, integrated and practical approach, the book offers a valuable tool for students and young professionals, especially those affiliated with oil companies.
