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Saturday, July 11, 2020 | History

1 edition of Topics in Non-Gaussian Signal Processing found in the catalog.

Topics in Non-Gaussian Signal Processing

by Edward J. Wegman

  • 103 Want to read
  • 11 Currently reading

Published by Springer New York in New York, NY .
Written in English

    Subjects:
  • Engineering,
  • Telecommunication

  • About the Edition

    The papers in this volume are the result of a fundamental reexamination of structure and inference methods for non- Gaussian stochastic processes together with the application of such processes as models in the context of filtering, estimation, detection and signal extraction. Considerable emphasis is placed on signal detection in the ocean en- vironment.

    Edition Notes

    Statementedited by Edward J. Wegman, Stuart C. Schwartz, John B. Thomas
    ContributionsSchwartz, Stuart C., Thomas, John B.
    Classifications
    LC ClassificationsTK1-9971
    The Physical Object
    Format[electronic resource] /
    Pagination1 online resource (xii, 235p. 83 illus.)
    Number of Pages235
    ID Numbers
    Open LibraryOL27092312M
    ISBN 101461388619, 1461388597
    ISBN 109781461388616, 9781461388593
    OCLC/WorldCa852790745

    Dr. Maria Sabrina Greco graduated in Electronic Engineering in and received a Ph.D. degree in Telecommunication Engineering in , from the University of Pisa, Italy. From December to May , she joined the Georgia Tech Research Institute, Atlanta, in the USA as a visiting research scholar where she carried on research activity in the field of radar detection in non-Gaussian. P. J. Buxbaum and R. A. Haddad, Recursive optimal estimation for a class of non-gaussian processes, in Proceedings of the Symposium on Computer Processing in Communications, (Polytechnic Institute.

    Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing. Bayesian-based signal processing is expected to dominate the future of model-based signal processing for years to come. This book develops the "Bayesian approach" to statistical signal processing for a variety of useful model sets with an emphasis on nonlinear/non-Gaussian problems, as well as classical techniques. Current applications and simple examples motivate the models and prepare the.

    Financial Signal Processing and Machine Learning May May Read More. Authors: Ali N. Akansu, ; Sanjeev R. Kulkarni, ; Dmitry M. Malioutov. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management .


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Topics in Non-Gaussian Signal Processing by Edward J. Wegman Download PDF EPUB FB2

Non-Gaussian Signal Processing is a child of a technological push. It is evident that we are moving from an era of simple signal processing with relatively primitive electronic cir­ cuits to one in which digital processing systems, in a combined hardware-software configura.­ tion, are quite capable of implementing advanced mathematical and statistical procedures.

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ISBN: OCLC Number: Notes: "A Dowden & Culver book." Description: xii, pages: illustrations ; 25 cm. For a nonlinear system, although the input signal follows Gaussian distribution, the output is a non-Gaussian signal.

In practice, there are many non-Gaussian and nonstationary signals that also need to be processed and analyzed. For these signals, high-order statistics is one effective and important tool to get the detailed characteristics. Get this from a library.

Topics in Non-Gaussian Signal Processing. [Edward J Wegman; Stuart C Schwartz; John B Thomas] -- The papers in this volume are the result of a fundamental reexamination of structure and inference methods for non- Gaussian stochastic processes together with the application of such processes as.

Book Abstract: Non-Gaussian Statistical Communication Theory. Since its inception in the late s, Statistical Communication Theory (SCT) has grown into a major field of study, applicable to many branches of science. This authoritative and provocative text is a legacy left behind by the late Dr.

David Middleton—a pioneer of SCT. e-books in Signal Processing category Bayesian Methods in the Search for MH by Samuel Davey, et al.

- Springer, Topics in Non-Gaussian Signal Processing book book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH flight paths. Nonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nonlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are non-Gaussian, rather than Gaussian, by: Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering.

This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing 4/5(1). The purpose of the book is to introduce Non-Gaussian statistical communication theory and demonstrate how the theory improves probabilistic model.

The book was originally planed to include 24 chapters as seen in the table of preface. Middleton completed first 10 chapters prior to his passing in In Time-Frequency Signal Analysis and Processing (Second Edition), Summary and Conclusions.

In this section, we have presented a method of analyzing complex multicomponent time-frequency signal structures without the usual trade-off of (t,f) resolution versus cross iterative approach is based on the MP of Ref. [17] but extended to include non-Gaussian signal types.

Focusing on non-Gaussian models, this book develops tools for studying nonlinear signal processing algorithms that emerge from statistical estimation principles.

Topics covered include order statistics, weighted median smoothers and filters, and weighted myriad filters. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering.

This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing 3/5(1). Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering.

This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing. Digital Signal Processing and Applications with the C and C DSK - Ebook written by Rulph Chassaing.

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Experiments with this detector on ambient arctic and shrimp noises show processing gains of to dB, respectively.

Signals with a moderate signal-to-noise ratio in non-Gaussian noise are modeled as fading narrowband signals. A new processor is developed which combines a robust estimator (for the fading signal) with a robust detection Cited by: 2. This book describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy.

Taking a gradual approach, it builds up concepts in a solid, step-by-step fashion so that the ideas and algorithms can be implemented in practical. signal processing. He has authored or co-authored about 75 journal papers, 70 conference papers, and two book chapters. Gini was an Associate Editor for the IEEE TRANSACTIONS ON SIGNAL PROCESSING from August to August He is a member of the Editorial Boards of the Eurasip Journal on Advances in Signal Processing and Signal.

A problem-solving approach to statistical signal processing for practicing engineers, technicians, and graduate students This book takes a pragmatic approach in solving a set of common problems engineers and technicians encounter when processing signals.

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Now, twoleading researchers synthesize the field's vast new literature, giving working engineers practicalguidance for designing.for adaptation, and yield s therefore solutions that are more accurate than MSE in non-Gaussian and non-linear signal processing [3]-[8]. Manuscript received April 6, Purchase Academic Press Library in Signal Processing, Volume 3 - 1st Edition.

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