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Alan V. Oppenheim. Demirtas, Sefa Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2014-09-19T21:32:55Z 2014-09-19T21:32:55Z 2014 2014 http://hdl.handle.net/1721.1/89989 890130929 Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. Cataloged from PDF version of thesis. Includes bibliographical references (pages 175-180). Functional composition, the application of one function to the results of another function, has a long history in the mathematics community, particularly in the context of polynomials and rational functions. This thesis articulates and explores a general framework for the use of functional composition in the context of signal processing. Its many potential applications to signal processing include utilization of the composition of simpler or lower order subfunctions to exactly or approximately represent a given function or data sequence. Although functional composition currently appears implicitly in a number of established signal processing algorithms, it is shown how the more general context developed and exploited in this thesis leads to significantly improved results for several important classes of functions that are ubiquitous in signal processing such as polynomials, frequency responses and discrete multivariate functions. Specifically, the functional composition framework is exploited in analyzing, designing and extending modular filters, separating marginalization computations into more manageable subcomputations and representing discrete sequences with fewer degrees of freedom than their length and region of support with implications for sparsity and efficiency. by Sefa Demirtas. Ph.D. 187 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Functional composition and decomposition for signal processing Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="477cab93-37a8-4481-af16-71a6d23aacbc"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Functional composition and decomposition for signal processing</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2014</PublicationDate> <Authors> <Author> <DisplayName>Demirtas, Sefa</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Functional composition, the application of one function to the results of another function, has a long history in the mathematics community, particularly in the context of polynomials and rational functions. This thesis articulates and explores a general framework for the use of functional composition in the context of signal processing. Its many potential applications to signal processing include utilization of the composition of simpler or lower order subfunctions to exactly or approximately represent a given function or data sequence. Although functional composition currently appears implicitly in a number of established signal processing algorithms, it is shown how the more general context developed and exploited in this thesis leads to significantly improved results for several important classes of functions that are ubiquitous in signal processing such as polynomials, frequency responses and discrete multivariate functions. Specifically, the functional composition framework is exploited in analyzing, designing and extending modular filters, separating marginalization computations into more manageable subcomputations and representing discrete sequences with fewer degrees of freedom than their length and region of support with implications for sparsity and efficiency.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>