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Stochastical and neuromimetic aspects of modeling electromagnetic composite materials

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Date Issued:
1994
Summary:
This dissertation is concerned primarily with the analytical modeling of a class of electromagnetic composite materials using the concepts of stochastical mixture theory, principles of electromagnetics and neuromimetic considerations. The global behavior of the test composite is ascertained in terms of the constitutive relations of the material parameters (having stochastical attributions) and the intramaterial hierarchy is modeled as massively interconnected, interacting units depicting such systems as mimetics of neural networks. Pertinent research efforts enclave the following specific tasks: (i) Modeling a multi-constituent electromagnetic composite medium in terms of the characteristics of its individual constituents and their spatial (random or orderly) dispositions. (ii) Assessment of nonspherical particulate effects (in terms of the stochastical attributes) on the global response of such composite materials. (iii) Evaluation of interparticle interactions and their implicit effects on the effective electromagnetic properties of the composite media. (iv) Assaying the transitional behavior of the test composites and, (v) modeling electromagnetic composites as neuromimetics correlating their effective material characteristics to the corresponding state-transitional response of a massively interconnected neural network. Results arising from these theoretical considerations are compared with data compiled via experimental studies performed (where feasible) or otherwise correlated with theoretical and/or experimental results available elsewhere in the literature. Specific experimental efforts carried out refer to piezoelectric rubber composites and their application in controlling acoustic beamforming via electrical 'pinch off' (which mimics the inhibitory response in a neuronal cell); as well as exclusive experimental tasks to verify the transitional lossy behavior model developed presently using a set of fast-ion conductor composites and dielectric-plus-conductor mixtures. Lastly, inferential conclusions are presented and discussed with an outline on the scope of extensions to the present work.
Title: Stochastical and neuromimetic aspects of modeling electromagnetic composite materials.
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Name(s): Park, Joseph C.
Florida Atlantic University, Degree grantor
Neelakanta, Perambur S., Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1994
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 214 p.
Language(s): English
Summary: This dissertation is concerned primarily with the analytical modeling of a class of electromagnetic composite materials using the concepts of stochastical mixture theory, principles of electromagnetics and neuromimetic considerations. The global behavior of the test composite is ascertained in terms of the constitutive relations of the material parameters (having stochastical attributions) and the intramaterial hierarchy is modeled as massively interconnected, interacting units depicting such systems as mimetics of neural networks. Pertinent research efforts enclave the following specific tasks: (i) Modeling a multi-constituent electromagnetic composite medium in terms of the characteristics of its individual constituents and their spatial (random or orderly) dispositions. (ii) Assessment of nonspherical particulate effects (in terms of the stochastical attributes) on the global response of such composite materials. (iii) Evaluation of interparticle interactions and their implicit effects on the effective electromagnetic properties of the composite media. (iv) Assaying the transitional behavior of the test composites and, (v) modeling electromagnetic composites as neuromimetics correlating their effective material characteristics to the corresponding state-transitional response of a massively interconnected neural network. Results arising from these theoretical considerations are compared with data compiled via experimental studies performed (where feasible) or otherwise correlated with theoretical and/or experimental results available elsewhere in the literature. Specific experimental efforts carried out refer to piezoelectric rubber composites and their application in controlling acoustic beamforming via electrical 'pinch off' (which mimics the inhibitory response in a neuronal cell); as well as exclusive experimental tasks to verify the transitional lossy behavior model developed presently using a set of fast-ion conductor composites and dielectric-plus-conductor mixtures. Lastly, inferential conclusions are presented and discussed with an outline on the scope of extensions to the present work.
Identifier: 12359 (digitool), FADT12359 (IID), fau:9260 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (Ph.D.)--Florida Atlantic University, 1994.
Subject(s): Composite materials--Electric properties
Composite materials--Magnetic properties
Stochastic processes
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/12359
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.