Park, Joseph C.

A computer algorithm is developed to provide real-time cross range spatial quantization for a single beam forward look SONAR similar in operation to a typical sidescan SONAR. This involves the computer simulation of return time signals generated by scanning a surface profile. The time signals are normalized with respect to the scanning altitude to simulate the application of a time varying gain, and then are used as input to the surface estimation algorithm. The algorithm requires two time signals acquired from adjacent scanning positions and solves a stereoscopic geometry in arriving at the surface estimate. Final estimates have an error of less than 1% in target height determination within a set range of operation.

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.