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dc.contributor.authorBaras, John S.en_US
dc.date.accessioned2007-05-23T09:41:49Z
dc.date.available2007-05-23T09:41:49Z
dc.date.issued1988en_US
dc.identifier.urihttp://hdl.handle.net/1903/4794
dc.description.abstractWe consider real-time sequential detection and estimation problems for non-gaussian signal and noise models. We develop optimal algorithms and several architectures for real-time implementation based on numerical algorithms, including asynchronous implementations of multigrid algorithms. These implementations are of high complexity, costly and cannot easily accomodate model variability. We then propose and analyze a different class of algorithms, which are symbolic, of the neural network type. The preliminary results presented here demonstrate that these algorithms have remarkably lower complexity and cost, work well under model variability and their performance is nearly optimal. We also discuss how these type of algorithms are incorporated in the DELPHI system for integrated design of signal processing systems.en_US
dc.format.extent1061436 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1988-68en_US
dc.titleSymbolic and Numeric Real-Time Signal Processing.en_US
dc.typeTechnical Reporten_US
dc.contributor.departmentISRen_US


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