Distributed Computing For Signal Processing Modeling Of Asynchronous Parallel Computation Appendix A

Distributed Computing for Signal Processing: Modeling of Asynchronous Parallel Computation. Appendix A.

Research in the area of distributed computing systems for digital signal processing applications is described. The work involves the modeling of asynchronous parallel processes and computer systems for executing these processes. The objective of the work is to develop techniques by which the compatibility of an architecture and an algorithm can be evaluated. The three part effort addresses: 1. Modeling of asynchronous parallel computer system architectures; 2. Modeling of asynchronous parallel computational processes; 3. Evaluation of alternative architectures relative to classes of computational the approach to the modeling of parallel processes and architectures is to examined the parallelism in a variety of one- and two-dimensional signal processing tasks. This includes a study of the ways in which different types of digital signal processing tasks can be executed on different types of architectures. The goal is to develop one set of features by which processes can be characterized, and another set of features by which parallel architectures can be characterized: and to use these features to obtain measures for the evaluation of process/architecture compatibility. This research will contribute to the understanding both of how distributed computer systems can be designed for the execution of a class of tasks, and of how signal processing tasks can be decomposed for execution on a distributed computing system. (Author).

Distributed Computing for Signal Processing: Modeling of Asynchronous Parallel Computation. Appendix F. Studies in Parallel Image Processing

The supervised relaxation operator combines the information from multiple ancillary data sources with the information from multispectral remote sensing image data and spatial context. Iterative calculation integrate information from the various sources, reaching a balance in consistency between these sources of information. The supervised relaxation operator is shown to produce substantial improvements in classification accuracy compared to the accuracy produced by the conventional maximum likelihood classifier using spectral data only. The convergence property of the supervised relaxation algorithm is also described. Improvement in classification accuracy by means of supervised relaxation comes at a high price in terms of computation. In order to overcome the computation-intensive problem, a distributed/parallel implementation is adopted to take advantage of a high degree of inherent parallelism in the algorithm.

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