This thesis develops a self-adaptive architecture for image understanding that addresses certain kinds of lack of robustness common in image understanding programs. The architecture provides support for making image understanding programs that can manipulate their own semantics and thereby adjust their structure in response to changes in the environment that might cause static image understanding systems to fail. The general approach taken has been to explore the ideas of self-adaptive software and implement an architectural framework that addresses a class of problems that we term "interpretation problems" common in image understanding. Self-adaptive software is a relatively new idea and this thesis represents one of the first implementations of the general idea. The general idea is that to make programs robust to changing environmen- tal conditions that they should be "aware" of their relationship with the environment and be able to restructure themselves at runtime in order to "track" changes in the environment. The implementation takes the form of a multi-layered reflective interpreter that ma- nipulates and runs simple agents. The interpreter framework utilizes Monte-Carlo sam- pling as a mechanism for estimating most likely solutions, uses Minimum Descriptin Length (MDL) as a central coordinating device, and includes a theorem prover based compiler to restructure the program when necessary. To test the architectural ideas developed in the thesis a test domain of interpreting aerial images was chosen. Much of the research described in the thesis addresses issues in that problem domain. The task of the program is to segment, label, and parse aerial images so as to produce an image description similar to descriptions produced by a human expert. An image corpus is developed that is used as the source of domain knowledge. The first processing stage of the program segments the aerial images into segments similar to those found in the annotated corpus. To accomplish this a new segmentation algorithm that we call semantic segmentation was developed that not only used MDL as a principle to drive the low-level segmentation but also allows higher level semantics to influence the segmentation. In our usage of the algorithm those semantics take the form of labeling and parsing the resulting segmentation. The second stage labels the regions and parses the regions into a parse tree. To do this we develop a 2D statistical parser. Rules of grammar are induced from the corpus and an MDL parser finds approximations to the most probable parse of the regions of the segmented image.

We consider the parallelization of Monte Carlo algorithms for analyzing numerical models of charge transport used in semiconductor device physics. Parallel algorithms for the standard k-space Monte Carlo simulation of a three band model of bulk GaAs on hypercube multicomputers are first presented. This Monte Carlo model includes scattering due to polar-optical, intervalley, and acoustic phonons, as well as electron-electron scattering. The k-space Monte Carlo program, excluding electron-electron scattering, is then extended to simulate a semiconductor device by the addition of the real space position of each simulated particle and the assignment of particle charge, using a cloud in cell scheme, to solve the Poisson's equation with particle dynamics. Techniques for effectively partitioning this device so as to balance the computational load while minimizing the communication overhead are discussed. Approaches for improving the efficiency of the parallel algorithm, either by dynamically balancing of load or by employing the usual techniques for enhancing rare events in Monte Carlo simulations are also considered. The parallel algorithms were implemented on a 64-node NCUBE multiprocessor and test results were generated to validate the parallel k-space, as well as the device simulation programs. Timing measurements were also made to study the variation of speedups as both the problem size and number of processors are varied. The effective exploitation of the computational power of message passing multiprocessors requires the efficient mapping of parallel programs onto processors so as to balance the computational load while minimizing the communication overhead between processors. A lower bound for this communication volume when mapping arbitrary task graphs onto distributed processor systems is derived. For a K processor system this lower bound can be computed from the K (possibly) largest eigenvalues of the adjacency matrix of the task graph and the eigenvalues of the adjacency matrix of the processor graph. We also derive the eigenvalues of the adjacency matrix of the processor graph for a hypercube and give test results comparing the lower bound for the communication volume with the values given by a heuristic algorithm for a number of task graphs. / Graduation date: 1992

The spin-lattice relaxation rates are calculated for a rigid magnetic spin cluster in an elastic medium in the presence of a magnetic eld using the latticerame method. This rate is then compared with both the rate calculated using the quantum mechanical densitymatrix method and with the Glauber dynamics. These calculation results are used in the contribution of various heat baths, such as a phonon bath in various dimensions or a fermionic bath, to transition rates that enter into dynamic Monte Carlo simulations of molecular magnets and nanomagnets.