Abstract: "PET reconstruction by the EM algorithm is an iterative computation of Poisson emission rates to maximize a likelihood function. The method is time-consuming and, for real scanner data, requires large numerical arrays. To speed up the computation on multiple processors which have their own local memory and communicate by passing messages on a network, a parallel method has been implemented in which processors compute several iterations before exchanging their latest data with other processors. This method is convenient for iterative reconstruction using a relatively small number of standard workstations on a local area network, i.e., for implementation on computer resources commonly available in clinical and research environments and for which reducing communication among processors is desirable. Computational aspects of the method are explained and illustrated with 2-D reconstructions from a simulation and from sinograms produced by a PET scanner. 512 iterations are computed on a local area network of workstations and, for reference, on a distributed- memory multiprocessor computer as well. The method is capable of producing high quality reconstructions with significant speed-up."

This report presents an algorithm for the inference of tree grammars and its application to pattern recognition. The algorithm takes each sample tree of a pattern class and expresses it as a set of expansive productions capable of generating only that sample tree. The procedure is then generalized by making combinations of nonterminals within each set based on the properties of self embedding, regularity and equivalence.

This is the final report for the year's work on the project to develop an "expert system" to assist in the analysis and interpretation of mesoscale features in the Atlantic Ocean off the U.S. east coast...

Abstract: "Optimal alignment of two strings of length m and n is computed in time O(mn) by dynamic programming. When the strings represent cyclic patterns, the alignment computation must consider all possible shifts and the computation complexity increases accordingly. We present an algorithm for efficient dynamic programming alignment of cyclic strings which uses a previously established channeling technique to reduce the complexity of each alignment and a new shift elimination technique to reduce the number of alignments carried out. The result is a data- dependent time complexity that varies between O(2mn) and O(mnlog2n). An experimental evaluation is provided using randomly generated sequences."