Compiling Prolog to Logic-inference Virtual Machine

The Logic-inference Virtual Machine (LVM) is a new Prolog execution model consisting of a set of high-level instructions and memory architecture for handling control and unification. Different from the well-known Warren's Abstract Machine [1], which uses Structure Copying method, the LVM adopts a hybrid of Program Sharing [2] and Structure Copying to represent first-order terms. In addition, the LVM employs a single stack paradigm for dynamic memory allocation and embeds a very efficient garbage collection algorithm to reclaim the useless memory cells. In order to construct a complete Prolog system based on the LVM, a corresponding compiler must be written. In this thesis, a design of such LVM compiler is presented and all important components of the compiler are described. The LVM compiler is developed to translate Prolog programs into LVM bytecode instructions, so that a Prolog program is compiled once and can run anywhere. The first version of LVM compiler (about 8000 lines of C code) has been developed. The compilation time is approximately proportional to the size of source codes. About 80 percent of the time are spent on the global analysis. Some compiled programs have been tested under a LVM emulator. Benchmarks show that the LVM system is very promising in memory utilization and performance

A Multiple-Systems Approach in the Symbolic Modelling of Human Vision

For most of the thirty years or so of machine vision research, activity has been concentrated mainly in the domain of metric-based approaches: there has been negligible attention to the psychological factors in human vision. With the recent resurgence of interest in neural systems, that is now changing. This thesis discusses relevant aspects of basic visual neuroanatomy, and psychological phenomena, in an attempt to relate the concepts to a model of human vision and the prospective goals of future machine vision systems. It is suggested that, while biological vision is complex, the underlying mechanisms of human vision are more tractable than is often believed. We also argue here that the controversial subject of direct vision plays a crucial role in natural vision, and we attempt to relate this to the model. The recognition of massive parallelism in natural vision has led to proposals for emulating aspects of neural networks in technology. The systems model developed in this work demonstrates software-simulated cellular automata (CAs) in the role of mainly low-level image processing. It is shown that CAs are able to efficiently provide both conventional and neurally-inspired vision functions. The thesis also discusses the use of Prolog as the means of realising higher level image understanding. The symbolic processing developed is basic, but is nevertheless sufficient for the purposes of the present. demonstrations. Extensions to the concepts can be easily achieved. The modular systems approach adopted blends together several ideas and processes, and results in a more robust model of human vision that is able to translate a noisy real image into an accessible symbolic form for expert-domain interpretation

2014-2015, University of Memphis bulletin

University of Memphis bulletin containing the graduate catalog for 2014-2015.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1433/thumbnail.jp

2010-2011, University of Memphis bulletin

University of Memphis bulletin containing the graduate catalog for 2010-2011.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1430/thumbnail.jp

2003-2005, University of Memphis bulletin

University of Memphis bulletin containing the graduate catalog for 2003-2005.https://digitalcommons.memphis.edu/speccoll-ua-pub-bulletins/1424/thumbnail.jp