Adaptive pattern recognition by mini-max neural networks as a part of an intelligent processor

In this decade and progressing into 21st Century, NASA will have missions including Space Station and the Earth related Planet Sciences. To support these missions, a high degree of sophistication in machine automation and an increasing amount of data processing throughput rate are necessary. Meeting these challenges requires intelligent machines, designed to support the necessary automations in a remote space and hazardous environment. There are two approaches to designing these intelligent machines. One of these is the knowledge-based expert system approach, namely AI. The other is a non-rule approach based on parallel and distributed computing for adaptive fault-tolerances, namely Neural or Natural Intelligence (NI). The union of AI and NI is the solution to the problem stated above. The NI segment of this unit extracts features automatically by applying Cauchy simulated annealing to a mini-max cost energy function. The feature discovered by NI can then be passed to the AI system for future processing, and vice versa. This passing increases reliability, for AI can follow the NI formulated algorithm exactly, and can provide the context knowledge base as the constraints of neurocomputing. The mini-max cost function that solves the unknown feature can furthermore give us a top-down architectural design of neural networks by means of Taylor series expansion of the cost function. A typical mini-max cost function consists of the sample variance of each class in the numerator, and separation of the center of each class in the denominator. Thus, when the total cost energy is minimized, the conflicting goals of intraclass clustering and interclass segregation are achieved simultaneously

Contributions to the solution of the symmetric travelling salesman problem

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Geostry - a Peer-to-Peer System for Location-based Information

An interesting development is summarized by the notion of ”Ubiquitous Computing”: In this area, miniature systems are integrated into everyday objects making these objects ”smart” and able to communicate. Thereby, everyday objects can gather information about their state and their environment. By embedding this information into a model of the real world, which nowadays can be modeled very realistically using sophisticated 3D modeling techniques, it is possible to generate powerful digital world models. Not only can existing objects of the real world and their state be mapped into these world models, but additional information can be linked to these objects as well. The result is a symbiosis of the real world and digital information spaces. In this thesis, we present a system that allows for an easy access to this information. In contrast to existing solutions our approach is not based on a server-client architecture. Geostry bases on a peer-to-peer system and thus incorporates all the advantages, such as self-organization, fairness (in terms of costs), scalability and many more. Setting up the network is realized through a decentralized bootstrapping protocol based on an existing Internet service to provide robustness and availability. To selectively find geographic-related information Geostry supports spatial queries. They - among other things - enable the user to search for information e.g. in a certain district only. Sometimes, a certain piece of information raises particular interest. To cope with the run on the single computer that provides this specific information, Geostry offers dynamic replication mechanisms. Thereby, the information is replicated for as long as the rush lasts. Thus, Geostry offers all aspects from setting up a network, providing access to geo-related information and replication methods to provide accessibility in times of high loads

Efficient Algorithms for Large-Scale Image Analysis

This work develops highly efficient algorithms for analyzing large images. Applications include object-based change detection and screening. The algorithms are 10-100 times as fast as existing software, sometimes even outperforming FGPA/GPU hardware, because they are designed to suit the computer architecture. This thesis describes the implementation details and the underlying algorithm engineering methodology, so that both may also be applied to other applications

Visualization Tools for Comparative Genomics applied to Convergent Evolution in Ash Trees

Assembly and analysis of whole genomes is now a routine part of genetic research, but effective tools for the visualization of whole genomes and their alignments are few. Here we present two approaches to allow such visualizations to be done in an efficient and user-friendly manner. These allow researchers to spot problems and patterns in their data and present them effectively. First, FluentDNA is developed to tackle single full genome visualization and assembly tasks by representing nucleotides as colored pixels in a zooming interface. This enables users to identify features without relying on algorithmic annotation. FluentDNA also supports visualizing pairwise alignments of wellassembled whole genomes from chromosome to nucleotide resolution. Second, Pantograph is developed to tackle the problem of visualizing variation among large numbers of whole genome sequences. This uses a graph genome approach, which addresses many of the technical challenges of whole genome multiple sequence alignments by representing aligned sequences as nodes which can be shared by many individuals. Pantograph is capable of scaling to thousands of individuals and is applied to SARS and A. thaliana pangenomes. Alongside the development of these new genomics tools, comparative genomic research was undertaken on worldwide species of ash trees. I assembled 13 ash genomes and used FluentDNA to quality check the results and discovered contaminants and a mitochondrial integration. I annotated protein coding genes in 28 ash assemblies and aligned their gene families. Using phylogenetic analysis, I identified gene duplications that likely occurred in an ancient whole genome duplication shared by all ash species. I examined the fate of these duplicated genes, showing that losses are concentrated in a subset of gene families more often than predicted by a null model simulation. I conclude that convergent evolution has occurred in the loss and retention of duplicated genes in different ash species.BBSRC BB/S004661/

The Design and Implementation of a Truly Integrated GIS Using the Persistent Programming Language Napier88

This thesis is concerned with the design and development of an integrated geographical information system (IGIS) based on the use of a persistent programming language called Napier88. It reports on the research carried out to implement a wholly new approach to deal with the problems of constructing a truly integrated GIS. The main aspects discussed within the context of this thesis are: an overview of the current status and trends in IGIS development; the characteristics and functions of the persistent programming language Napier88; the design considerations and the definition of the system architecture of an IGIS; the integration of vector map data and raster image data in a persistent store; the multiple data modelling of geographical data; the superimposition and cross indexing of vector maps and raster images; the spatial indexing and querying of geographical data; the management of geographical data in a persistent database environment; and the implementation of a prototype IGIS. This thesis concludes that the Napier88 language can provide a sound framework for the construction of a truly integrated GIS, although some current deficiencies in the language need to be overcome. Since persistent programming languages are still in the stage of research and development, more research is necessary to investigate other features that they could provide which may be beneficial to the development of a truly integrated GIS