An investigation to study the feasibility of on-line bibliographic information retrieval system using an APP

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This thesis reports an investigation on the feasibility study of a searching mechanism using an APP suitable for an on-line bibliographic retrieval, operation, especially for retrospective searches. From the study of the searching methods used in the conventional systems it is seen that elaborate file- and data- structures are introduced to improve the response time of the system. These consequently lead to software and hardware redundancies. To mask these complexities of the system an expensive computer with higher capabilities and more powerful instruction set is commonly used. Thus the service of the systen becomes cost-ineffective. On the other hand the primitive operations of a searching mechanism, such as, association, domain selection, intersection and unions, are the intrinsic features of an associative parallel processor. Therefore it is important to establish the feasibility of an APP as a cost-effective searching mechanise. In this thesis a searching mechanism using an 'ON-THE-FLY' searching technique has been proposed. The parallel search unit uses a Byte-oriented VRL-APP for efficient character string processing. At the time of undertaking this work the specification for neither the retrieval systems nor the BO-VRL APP's were well established; hence a two-phase investigation was originated. In the Phase I of the work a bottom up approach was adopted to derive a formal and precise specification for the BO-VRL-APP. During the Phase II of the work a top-down approach was opted for the implementation of the searching mechanism. An experimental research vehicle has been developed to establish the feasibility of an APP as a cost-effective searching mechanism. Although rigorous proof of the feasibility has not been obtained, the thesis establishes that the APP is well suited for on-line bibligraphic information retrieval operations where substring searches including boolean selection and threshold weights are efficiently supported

Mammalian Brain As a Network of Networks

Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD

Spaceborne memory organization Interim report

Associative memory applications in unmanned space vehicle

The hippocampus and cerebellum in adaptively timed learning, recognition, and movement

The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensory-motor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contain circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar timing controls the release of conditioned responses. This property is part of the model's description of how cognitive-emotional interactions focus attention on motivationally valued cues, and how this process breaks down due to hippocampal ablation. The model suggests that the hippocampal mechanisms that help to rapidly draw attention to salient cues could prematurely release motor commands were not the release of these commands adaptively timed by the cerebellum. The model hippocampal system modulates cortical recognition learning without actually encoding the representational information that the cortex encodes. These properties avoid the difficulties faced by several models that propose a direct hippocampal role in recognition learning. Learning within the model hippocampal system controls adaptive timing and spatial orientation. Model properties hereby clarify how hippocampal ablations cause amnesic symptoms and difficulties with tasks which combine task delays, novelty detection, and attention towards goal objects amid distractions. When these model recognition, reinforcement, sensory-motor, and timing processes work together, they suggest how the brain can accomplish conditioning of multiple sensory events to delayed rewards, as during serial compound conditioning.Air Force Office of Scientific Research (F49620-92-J-0225, F49620-86-C-0037, 90-0128); Advanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-92-J-1904); National Institute of Mental Health (MH-42900

Contributions to the analysis and segmentation of remote sensing hyperspectral images

142 p.This PhD Thesis deals with the segmentation of hyperspectral images from the point of view of Lattice Computing. We have introduced the application of Associative Morphological Memories as a tool to detect strong lattice independence, which has been proven equivalent to affine independence. Therefore, sets of strong lattice independent vectors found using our algorithms correspond to the vertices of convex sets that cover most of the data. Unmixing the data relative to these endmembers provides a collection of abundance images which can be assumed either as unsupervised segmentations of the images or as features extracted from the hyperspectral image pixels. Besides, we have applied this feature extraction to propose a content based image retrieval approach based on the image spectral characterization provided by the endmembers. Finally, we extended our ideas to the proposal of Morphological Cellular Automata whose dynamics are guided by the morphological/lattice independence properties of the image pixels. Our works have also explored the applicability of Evolution Strategies to the endmember induction from the hyperspectral image data