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Physical Sciences and Mathematics Commons™

Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences

2002

Louisiana State University

Articles 1 - 7 of 7

Full-Text Articles in Physical Sciences and Mathematics

Efficient Parallel Computation On Multiprocessors With Optical Interconnection Networks, Min He Jan 2002

Efficient Parallel Computation On Multiprocessors With Optical Interconnection Networks, Min He

LSU Doctoral Dissertations

This dissertation studies optical interconnection networks, their architecture, address schemes, and computation and communication capabilities. We focus on a simple but powerful optical interconnection network model - the Linear Array with Reconfigurable pipelined Bus System (LARPBS). We extend the LARPBS model to a simplified higher dimensional LAPRBS and provide a set of basic computation operations. We then study the following two groups of parallel computation problems on both one dimensional LARPBS's as well as multi-dimensional LARPBS's: parallel comparison problems, including sorting, merging, and selection; Boolean matrix multiplication, transitive closure and their applications to connected component problems. We implement an optimal …

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Decentralized And Adaptive Sensor Data Routing, Mengxia Zhu Jan 2002

Decentralized And Adaptive Sensor Data Routing, Mengxia Zhu

LSU Master's Theses

Wireless sensor network (WSN) has been attracting research efforts due to the rapidly increasing applications in military and civilian fields. An important issue in wireless sensor network is how to send information in an efficient and adaptive way. Information can be directly sent back to the base station or through a sequence of intermediate nodes. In the later case, it becomes the problem of routing. Current routing protocols can be categorized into two groups, namely table-drive (proactive) routing protocols and source-initiated on-demand (reactive) routing. For ad hoc wireless sensor network, routing protocols must deal with some unique constraints such as …

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Experience-Based Language Acquisition: A Computational Model Of Human Language Acquisition, Brian Edward Pangburn Jan 2002

Experience-Based Language Acquisition: A Computational Model Of Human Language Acquisition, Brian Edward Pangburn

LSU Doctoral Dissertations

Almost from the very beginning of the digital age, people have sought better ways to communicate with computers. This research investigates how computers might be enabled to understand natural language in a more humanlike way. Based, in part, on cognitive development in infants, we introduce an open computational framework for visual perception and grounded language acquisition called Experience-Based Language Acquisition (EBLA). EBLA can “watch” a series of short videos and acquire a simple language of nouns and verbs corresponding to the objects and object-object relations in those videos. Upon acquiring this protolanguage, EBLA can perform basic scene analysis to generate …

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Fast Scalable Visualization Techniques For Interactive Billion-Particle Walkthrough, Xinlian Liu Jan 2002

Fast Scalable Visualization Techniques For Interactive Billion-Particle Walkthrough, Xinlian Liu

LSU Doctoral Dissertations

This research develops a comprehensive framework for interactive walkthrough involving one billion particles in an immersive virtual environment to enable interrogative visualization of large atomistic simulation data. As a mixture of scientific and engineering approaches, the framework is based on four key techniques: adaptive data compression based on space-filling curves, octree-based visibility and occlusion culling, predictive caching based on machine learning, and scalable data reduction based on parallel and distributed processing. In terms of parallel rendering, this system combines functional parallelism, data parallelism, and temporal parallelism to improve interactivity. The visualization framework will be applicable not only to material simulation, …

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Techniques And Algorithms For Immersive And Interactive Visualization Of Large Datasets, Ashish Sharma Jan 2002

Techniques And Algorithms For Immersive And Interactive Visualization Of Large Datasets, Ashish Sharma

LSU Master's Theses

Advances in computing power have made it possible for scientists to perform atomistic simulations of material systems that range in size, from a few hundred thousand atoms to one billion atoms. An immersive and interactive walkthrough of such datasets is an ideal method for exploring and understanding the complex material processes in these simulations. However rendering such large datasets at interactive frame rates is a major challenge. A scalable visualization platform is developed that is scalable and allows interactive exploration in an immersive, virtual environment. The system uses an octree based data management system that forms the core of the …

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Techniques To Explore Time-Related Correlation In Large Datasets, Sumeet Dua Jan 2002

Techniques To Explore Time-Related Correlation In Large Datasets, Sumeet Dua

LSU Doctoral Dissertations

The next generation of database management and computing systems will be significantly complex with data distributed both in functionality and operation. The complexity arises, at least in part, due to data types involved and types of information request rendered by the database user. Time sequence databases are generated in many practical applications. Detecting similar sequences and subsequences within these databases is an important research area and has generated lot of interest recently. Previous studies in this area have concentrated on calculating similitude between (sub)sequences of equal sizes. The question of unequal sized (sub)sequence comparison to report similitude has been an …

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Complexity And Heuristics In Ruled-Based Algorithmic Music Composition, Nigel Gwee Jan 2002

Complexity And Heuristics In Ruled-Based Algorithmic Music Composition, Nigel Gwee

LSU Doctoral Dissertations

Successful algorithmic music composition requires the efficient creation of works that reflect human preferences. In examining this key issue, we make two main contributions in this dissertation: analysis of the computational complexity of algorithmic music composition, and methods to produce music that approximates a commendable human effort. We use species counterpoint as our compositional model, wherein a set of stylistic and grammatical rules governs the search for suitable countermelodies to match a given melody. Our analysis of the complexity of rule-based music composition considers four different types of computational problems: decision, enumeration, number, and optimization. For restricted versions of the …

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