Neural Network Based Lossless Data Compression Schemes For Telemetry Data

Data compression deals with removal of redundancy, reducing bandwidth and storage requirements, and thus lowering transmission and storage costs of data acquired from remote sensors. Some telemetry data are sensitive to inaccuracies and reguire lossless compression techniques to enable exact reconst...

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Main Author:	N. Rajasvaran, R.Logeswaran
Format:	Thesis
Published:	2000
Subjects:	QA76.75-76.765 Computer software
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id	my-mmu-ep.362
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spelling	my-mmu-ep.3622010-06-03T02:01:35Z Neural Network Based Lossless Data Compression Schemes For Telemetry Data 2000 N. Rajasvaran, R.Logeswaran QA76.75-76.765 Computer software Data compression deals with removal of redundancy, reducing bandwidth and storage requirements, and thus lowering transmission and storage costs of data acquired from remote sensors. Some telemetry data are sensitive to inaccuracies and reguire lossless compression techniques to enable exact reconstruction of the data at the receiver. One technology that has been successfully applied in a wide range of lossy compression applications is neural networks, a massively parallel system with pattern recognition capabilities. This report describes simulation results of implementing this technology for lossless compression of telemetry data. The general approach taken is to modify a two-stage scheme consisting of a predictor-encoder combinations in the past. Adaptations to enhance the scheme for the neural implementation are introduced. A variety of neural network models from a selection of different network types, including feedforward, recurrent and radial basis configurations are tested. Various schemes are proposed and implemented to determine suitable strategies for training the network. Characteristic features of the models, transmission issues and other practical considerations are taken into account to determine optimised configuration and implementation of the schemes. Variations of the two-stage scheme are introduced for both the traditional and neural schemes. Significant observations and results of the strengths and weaknesses observed amongst over 50 implementations simulated with the telemetry data in the report reveal interesting results for existing and proposed neural schemes. The distributed processing characteristic of neural models implies speed and error tolerance. Simulation results, combined with estimations are used for comparisons of these characteristics with the existing schemes. The findings and shortcomings of the research may be beneficial for future work, such as in the hardware implementations of dedicated neural chips for lossless compression. 2000 Thesis http://shdl.mmu.edu.my/362/ http://myto.perpun.net.my/metoalogin/logina.php masters Multimedia University Research Library
institution	Multimedia University
collection	MMU Institutional Repository
topic	QA76.75-76.765 Computer software
spellingShingle	QA76.75-76.765 Computer software N. Rajasvaran, R.Logeswaran Neural Network Based Lossless Data Compression Schemes For Telemetry Data
description	Data compression deals with removal of redundancy, reducing bandwidth and storage requirements, and thus lowering transmission and storage costs of data acquired from remote sensors. Some telemetry data are sensitive to inaccuracies and reguire lossless compression techniques to enable exact reconstruction of the data at the receiver. One technology that has been successfully applied in a wide range of lossy compression applications is neural networks, a massively parallel system with pattern recognition capabilities. This report describes simulation results of implementing this technology for lossless compression of telemetry data. The general approach taken is to modify a two-stage scheme consisting of a predictor-encoder combinations in the past. Adaptations to enhance the scheme for the neural implementation are introduced. A variety of neural network models from a selection of different network types, including feedforward, recurrent and radial basis configurations are tested. Various schemes are proposed and implemented to determine suitable strategies for training the network. Characteristic features of the models, transmission issues and other practical considerations are taken into account to determine optimised configuration and implementation of the schemes. Variations of the two-stage scheme are introduced for both the traditional and neural schemes. Significant observations and results of the strengths and weaknesses observed amongst over 50 implementations simulated with the telemetry data in the report reveal interesting results for existing and proposed neural schemes. The distributed processing characteristic of neural models implies speed and error tolerance. Simulation results, combined with estimations are used for comparisons of these characteristics with the existing schemes. The findings and shortcomings of the research may be beneficial for future work, such as in the hardware implementations of dedicated neural chips for lossless compression.
format	Thesis
qualification_level	Master's degree
author	N. Rajasvaran, R.Logeswaran
author_facet	N. Rajasvaran, R.Logeswaran
author_sort	N. Rajasvaran, R.Logeswaran
title	Neural Network Based Lossless Data Compression Schemes For Telemetry Data
title_short	Neural Network Based Lossless Data Compression Schemes For Telemetry Data
title_full	Neural Network Based Lossless Data Compression Schemes For Telemetry Data
title_fullStr	Neural Network Based Lossless Data Compression Schemes For Telemetry Data
title_full_unstemmed	Neural Network Based Lossless Data Compression Schemes For Telemetry Data
title_sort	neural network based lossless data compression schemes for telemetry data
granting_institution	Multimedia University
granting_department	Research Library
publishDate	2000
_version_	1747829115798421504

Neural Network Based Lossless Data Compression Schemes For Telemetry Data

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