Author: So, Tung
Title: Signal compression by discrete recurrent neural networks
Degree: M.Sc.
Year: 1998
Subject: Signal processing -- Digital techniques
Neural networks (Computer science)
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Department of Applied Mathematics
Pages: vii, 158 leaves : ill. ; 31 cm
Language: English
Abstract: The importance and demands for signal compression have been increasing abruptly, especially with the growing popularity of Internet access and personal entertainment. The speed and the ratio of compressing signal are the main concern when judging the compression algorithm. At present, two of the most popular algorithms are Fast Fourier Transform and Wavelet Transform which represent the signals as linear sum of the basic functions, by retaining a finite number of integration coefficients to achieve the goal of compression. The newest focus is on Fractal compression which try to seek the similarities in the signals such that compression can be achieved. In this project, we try to seek an algorithm which compress signals in a totally different way than the ones mentioned above. We use recurrent neural network to carry out the compression process. Signal samples were obtained from some chemical experiments such that the network can be tested without losing of generality.
Access: restricted access

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