Compression of UV spectrum with recurrent neural network

Leong Kwan Li, Ka Fai Cedric Yiu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In order to save time or storage space, compression techniques are applied. Recently compression techniques based on approximation theory are dominated by the fast Fourier and the wavelet transforms if noise is tolerated. For a given sequence, the compressed signal is represented as a linear sum of basic functions. In this note, we introduce a dynamical system approach for signal compressions. We demonstrate how to compress a UV spectrum by a discrete-time recurrent neural network. As an initial valued problem, the parameters we stored are the connection weights of the neural network and also the initial states. Compression ratio is also discussed. Storage space and energy is saved if good compression techniques are applied.
Original languageEnglish
Title of host publication1st International Conference on Green Circuits and Systems, ICGCS 2010
Pages365-369
Number of pages5
DOIs
Publication statusPublished - 20 Sep 2010
Event1st International Conference on Green Circuits and Systems, ICGCS 2010 - Shanghai, China
Duration: 21 Jun 201023 Jun 2010

Conference

Conference1st International Conference on Green Circuits and Systems, ICGCS 2010
Country/TerritoryChina
CityShanghai
Period21/06/1023/06/10

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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