File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1109/ICGCS.2010.5543038
- Scopus: eid_2-s2.0-77956588290
Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Conference Paper: Compression of UV spectrum with recurrent neural network
Title | Compression of UV spectrum with recurrent neural network |
---|---|
Authors | |
Issue Date | 2010 |
Citation | 1St International Conference On Green Circuits And Systems, Icgcs 2010, 2010, p. 365-369 How to Cite? |
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. © 2010 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/155932 |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Li, LK | en_US |
dc.contributor.author | Yiu, KFC | en_US |
dc.date.accessioned | 2012-08-08T08:38:29Z | - |
dc.date.available | 2012-08-08T08:38:29Z | - |
dc.date.issued | 2010 | en_US |
dc.identifier.citation | 1St International Conference On Green Circuits And Systems, Icgcs 2010, 2010, p. 365-369 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/155932 | - |
dc.description.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. © 2010 IEEE. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | 1st International Conference on Green Circuits and Systems, ICGCS 2010 | en_US |
dc.title | Compression of UV spectrum with recurrent neural network | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Yiu, KFC:cedric@hkucc.hku.hk | en_US |
dc.identifier.authority | Yiu, KFC=rp00206 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1109/ICGCS.2010.5543038 | en_US |
dc.identifier.scopus | eid_2-s2.0-77956588290 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-77956588290&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.spage | 365 | en_US |
dc.identifier.epage | 369 | en_US |
dc.identifier.scopusauthorid | Li, LK=7501447410 | en_US |
dc.identifier.scopusauthorid | Yiu, KFC=24802813000 | en_US |