Blind Deconvolution of SISO Systems with Binary Source Based on Recursive Channel Shortening
We treat the problem of Blind Deconvolution of Single Input – Single Output (SISO) systems with real or complex binary sources. We explicate the basic mathematical idea by focusing on the noiseless case. Our approach leads to a recursive channel shortening algorithm based on simple data gouping. The...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 553 |
|---|---|
| container_issue | |
| container_start_page | 548 |
| container_title | |
| container_volume | |
| creator | Diamantaras, Konstantinos I. Papadimitriou, Theophilos |
| description | We treat the problem of Blind Deconvolution of Single Input – Single Output (SISO) systems with real or complex binary sources. We explicate the basic mathematical idea by focusing on the noiseless case. Our approach leads to a recursive channel shortening algorithm based on simple data gouping. The channel shortening process eventually results in an instantaneous binary system with trivial solution. The method is both deterministic and very fast. It does not involve any iterative optimization or stochastic approximation procedure. It does however, require sufficiently large datasets in order to meet the source richness condition. |
| doi_str_mv | 10.1007/978-3-540-30110-3_70 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>pascalfrancis_sprin</sourceid><recordid>TN_cdi_pascalfrancis_primary_16176798</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>16176798</sourcerecordid><originalsourceid>FETCH-LOGICAL-p228t-a1647e312c0dc1170d26b98ff7de749f9b5f861b5d534f55cf981e4fac8eed03</originalsourceid><addsrcrecordid>eNotkE1vAiEQhulXUmv9Bz1w6ZEWFnaBY7VfJiYmXY9NCLKgtCuYZbXx3xdt5zCTvPNkMnkAuCP4gWDMHyUXiKKSYUQxIbkrjs_ADc3JKcDnYEAqQhClTF6AUeaPu4LismKXYJCpAknO6DUYpfSFcxEuiGAD8DlufWjgszUx7GO7630MMDpYT-s5rA-pt5sEf3y_hmMfdHeAddx1xsKxTraBmf2wZtclv7dwstYh2BbW69j1NviwugVXTrfJjv7nECxeXxaTdzSbv00nTzO0LQrRI00qxi0lhcGNIYTjpqiWUjjHG8uZdHJZOlGRZdmUlLmyNE4KYpnTRljbYDoE939ntzoZ3bpOB-OT2nZ-kz9W2QyvuBSZK_64lFdhZTu1jPE7KYLVUbPK2hRVWZw6WVVHzfQXgUhrbQ</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Blind Deconvolution of SISO Systems with Binary Source Based on Recursive Channel Shortening</title><source>Springer Books</source><creator>Diamantaras, Konstantinos I. ; Papadimitriou, Theophilos</creator><contributor>Puntonet, Carlos G. ; Prieto, Alberto</contributor><creatorcontrib>Diamantaras, Konstantinos I. ; Papadimitriou, Theophilos ; Puntonet, Carlos G. ; Prieto, Alberto</creatorcontrib><description>We treat the problem of Blind Deconvolution of Single Input – Single Output (SISO) systems with real or complex binary sources. We explicate the basic mathematical idea by focusing on the noiseless case. Our approach leads to a recursive channel shortening algorithm based on simple data gouping. The channel shortening process eventually results in an instantaneous binary system with trivial solution. The method is both deterministic and very fast. It does not involve any iterative optimization or stochastic approximation procedure. It does however, require sufficiently large datasets in order to meet the source richness condition.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540230564</identifier><identifier>ISBN: 3540230564</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540301100</identifier><identifier>EISBN: 9783540301103</identifier><identifier>DOI: 10.1007/978-3-540-30110-3_70</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Blind Deconvolution ; Blind Separation ; Blind Source Separation ; Detection, estimation, filtering, equalization, prediction ; Exact sciences and technology ; Information, signal and communications theory ; Noiseless Case ; Signal and communications theory ; Signal, noise ; Source Vector ; Telecommunications and information theory</subject><ispartof>Independent Component Analysis and Blind Signal Separation, 2004, p.548-553</ispartof><rights>Springer-Verlag Berlin Heidelberg 2004</rights><rights>2004 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/978-3-540-30110-3_70$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/978-3-540-30110-3_70$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,779,780,784,789,790,793,4048,4049,27924,38254,41441,42510</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16176798$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Puntonet, Carlos G.</contributor><contributor>Prieto, Alberto</contributor><creatorcontrib>Diamantaras, Konstantinos I.</creatorcontrib><creatorcontrib>Papadimitriou, Theophilos</creatorcontrib><title>Blind Deconvolution of SISO Systems with Binary Source Based on Recursive Channel Shortening</title><title>Independent Component Analysis and Blind Signal Separation</title><description>We treat the problem of Blind Deconvolution of Single Input – Single Output (SISO) systems with real or complex binary sources. We explicate the basic mathematical idea by focusing on the noiseless case. Our approach leads to a recursive channel shortening algorithm based on simple data gouping. The channel shortening process eventually results in an instantaneous binary system with trivial solution. The method is both deterministic and very fast. It does not involve any iterative optimization or stochastic approximation procedure. It does however, require sufficiently large datasets in order to meet the source richness condition.</description><subject>Applied sciences</subject><subject>Blind Deconvolution</subject><subject>Blind Separation</subject><subject>Blind Source Separation</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Exact sciences and technology</subject><subject>Information, signal and communications theory</subject><subject>Noiseless Case</subject><subject>Signal and communications theory</subject><subject>Signal, noise</subject><subject>Source Vector</subject><subject>Telecommunications and information theory</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540230564</isbn><isbn>3540230564</isbn><isbn>3540301100</isbn><isbn>9783540301103</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2004</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotkE1vAiEQhulXUmv9Bz1w6ZEWFnaBY7VfJiYmXY9NCLKgtCuYZbXx3xdt5zCTvPNkMnkAuCP4gWDMHyUXiKKSYUQxIbkrjs_ADc3JKcDnYEAqQhClTF6AUeaPu4LismKXYJCpAknO6DUYpfSFcxEuiGAD8DlufWjgszUx7GO7630MMDpYT-s5rA-pt5sEf3y_hmMfdHeAddx1xsKxTraBmf2wZtclv7dwstYh2BbW69j1NviwugVXTrfJjv7nECxeXxaTdzSbv00nTzO0LQrRI00qxi0lhcGNIYTjpqiWUjjHG8uZdHJZOlGRZdmUlLmyNE4KYpnTRljbYDoE939ntzoZ3bpOB-OT2nZ-kz9W2QyvuBSZK_64lFdhZTu1jPE7KYLVUbPK2hRVWZw6WVVHzfQXgUhrbQ</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Diamantaras, Konstantinos I.</creator><creator>Papadimitriou, Theophilos</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2004</creationdate><title>Blind Deconvolution of SISO Systems with Binary Source Based on Recursive Channel Shortening</title><author>Diamantaras, Konstantinos I. ; Papadimitriou, Theophilos</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p228t-a1647e312c0dc1170d26b98ff7de749f9b5f861b5d534f55cf981e4fac8eed03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Applied sciences</topic><topic>Blind Deconvolution</topic><topic>Blind Separation</topic><topic>Blind Source Separation</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Exact sciences and technology</topic><topic>Information, signal and communications theory</topic><topic>Noiseless Case</topic><topic>Signal and communications theory</topic><topic>Signal, noise</topic><topic>Source Vector</topic><topic>Telecommunications and information theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Diamantaras, Konstantinos I.</creatorcontrib><creatorcontrib>Papadimitriou, Theophilos</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Diamantaras, Konstantinos I.</au><au>Papadimitriou, Theophilos</au><au>Puntonet, Carlos G.</au><au>Prieto, Alberto</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Blind Deconvolution of SISO Systems with Binary Source Based on Recursive Channel Shortening</atitle><btitle>Independent Component Analysis and Blind Signal Separation</btitle><date>2004</date><risdate>2004</risdate><spage>548</spage><epage>553</epage><pages>548-553</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540230564</isbn><isbn>3540230564</isbn><eisbn>3540301100</eisbn><eisbn>9783540301103</eisbn><abstract>We treat the problem of Blind Deconvolution of Single Input – Single Output (SISO) systems with real or complex binary sources. We explicate the basic mathematical idea by focusing on the noiseless case. Our approach leads to a recursive channel shortening algorithm based on simple data gouping. The channel shortening process eventually results in an instantaneous binary system with trivial solution. The method is both deterministic and very fast. It does not involve any iterative optimization or stochastic approximation procedure. It does however, require sufficiently large datasets in order to meet the source richness condition.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/978-3-540-30110-3_70</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Independent Component Analysis and Blind Signal Separation, 2004, p.548-553 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16176798 |
| source | Springer Books |
| subjects | Applied sciences Blind Deconvolution Blind Separation Blind Source Separation Detection, estimation, filtering, equalization, prediction Exact sciences and technology Information, signal and communications theory Noiseless Case Signal and communications theory Signal, noise Source Vector Telecommunications and information theory |
| title | Blind Deconvolution of SISO Systems with Binary Source Based on Recursive Channel Shortening |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T02%3A56%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pascalfrancis_sprin&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Blind%20Deconvolution%20of%20SISO%20Systems%20with%20Binary%20Source%20Based%20on%20Recursive%20Channel%20Shortening&rft.btitle=Independent%20Component%20Analysis%20and%20Blind%20Signal%20Separation&rft.au=Diamantaras,%20Konstantinos%20I.&rft.date=2004&rft.spage=548&rft.epage=553&rft.pages=548-553&rft.issn=0302-9743&rft.eissn=1611-3349&rft.isbn=9783540230564&rft.isbn_list=3540230564&rft_id=info:doi/10.1007/978-3-540-30110-3_70&rft_dat=%3Cpascalfrancis_sprin%3E16176798%3C/pascalfrancis_sprin%3E%3Curl%3E%3C/url%3E&rft.eisbn=3540301100&rft.eisbn_list=9783540301103&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |