Extracting astrophysical sources from channel-dependent convolutional mixtures by correlated component analysis in the frequency domain (Articolo in rivista)

Type

• Articolo in rivista (Classe)
• Prodotto della ricerca (Classe)

Label

• Extracting astrophysical sources from channel-dependent convolutional mixtures by correlated component analysis in the frequency domain (Articolo in rivista) (literal)

Anno

• 2007-01-01T00:00:00+01:00 (literal)

Alternative label

• Bedini L.; Salerno E. (2007)
  Extracting astrophysical sources from channel-dependent convolutional mixtures by correlated component analysis in the frequency domain
  in Lecture notes in computer science
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Bedini L.; Salerno E. (literal)

Pagina inizio

• 9 (literal)

Pagina fine

• 16 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 4694 (literal)

Rivista

• Lecture notes in computer science (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

• In: Knowledge-Based Intelligent Information and Engineering Systems. 11th International Conference KES 2007, XVII Italian Workshop on Neural Networks (Vietri sul Mare, 12-14 September 2007). Proceedings, pp. 9 - 16. Bruno Apolloni, Robert J. Howlett and Lakhmi Jain (eds.). (Lecture Notes in Artificial Intelligence, vol. 4694). Springer, 2007. (literal)

Note

• ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• ISTI-CNR Pisa (literal)

Titolo

• Extracting astrophysical sources from channel-dependent convolutional mixtures by correlated component analysis in the frequency domain (literal)

Abstract

• A second-order statistical technique (FD-CCA) for semi-blind source separation from multiple-sensor data is presented. It works in the Fourier domain and allows us to both learn the unknown mixing operator and estimate the source cross-spectra before applying the proper source separation step. If applied to small sky patches, our algorithm can be used to extract diffuse astrophysical sources from the mixed maps obtained by radioastronomical surveys, even though their resolution depends on the measurement channel. Unlike the independent component analysis approach, FD-CCA does not need mutual independence between sources, but exploits their spatial autocorrelations. We describe our algorithm, derived from a previous pixel-domain strategy, and present some results from simulated data. (literal)

Prodotto di

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Elaborazione di segnali e immagini per impieghi diagnostici e interpretazione di immagini multisorgente (ICT.P10.012.001) (Modulo)

Autore CNR

• LUIGI BEDINI (Persona)

Insieme di parole chiave

• Parole chiave di "Extracting astrophysical sources from channel-dependent convolutional mixtures by correlated component analysis in the frequency domain" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di scienza e tecnologie dell'informazione \"Alessandro Faedo\" (ISTI) (Istituto)
• Elaborazione di segnali e immagini per impieghi diagnostici e interpretazione di immagini multisorgente (ICT.P10.012.001) (Modulo)

Autore CNR di

• LUIGI BEDINI (Persona)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• Lecture notes in computer science (Rivista)

Insieme di parole chiave di

• Parole chiave di "Extracting astrophysical sources from channel-dependent convolutional mixtures by correlated component analysis in the frequency domain" (Insieme di parole chiave)