This HTML5 document contains 45 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n6http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n17http://localhost/temp/predkladatel/
n11http://purl.org/net/nknouf/ns/bibtex#
n21http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n4http://linked.opendata.cz/resource/domain/vavai/projekt/
n13http://linked.opendata.cz/ontology/domain/vavai/
n22http://linked.opendata.cz/resource/domain/vavai/zamer/
n14https://schema.org/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n5http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F00216305%3A26220%2F01%3APU20798%21RIV%2F2002%2FGA0%2F262202%2FN/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n12http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n8http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n20http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n9http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n19http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n16http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n15http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F01%3APU20798%21RIV%2F2002%2FGA0%2F262202%2FN
rdf:type
n13:Vysledek skos:Concept
dcterms:description
Current methods used to improve the signal–to–noise ratio mainly employ the Wiener filtering, or methods derived from it, such as spectral subtraction. All these methods assume that it is possible to determine or at least to estimate noise spectral characteristics. As can be derived, the estimation by the periodogram is not exact, but it contains a disturbance. Averaging the power spectra offers better results but the properties are simultaneously downgraded by a non-stationary disturbance. That is why the paper deals with the improvement of the power spectral density (PSD) estimation. To achieve this purpose we use the method of thresholding wavelet–transform coefficients, which we apply to each periodogram separately. Then the resultant estimation is used for the Wiener filter. The differences between the estimations obtained by the periodogram, by averaging and by this new method are shown in the paper. When we use wavelet transformation, a marked improvement in suppressing the disturbing signal a Current methods used to improve the signal–to–noise ratio mainly employ the Wiener filtering, or methods derived from it, such as spectral subtraction. All these methods assume that it is possible to determine or at least to estimate noise spectral characteristics. As can be derived, the estimation by the periodogram is not exact, but it contains a disturbance. Averaging the power spectra offers better results but the properties are simultaneously downgraded by a non-stationary disturbance. That is why the paper deals with the improvement of the power spectral density (PSD) estimation. To achieve this purpose we use the method of thresholding wavelet–transform coefficients, which we apply to each periodogram separately. Then the resultant estimation is used for the Wiener filter. The differences between the estimations obtained by the periodogram, by averaging and by this new method are shown in the paper. When we use wavelet transformation, a marked improvement in suppressing the disturbing signal a
dcterms:title
Wiener Filtering with Spectrum Estimation by Wavelet Transformation Wiener Filtering with Spectrum Estimation by Wavelet Transformation
skos:prefLabel
Wiener Filtering with Spectrum Estimation by Wavelet Transformation Wiener Filtering with Spectrum Estimation by Wavelet Transformation
skos:notation
RIV/00216305:26220/01:PU20798!RIV/2002/GA0/262202/N
n3:strany
471-474
n3:aktivita
n9:Z n9:P
n3:aktivity
P(GA102/00/1084), Z(MSM 262200011)
n3:dodaniDat
n15:2002
n3:domaciTvurceVysledku
n21:6209769
n3:druhVysledku
n19:D
n3:duvernostUdaju
n8:S
n3:entitaPredkladatele
n5:predkladatel
n3:idSjednocenehoVysledku
702260
n3:idVysledku
RIV/00216305:26220/01:PU20798
n3:jazykVysledku
n20:eng
n3:klicovaSlova
Wiener, filter, wavelet, estimation
n3:klicoveSlovo
n12:Wiener n12:estimation n12:filter n12:wavelet
n3:kontrolniKodProRIV
[8DA7425F0212]
n3:mistoKonaniAkce
Bratislava
n3:mistoVydani
Bratislava, Slovensko
n3:nazevZdroje
Proceedings of International Conference on Trends in Communications
n3:obor
n16:JA
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:pocetUcastnikuAkce
0
n3:pocetZahranicnichUcastnikuAkce
0
n3:projekt
n4:GA102%2F00%2F1084
n3:rokUplatneniVysledku
n15:2001
n3:tvurceVysledku
Sysel, Petr
n3:typAkce
n6:WRD
n3:zahajeniAkce
2001-07-04+02:00
n3:zamer
n22:MSM%20262200011
s:numberOfPages
4
n11:hasPublisher
Slovenská technická univerzita v Bratislave. Fakulta elektrotechniky a informatiky
n14:isbn
0-7803-6490-2
n17:organizacniJednotka
26220