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Statements

Subject Item
n2:RIV%2F68407700%3A21260%2F13%3A00210281%21RIV14-GA0-21260___
rdf:type
n12:Vysledek skos:Concept
rdfs:seeAlso
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6662335&refinements%3D4279724884%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6662190%29
dcterms:description
The Approximate Discrete Zolotarev Transform (ADZT) has been introduced recently. The aim of this paper is to describe the possibilities and limitations of ADZT when used for a signal reconstruction. The signal reconstruction is shown for three types of signals. Only one signal segment is used, that is why there is no need of an overlap-add (or overlap-save) technique. ADZT behaviour is compared to DFT behaviour. Unlike DFT synthesis vectors, the ADZT synthesis vectors are signal dependent. There is only one constraint: these vectors are given by the inversion of a linear combination of the DFT base vectors. This fact forms the main difference between DFT and ADZT syntheses. The signal-dependent base of ADZT brings some advantages but also some new problems with the interpretation of the results. The Approximate Discrete Zolotarev Transform (ADZT) has been introduced recently. The aim of this paper is to describe the possibilities and limitations of ADZT when used for a signal reconstruction. The signal reconstruction is shown for three types of signals. Only one signal segment is used, that is why there is no need of an overlap-add (or overlap-save) technique. ADZT behaviour is compared to DFT behaviour. Unlike DFT synthesis vectors, the ADZT synthesis vectors are signal dependent. There is only one constraint: these vectors are given by the inversion of a linear combination of the DFT base vectors. This fact forms the main difference between DFT and ADZT syntheses. The signal-dependent base of ADZT brings some advantages but also some new problems with the interpretation of the results.
dcterms:title
Using ADZT for Signal Reconstruction Using ADZT for Signal Reconstruction
skos:prefLabel
Using ADZT for Signal Reconstruction Using ADZT for Signal Reconstruction
skos:notation
RIV/68407700:21260/13:00210281!RIV14-GA0-21260___
n12:predkladatel
n24:orjk%3A21260
n3:aktivita
n11:P
n3:aktivity
P(GAP102/11/1795)
n3:dodaniDat
n5:2014
n3:domaciTvurceVysledku
n10:8590060
n3:druhVysledku
n7:D
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
113095
n3:idVysledku
RIV/68407700:21260/13:00210281
n3:jazykVysledku
n9:eng
n3:klicovaSlova
discrete Fourier transforms; approximate discrete Zolotarev transform; signal reconstruction
n3:klicoveSlovo
n19:discrete%20Fourier%20transforms n19:approximate%20discrete%20Zolotarev%20transform n19:signal%20reconstruction
n3:kontrolniKodProRIV
[3072F8FACC95]
n3:mistoKonaniAkce
Dresden
n3:mistoVydani
Dresden
n3:nazevZdroje
Circuit Theory and Design (ECCTD), 2013 European Conference on
n3:obor
n4:JA
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
4
n3:projekt
n21:GAP102%2F11%2F1795
n3:rokUplatneniVysledku
n5:2013
n3:tvurceVysledku
Máša, Pavel Vlček, Miroslav Špetík, Radim Sovka, Pavel
n3:typAkce
n8:WRD
n3:zahajeniAkce
2013-09-08+02:00
s:numberOfPages
4
n15:doi
10.1109/ECCTD.2013.6662335
n13:hasPublisher
Technische Universität
n18:isbn
978-3-00-043430-3
n22:organizacniJednotka
21260