Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 951418

Sparse TFD Reconstruction Using the Information on the Instantaneous Number of Signal Components


Volarić, Ivan; Sučić, Viktor
Sparse TFD Reconstruction Using the Information on the Instantaneous Number of Signal Components // Proceedings of International Conference on Innovative Technologies IN-TECH 2018
Zagreb, Hrvatska, 2018. str. 9-12 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 951418 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Sparse TFD Reconstruction Using the Information on the Instantaneous Number of Signal Components

Autori
Volarić, Ivan ; Sučić, Viktor

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of International Conference on Innovative Technologies IN-TECH 2018 / - , 2018, 9-12

Skup
Internarional Conference on Inovative Technologies (IN-TECH 2018)

Mjesto i datum
Zagreb, Hrvatska, 05.09.2018. - 07.09.2018

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Signal sparsity, Compressive sensing, Time-frequency distributions, Ambiguity function, l0-norm, Renyi entropy, Signal reconstruction.

Sažetak
Over the last few years, signal sparsity has become a popular topic of research in the field of signal processing, exploiting the fact that the heavily undersampled signal can be successfully reconstructed if the considered signal has a sparse representation. The time- frequency (TF) domain often induces sparsity in many real-life signals, hence allowing application of the compressive sensing (CS) based methods. Utilization of the CS based methods achieves better interference suppression and signal concentration enhancement when compared to the traditional TF signal processing methods ; both achieved through the sparse reconstruction algorithm. In this paper, we propose a sparse TF distribution (TFD) reconstruction algorithm which utilizes the information on the instantaneous number of signal components in order to hard-threshold each TFD time slice independently. The performance of the proposed algorithm has been compared to the state-of-the-art sparse reconstruction algorithms in terms of the resulting sparse TFD concentration measure and the algorithms execution time.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA


Ustanove:
Tehnički fakultet, Rijeka

Profili:

Avatar Url Ivan Volarić (autor)

Avatar Url Viktor Sučić (autor)


Citiraj ovu publikaciju:

Volarić, Ivan; Sučić, Viktor
Sparse TFD Reconstruction Using the Information on the Instantaneous Number of Signal Components // Proceedings of International Conference on Innovative Technologies IN-TECH 2018
Zagreb, Hrvatska, 2018. str. 9-12 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Volarić, I. & Sučić, V. (2018) Sparse TFD Reconstruction Using the Information on the Instantaneous Number of Signal Components. U: Proceedings of International Conference on Innovative Technologies IN-TECH 2018.
@article{article, author = {Volari\'{c}, Ivan and Su\v{c}i\'{c}, Viktor}, year = {2018}, pages = {9-12}, keywords = {Signal sparsity, Compressive sensing, Time-frequency distributions, Ambiguity function, l0-norm, Renyi entropy, Signal reconstruction.}, title = {Sparse TFD Reconstruction Using the Information on the Instantaneous Number of Signal Components}, keyword = {Signal sparsity, Compressive sensing, Time-frequency distributions, Ambiguity function, l0-norm, Renyi entropy, Signal reconstruction.}, publisherplace = {Zagreb, Hrvatska} }
@article{article, author = {Volari\'{c}, Ivan and Su\v{c}i\'{c}, Viktor}, year = {2018}, pages = {9-12}, keywords = {Signal sparsity, Compressive sensing, Time-frequency distributions, Ambiguity function, l0-norm, Renyi entropy, Signal reconstruction.}, title = {Sparse TFD Reconstruction Using the Information on the Instantaneous Number of Signal Components}, keyword = {Signal sparsity, Compressive sensing, Time-frequency distributions, Ambiguity function, l0-norm, Renyi entropy, Signal reconstruction.}, publisherplace = {Zagreb, Hrvatska} }




Contrast
Increase Font
Decrease Font
Dyslexic Font