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Wyszukujesz frazę "sparse coding" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
Resource Tuned Optimal Random Network Coding for Single Hop Multicast future 5G Networks
Autorzy:
Dong, Dhawa Sang
Pokhrel, Yagnya Murti
Gachhadar, Anand
Qamar, Faizan
Amiri, Iraj Sadegh
Maharjan, Ram Krishna
Powiązania:
https://bibliotekanauki.pl/articles/226605.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
coding coefficients
computational complexity
lower triangular matrix
random network coding
sparse coding
coefficients
Opis:
Optimal random network coding is reduced complexity in computation of coding coefficients, computation of encoded packets and coefficients are such that minimal transmission bandwidth is enough to transmit coding coefficient to the destinations and decoding process can be carried out as soon as encoded packets are started being received at the destination and decoding process has lower computational complexity. But in traditional random network coding, decoding process is possible only after receiving all encoded packets at receiving nodes. Optimal random network coding also reduces the cost of computation. In this research work, coding coefficient matrix size is determined by the size of layers which defines the number of symbols or packets being involved in coding process. Coding coefficient matrix elements are defined such that it has minimal operations of addition and multiplication during coding and decoding process reducing computational complexity by introducing sparseness in coding coefficients and partial decoding is also possible with the given coding coefficient matrix with systematic sparseness in coding coefficients resulting lower triangular coding coefficients matrix. For the optimal utility of computational resources, depending upon the computational resources unoccupied such as memory available resources budget tuned windowing size is used to define the size of the coefficient matrix.
Źródło:
International Journal of Electronics and Telecommunications; 2019, 65, 3; 463-469
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Incoherent Discriminative Dictionary Learning for Speech Enhancement
Autorzy:
Shaheen, D.
Dakkak, O. A.
Wainakh, M.
Powiązania:
https://bibliotekanauki.pl/articles/308116.pdf
Data publikacji:
2018
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
ADMM
l1 minimization algorithms
sparse coding
speech enhancement
supervised dictionary learning
Opis:
Speech enhancement is one of the many challenging tasks in signal processing, especially in the case of nonstationary speech-like noise. In this paper a new incoherent discriminative dictionary learning algorithm is proposed to model both speech and noise, where the cost function accounts for both “source confusion” and “source distortion” errors, with a regularization term that penalizes the coherence between speech and noise sub-dictionaries. At the enhancement stage, we use sparse coding on the learnt dictionary to find an estimate for both clean speech and noise amplitude spectrum. In the final phase, the Wiener filter is used to refine the clean speech estimate. Experiments on the Noizeus dataset, using two objective speech enhancement measures: frequency-weighted segmental SNR and Perceptual Evaluation of Speech Quality (PESQ) demonstrate that the proposed algorithm outperforms other speech enhancement methods tested.
Źródło:
Journal of Telecommunications and Information Technology; 2018, 3; 42-54
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Large-scale hyperspectral image compression via sparse representations based on online learning
Autorzy:
Ülkü, İ.
Kizgut, E.
Powiązania:
https://bibliotekanauki.pl/articles/331241.pdf
Data publikacji:
2018
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
hyperspectral imaging
compression algorithm
dictionary learning
sparse coding
obrazowanie wielospektralne
algorytm kompresji
nauczanie online
kodowanie rzadkie
Opis:
In this study, proximity based optimization algorithms are used for lossy compression of hyperspectral images that are inherently large scale. This is the first time that such proximity based optimization algorithms are implemented with an online dictionary learning method. Compression performances are compared with the one obtained by various sparse representation algorithms. As a result, proximity based optimization algorithms are listed among the three best ones in terms of compression performance values for all hyperspectral images. Additionally, the applicability of anomaly detection is tested on the reconstructed images.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2018, 28, 1; 197-207
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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