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    Wyświetlanie 1-4 z 4
    Tytuł:
    Area-oriented technology mapping for LUT-based logic blocks
    Autorzy:
    Kubica, M.
    Kania, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/331370.pdf
    Data publikacji:
    2017
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    SMTBDD
    FPGA
    synthesis method
    decomposition technique
    metoda syntezy
    technika rozkładu
    Opis:
    One of the main aspects of logic synthesis dedicated to FPGA is the problem of technology mapping, which is directly associated with the logic decomposition technique. This paper focuses on using configurable properties of CLBs in the process of logic decomposition and technology mapping. A novel theory and a set of efficient techniques for logic decomposition based on a BDD are proposed. The paper shows that logic optimization can be efficiently carried out by using multiple decomposition. The essence of the proposed synthesis method is multiple cutting of a BDD. A new diagram form called an SMTBDD is proposed. Moreover, techniques that allow finding the best technology mapping oriented to configurability of CLBs are presented. In the experimental section, the presented method (MultiDec) is compared with academic and commercial tools. The experimental results show that the proposed technology mapping strategy leads to good results in terms of the number of CLBs.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2017, 27, 1; 207-222
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An efficient algorithm for adaptive total variation based image decomposition and restoration
    Autorzy:
    Liu, X.
    Huang, L.
    Powiązania:
    https://bibliotekanauki.pl/articles/330619.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    image decomposition
    image restoration
    adaptive total variation
    H-1 norm
    split Bregman method
    dekompozycja obrazu
    odtworzenie obrazu
    wariacja zupełna adaptacyjna
    metoda Bregmana
    Opis:
    With the aim to better preserve sharp edges and important structure features in the recovered image, this article researches an improved adaptive total variation regularization and H-1 norm fidelity based strategy for image decomposition and restoration. Computationally, for minimizing the proposed energy functional, we investigate an efficient numerical algorithm—the split Bregman method, and briefly prove its convergence. In addition, comparisons are also made with the classical OSV (Osher–Sole–Vese) model (Osher et al., 2003) and the TV-Gabor model (Aujol et al., 2006), in terms of the edge-preserving capability and the recovered results. Numerical experiments markedly demonstrate that our novel scheme yields significantly better outcomes in image decomposition and denoising than the existing models.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 2; 405-415
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Stabilized model reduction for nonlinear dynamical systems through a contractivity-preserving framework
    Autorzy:
    Chaturantabut, Saifon
    Powiązania:
    https://bibliotekanauki.pl/articles/1838159.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    model order reduction
    ordinary differential equation
    partial differential equation
    proper orthogonal decomposition
    discrete empirical interpolation method
    redukcja rzędu modelu
    równanie różniczkowe zwyczajne
    równanie różniczkowe cząstkowe
    rozkład ortogonalny
    Opis:
    This work develops a technique for constructing a reduced-order system that not only has low computational complexity, but also maintains the stability of the original nonlinear dynamical system. The proposed framework is designed to preserve the contractivity of the vector field in the original system, which can further guarantee stability preservation, as well as provide an error bound for the approximated equilibrium solution of the resulting reduced system. This technique employs a low-dimensional basis from proper orthogonal decomposition to optimally capture the dominant dynamics of the original system, and modifies the discrete empirical interpolation method by enforcing certain structure for the nonlinear approximation. The efficiency and accuracy of the proposed method are illustrated through numerical tests on a nonlinear reaction diffusion problem.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 4; 615-628
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A fast neural network learning algorithm with approximate singular value decomposition
    Autorzy:
    Jankowski, Norbert
    Linowiecki, Rafał
    Powiązania:
    https://bibliotekanauki.pl/articles/330870.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    Moore–Penrose pseudoinverse
    radial basis function network
    extreme learning machine
    kernel method
    machine learning
    singular value decomposition
    deep extreme learning
    principal component analysis
    pseudoodwrotność Moore–Penrose
    radialna funkcja bazowa
    maszyna uczenia ekstremalnego
    uczenie maszynowe
    analiza składników głównych
    Opis:
    The learning of neural networks is becoming more and more important. Researchers have constructed dozens of learning algorithms, but it is still necessary to develop faster, more flexible, or more accurate learning algorithms. With fast learning we can examine more learning scenarios for a given problem, especially in the case of meta-learning. In this article we focus on the construction of a much faster learning algorithm and its modifications, especially for nonlinear versions of neural networks. The main idea of this algorithm lies in the usage of fast approximation of the Moore–Penrose pseudo-inverse matrix. The complexity of the original singular value decomposition algorithm is O(mn2). We consider algorithms with a complexity of O(mnl), where l < n and l is often significantly smaller than n. Such learning algorithms can be applied to the learning of radial basis function networks, extreme learning machines or deep ELMs, principal component analysis or even missing data imputation.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 581-594
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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