Browsing by Subject "Digital arithmetic"

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    Binary morphological subband decomposition for image coding
    (IEEE, 1996) Gürcan, Metin Nafi; Gerek, Ömer Nezih; Çetin, A. Enis
    In this paper a binary waveform coding method based on morphological subband decomposition coupled with embedded zero-tree and entropy coding is described. This method can be utilized in text compression or bit-plane coding of images. Binary morphological subband decomposition operations are carried out in the Gallois Field, resulting in a computationally efficient structure. Simulation studies are presented.
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    Efficient vectorization of forward/backward substitutions in solving sparse linear equations
    (IEEE, 1994) Aykanat, Cevdet; Özgü, Özlem; Güven, N.
    Vector processors have promised an enormous increase in computing speed for computationally intensive and time-critical power system problems which require the repeated solution of sparse linear equations. Due to short vectors processed in these applications, standard sparsity-based algorithms need to be restructured for efficient vectorization. This paper presents a novel data storage scheme and an efficient vectorization algorithm that exploits the intrinsic architectural features of vector computers such as sectioning and chaining. As the benchmark, the solution phase of the Fast Decoupled Load Flow algorithm is used in simulations. The relative performances of the proposed and existing vectorization schemes are evaluated, both theoretically and experimentally, on IBM 3090/VF.
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    Morphological subband decomposition structure using GF(N) arithmetic
    (IEEE, 1996-09) Gürcan, Metin Nafi; Çetin, A. Enis; Gerek, Ömer, N.
    Linear filter banks with critical subsampling and perfect reconstruction (PR) property have received much interest and found numerous applications in signal and image processing. Recently, nonlinear filter bank structures with PR and critical subsampling have been proposed and used in image coding. In this paper, it is shown that PR nonlinear subband decomposition can be performed using the Gallois Field (GF) arithmetic. The result of the decomposition of an n-ary (e.g. 256-ary) input signal is still n-ary at different resolutions. This decomposition structure can be utilized for binary and 2k (k is an integer) level signal decompositions. Simulation studies are presented.