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    Wyświetlanie 1-13 z 13
    Tytuł:
    Semantic Sparse Representation of Disease Patterns
    Autorzy:
    Przelaskowski, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/226810.pdf
    Data publikacji:
    2010
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    sparse representation
    compressive sensing
    information theory
    semantic information
    disease pattern
    Opis:
    Sparse data representation is discussed in a context of useful fundamentals led to semantic content description and extraction of information. Disease patterns as semantic information extracted from medical images were underlined because of discussed application of computer-aided diagnosis. Compressive sensing rules were adjusted to the requirements of diagnostic pattern recognition. Proposed methodology of sparse disease patterns considers accuracy of sparse representation to estimate target content for detailed analysis. Semantics of sparse representation were modeled by morphological content analysis. Subtle or hidden components were extracted and displayed to increase information completeness. Usefulness of sparsity was verified for computer-aided diagnosis of stroke based on brain CT scans. Implemented method was based on selective and sparse representation of subtle hypodensity to improve diagnosis. Visual expression of disease signatures was fixed to radiologist requirements, domain knowledge and experimental analysis issues. Diagnosis assistance suitability was proven by experimental subjective rating and automatic recognition.
    Źródło:
    International Journal of Electronics and Telecommunications; 2010, 56, 3; 273-280
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Design of Sparse FIR Filters with Low Group Delay
    Autorzy:
    Konopacki, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/1844604.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    digital filters
    FIR filter design
    sparse filters
    reduced group delay filters
    Opis:
    The aim of the work is to present the method for designing sparse FIR filters with very low group delay and approximately linear-phase in the passband. Significant reduction of the group delay, e.g. several times in relation to the linear phase filter, may cause the occurrence of undesirable overshoot in the magnitude frequency response. The method proposed in this work consists of two stages. In the first stage, FIR filter with low group delay is designed using minimax constrained optimization that provides overshoot elimination. In the second stage, the same process is applied iteratively to reach sparse solution. Design examples demonstrate the effectiveness of the proposed method.
    Źródło:
    International Journal of Electronics and Telecommunications; 2021, 67, 1; 121-126
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Single Image Super-Resolution through Sparse Representation via Coupled Dictionary learning
    Autorzy:
    Patel, Rutul
    Thakar, Vishvjit
    Joshi, Rutvij
    Powiązania:
    https://bibliotekanauki.pl/articles/226607.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    single image super-resolution
    Dictionary Learning
    Sparse representation
    Opis:
    Single Image Super-Resolution (SISR) through sparse representation has received much attention in the past decade due to significant development in sparse coding algorithms. However, recovering high-frequency textures is a major bottleneck of existing SISR algorithms. Considering this, dictionary learning approaches are to be utilized to extract high-frequency textures which improve SISR performance significantly. In this paper, we have proposed the SISR algorithm through sparse representation which involves learning of Low Resolution (LR) and High Resolution (HR) dictionaries simultaneously from the training set. The idea of training coupled dictionaries preserves correlation between HR and LR patches to enhance the Super-resolved image. To demonstrate the effectiveness of the proposed algorithm, a visual comparison is made with popular SISR algorithms and also quantified through quality metrics. The proposed algorithm outperforms compared to existing SISR algorithms qualitatively and quantitatively as shown in experimental results. Furthermore, the performance of our algorithm is remarkable for a smaller training set which involves lesser computational complexity. Therefore, the proposed approach is proven to be superior based upon visual comparisons and quality metrics and have noticeable results at reduced computational complexity.
    Źródło:
    International Journal of Electronics and Telecommunications; 2020, 66, 2; 347-353
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Speech emotion recognition based on sparse representation
    Autorzy:
    Yan, J.
    Wang, X.
    Gu, W.
    Ma, L.
    Powiązania:
    https://bibliotekanauki.pl/articles/177778.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    speech emotion recognition
    sparse partial least squares regression SPLSR
    SPLSR
    feature selection and dimensionality reduction
    Opis:
    Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.
    Źródło:
    Archives of Acoustics; 2013, 38, 4; 465-470
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Signal Subspace Speech Enhancement Approach Based on Joint Low-Rank and Sparse Matrix Decomposition
    Autorzy:
    Sun, C.
    Xie, J.
    Leng, Y.
    Powiązania:
    https://bibliotekanauki.pl/articles/177990.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    subspace speech enhancement
    singular value decomposition
    joint low-rank and sparse matrix decomposition
    Opis:
    Subspace-based methods have been effectively used to estimate enhanced speech from noisy speech samples. In the traditional subspace approaches, a critical step is splitting of two invariant subspaces associated with signal and noise via subspace decomposition, which is often performed by singular-value decomposition or eigenvalue decomposition. However, these decomposition algorithms are highly sensitive to the presence of large corruptions, resulting in a large amount of residual noise within enhanced speech in low signal-to-noise ratio (SNR) situations. In this paper, a joint low-rank and sparse matrix decomposition (JLSMD) based subspace method is proposed for speech enhancement. In the proposed method, we firstly structure the corrupted data as a Toeplitz matrix and estimate its effective rank value for the underlying clean speech matrix. Then the subspace decomposition is performed by means of JLSMD, where the decomposed low-rank part corresponds to enhanced speech and the sparse part corresponds to noise signal, respectively. An extensive set of experiments have been carried out for both of white Gaussian noise and real-world noise. Experimental results show that the proposed method performs better than conventional methods in many types of strong noise conditions, in terms of yielding less residual noise and lower speech distortion.
    Źródło:
    Archives of Acoustics; 2016, 41, 2; 245-254
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Frequency Selection Based Separation of Speech Signals with Reduced Computational Time Using Sparse NMF
    Autorzy:
    Varshney, Y. V.
    Abbasi, Z. A.
    Abidi, M. R.
    Farooq, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/176829.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    sparse NMF
    non-negative matrix factorisation
    mixed speech recognition
    machine learning
    Opis:
    Application of wavelet decomposition is described to speed up the mixed speech signal separation with the help of non-negative matrix factorisation (NMF). It is assumed that the basis vectors of training data of individual speakers had been recorded. In this paper, the spectrogram magnitude of a mixed signal has been factorised with the help of NMF with consideration of sparseness of speech signals. The high frequency components of signal contain very small amount of signal energy. By rejecting the high frequency components, the size of input signal is reduced, which reduces the computational time of matrix factorisation. The signal of lower energy has been separated by using wavelet decomposition. The present work is done for wideband microphone speech signal and standard audio signal from digital video equipment. This shows an improvement in the separation capability using the proposed model as compared with an existing one in terms of correlation between separated and original signals. Obtained signal to distortion ratio (SDR) and signal to interference ratio (SIR) are also larger as compare of the existing model. The proposed model also shows a reduction in computational time, which results in faster operation.
    Źródło:
    Archives of Acoustics; 2017, 42, 2; 287-295
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modified Block Sparse Bayesian Learning-Based Compressive Sensing Scheme For EEG Signals
    Autorzy:
    Upadhyaya, Vivek
    Salim, Mohammad
    Powiązania:
    https://bibliotekanauki.pl/articles/1844532.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    compressive sensing
    CS
    mean square error
    MSE
    structural similarity index measure
    SSIM
    electroencephalogram
    EEG
    digital signal processing
    DSP
    block sparse Bayesian learning
    BSBL
    Opis:
    Advancement in medical technology creates some issues related to data transmission as well as storage. In real-time processing, it is too tedious to limit the flow of data as it may reduce the meaningful information too. So, an efficient technique is required to compress the data. This problem arises in Magnetic Resonance Imaging (MRI), Electrocardiogram (ECG), Electroencephalogram (EEG), and other medical signal processing domains. In this paper, we demonstrate Block Sparse Bayesian Learning (BSBL) based compressive sensing technique on an Electroencephalogram (EEG) signal. The efficiency of the algorithm is described using the Mean Square Error (MSE) and Structural Similarity Index Measure (SSIM) value. Apart from this analysis we also use different combinations of sensing matrices too, to demonstrate the effect of sensing matrices on MSE and SSIM value. And here we got that the exponential and chi-square random matrices as a sensing matrix are showing a significant change in the value of MSE and SSIM. So, in real-time body sensor networks, this scheme will contribute a significant reduction in power requirement due to its data compression ability as well as it will reduce the cost and the size of the device used for real-time monitoring.
    Źródło:
    International Journal of Electronics and Telecommunications; 2021, 67, 3; 331-336
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Computational aspects of GPU - accelerated sparse matrix - vector multiplication for solving Markov models
    Obliczeniowe aspekty mnożenia macierzy rzadkiej przez wektor dla rozwiązywania modeli Markowa przyspieszanego przez karty GPU
    Autorzy:
    Bylina, B.
    Bylina, J.
    Karwacki, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/375696.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Markovian models
    wireless network models
    GPU
    matrix-vector multiplication
    sparse matrices
    Opis:
    In this article we investigate some computational aspects of GPU-accelerated matrix-vector multiplication where matrix is sparse. Particularly, we deal with sparse matrices appearing in modelling with Markovian queuing models. The model we use for research is a Markovian queuing model of a wireless device. This model describes the device’s behavior during possible channel occupation by other devices. We study the efficiency of multiplication of a sparse matrix by a dense vector with the use of an appropriate, ready-to-use GPU-accelerated mathematical library, namely CUSP. For the CUSP library we discuss data structures and their impact on the CUDA platform for the fine-grained parallel architecture of the GPU. Our aim is to find the best format for storing a sparse matrix for GPU-computation (especially one associated with the Markovian model of a wireless device). We compare the time, the performance and the speed-up for the card NVIDIA Tesla C2050 (with ECC ON). For unstructured matrices (as our Markovian matrices), we observe speed-ups (in respect to CPU-only computations) of over 8 times.
    Łańcuchy Markowa są przydatnym narzędziem do modelowania systemów złożonych, takich jak systemy i sieci komputerowe. W ostatnich latach łańcuchy Markowa zostały z powodzeniem wykorzystane do oceny pracy sieci bezprzewodowych. Jednym z problemów jaki się pojawia przy wykorzystywaniu łańcuchów Markowa w modelowaniu sieci są problemy natury obliczeniowej. W artykule zajmiemy się badaniem mnożenia macierzy rzadkiej przez wektor, które jest jedną z głównych operacji podczas numerycznego rozwiązywania modeli Markowowskich. Aby, przyspieszyć czas obliczeń mnożenia macierz rzadkiej przez wektor wykorzystano funkcje z biblioteki CUSP. Biblioteka jest zbiorem funkcji wykonywanych na GPU (ang.Graphics Processing Unit) celem skrócenia czasu obliczeń. Do testowania operacji mnożenia macierzy rzadkiej przez wektor badano macierze z Markowowskiego modelu pracy sieci bezprzewodowej. Model ten opisuje zachowanie urządzenia, gdy kanał transmisyjnych może być zajęty przez inne urządzenia. Macierz przejść wspomnianego modelu jest macierzą rzadką i potrzeba specialnej struktury danych do jej przechowywania, dlatego w artykule dyskutowane są różne struktury danych dla macierzy rzadkich i ich przydatność do obliczen na kartach graficznych. W pracy porównano czas, wydajność i przyspieszenie jakie otrzymano podczas testowania biblioteki CUSP na karcie NVIDIA Tesla C2050 dla niestrukturalnych macierzy rzadkich opisujących model zajętości węzła w sieciach bezprzewodowych przy różnych formatach przechowywania macierzy rzadkich. Dla testowanych macierzy zauważono ośmiokrotne przyspieszenie obliczeń przy wykorzystaniu karty graficznej.
    Źródło:
    Theoretical and Applied Informatics; 2011, 23, 2; 127-145
    1896-5334
    Pojawia się w:
    Theoretical and Applied Informatics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Low Delay Sparse and Mixed Excitation CELP Coders for Wideband Speech Coding
    Autorzy:
    Dymarski, Przemysław
    Powiązania:
    https://bibliotekanauki.pl/articles/226657.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Low-Delay CELP
    MP-MLQ
    MOS
    variable bit rate
    Opis:
    Code Excited Linear Prediction (CELP) algorithms are proposed for compression of speech in 8 kHz band at switched or variable bit rate and algorithmic delay not exceeding 2 msec. Two structures of Low-Delay CELP coders are analyzed: Low-delay sparse excitation and mixed excitation CELP. Sparse excitation is based on MP-MLQ and multilayer models. Mixed excitation CELP algorithm stems from the narrowband G.728 standard. As opposed to G.728 LD-CELP coder, mixed excitation codebook consists of pseudorandom vectors and sequences obtained with Long-Term Prediction (LTP). Variable rate coding consists in maximizing vector dimension while keeping the required speech quality. Good speech quality (MOS=3.9 according to PESQ algorithm) is obtained at average bit rate 33.5 kbit/sec.
    Źródło:
    International Journal of Electronics and Telecommunications; 2020, 66, 1; 69-76
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Real-time motion tracking using optical flow on multiple GPUs
    Autorzy:
    Mahmoudi, S. A.
    Kierzynka, M.
    Manneback, P.
    Kurowski, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/200476.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Lucas-Kanade method
    sparse optical flow
    multiple GPU computations
    Opis:
    Motion tracking algorithms are widely used in computer vision related research. However, the new video standards, especially those in high resolutions, cause that current implementations, even running on modern hardware, no longer meet the needs of real-time processing. To overcome this challenge several GPU (Graphics Processing Unit) computing approaches have recently been proposed. Although they present a great potential of a GPU platform, hardly any is able to process high definition video sequences efficiently. Thus, a need arose to develop a tool being able to address the outlined problem. In this paper we present software that implements optical flow motion tracking using the Lucas-Kanade algorithm. It is also integrated with the Harris corner detector and therefore the algorithm may perform sparse tracking, i.e. tracking of the meaningful pixels only. This allows to substantially lower the computational burden of the method. Moreover, both parts of the algorithm, i.e. corner selection and tracking, are implemented on GPU and, as a result, the software is immensely fast, allowing for real-time motion tracking on videos in Full HD or even 4K format. In order to deliver the highest performance, it also supports multiple GPU systems, where it scales up very well.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2014, 62, 1; 139-150
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    ZA-APA with Adaptive Zero Attractor Controller for Variable Sparsity Environment
    Autorzy:
    Radhika, S.
    Chandrasekar, A.
    Nirmalraj, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/1844469.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Zero Attraction APA
    sparse channel
    convergence
    steady state mean square error
    variable zero attraction controller
    Opis:
    The zero attraction affine projection algorithm (ZA-APA) achieves better performance in terms of convergence rate and steady state error than standard APA when the system is sparse. It uses l1 norm penalty to exploit sparsity of the channel. The performance of ZA-APA depends on the value of zero attractor controller. Moreover a fixed attractor controller is not suitable for varying sparsity environment. This paper proposes an optimal adaptive zero attractor controller based on Mean Square Deviation (MSD) error to work in variable sparsity environment. Experiments were conducted to prove the suitability of the proposed algorithm for identification of unknown variable sparse system.
    Źródło:
    International Journal of Electronics and Telecommunications; 2020, 66, 4; 695-700
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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:
    Polynomial chaos expansion method in estimating probability distribution of rotor-shaft dynamic responses
    Autorzy:
    Lasota, R.
    Stocki, R.
    Tauzowski, P.
    Szolc, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/200053.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    stochastic moment estimation
    sparse polynomial chaos expansion
    maximum entropy principle
    rotor
    uncertainties
    hybrid mechanical model
    random unbalance distribution
    zasada maksymalnej entropii
    wirnik
    niepewności
    model hybrydowy
    losowy rozkład asymetrii
    Opis:
    The main purpose of the study is an assessment of computational efficiency of selected numerical methods for estimation of vibrational response statistics of a large multi-bearing turbo-generator rotor-shaft system. The effective estimation of the probability distribution of structural responses is essential for robust design optimization and reliability analysis of such systems. The analyzed scatter of responses is caused by random residual unbalances as well as random stiffness and damping parameters of the journal bearings. A proper representation of these uncertain parameters leads to multidimensional stochastic models. Three estimation techniques are compared: Monte Carlo sampling, Latin hypercube sampling and the sparse polynomial chaos expansion method. Based on the estimated values of the first four statistical moments the probability density function of the maximal vibration amplitude is evaluated by the maximal entropy principle method. The method is inherently suited for an accurate representation of the probability density functions with an exponential behavior, which appears to be characteristic for the investigated rotor-shaft responses. Performing multiple numerical tests for a range of sample sizes it was found that the sparse polynomial chaos method provides the best balance between the accuracy and computational effectiveness in estimating the unknown probability distribution of the maximal vibration amplitude.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 2; 413-422
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-13 z 13

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