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Wyświetlanie 1-15 z 69
Tytuł:
Blind Estimation of Linear and Nonlinear Sparse Channels
Autorzy:
Georgoulakis, K.
Powiązania:
https://bibliotekanauki.pl/articles/307846.pdf
Data publikacji:
2013
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
blind estimation and equalization
clustering techniques
sparse zero pad channels
Opis:
This paper presents a Clustering Based Blind Channel Estimator for a special case of sparse channels - the zero pad channels. The proposed algorithm uses an unsupervised clustering technique for the estimation of data clusters. Clusters labelling is performed by a Hidden Markov Model of the observation sequence appropriately modified to exploit channel sparsity. The algorithm achieves a substantial complexity reduction compared to the fully evaluated technique. The proposed algorithm is used in conjunction with a Parallel Trellis Viterbi Algorithm for data detection and simulation results show that the overall scheme exhibits the reduced complexity benefits without performance reduction.
Źródło:
Journal of Telecommunications and Information Technology; 2013, 1; 65-71
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Incoherent Dictionary Learning for Sparse Representation in Network Anomaly Detection
Autorzy:
Andrysiak, Tomasz
Saganowski, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/1373708.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
Tematy:
dictionary learning
sparse representation
anomaly detection
Opis:
In this article we present the use of sparse representation of a signal and incoherent dictionary learning method for the purpose of network traffic analysis. In learning process we use 1D INK-SVD algorithm to detect proper dictionary structure. Anomaly detection is realized by parameter estimation of the analyzed signal and its comparative analysis to network traffic profiles. Efficiency of our method is examined with the use of extended set of test traces from real network traffic. Received experimental results confirm effectiveness of the presented method.
Źródło:
Schedae Informaticae; 2015, 24; 63-71
0860-0295
2083-8476
Pojawia się w:
Schedae Informaticae
Dostawca treści:
Biblioteka Nauki
Artykuł
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:
The versatile hardware accelerator framework for sparse vector calculations
Autorzy:
Karwatowski, R.
Wiatr, K.
Powiązania:
https://bibliotekanauki.pl/articles/114705.pdf
Data publikacji:
2015
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
FPGA
sparse vectors
cosine similarity
Zynq
hardware accelerator
Opis:
In this paper, we present the advantage of the ability of FPGAs to perform various computationally complex calculations using deep pipelining and parallelism. We propose an architecture that consists of many small stream processing blocks. The designed framework maintains proper data movement and synchronization. The architecture can be easily adapted to be implemented in FPGA devices of a various size and cost - from small SoC devices to high-end PCIe accelerator cards. It is capable to perform a selected operation on a sparse data that are loaded as the stream of vectors. As an example application, we have implemented the cosine similarity measure for the text similarity calculations that uses the TF-IDF weighting scheme. The presented example application calculates the similarity of texts from the set of input documents to documents from the large database. The scheme is used to find the most similar documents. The proposed design can decrease the service time of search queries in computer centers while reducing power consumption.
Źródło:
Measurement Automation Monitoring; 2015, 61, 7; 327-329
2450-2855
Pojawia się w:
Measurement Automation Monitoring
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effects of Sparse Initialization in Deep Belief Networks
Autorzy:
Grzegorczyk, K.
Kurdziel, M.
Wójcik, P. I.
Powiązania:
https://bibliotekanauki.pl/articles/305264.pdf
Data publikacji:
2015
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
sparse initialization
Deep Belief Networks
Noisy Rectified Linear Units
Opis:
Deep neural networks are often trained in two phases: first, hidden layers are pretrained in an unsupervised manner, and then the network is fine-tuned with error backpropagation. Pretraining is often carried out using Deep Belief Networks (DBNs), with initial weights set to small random values. However, recent results established that well-designed initialization schemes, e.g., Sparse Initialization (SI), can greatly improve the performance of networks that do not use pretraining. An interesting question arising from these results is whether such initialization techniques wouldn’t also improve pretrained networks. To shed light on this question, in this work we evaluate SI in DBNs that are used to pretrain discriminative networks. The motivation behind this research is our observation that SI has an impact on the features learned by a DBN during pretraining. Our results demonstrate that this improves network performance: when pretraining starts from sparsely initialized weight matrices, networks achieve lower classification errors after fine-tuning.
Źródło:
Computer Science; 2015, 16 (4); 313-327
1508-2806
2300-7036
Pojawia się w:
Computer Science
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:
Application of sparse linear discriminant analysis for prediction of protein-protein interactions
Autorzy:
Stąpor, K.
Fabian, P.
Powiązania:
https://bibliotekanauki.pl/articles/95137.pdf
Data publikacji:
2016
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
sparse discriminant analysis
feature selection
protein-protein interaction
Opis:
To understand the complex cellular mechanisms involved in a biological system, it is necessary to study protein-protein interactions (PPIs) at the molecular level, in which prediction of PPIs plays a significant role. In this paper we propose a new classification approach based on the sparse discriminant analysis [10] to predict obligate (permanent) and non-obligate (transient) protein-protein interactions. The sparse discriminant analysis [10] circumvents the limitations of the classical discriminant analysis [4, 9] in the high dimensional low sample size settings by incorporating inherently the feature selection into the optimization procedure. To characterize properties of protein interaction, we proposed to use the binding free energies. The performance of our proposed classifier is 75% ± 5%.
Źródło:
Information Systems in Management; 2016, 5, 1; 109-118
2084-5537
2544-1728
Pojawia się w:
Information Systems in Management
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:
High-resolution Direction of Arrival Estimation Method Based on Sparse Arrays with Minimum Number of Elements
Autorzy:
Mohammed, Jafar Ramadhan
Powiązania:
https://bibliotekanauki.pl/articles/1839475.pdf
Data publikacji:
2021
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
compressed sensing
direction of arrival
DOA
estimation
sparse array
Opis:
Regular fully filled antenna arrays have been widely used in direction of arrival (DOA) estimation. However, practical implementation of these arrays is rather complex and their resolutions are limited to the beamwidth of the array pattern. Therefore, higher resolution and simpler methods are desirable. In this paper, the compressed sensing method is first applied to an initial fully filled array to randomly select the most prominent and effective elements which are used to form the sparse array. To keep the dimension of the sparse array equal to that of the fully filled array, the first and the last elements were excluded from the sparseness process. In addition, some constraints on the sparse spectrum are applied to increase estimation accuracy. The optimization problem is then solved iteratively using the iterative reweighted l1 norm. Finally, a simple searching algorithm is used to detect peaks in the spectrum solution that correspond to the directions of the arriving signals. Compared with the existing scanned beam methods, such as the minimum variance distortionless response (MVDR) technique, and with subspace approaches, such as multiple signal classification (MUSIC) and ESPIRT algorithms, the proposed sparse array method offers better performance even with a lower number of array elements and in severely noisy environments. Effectiveness of the proposed sparse array method is verified via computer simulations.
Źródło:
Journal of Telecommunications and Information Technology; 2021, 1; 8-14
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
High-resolution Direction of Arrival Estimation Method Based on Sparse Arrays with Minimum Number of Elements
Autorzy:
Mohammed, Jafar Ramadhan
Powiązania:
https://bibliotekanauki.pl/articles/1839489.pdf
Data publikacji:
2021
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
compressed sensing
direction of arrival
DOA
estimation
sparse array
Opis:
Regular fully filled antenna arrays have been widely used in direction of arrival (DOA) estimation. However, practical implementation of these arrays is rather complex and their resolutions are limited to the beamwidth of the array pattern. Therefore, higher resolution and simpler methods are desirable. In this paper, the compressed sensing method is first applied to an initial fully filled array to randomly select the most prominent and effective elements which are used to form the sparse array. To keep the dimension of the sparse array equal to that of the fully filled array, the first and the last elements were excluded from the sparseness process. In addition, some constraints on the sparse spectrum are applied to increase estimation accuracy. The optimization problem is then solved iteratively using the iterative reweighted l1 norm. Finally, a simple searching algorithm is used to detect peaks in the spectrum solution that correspond to the directions of the arriving signals. Compared with the existing scanned beam methods, such as the minimum variance distortionless response (MVDR) technique, and with subspace approaches, such as multiple signal classification (MUSIC) and ESPIRT algorithms, the proposed sparse array method offers better performance even with a lower number of array elements and in severely noisy environments. Effectiveness of the proposed sparse array method is verified via computer simulations.
Źródło:
Journal of Telecommunications and Information Technology; 2021, 1; 8-14
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pre-trained deep neural network using sparse autoencoders and scattering wavelet transform for musical genre recognition
Autorzy:
Kleć, M.
Korzinek, D.
Powiązania:
https://bibliotekanauki.pl/articles/952940.pdf
Data publikacji:
2015
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
Sparse Autoencoders
deep learning
genre recognition
Scattering Wavelet Transform
Opis:
Research described in this paper tries to combine the approach of Deep Neural Networks (DNN) with the novel audio features extracted using the Scatter- Ing Wavelet Transform (SWT) for classifying musical genres. The SWT uses A sequence of Wavelet Transforms to compute the modulation spectrum coef- Ficients of multiple orders, which has already shown to be promising for this Task. The DNN in this work uses pre-trained layers using Sparse Autoencoders (SAE). Data obtained from the Creative Commons website jamendo.com is Used to boost the well-known GTZAN database, which is a standard bench- mark for this task. The final classifier is tested using a 10-fold cross validation To achieve results similar to other state-of-the-art approaches.
Źródło:
Computer Science; 2015, 16 (2); 133-144
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Markowitz scheme for the sparse WZ factorization
Autorzy:
Bylina, Beata
Bylina, Jarosław
Powiązania:
https://bibliotekanauki.pl/articles/764599.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Źródło:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2007, 6, 1
1732-1360
2083-3628
Pojawia się w:
Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
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:
Quick offline sparse matrices
Szybkie rzadkie macierze przechowywane na dysku
Autorzy:
Wicijowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/160240.pdf
Data publikacji:
2010
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Elektrotechniki
Tematy:
macierz rzadka
mnożenie
przechowywanie w trybie offline
wielki zbiór danych
sparse matrix
multiplication
offline storage
huge datasets
Opis:
When dealing with large datasets, computer memory constraints are a common problem. With the volumes of data exceeding 1 GiB of size, storage of the whole datasets in RAM becomes infeasible. Since in most applications one deals with only a portion of dataset at a time, the rest may be kept offline on nonvolatile memory that provides larger capacities. The access to nonvolatile memory is typically a few orders of magnitude slower than of RAM, so an efficient method of storage should be proposed to keep the number of disc accesses count as small as possible. In the paper I describe the offline storage of sparse matrices that is built on top of Hierarchical Data Format (precisely, on the latest revision - HDF5) addressing the problem of matrix-vector multiplication.
Ograniczenia pamięci komputera są powszechnym problemem przy obliczeniach przeprowadzanych na wielkich zbiorach danych. Przy danych roboczych przekraczających 1 GiB, składowanie całości w pamięci operacyjnej staje się utrudnione, a często nawet nieosiągalne. Ponieważ w większości aplikacji wykonuje się działania jedynie na fragmencie zbioru danych, reszta może być przechowywana w pamięci stałej, która zapewnia dużo większe pojemności. Dostęp do pamięci stałej jest zazwyczaj kilka rzędów wielkości wolniejszy niż do RAMu, zatem należy przedstawić metodę składowania ograniczającą do minimum ilość dostępów do dysku. W artykule opisuję format przechowywania macierzy rzadkich na dysku, zbudowanym na bazie formatu HDF5 (Hierarchical Data Format) pod kątem minimalizacji czasu mnożenia tej macierzy przez wektor.
Źródło:
Prace Instytutu Elektrotechniki; 2010, 247; 209-222
0032-6216
Pojawia się w:
Prace Instytutu Elektrotechniki
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 69

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