Temat: hybrid detector - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Performance of Hybrid Sensing Method in Environment with Noise Uncertainty
Autorzy:: Kustra, M.
Kosmowski, K.
Suchański, M.
Powiązania:: https://bibliotekanauki.pl/articles/308868.pdf
Data publikacji:: 2018
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Tematy:: Covariance Absolute Value
Cyclic Autocorrelation Function
hybrid detector
noise uncertainty
OFDM
SNR wall
WiMAX
Opis:: The paper presents a novel hybrid spectrum sensing method used in cognitive radio and presents a hybrid detector (HD) which improves the sensing performance. The proposed HD takes advantage of the energy detection (ED) principle and a method based on Covariance Absolute Value (CAV), as well as on Cyclic Autocorrelation Function (CAF). The paper shows the limitations of using ED, resulting from the uncertainty of spectral density of noise power estimation, known as the SNR wall. The paper describes a system model and presents simulation results for the OFDM signal of a WiMAX-based communications system. The simulation results refer to an ideal environment with well-known parameters, and to an environment with uncertain spectral density of noise power estimation.
Źródło:: Journal of Telecommunications and Information Technology; 2018, 1; 51-57
1509-4553
1899-8852
Pojawia się w:: Journal of Telecommunications and Information Technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Efficient dead time correction of G-M counters using feed forward artificial neural network
Autorzy:: Arkani, M.
Khalafi, A.
Powiązania:: https://bibliotekanauki.pl/articles/146121.pdf
Data publikacji:: 2013
Wydawca:: Instytut Chemii i Techniki Jądrowej
Tematy:: dead time
artificial neural network (ANN)
Geiger-Müller (G-M) detector
hybrid model
source decaying experiment
Opis:: Dead time parameter of Geiger-Müller (G-M) counters causes a great uncertainty in their response to the incident radiation intensity at high counting rates. As their applications in experimental nuclear science are widespread, many attempts have been done on improvements of their nonlinear response. In this work, response of a G-M counter system is optimized and corrected efficiently using feed forward artificial neural network (ANN). This method is simple, fast, and provides the answer to the problem explicitly with no need for iteration. The method is applied to a set of decaying source experimental data measured by a fairly large G-M tube. The results are compared with those predicted by a given analytical model which is called hybrid model. The maximum deviation of the corrected results from the true counting rates is less than 4% which is a significant improvement in comparison with the results obtained by the analytical method. Results of this study show that by using a proper artificial neural network structure, the dead time effects of G-M counters can be tolerated significantly.
Źródło:: Nukleonika; 2013, 58, 2; 317-321
0029-5922
1508-5791
Pojawia się w:: Nukleonika
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Combining Rough Sets and Neural Network Approaches in Pattern Recognition
Autorzy:: Cyran, K.
Powiązania:: https://bibliotekanauki.pl/articles/92799.pdf
Data publikacji:: 2005
Wydawca:: Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:: pattern recognition
neural networks
rough sets
hybrid methods
evolutionary optimization
holographic ring-wedge detector
Opis:: The paper focuses on problems which arise when two different types of AI methods are combined in one design. The first type is rule based, rough set methodology operating is highly discretized attribute space. The discretization is a consequence of the granular nature of knowledge representation in the theory of rough sets. The second type is neural network working in continuous space. Problems of combining these different types of knowledge processing are illustrated in a system used for recognition of diffraction patterns. The feature extraction is performed with the use of holographic ring wedge detector, generating the continuous feature space. No doubt, this is a feature space natural for application of the neural network. However, the criterion of optimization of the feature extractor uses rough set based knowledge representation. This latter, requires the discretization of conditional attributes generating the feature space. The novel enhanced method of optimization of holographic ring wedge detector is proposed, as a result of modification of indiscernibility relation in the theory of rough sets.
Źródło:: Studia Informatica : systems and information technology; 2005, 2(6); 7-20
1731-2264
Pojawia się w:: Studia Informatica : systems and information technology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: An improved formula for dead time correction of G-M detectors
Autorzy:: Arkani, M.
Khalafi, H.
Powiązania:: https://bibliotekanauki.pl/articles/147009.pdf
Data publikacji:: 2013
Wydawca:: Instytut Chemii i Techniki Jądrowej
Tematy:: dead time model
Geiger-Müller (G-M) detector
decaying source experiment
hybrid model
Opis:: Different analytical formulae have been described in the literature to modify response of Geiger-Müller (G-M) detectors. In this work, improvement of a previously proposed dead time correction formula was investigated. A set of experimental data of a decaying source was the basis of the analysis. A general agreement is seen with the experimental data. The result was compared with those obtained by the original work. Numerical aspects were also examined.
Źródło:: Nukleonika; 2013, 58, 4; 533-536
0029-5922
1508-5791
Pojawia się w:: Nukleonika
Dostawca treści:: Biblioteka Nauki

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