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Artykuły w czasopismach na temat "Convolutional code-word"
Sidorenko, A. A. "Decoding of the turbo code created on the basis of the block code using the syndrome grid". Journal of Physics: Conference Series 2388, nr 1 (1.12.2022): 012029. http://dx.doi.org/10.1088/1742-6596/2388/1/012029.
Pełny tekst źródłaWang, Yilin, Siqing Xue i Jun Song. "A Malicious Webpage Detection Method Based on Graph Convolutional Network". Mathematics 10, nr 19 (25.09.2022): 3496. http://dx.doi.org/10.3390/math10193496.
Pełny tekst źródłaRamanna, Dasari, i V. Ganesan. "Low-Power VLSI Implementation of Novel Hybrid Adaptive Variable-Rate and Recursive Systematic Convolutional Encoder for Resource Constrained Wireless Communication Systems". International Journal of Electrical and Electronics Research 10, nr 3 (30.09.2022): 523–28. http://dx.doi.org/10.37391/ijeer.100320.
Pełny tekst źródłaEt.al, Vishaal Saravanan. "Automated Web Design And Code Generation Using Deep Learning". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, nr 6 (10.04.2021): 364–73. http://dx.doi.org/10.17762/turcomat.v12i6.1401.
Pełny tekst źródłaFarid, Ahmed Bahaa, Enas Mohamed Fathy, Ahmed Sharaf Eldin i Laila A. Abd-Elmegid. "Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM)". PeerJ Computer Science 7 (16.11.2021): e739. http://dx.doi.org/10.7717/peerj-cs.739.
Pełny tekst źródłaHsu, Jia-Lien, Teng-Jie Hsu, Chung-Ho Hsieh i Anandakumar Singaravelan. "Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records". Sensors 20, nr 24 (11.12.2020): 7116. http://dx.doi.org/10.3390/s20247116.
Pełny tekst źródłaBanerjee, Suman, i Mitesh M. Khapra. "Graph Convolutional Network with Sequential Attention for Goal-Oriented Dialogue Systems". Transactions of the Association for Computational Linguistics 7 (listopad 2019): 485–500. http://dx.doi.org/10.1162/tacl_a_00284.
Pełny tekst źródłaRao, Jinfeng, Wei Yang, Yuhao Zhang, Ferhan Ture i Jimmy Lin. "Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 232–40. http://dx.doi.org/10.1609/aaai.v33i01.3301232.
Pełny tekst źródłaSimistira Liwicki, Foteini, Vibha Gupta, Rajkumar Saini, Kanjar De i Marcus Liwicki. "Rethinking the Methods and Algorithms for Inner Speech Decoding and Making Them Reproducible". NeuroSci 3, nr 2 (19.04.2022): 226–44. http://dx.doi.org/10.3390/neurosci3020017.
Pełny tekst źródłaZhang, Min, Yujin Yan, Hai Wang i Wei Zhao. "An Algorithm for Natural Images Text Recognition Using Four Direction Features". Electronics 8, nr 9 (31.08.2019): 971. http://dx.doi.org/10.3390/electronics8090971.
Pełny tekst źródłaRozprawy doktorskie na temat "Convolutional code-word"
Bellard, Marion. "Influence du mapping sur la reconnaissance d'un système de communication". Electronic Thesis or Diss., Paris 6, 2014. http://www.theses.fr/2014PA066008.
Pełny tekst źródłaThe context of this thesis is the recognition of communication systems in a non-cooperative context. We are interested in the convolutional code reconstruction problem and in the constellation labeling reconstruction (the mapping used to associate a binary sequence to a modulated signal). We have defined a new statistical method for detecting if a given binary sequence is a noisy convolutional code-word obtained from an unknown convolutional code. It consists in forming blocks of sequence which are big enough to contain the support of a parity check equation and counting the number of blocks which are equal. It gives the length of the convolutional code without knowledge of the constellation labeling. This method can also be used to reconstruct the dual of a convolutional code when the constellation labeling is known. Moreover we propose a constellation labeling recognition algorithm using some equivalence classes. Two types of classes are defined: linear and affine. We observe a noisy signal which is partially demodulated (with a default labeling) and assume that the data are coded by a convolutional encoder. Thus we use the reconstruction of a code as a test and run through the classes which reveal a code structure. This classification improves the complexity of the search for small constellations (4-PSK and 8-PSK). In case of 16-QAM to 256-QAM constellations we apply the algorithm to Gray or quasi-Gray labelings. The algorithm does not give a unique result but it allows to find a small set of possible constellation labelings from noisy data
Części książek na temat "Convolutional code-word"
Lei, Jing Sheng, Chen Si Cong Zhu, Sheng Ying Yang, Guan Mian Liang, Cong Hu i Wei Song. "Interpretable Dual-Feature Recommender System Using Reviews1". W Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210175.
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