Auswahl der wissenschaftlichen Literatur zum Thema „Convolutional code-word“
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Zeitschriftenartikel zum Thema "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 (01.12.2022): 012029. http://dx.doi.org/10.1088/1742-6596/2388/1/012029.
Der volle Inhalt der QuelleWang, Yilin, Siqing Xue und 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.
Der volle Inhalt der QuelleRamanna, Dasari, und 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.
Der volle Inhalt der QuelleEt.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.
Der volle Inhalt der QuelleFarid, Ahmed Bahaa, Enas Mohamed Fathy, Ahmed Sharaf Eldin und 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.
Der volle Inhalt der QuelleHsu, Jia-Lien, Teng-Jie Hsu, Chung-Ho Hsieh und 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.
Der volle Inhalt der QuelleBanerjee, Suman, und Mitesh M. Khapra. „Graph Convolutional Network with Sequential Attention for Goal-Oriented Dialogue Systems“. Transactions of the Association for Computational Linguistics 7 (November 2019): 485–500. http://dx.doi.org/10.1162/tacl_a_00284.
Der volle Inhalt der QuelleRao, Jinfeng, Wei Yang, Yuhao Zhang, Ferhan Ture und 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.
Der volle Inhalt der QuelleSimistira Liwicki, Foteini, Vibha Gupta, Rajkumar Saini, Kanjar De und 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.
Der volle Inhalt der QuelleZhang, Min, Yujin Yan, Hai Wang und 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.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleThe 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
Buchteile zum Thema "Convolutional code-word"
Lei, Jing Sheng, Chen Si Cong Zhu, Sheng Ying Yang, Guan Mian Liang, Cong Hu und Wei Song. „Interpretable Dual-Feature Recommender System Using Reviews1“. In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210175.
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