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Metalexicographical Investigations with the DiCo Database
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In: ISSN: 0950-3846 ; EISSN: 1477-4577 ; International Journal of Lexicography ; https://halshs.archives-ouvertes.fr/halshs-03605596 ; International Journal of Lexicography, Oxford University Press (OUP), 2022, 35 (1), pp.75-106. ⟨10.1093/ijl/ecab017⟩ (2022)
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Italian Language in Exile in France in the First Half of the Nineteenth Century
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In: Italianness and Migration from the Risorgimento to the 1960s ; https://hal.archives-ouvertes.fr/hal-03626136 ; Italianness and Migration from the Risorgimento to the 1960s, Springer International Publishing, pp.133-142, 2022, ⟨10.1007/978-3-030-88964-7_11⟩ (2022)
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СОИБИ ТАБРЕЗӢ ВА МАЪНОҲОИ МАҶҲУЛИ ЯК ФЕЪЛИ МАЪМУЛ ... : SOIB TABREZI AND FORGOTTEN MEANINGS OF ONE FREQUENTLY USED VERB OF THE TAJIK LANGUAGE ...
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Machine-readable Northern Karelian Proper-Livvi bilingual translation dictionary ...
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Machine-readable Northern Karelian Proper-Livvi bilingual translation dictionary ...
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A Study on Dictionary Selection in Compressive Sensing for ECG Signals Compression and Classification
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In: Biosensors; Volume 12; Issue 3; Pages: 146 (2022)
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Abstract:
The paper proposes a comparative analysis of the projection matrices and dictionaries used for compressive sensing (CS) of electrocardiographic signals (ECG), highlighting the compromises between the complexity of preprocessing and the accuracy of reconstruction. Starting from the basic notions of CS theory, this paper proposes the construction of dictionaries (constructed directly by cardiac patterns with R-waves, centered or not-centered) specific to the application and the results of their testing. Several types of projection matrices are also analyzed and discussed. The reconstructed signals are analyzed quantitatively and qualitatively by standard distortion measures and by the classification of the reconstructed signals. We used a k-nearest neighbors (KNN) classifier to evaluate the reconstructed models. The KNN module was trained with the models from the mega-dictionary used in the classification block and tested with the models reconstructed with class-specific dictionaries. In addition to the KNN classifier, a neural network was used to test the reconstructed signals. The neural network was a multilayer perceptron (MLP). Moreover, the results are compared with those obtained with other compression methods, and ours proved to be superior.
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Keyword:
compressed sensing; ECG signal; projection matrices; reconstruction dictionaries; signal classifications
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URL: https://doi.org/10.3390/bios12030146
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STRUCTURAL FEATURES OF LEGAL TERMS IN GENERAL DICTIONARIES OF UZBEK LANGUAGE ...
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Dictionaries [998] لوامع النجوم المستضيئة من شمس العلوم ...
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STRUCTURAL FEATURES OF LEGAL TERMS IN GENERAL DICTIONARIES OF UZBEK LANGUAGE ...
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Dictionaries [998] لوامع النجوم المستضيئة من شمس العلوم ...
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