Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/18371
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKozlenko, Mykola-
dc.contributor.authorКозленко, Микола Іванович-
dc.date.accessioned2024-01-09T07:14:10Z-
dc.date.available2024-01-09T07:14:10Z-
dc.date.issued2023-11-29-
dc.identifier.citationM. Kozlenko, "Weak sinusoidal signal extraction from white noise using convolutional neural network," 2023 2nd International Conference on Innovative Solutions in Software Engineering (ICISSE), Vasyl Stefanyk Precarpathian National University, Ivano-Frankivsk, Ukraine, Nov. 29-30, 2023, doi: 10.5281/zenodo.10467333uk_UA
dc.identifier.isbn978-966-640-549-7-
dc.identifier.other10.5281/zenodo.10467333-
dc.identifier.urihttps://doi.org/10.5281/zenodo.10467333-
dc.identifier.urihttps://zenodo.org/records/10467333-
dc.identifier.urihttp://hdl.handle.net/123456789/18371-
dc.description.abstractA great number of analog and digital data communications schemes use the sinusoidal waveform as a basic elementary signal, including the spread spectrum data exchange techniques. Detection of the presence of the sinusoidal waveform in a mixture of signal and noise is a common task, regardless the specific modulation scheme. This paper presents the machine learning-based approach for detection of the sinusoidal wave. It presents the structure of the convolutional neural network, as well as the performance metrics for the sinusoidal signals detection. The paper provides an assessment of the overall accuracy for the binary signals. It reports the overall accuracy value of 0.93 for the sinusoidal signal detection in the presence of additive white Gaussian noise at the signal-to-noise ratio value of −20 dB for a balanced dataset.uk_UA
dc.language.isoen_USuk_UA
dc.publisherVasyl Stefanyk Precarpathian National Universityuk_UA
dc.subjectdigital communicationsuk_UA
dc.subjectmodulationuk_UA
dc.subjectmanipulation keyinguk_UA
dc.subjectdemodulationuk_UA
dc.subjectdetectionuk_UA
dc.subjectbit error rateuk_UA
dc.subjectmachine learninguk_UA
dc.subjectdeep learninguk_UA
dc.subjectconvolutional neural networkuk_UA
dc.subjectJT65uk_UA
dc.titleWeak sinusoidal signal extraction from white noise using convolutional neural networkuk_UA
dc.typeArticleuk_UA
Appears in Collections:Статті та тези (ФМІ)

Files in This Item:
File Description SizeFormat 
2023_ICISSE_paper_77_final.pdf356.3 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.