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Object detection for signal separation with different time-frequency representations
Dissertation (MEng (Computer Engineering))--University of Pretoria, 2017.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2021
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| _version_ | 1867613552269852672 |
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| access_status_str | Open Access |
| author2 | Du Plessis, Warren Paul |
| author_browse | Du Plessis, Warren Paul |
| author_facet | Du Plessis, Warren Paul |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Computer Engineering))--University of Pretoria, 2017. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/81153 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:57.427Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/81153 Object detection for signal separation with different time-frequency representations Du Plessis, Warren Paul llewellyn.strydom@gmail.com Strydom, Llewellyn Signal separation Signal classification Machine learning Object detection Joint time-frequency analysis UCTD Dissertation (MEng (Computer Engineering))--University of Pretoria, 2017. The task of detecting and separating multiple radio-frequency signals in a wideband scenario has attracted much interest recently, especially from the cognitive radio community. Many successful approaches in this field have been based on machine learning and computer vision methods using the wideband signal spectrogram as an input feature. YOLO and R-CNN are deep learning-based object detection algorithms that have been used to obtain state-of-the-art results on several computer vision benchmark tests. In this work, YOLOv2 and Faster R-CNN are implemented, trained and tested, to solve the signal separation task. Previous signal separation research does not consider representations other than the spectrogram. Here, specific focus is placed on investigating different time-frequency representations based on the short-time Fourier transform. Results are presented in terms of traditional object detection metrics, with Faster R-CNN and YOLOv2 achieving mean average precision scores of up to 89.3% and 88.8% respectively. Saab Grintek Defence University of Pretoria Electrical, Electronic and Computer Engineering MEng (Computer Engineering) Unrestricted 2021-08-04T13:29:59Z 2021-08-04T13:29:59Z 2021 2021 Dissertation * S2021 http://hdl.handle.net/2263/81153 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Signal separation Signal classification Machine learning Object detection Joint time-frequency analysis UCTD Object detection for signal separation with different time-frequency representations |
| title | Object detection for signal separation with different time-frequency representations |
| title_full | Object detection for signal separation with different time-frequency representations |
| title_fullStr | Object detection for signal separation with different time-frequency representations |
| title_full_unstemmed | Object detection for signal separation with different time-frequency representations |
| title_short | Object detection for signal separation with different time-frequency representations |
| title_sort | object detection for signal separation with different time frequency representations |
| topic | Signal separation Signal classification Machine learning Object detection Joint time-frequency analysis UCTD |
| url | http://hdl.handle.net/2263/81153 |