Signal processing of microwave imaging brain tumor detection using superposition windowing
This paper discusses the selection of window function for signal processing in microwave imaging brain tumor detection. Most of the window functions are non-negative bell-shaped curves. This paper proposed a superposition windowing function for better time series data analyses and enhancement. The p...
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| Format: | Article |
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Trans Tech Publications
2014
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| author | Sudirman, Rubita Seman, Norhudah Yong, Ching Yee Chew, Kim Mey |
| author_facet | Sudirman, Rubita Seman, Norhudah Yong, Ching Yee Chew, Kim Mey |
| author_sort | Sudirman, Rubita |
| collection | ePrints |
| description | This paper discusses the selection of window function for signal processing in microwave imaging brain tumor detection. Most of the window functions are non-negative bell-shaped curves. This paper proposed a superposition windowing function for better time series data analyses and enhancement. The performance of the selected five window functions (Hamming, Blackman-Harris, Parzen, Chebyshev and Bartlett-Hanning) and the proposed superposition window were compared and evaluated. The results show the superposition window function is potentially reduce the unwanted noise and preserve important information of the signals. |
| first_indexed | 2024-03-05T19:54:09Z |
| format | Article |
| id | utm.eprints-62572 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:54:09Z |
| publishDate | 2014 |
| publisher | Trans Tech Publications |
| record_format | dspace |
| spelling | utm.eprints-625722017-06-18T06:35:21Z http://eprints.utm.my/62572/ Signal processing of microwave imaging brain tumor detection using superposition windowing Sudirman, Rubita Seman, Norhudah Yong, Ching Yee Chew, Kim Mey TK Electrical engineering. Electronics Nuclear engineering This paper discusses the selection of window function for signal processing in microwave imaging brain tumor detection. Most of the window functions are non-negative bell-shaped curves. This paper proposed a superposition windowing function for better time series data analyses and enhancement. The performance of the selected five window functions (Hamming, Blackman-Harris, Parzen, Chebyshev and Bartlett-Hanning) and the proposed superposition window were compared and evaluated. The results show the superposition window function is potentially reduce the unwanted noise and preserve important information of the signals. Trans Tech Publications 2014 Article PeerReviewed Sudirman, Rubita and Seman, Norhudah and Yong, Ching Yee and Chew, Kim Mey (2014) Signal processing of microwave imaging brain tumor detection using superposition windowing. Applied Mechanics and Materials, 654 . pp. 321-326. ISSN 1660-9336 http://dx.doi.org/10.4028/www.scientific.net/AMM.654.321 DOI:10.4028/www.scientific.net/AMM.654.321 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Sudirman, Rubita Seman, Norhudah Yong, Ching Yee Chew, Kim Mey Signal processing of microwave imaging brain tumor detection using superposition windowing |
| title | Signal processing of microwave imaging brain tumor detection using superposition windowing |
| title_full | Signal processing of microwave imaging brain tumor detection using superposition windowing |
| title_fullStr | Signal processing of microwave imaging brain tumor detection using superposition windowing |
| title_full_unstemmed | Signal processing of microwave imaging brain tumor detection using superposition windowing |
| title_short | Signal processing of microwave imaging brain tumor detection using superposition windowing |
| title_sort | signal processing of microwave imaging brain tumor detection using superposition windowing |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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