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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tsung-Ying Sun Chan-Cheng Liu Tsung-Ying Tsai Jyun-Hong Jheng Sheng-Ta Hsieh |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Electr. Eng., Nat. Dong Hwa Univ., Hualien (Tsung-Ying Sun; Chan-Cheng Liu; Tsung-Ying Tsai; Jyun-Hong Jheng) || Dept. of Commun. Eng., Oriental Inst. of Technol., Taipei (Sheng-Ta Hsieh) |
| Abstract | Recently, the research which based on wavelet representation to reduce noise has gotten a lot of attention. The most typical method, universal threshold proposed by Donoho, and its derivative methods have verified their efficiency on varied applications. Some settings which are signal-dependence are critical; however, they were usually given by trial and error or a rough estimate in existing algorithms. This paper addresses the assumption that source signals and additive noise are mutually independent to deal with de-noisy problem without extra prior knowledge. First, the objective function developed from a technique of blind source separation (BSS) is applied on particle swarm optimizer (PSO) to guide the determination of wavelet threshold. Second, the evaluation which is able to determine the most profitable decomposition scale for de-noisy is proposed. Therefore, the best performance of de-noisy could be obtained. In order to confirm the validity and efficiency of the proposed algorithm, several simulations which include four benchmarks with different noise degree are designed. The performance of proposed algorithm further compared with that of other existing algorithms. |
| Starting Page | 2652 |
| Ending Page | 2657 |
| File Size | 459585 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424423835 |
| ISSN | 1062922X |
| DOI | 10.1109/ICSMC.2008.4811696 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-10-12 |
| Publisher Place | Singapore |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Particle swarm optimization Noise reduction Wavelet domain Additive noise Space technology Wavelet transforms Signal processing Filters Wavelet coefficients Sun wavelet threshold determination particle swarm optimization noise reduction best decomposition scale |
| Content Type | Text |
| Resource Type | Article |
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