Spatial Frequency Extraction using Gradient-liked Operator
碩士 === 國立交通大學 === 資訊科學與工程研究所 === 106 === The multi-dimensional ensemble empirical mode decomposition (MEEMD) is usually used for temporal-spatial data decomposition. One of the major issue is its high time complexity. A new gradient-liked approach to mimic similar spatial data decomposition results...
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| Format: | Others |
| Language: | en_US |
| Published: |
2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/23fy97 |
| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 106 === The multi-dimensional ensemble empirical mode decomposition (MEEMD) is usually used for temporal-spatial data decomposition. One of the major issue is its high time complexity. A new gradient-liked approach to mimic similar spatial data decomposition results with more than 10x speedup. The GPGPU version of our approach can reach 500x speedup. Our novel approach use gradient-liked operator to evaluate the spatial frequency on different radius and integral the gradient result to spatial frame which is similar to BIMFs of MEEMD.
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