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| Content Provider | IET Digital Library |
|---|---|
| Author | Liu, Ji xin Sun, Quan sen |
| Abstract | This study presents a novel compressive sensing (CS) framework to solve the high dimensional mass spectrometry (MS) signal processing in Bioinformatics. As a hot research topic, CS has attracted a great deal of attention in many fields. In theory, high sparsity is one precondition for any CS framework. However, in Bioinformatics, one application bottleneck is that only a few MS data can be considered as sparse. So sparse representation (SR) become necessary. However, this will create a new problem that the SR computation cost will be too huge to MS signal because of its high data dimensionality (usually tens of thousands or more). Therefore the authors propose theconcept ofsparse difference (SD) to realise a new CS framework. Firstly, it canacquire the prior MS information through fractal and entropy recognition. Secondly, the original signal can be perfectly recovered by SD based on the previous recognition result. The feasibility and validity of this CS framework isproved by experiments. |
| Starting Page | 201 |
| Ending Page | 209 |
| Page Count | 9 |
| ISSN | 17519675 |
| Volume Number | 7 |
| e-ISSN | 17519683 |
| Issue Number | Issue 3, May (2013) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-spr/7/3 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2011.0219 |
| Journal | IET Signal Processing |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Algebra Bioinformatics Biology And Medical Computing Biomedical Engineering Biomedical Measurement And Imaging Compressed Sensing Compressive Sensing CS Framework Digital Signal Processing Entropy Recognition Fractal Fractals High Data Dimensionality High Dimensional Mass Spectrometry Signal Processing Mass Spectrometry Sensing Data Mass Spectroscopy Medical Signal Processing MS Signal Processing Signal Processing And Detection Sparse Difference Sparse Matrix Sparse Representation SR Computation Cost |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Electrical and Electronic Engineering |
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