文件列表:

BSBL-FM (0, 2016-10-21)
BSBL-FM\BSBL_BO.m (15593, 2014-03-27)
BSBL-FM\BSBL_FM_subfunc.m (12997, 2016-10-21)
BSBL-FM\Phi.mat (131256, 2014-03-27)
BSBL-FM\demo.mat (1610, 2014-03-27)
BSBL-FM\demo_fecg.m (1481, 2014-03-27)
BSBL-FM\demo_mmv.m (2194, 2014-03-27)
BSBL-FM\demo_real.m (1964, 2014-03-27)
BSBL-FM\demo_smv_complex.m (2152, 2014-03-27)
BSBL-FM\demo_smv_real.m (2017, 2014-03-27)
BSBL-FM\signal_01.mat (18304240, 2014-03-27)
BSBL-FM\耦合模式参考文献18 Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation 未知先验的块稀疏贝叶斯学习.pdf (1452287, 2016-10-19)

# BSBL-FM # This is a fast implementation of the Block Sparse Bayesian Learning [BSBL](https://sites.google.com/site/researchbyzhang/) algorithm. The developed algorith is based on the Fast Marginalized **(FM)** likelihood maximization algorithm, which yields ~8 times speedup while also pertains nearly the same recovery performances. # A Short Introduction # A CS algorithm aims to solve, **Y** = **Phi** **X** + **N**, where **Y** is the measurement matrix of size M times T, **Phi** is the under-determined sensing matrix of size M times N, **X** is the signal. Compressed sensing, can recover **X** given **Y** and the under-determined matrix **Phi**. When **T=1**, we called it Single Measurement Vector (**SMV**) Model, with **T>1**, it is the Multiple Measurement Vector (**MMV**) model. Block Sparse, assumes that **x** can be partitioned into blocks **x** = {**x_1**, ... , **x_g**}. The non-zero entries cluster within some blocks and *zeros* otherwise. If *d* out of *g* blocks are non-zero, then the block sparsity is defined as *d/g*. Exploiting both the block sparsity and the intra-block correlation is the source of the magic of all BSBL algorithms. Our **BSBL-FM** algorithm, is an ultra fast implementation of the original BSBL framework, which brings about **~8** times speedup. What's more, It can worked in all the scenarios include: > - SMV sparse > - MMV sparse > - SMV block sparse > - MMV block sparse > - Real-valued > - Complex-valued See the demos and implementations below for more details. # Codes and Data # The `.m` codes are: > **CODE:** > > - **BSBL_FM.m**: the main algorithm, also called **MBSBL-FM** in MMV model > - **BSBL_BO.m**: Zhilin's BSBL-BO algorithm. > - **demo_smv_real.m**: the real valued SMV block sparse demo > - **demo_smv_complex.m**: the complexed valued SMV block sparse demo > - **demo_mmv.m**: the real valued MMV block sparse demo > - **demo_fecg.m**: the demo code for FECG dataset recovery The `.mat` data files are: > **DATA:** > > - **demo.mat**: the data for SMV case, contains *re*, *im* vectors > - **signal_01.mat**: FECG datasets used in BSBL-BO by Zhilin > - **Phi.mat**: the sensing matrix for CS FECG data # Citations # If you find the **BSBL-FM** algorithm useful, please cite: ```bibtex @Article{liu2013energy, Title = {Energy Efficient Telemonitoring of Physiological Signals via Compressed Sensing: A Fast Algorithm and Power Consumption Evaluation}, Author = {Liu, Benyuan and Zhang, Zhilin and Xu, Gary and Fan, Hongqi and Fu, Qiang}, Journal = {Biomedical Signal Processing and Control}, Year = {2014}, Pages = {80--88}, Volume = {11C} } ``` ```bibtex @InProceedings{liu2013compression, Title = {Compression via Compressive Sensing: A Low-Power Framework for the Telemonitoring of Multi-Channel Physiological Signals}, Author = {Benyuan Liu and Zhilin Zhang and Hongqi Fan and Qiang Fu}, Booktitle = {2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, Year = {2013}, Organization = {IEEE}, Pages = {9--12} } ``` More powerful **STSBL** algorithm developed by Zhilin Zhang is available on-line: ```bibtex @InProceedings{ZhangAsilomar2013, Title = {Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities}, Author = {Zhilin Zhang and Bhaskar D. Rao and Tzyy-Ping Jung}, Booktitle = {Asilomar Conference on Signals, Systems, and Computers (Asilomar 2013)}, Year = {2013} } ``` ```bibtex @Article{zhang2014spatiotemporal, Title = {Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel EEG for Wireless Telemonitoring and Brain-Computer Interfaces}, Author = {Zhilin Zhang and Tzyy-Ping Jung and Scott Makeig and Bhaskar D. Rao and Zhouyue Pi}, Journal = {(Accepted) IEEE Trans. on Neural Systems and Rehabilitation Engineering}, Year = {2014} } ```

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