Research not for publishing papers, but for fun, for satisfying curiosity, and for revealing the truth.

This blog reports latest progresses in
(1) Signal Processing and Machine Learning for Biomedicine, Neuroimaging, Wearable Healthcare, and Smart-Home
(2) Sparse Signal Recovery and Compressed Sensing of Signals by Exploiting Spatiotemporal Structures
(3) My Works

Monday, May 23, 2011

New Versions of T-MSBL/T-SBL are Available for Download

Finally, I almost re-coded the two algorithms for convenient use. The main feature of the new versions  is, for general users who don't know much about SBL and don't want to tune parameters, they just need to type the command:

X_est = TMSBL(Phi, Y); % for most noisy cases (SNR from 7-23dB)

or according to your rough guess about the SNR, type the command:

X_est = TMSBL(Phi, Y, 'noise', 'large');  % for SNR < 7dB
X_est = TMSBL(Phi, Y, 'noise', 'mild') ;  % for SNR from 7-23dB
X_est = TMSBL(Phi, Y, 'noise', 'small') ; % for SNR > 23dB
X_est = TMSBL(Phi, Y, 'noise', 'no') ;      % no noise

Each command uses a set of pre-defined parameter values, which probably are suitable for most compressed sensing experiments. But they may not be optimal for your specific task. For example, if the row-norms of your X are very small or very large, you may need to change the input argument 'prune_gamma'. Please read the demo files to see the experiment settings. If you want to get the best performance for your task, you can read my suggestions on tuning parameters from the cookbook: I strongly suggest everyone to read the short cookbook before using the codes.

The codes can be downloaded at:

If you have any questions or suggestions, please feel free to contact me. And, if you find the codes do not perform well, please let me know.

There are two pictures of my plants in that cookbook. Interesting, right? Just relax :)
It is called cobra plant, because it is like a cobra :)

Another one is called N. ampullaria (red) x N. sibuyanensis. It is a nepenthes.

No comments:

Post a Comment