Iterative Reweighted Least Squares (IRLS) algorithm is added to RxCS software. This implementation of the IRLS allows to solve an l1 optimization problem using an l2 solver. You can find the IRLS algorithm implemented in Python on GitHub. An example of using IRLS to solve an underdetermined system is here.
A wrapper which employs IRLS to solve a compressed sensing problem is also availabe on GitHub, an example of usage is here.
Reproducible materials for “Frequency Selective Compressed Sensing” are available here . Reproducible research scripts are available on GitHub .
A new module is present in RxCS software: Inverse Discrete Hartley Transform . You can find the module on RxCS’s GitHub page , an example of how to use the module is also available on GitHub.
Workshop on Compressed Sensing in Wireless Communication will be held at the European Wireless 2015 conference . You can find the workshop’s website here.
Our paper “Frequency Selective Compressed Sensing” submitted to IEEE Signal Processing Letters is now available on arXiv.org
Paper “Frequency Selective Compressed Sensing” was submitted to IEEE Signal Processing Letters. The paper will be soon available in arXiv, reproducible research Python scripts will be available on the website in a couple of days.