Sparse is very hot in compressive sensing research.  When I just read a paper,  an idea got to my mind. Why not register a related domain for it? I love this idea, and search for sparselab.com, amazing it is available!

It cost me arround 9 dollars to get it at namecheap instead of godaddy, because namecheap is cheaper:)

Compressive Sensing · sparse

successive interference cancellation under cdma and compressive sensing!

sic

In this paper, the CS framework is introduced firstly, and then the short-term stability of speech signal and the sparsity in the discrete cosine transform basis of speech signal are analyzed. Secondly, a new distributed speech signal compression and reconstruction framework based on compressed sensing theory is proposed. Via basis pursuit(BP) and orthogonal matching pursuit(OMP), it is demonstrated that the performance of reconstruction is correlated with the number of measurements and the length of frames.

By SUN Lin-hui and YANG Zhen, Nanjing University of Posts and Telecommnunications

Compressive Sensing

yes,I would like to do more research on it thought it is already very hot now.

If you are interested in this big picture,feel free to contact me.

Compressive Sensing

This paper was published at August,2009 on J. Infrared Millim. Waves which is a chinese journal.

The authors are Liu Dan-Hua, Shi Guang-Ming, Zhou Jia-She, Gao Da-Hua, Wu Jia-Ji(Key Lab of Intelligent perception and Image Understanding of Ministry of Education and School of Science, Air Force Engineering University Xi’an).

The abstract is below:

“Based on compressed sensing(CS), a new multiple description coding method(CS-MDC) was presented. The new method is robust to packet loss or bit error, and has the advantages of simple structure and easy implementation. The method partitioned an image into several blocks by interleaving extracting in the wavelet domain, and made random measurements of the image blocks, and then formed multiple descriptions after quantizing and packing. At the decoding end, it reconstructed the original image approximately or exactly with the received bit streams by solving an optimization problem. The proposed method can construct more descriptions with lower complexity because the process of random measuring is simple and easy to realize. Experimental results show that the proposed method exhibits its superiority over SPIHT-MDC with the same packet loss probability, and it can easily generate more descriptions ”

compressive sensing

This paper was published in July 2009 on “Chinese Journal of Scientific Instument”.  The authors are Lian Qiusheng, Gao Yangyan, Chen Shuzhen(Institute of Information Science and Technology, Yanshan University, Qinhuangdao 066004, China).

Abstract is below:

“Compressed sening system can reconstruct original image from fewer measurements using sparse priors of the image. In current compressed sensing literature,people always use orthogonal wavelet with three directions to represent the image, and use iterative shrinkage to solve the optimization problem. However, traditional reconstruction algorithm suffers from lower convergence rate and pseudo-Gibbs effect in the reconstructed image. Aiming at this problem, this paper presents an image reconstruction algorithm based on sparse representation of the image in dual-tree complex wavelet transform domain and two-step iterative shrinkage, which uses two previous estimations to obtain a new one. The results of experiments show that the reconstructed image has better vision quality and the convergence rate is faster than that of conventional reconstrucion algorithm“.

Comments are welcome!

Chinese · compressive sensing