The aim of antenna array synthesis is to achieve a desired radiation pattern with the minimum number of antenna elements. In this paper the antenna synthesis problem is studied from a totally new perspective. One of the key principles of compressive sensing is that the signal to be sensed should be sparse or compressible. This coincides with the requirement of minimum number of element in the antenna array synthesis problem. In this paper the antenna element of the array can be efficiently reduced via compressive sensing, which shows a great improvement to the existing antenna synthesis method. Moreover, the desired radiation pattern can be achieved in a very computation time which is even shorter than the existing method. Numerical examples are presented to show the high efficiency of the proposed method.
We consider the problem of recovering a matrix from its action on a known vector in the setting where the matrix can be represented efficiently in a known matrix dictionary. Connections with sparse signal recovery allows for the use of efficient reconstruction techniques such as Basis Pursuit. Of particular interest is the dictionary of time-frequency shift matrices and its role for channel estimation and identification in communications engineering. We present recovery results for Basis Pursuit with the time-frequency shift dictionary and various dictionaries of random matrices.
Boufounos P., "Compressive Sensing and Beyond: New approaches to signal acquisition and processing."
Variations presented at:
- Mitsubishi Electric Research Laboratories, May 19, 2008, Boston, MA
- Electrical Engineering Departmental Seminar, Princeton University, April 14, 2008, Princeton, NJ.
- Center for Signal and Image Processing Seminar, Georgia Institute of Technology, March 17, 2008.
- Electrical Engineering Seminar, Columbia University, February 19, 2008, New York, NY.Boufounos P., "L1 Minimization Without Amplitude Information." Nonlinear Approximation Techniques Using L1 Workshop, Texas A&M University, May 16-18, 2008, College Station, TX.
Boufounos P., Baraniuk R. G. "1-Bit Compressive Sensing." 42nd annual Conference on Information Sciences and Systems (CISS) 2008, March 19-21 2008, Princeton, NJ.
Boufounos P., Baraniuk R. G. "Reconstructing Sparse Signals From their Zero Crossings." IEEE International Conference on Acoustics, Speech, and Signal Processing 2008, (ICASSP 2008), March 30-April 4, Las Vegas,NV.
- Marco Duarte, Fast reconstruction from random incoherent projections. (Rice ECE Department Technical Report TREE 0507, May 2005)
- Yonina Eldar, Beyond bandlimited sampling: Nonideal sampling, smoothness, and sparsity (EUSIPCO, Lausanne, Switzerland, August 2008)
In the meantime, of course population genomics is what we're all about here in the Hawks lab. Single-locus genetics has gone the way of the dodo. Er...I suppose if you study dodos, you'd better go whole-genome with them, too. My only question: exactly how much hard drive space am I expected to have, if I'm going to deal with 100,000 genomes?
- to complain to the site owner (but in my case, I have no power on either blogger nor googlepages)
- use a different ISP, or
- use anonymous proxies like the one mentioned in the Tor Project.
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