Moshe Mishali

Computational Mechanics · 2007–2009

A researcher in the Science corpus: 5 papers; trusts 39 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 66 citations into it; #577 of 10,001 by trust here, #1,304 by citations
• Computer Vision and Pattern Recognition — 30 citations into it; #4,282 of 21,575 by trust here, #8,612 by citations
• Signal Processing — 29 citations into it; #985 of 7,572 by trust here, #2,276 by citations
• Biomedical Engineering — 28 citations into it; #749 of 10,734 by trust here, #4,781 by citations
• Applied Mathematics — 18 citations into it; #1,040 of 7,620 by trust here, #1,710 by citations
• Radiology, Nuclear Medicine and Imaging — 9 citations into it
• Electrical and Electronic Engineering — 8 citations into it; #2,568 of 16,539 by trust here, #8,802 by citations
• Computer Networks and Communications — 7 citations into it

Whom Moshe Mishali trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Joel A. Tropp
2. Yonina C. Eldar
3. David L. Donoho
4. Joseph B. Kruskal
5. Jie Chen
6. Shane F. Cotter
7. E.J. Candes
8. Xiaoming Huo
9. Terence Tao
10. Bhaskar D. Rao

Who trusts Moshe Mishali overall

1. Yonina C. Eldar
2. Marco F. Duarte
3. D. P. Palomar
4. Richard Baraniuk
5. Andreas Bollig
6. Zhilin Zhang
7. Patrick Kuppinger
8. M. Davies
9. Niall P. Hurley
10. Zhi-Qin John Xu

Papers from Moshe Mishali's perspective

Ordered by how strongly the researchers Moshe Mishali trusts endorse each paper. Same corpus, a different order for every researcher.

1. Stable signal recovery from incomplete and inaccurate measurements
2. Uncertainty principles and ideal atomic decomposition
3. Atomic Decomposition by Basis Pursuit
4. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
5. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
6. Matching pursuits with time-frequency dictionaries
7. Greed is good: algorithmic results for sparse approximation (IEEE Transactions on Information Theory)
8. Decoding by linear programming
9. Regression Shrinkage and Selection via the Lasso
10. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization

Their papers

• From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals (2009) — 1,201 citations
• Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors (2008) — 367 citations
• Xampling: Analog to digital at sub-Nyquist rates (2009) — 430 citations
• Robust Recovery of Signals From a Structured Union of Subspaces (2008) — 1,052 citations
• Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals (2007) — 858 citations