Sparsity adaptive matching pursuit algorithm for practical compressed sensing

By Thong T. Do, Lu Gan, Nam H. Nguyen, Trac Duy Tran. Asilomar Conference on Signals, Systems and Computers, 2008

580 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bb11c6474f573de11a2ef8b1bee65c3970b2b041

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. James E. Fowler
2. Sungkwang Mun
3. Trac Duy Tran
4. Nam H. Nguyen
5. Zhen Gao
6. Thong T. Do
7. Linglong Dai
8. Lu Gan

In Computational Mechanics

1. James E. Fowler
2. Sungkwang Mun
3. Trac Duy Tran
4. Nam H. Nguyen
5. Zhen Gao
6. Thong T. Do
7. Linglong Dai
8. Lu Gan

In Signal Processing

1. James E. Fowler
2. Sungkwang Mun
3. Zhen Gao
4. Thong T. Do
5. Trac Duy Tran
6. Linglong Dai
7. Nam H. Nguyen
8. Lu Gan

Citations in the corpus

• Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO (2015, methodology): “This algorithm is developed from the SAMP algorithm [22].”
• Fast and Efficient Compressive Sensing Using Structurally Random Matrices (2011, methodology): “Linear programming [1], [2] and other convex optimization algorithms [3], [4], [5] have been proposed to solve thel1 minimization.”
• Block Compressed Sensing of Images Using Directional Transforms (2010, methodology): “Recently, several fast algorithms (e.g., [4] [5] [6]) have been developed for CS reconstruction, while the latter challenge was addressed in [7] using a block-based sampling operation.”