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All issues Volume 159 (2018) MATEC Web of Conferences, 159 (2018) 01061 References
Open Access
Issue
MATEC Web of Conferences
Volume 159, 2018
The 2nd International Joint Conference on Advanced Engineering and Technology (IJCAET 2017) and International Symposium on Advanced Mechanical and Power Engineering (ISAMPE 2017)
Article Number 01061
Number of page(s) 6
Section Built Environment
DOI https://doi.org/10.1051/matecconf/201815901061
Published online 30 March 2018
  1. D.L. Donoho, Compressed Sensing, IEEE Trans. Inf. Theory 52(4), 1289-1306 (2006). [CrossRef] [MathSciNet] [Google Scholar]
  2. E.J. Candès, J.R, T. Tao, Robust Uncertainty Principles: Exact Signal Reconstruction From Highly Incomplete Frequency Information, IEEE Trans. Inf. Theory 52(2), 489-509 (2006). [CrossRef] [MathSciNet] [Google Scholar]
  3. J.A.M Lorenzo, R. Obermeier, Sensing Matrix Design via Mutual Coherence Minimization for Electromagnetic Compressive Imaging Applications, IEEE Trans. Comp. Img 3(2), 217-229 (2017). [Google Scholar]
  4. S.Qaisar, R.M. Bilal, W. Iqbal, M. Naureen, S. Lee, Compressive Sensing: From Theory to Applications, a Survey, ComNet J 15(5), 443 – 456 (2013). [Google Scholar]
  5. A. M. Bruckstein, D. L. Donoho, M. Elad, From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images, SIAM Rev 51(1), 34–81 (2009). [NASA ADS] [CrossRef] [Google Scholar]
  6. S. Nam, M. Davies, M. Elad, R.Gribonval, The Cosparse Analysis Model and Algorithms, Appl. Comput. Harmon. Anal 34(1), 30–56 (2013). [CrossRef] [Google Scholar]
  7. O. Endra, D. Gunawan, Comparison of Synthesis-Based and Analysis-Based Compressive Sensing, In Proceedings of IEEE International Conference QiR, 167-170 (2015). [Google Scholar]
  8. R. Saiprasad, Y. Bresler, Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing, IEEE Trans. Comp. Img. 2(3), 294-309 (2016). [Google Scholar]
  9. M. Aharon, M. Elad, A. Bruckstein, The KSVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Trans. Sig. Proc, 54(11), 4311-4322 (2006). [NASA ADS] [CrossRef] [Google Scholar]
  10. I. Kviatkovsky, M. Gabel, E. Rivlin, I. Shimshoni, On the Equivalence of the LC-KSVD and the D-KSVD Algorithms, IEEE Trans. Pattern Analysis and Machine Intelligence, 39(2), 411-416 (2017). [CrossRef] [Google Scholar]
  11. M.F. Duarte, V. Cevher, R.G. Baraniuk, Model-based compressive sensing for signal ensembles, In Proceedings of 47th Annual IEEE Allerton Conference on Communication, Control, and Computing, 244-250 (2009). [Google Scholar]
  12. C. Hegde, P. Indyk, L. Schmidt, Approximation-tolerant model-based compressive sensing, In Proceedings of 25th Annual ACM-SIAM Symposium on Discrete Algorithms, 1544-1561 (2014). [CrossRef] [Google Scholar]
  13. R. Rubinstein, T. Peleg, M. Elad, Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model. IEEE Trans. Sig. Proc, 61(3), 661-677 (2013). [NASA ADS] [CrossRef] [Google Scholar]
  14. S. Ravishankar, Y. Bresler, Learning Sparsifying Transforms, IEEE Trans. Sig. Proc 61(5), 1072-1086 (2013). [CrossRef] [Google Scholar]
  15. B. Hou, Z. Zhu, G. Li, A. Yu, An Efficient Algorithm for Overcomplete Sparsifying Transform Learning with Signal Denoising, Math. Problems. in Eng. (2016). [Google Scholar]
  16. Y. Arjoune, N. Kaabouch, H.E. Ghazi, A. Tamtaoui, Compressive Sensing: Performance Comparison of Sparse Recovery Algorithms, In IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), 1-7 (2017). [Google Scholar]
  17. R.Giryes, A Greedy Algorithm for the Analysis Transform Domain, Neurocomputing 173, 278-289 (2016). [CrossRef] [Google Scholar]
  18. M. Elad, Optimized Projections for Compressed Sensing, IEEE Trans. Sig. Proc, 55(12), 5695–5702 (2007). [CrossRef] [Google Scholar]
  19. H. Bai, S. Li, X. He, Sensing Matrix Optimization Based on Equiangular Tight Frames with Consideration of Sparse Representation Error, IEEE Trans. Multimedia, 18(10), 2040-2053 (2016). [CrossRef] [Google Scholar]
  20. T. Strohmer, R.W. Heath, Grassmannian Frames with Applications to Coding and Communication, Appl. Comp. Harmon. Analysis, 14(3) (2003). [Google Scholar]
  21. B. C. Russell, A. Torralba, K. P. Murphy, W. T. Freeman, LabelMe: A Database and Web-Based Tool for Image Annotation, Comput. Vis. J. 77(1), 157–173 (2008). [CrossRef] [Google Scholar]
  22. R. Uetz, S. Behnke, Large-Scale Object Recognition with CUDA-Accelerated Hierarchical Neural Networks, In Proceedings of IEEE ICIS, 536-541 (2009). [Google Scholar]
  23. J. A. Tropp, A.C. Gilbert, Signal Recovery from Random Measurements via Orthogonal Matching Pursuit, IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007). [CrossRef] [Google Scholar]

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