| Sliding modes after the first decade of the 21st century L Fridman, J Moreno, R Iriarte Lecture notes in control and information sciences 412, 113-149, 2011 | 249 | 2011 |
| Compressive sensing with chaotic sequence L Yu, JP Barbot, G Zheng, H Sun IEEE Signal Processing Letters 17 (8), 731-734, 2010 | 201 | 2010 |
| Bayesian compressive sensing for cluster structured sparse signals L Yu, H Sun, JP Barbot, G Zheng Signal Processing, 2012 | 199 | 2012 |
| Compressive sensing for cluster structured sparse signals: variational bayes approach L Yu, JP Barbot, G Zheng, H Sun IET Signal Processing 10 (7), 770-779, 2016 | 67 | 2016 |
| Compressive sensing matrix designed by tent map, for secure data transmission M Frunzete, L Yu, JP Barbot, A Vlad Signal Processing Algorithms, Architectures, Arrangements, and Applications …, 2011 | 44 | 2011 |
| Toeplitz-structured chaotic sensing matrix for compressive sensing L Yu, JP Barbot, G Zheng, H Sun 2010 7th International Symposium on Communication Systems, Networks …, 2010 | 42 | 2010 |
| Model based Bayesian compressive sensing via local beta process L Yu, H Sun, G Zheng, JP Barbot Signal Processing 108, 259-271, 2015 | 37 | 2015 |
| Dynamical sparse recovery with finite-time convergence L Yu, G Zheng, JP Barbot IEEE Transactions on Signal Processing 65 (23), 6146-6157, 2017 | 30 | 2017 |
| Frequency estimation of multiple sinusoids with three sub-Nyquist channels S Huang, H Zhang, H Sun, L Yu, L Chen Signal Processing 139, 96-101, 2017 | 28 | 2017 |
| Underdetermined blind separation of overlapped speech mixtures in time-frequency domain with estimated number of sources H Zhang, G Hua, L Yu, Y Cai, G Bi Speech Communication 89, 1-16, 2017 | 26 | 2017 |
| 从稀疏到结构化稀疏: 贝叶斯方法 孙洪， 张智林， 余磊 信号处理 28 (6), 759-773, 2012 | 24 | 2012 |
| Event enhanced high-quality image recovery B Wang, J He, L Yu, GS Xia, W Yang European Conference on Computer Vision, 155-171, 2020 | 23 | 2020 |
| Det: A high-resolution dvs dataset for lane extraction W Cheng, H Luo, W Yang, L Yu, S Chen, W Li Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 21 | 2019 |
| Cyber-attack reconstruction via sliding mode differentiation and sparse recovery algorithm: Electrical power networks application S Nateghi, Y Shtessel, JP Barbot, G Zheng, L Yu 2018 15th International workshop on variable structure systems (VSS), 285-290, 2018 | 20 | 2018 |
| Bayesian compressive sensing for clustered sparse signals L Yu, H Sun, JP Barbot, G Zheng 2011 IEEE International Conference on Acoustics, Speech and Signal …, 2011 | 20 | 2011 |
| Iterative time-frequency filtering of sinusoidal signals with updated frequency estimation H Zhang, L Yu, GS Xia IEEE Signal Processing Letters 23 (1), 139-143, 2015 | 16 | 2015 |
| Structured Bayesian learning for recovery of clustered sparse signal L Wang, L Zhao, L Yu, J Wang, G Bi Signal Processing 166, 107255, 2020 | 14 | 2020 |
| Block-sparsity recovery via recurrent neural network C Lyu, Z Liu, L Yu Signal Processing 154, 129-135, 2019 | 14 | 2019 |
| Observability forms for switched systems with zeno phenomenon or high switching frequency L Yu, JP Barbot, D Boutat, D Benmerzouk IEEE transactions on automatic control 56 (2), 436-441, 2010 | 13 | 2010 |
| Robust motion compensation for event cameras with smooth constraint J Xu, M Jiang, L Yu, W Yang, W Wang IEEE Transactions on Computational Imaging 6, 604-614, 2020 | 12 | 2020 |
Jean-Pierre BarbotProfessor at ENSEA, Cergy-Pontoise, Quartz Laboratory (Actually in secondement at LS2N-CNRS)Verified email at ensea.fr
