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Wei-Jie Liang
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2020 – today
- 2020
- [j3]Sung-Hsien Hsieh, Wei-Jie Liang, Chun-Shien Lu, Soo-Chang Pei:
Distributed Compressive Sensing: Performance Analysis With Diverse Signal Ensembles. IEEE Trans. Signal Process. 68: 3500-3514 (2020)
2010 – 2019
- 2017
- [j2]Wei-Jie Liang, Tsung-Hsun Chien, Chun-Shien Lu:
Theoretical stopping criteria guided Greedy Algorithm for Compressive Cooperative Spectrum Sensing. Comput. Commun. 111: 165-175 (2017) - [j1]Wei-Jie Liang, Gang-Xuan Lin, Chun-Shien Lu:
Tree Structure Sparsity Pattern Guided Convex Optimization for Compressive Sensing of Large-Scale Images. IEEE Trans. Image Process. 26(2): 847-859 (2017) - [c5]Sung-Hsien Hsieh, Wei-Jie Liang, Chun-Shien Lu, Soo-Chang Pei:
Distributed compressive sensing: Performance analysis with diverse signal ensembles. EUSIPCO 2017: 1324-1328 - 2016
- [c4]Shih-Wei Hu, Gang-Xuan Lin, Sung-Hsien Hsieh, Wei-Jie Liang, Chun-Shien Lu:
Performance analysis of joint-sparse recovery from multiple measurement vectors with prior information via convex optimization. ICASSP 2016: 4368-4372 - [i3]Sung-Hsien Hsieh, Wei-Jie Liang, Chun-Shien Lu, Soo-Chang Pei:
Distributed Compressive Sensing: Performance Analysis with Diverse Signal Ensembles. CoRR abs/1609.01899 (2016) - 2015
- [c3]Wei-Jie Liang, Gang-Xuan Lin, Chun-Shien Lu:
Compressive large-scale image sensing. GlobalSIP 2015: 378-382 - [i2]Wei-Jie Liang, Gang-Xuan Lin, Chun-Shien Lu:
Compressive Sensing of Large-Scale Images: An Assumption-Free Approach. CoRR abs/1505.05407 (2015) - [i1]Shih-Wei Hu, Gang-Xuan Lin, Sung-Hsien Hsieh, Wei-Jie Liang, Chun-Shien Lu:
Performance Analysis of Joint-Sparse Recovery from Multiple Measurements and Prior Information via Convex Optimization. CoRR abs/1509.06655 (2015) - 2014
- [c2]Chun-Shien Lu, Wei-Jie Liang:
Fast compressive sensing of high-dimensional signals with tree-structure sparsity pattern. ChinaSIP 2014: 738-742 - [c1]Tsung-Hsun Chien, Wei-Jie Liang, Chun-Shien Lu:
A practical subspace multiple measurement vectors algorithm for cooperative spectrum sensing. GLOBECOM 2014: 787-792
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