7 citations · 12 across the 8 of their papers we have counts for
8 papers
Greedy Algorithms for Hybrid Compressed Sensing
Ching-Lun Tai, Sung-Hsien Hsieh, Chun-Shien Lu
Compressed sensing (CS) is a technique which uses fewer measurements than dictated by the Nyquist sampling theorem. The traditional CS with linear measurements achieves efficient r…
Link Delay Estimation Using Sparse Recovery for Dynamic Network Tomography
Hao-Ting Wei, Sung-Hsien Hsieh, Wen-Liang Hwang +2
When the scale of communication networks has been growing rapidly in the past decades, it becomes a critical challenge to extract fast and accurate estimation of key state paramete…
Distributed Compressive Sensing: Performance Analysis with Diverse Signal Ensembles
Sung-Hsien Hsieh, Wei-Jie Liang, Chun-Shien Lu +1
Distributed compressive sensing is a framework considering jointly sparsity within signal ensembles along with multiple measurement vectors (MMVs). The current theoretical bound of…
Fast Binary Embedding via Circulant Downsampled Matrix -- A Data-Independent Approach
Sung-Hsien Hsieh, Chun-Shien Lu, Soo-Chang Pei
Binary embedding of high-dimensional data aims to produce low-dimensional binary codes while preserving discriminative power. State-of-the-art methods often suffer from high comput…
Performance Analysis of Joint-Sparse Recovery from Multiple Measurements and Prior Information via Convex Optimization
Shih-Wei Hu, Gang-Xuan Lin, Sung-Hsien Hsieh +2
We address the problem of compressed sensing with multiple measurement vectors associated with prior information in order to better reconstruct an original sparse matrix signal. $\…
Fast Template Matching by Subsampled Circulant Matrix
Sung-Hsien Hsieh, Chun-Shien Lu, and Soo-Chang Pei
Template matching is widely used for many applications in image and signal processing and usually is time-critical. Traditional methods usually focus on how to reduce the search lo…
Fast Greedy Approaches for Compressive Sensing of Large-Scale Signals
Sung-Hsien Hsieh, Chun-Shien Lu, Soo-Chang Pei
Cost-efficient compressive sensing is challenging when facing large-scale data, {\em i.e.}, data with large sizes. Conventional compressive sensing methods for large-scale data wil…
Sparse Fast Fourier Transform for Exactly and Generally K-Sparse Signals by Downsampling and Sparse Recovery
Sung-Hsien Hsieh, Chun-Shien Lu, Soo-Chang Pei
Fast Fourier Transform (FFT) is one of the most important tools in digital signal processing. FFT costs O(N \log N) for transforming a signal of length N. Recently, Sparse Fourier…