Petros T. Boufounos
Mitsubishi Electric Research Laboratories
250 Papers
2K Citations
Petros T. Boufounos is an academic researcher from Mitsubishi Electric Research Laboratories. The author has contributed to research in topics: Compressed sensing & Signal. The author has an hindex of 31, co-authored 223 publications. Previous affiliations of Petros T. Boufounos include Mitsubishi Electric & Chalmers University of Technology.
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Papers
1-Bit compressive sensing
Petros T. Boufounos,Richard G. Baraniuk +1 more
- 19 Mar 2008
TL;DR: This paper reformulates the problem by treating the 1-bit measurements as sign constraints and further constraining the optimization to recover a signal on the unit sphere, and demonstrates that this approach performs significantly better compared to the classical compressive sensing reconstruction methods, even as the signal becomes less sparse and as the number of measurements increases.
Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors
TL;DR: This paper investigates an alternative CS approach that shifts the emphasis from the sampling rate to the number of bits per measurement, and introduces the binary iterative hard thresholding algorithm for signal reconstruction from 1-bit measurements that offers state-of-the-art performance.
820
Signal Processing With Compressive Measurements
TL;DR: This paper takes some first steps in the direction of solving inference problems-such as detection, classification, or estimation-and filtering problems using only compressive measurements and without ever reconstructing the signals involved.
737
•Posted Content
Robust 1-Bit Compressive Sensing via Binary Stable Embeddings of Sparse Vectors
TL;DR: In this paper, the authors consider the case of 1-bit CS measurements and provide a lower bound on the best achievable reconstruction error, and show that the same class of matrices that provide almost optimal noiseless performance also enable a robust mapping.
462
Democracy in Action: Quantization, Saturation, and Compressive Sensing
TL;DR: A series of computational experiments indicate that the signal acquisi- tion error is minimized when a significant fraction of the CS measurements is allowed to saturate, challenging the conventional wisdom of both conventional sampling and CS.
284