WebFor the sub-Nyquist sampling process, the design of the sensing matrix is the key of a successful compressive sensing solution. Random matrices [ 19] are the most widely … WebThe sensing matrix compresses the sig- nal by mapping a high-dimensional vector into a lower- dimensional vector. This leads to a much shorter sam- pling rate than that of the classical Shannon/Nyquist sam- pling theorem [26]. There a two keys to the success of compressed sensing.

Compressed sensing of low-rank plus sparse matrices

WebSensing Matrix Design and Sparse Recovery on the Sphere and the Rotation Group Abstract: In this article, the goal is to design deterministic sampling patterns on the sphere and the … WebI am the leader of the High-Dimensional Signal Processing (HDSP) Research group. My research interests include high-dimensional signal processing, computational imaging, optical imaging, compressed sensing, seismic imaging, medical imaging, hyperspectral imaging, and deep optical sensor design. A current research topic is binary codification … find first repeating character in a string

Sensing Matrix Design and Sparse Recovery on the Sphere and the …

Web30 Nov 2016 · Even though, the deterministic sensing matrices are an incapable to satisfy RIP condition, they are very useful in practice because of the deterministic nature of the sampler and might be able to advance some features like compression ratio and computational complexity. Web1. Introduction. Compressive sensing (CS) theory (Candes & Tao, 2005; Donoho et al., 2006; Foucart & Rauhut, 2013) shows us how to compressively and non-adaptively sample low-complexity signals, such as sparse vectors or low-rank matrices, in high-dimensional domains.In this framework, by exploiting the low-complexity nature of these signals, we … find first space in string sql