scholarly journals On full spark frames via Cauchy matrices

Author(s):  
Dongwei Li

Full spark frames have been widely applied in sparse signal processing, signal reconstruction with erasures and phase retrieval. Since testing whether a given frame is full spark is hard for NP under randomized polynomial-time reductions, hence the deterministic full spark (DFS) frames are particularly significant. However, the degree of freedom of choices of DFS frames is not enough in practical applications because the DFS frames are well known as Vandermonde frames and harmonic frames. In this paper, we focus on the deterministic constructions of full spark frames. We present a new and effective method to construct DFS frames by using Cauchy matrices. We also construct the DFS frames by using Cauchy-Vandermonde matrices. Finally, we show that full spark tight frames can be constructed from generalized Cauchy matrices.

2020 ◽  
Vol 4 (9) ◽  
pp. 1-4
Author(s):  
Udaya S. K. P. Miriya Thanthrige ◽  
Jan Barowski ◽  
Ilona Rolfes ◽  
Daniel Erni ◽  
Thomas Kaiser ◽  
...  

2017 ◽  
Vol 30 (4) ◽  
pp. 477-510 ◽  
Author(s):  
Andjela Draganic ◽  
Irena Orovic ◽  
Srdjan Stankovic

Compressive Sensing, as an emerging technique in signal processing is reviewed in this paper together with its common applications. As an alternative to the traditional signal sampling, Compressive Sensing allows a new acquisition strategy with significantly reduced number of samples needed for accurate signal reconstruction. The basic ideas and motivation behind this approach are provided in the theoretical part of the paper. The commonly used algorithms for missing data reconstruction are presented. The Compressive Sensing applications have gained significant attention leading to an intensive growth of signal processing possibilities. Hence, some of the existing practical applications assuming different types of signals in real-world scenarios are described and analyzed as well.


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