On the Use of Unit-Norm Tight Frames to Improve the Average MSE Performance in Compressive Sensing Applications

2012 ◽  
Vol 19 (1) ◽  
pp. 8-11 ◽  
Author(s):  
Wei Chen ◽  
Miguel R. D. Rodrigues ◽  
Ian J. Wassell
Keyword(s):  
Compressive Sensing ◽  
Tight Frames ◽  
Sensing Applications ◽  
Unit Norm

scholarly journals Tetris Tight Frames Construction via Hadamard Matrices

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
A. Abdollahi ◽  
M. Monfaredpour
Keyword(s):  
Hadamard Matrices ◽  
New Method ◽  
Tight Frames ◽  
Frame Operator ◽  
Elementary Construction ◽  
Unit Norm

We present a new method to construct unit norm tight frames by applying altered Hadamard matrices. Also we determine an elementary construction which can be used to produce a unit norm frame with prescribed spectrum of frame operator.

scholarly journals On some common compressive sensing recovery algorithms and applications

2017 ◽  
Vol 30 (4) ◽  
pp. 477-510 ◽  
Author(s):  
Andjela Draganic ◽  
Irena Orovic ◽  
Srdjan Stankovic
Keyword(s):  
Signal Processing ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Practical Applications ◽  
Sensing Applications ◽  
Different Types ◽  
Acquisition Strategy ◽  
Basic Ideas ◽  
Significant Attention ◽  
Intensive Growth

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.

scholarly journals Projection Matrix Design for Co-Sparse Analysis Model Based Compressive Sensing

2018 ◽  
Vol 159 ◽  
pp. 01061
Author(s):  
Endra Oey ◽  
Dadang Gunawan ◽  
Dodi Sudiana
Keyword(s):  
Compressive Sensing ◽  
Projection Matrix ◽  
Signal Quality ◽  
Tight Frames ◽  
Analysis Model ◽  
Mutual Coherence ◽  
Model Based ◽  
Equiangular Tight Frames ◽  
Simulation Results ◽  
Matrix Design

Co-sparse analysis model based-compressive sensing (CAMBCS) has gained attention in recent years as alternative to conventional sparse synthesis model based (SSMB)-CS. The equivalent operator as counterpart of the equivalent dictionary in the SSMB-CS is introduced in the CAMB-CS as the product of projection matrix and transpose of the analysis dictionary. This paper proposes an algorithm for designing suitable projection matrix for CAMB-CS by minimizing the mutual coherence of the equivalent operator based on equiangular tight frames design. The simulation results show that the CAMB-CS with the proposed projection matrix outperforms the SSMB-CS in terms of the signal quality reconstruction.

scholarly journals Compressive Sensing Approach in the Hermite Transform Domain

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Srdjan Stanković ◽  
Ljubiša Stanković ◽  
Irena Orović
Keyword(s):  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Transform Domain ◽  
Numerical Examples ◽  
Hermite Transform ◽  
Sensing Applications ◽  
Random Samples ◽  
Significant Interest ◽  
Small Set ◽  
Short Time

Compressive sensing has attracted significant interest of researchers providing an alternative way to sample and reconstruct the signals. This approach allows us to recover the entire signal from just a small set of random samples, whenever the signal is sparse in certain transform domain. Therefore, exploring the possibilities of using different transform basis is an important task, needed to extend the field of compressive sensing applications. In this paper, a compressive sensing approach based on the Hermite transform is proposed. The Hermite transform by itself provides compressed signal representation based on a smaller number of Hermite coefficients compared to the signal length. Here, it is shown that, for a wide class of signals characterized by sparsity in the Hermite domain, accurate signal reconstruction can be achieved even if incomplete set of measurements is used. Advantages of the proposed method are demonstrated on numerical examples. The presented concept is generalized for the short-time Hermite transform and combined transform.

Compressive sensing based data quality improvement for crowd-sensing applications

2017 ◽  
Vol 77 ◽  
pp. 123-134 ◽  
Author(s):  
Long Cheng ◽  
Jianwei Niu ◽  
Linghe Kong ◽  
Chengwen Luo ◽  
Yu Gu ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Data Quality ◽  
Compressive Sensing ◽  
Sensing Applications ◽  
Crowd Sensing
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