2017 25th European Signal Processing Conference (EUSIPCO)

A survey on compressive sensing techniques for cognitive radio networks

A hybrid conjugate gradient algorithm for constrained monotone equations with application in compressive sensing

Electricity can be provided to small-scale communities like commercial areas and villages through microgrid, one of the small-scale, advanced, and independent electricity systems out of the grid. Microgrid is an appropriate choice for specific purposes reducing emission and generation cost and increasing efficiency, reliability, and the utilization of renewable energy sources. The main objective of this paper is to elucidate the combined economic emission dispatch CEED problem in the microgrid to attain optimal generation cost. A combined cost optimization approach is examined to minimize operational cost and emission levels while satisfying the load demand of the microgrid. With this background, the authors proposed a novel improved mayfly algorithm incorporating Levy flight to resolve the combined economic emission dispatch problem encountered in microgrids. The islanded mode microgrid test system considered in this study comprises thermal power, solar-powered, and wind power generating units. The simulation results were considered for 24 hours with varying power demands. The minimization of total cost and emission is attained for four different scenarios. Optimization results obtained for all scenarios using IMA give a comparatively better reduction in system cost than MA and other optimization algorithms considered revealing the efficacy of IMA taken for comparison with the same data. The proposed IMA algorithm can solve the CEED problem in a grid-connected microgrid.

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Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm

For wideband spectrum sensing, compressive sensing has been proposed as a solution to speed up the high dimensional signals sensing and reduce the computational complexity. Compressive sensing consists of acquiring the essential information from a sparse signal and recovering it at the receiver based on an efficient sampling matrix and a reconstruction technique. In order to deal with the uncertainty, improve the signal acquisition performance, and reduce the randomness during the sensing and reconstruction processes, compressive sensing requires a robust sampling matrix and an efficient reconstruction technique. In this paper, we propose an approach that combines the advantages of a Circulant matrix with Bayesian models. This approach is implemented, extensively tested, and using several metrics its results are compared to those of £1 norm minimization with Circulant and random matrices. These metrics are Mean Square Error, reconstruction error, correlation, recovery time, sampling time, and processing time. The results show that our technique is faster and more efficient in compressing and recovering signals.

/pdf/bayesian-compressive-sensing-with-circulant-matrix-for-3mt9sxfqq1.pdf

Bayesian compressive sensing with circulant matrix for spectrum sensing in cognitive radio networks

Fusion based Compressive Sensing (CS) reconstruction algorithms combine multiple CS reconstruction algorithms, which worked with different principles, to obtain a better signal estimate. Examples include Fusion of Algorithms for Compressed Sensing (FACS) and Committee Machine Approach for Compressed Sensing (CoMACS). However, these algorithms involve solving a least squares problem which may be ill-conditioned. Modified CS algorithms such as Modified Basis Pursuit (Mod-BP) ensured a sparse signal can efficiently be reconstructed when a part of its support is known. Since Mod-BP makes use of available signal knowledge to improve upon BP, we propose to employ multiple Greedy Pursuits (GPs) to derive a partial support for Mod-BP. As Mod-BP makes use of signal knowledge derived using GPs, we term our proposed algorithm as Greedy Pursuits Assisted Basis Pursuit (GPABP). Experimental results show that our proposed algorithm performs better than the state-of-the-art algorithms — FACS and its variants.

Greedy pursuits assisted basis pursuit for compressive sensing

https://dr.ntu.edu.sg/bitstream/10356/85099/1/Greedy%20Pursuits%20Assisted%20Basis%20Pursuit%20for%20reconstruction%20of%20joint-sparse%20signals.pdf

Greedy Pursuits Assisted Basis Pursuit for reconstruction of joint-sparse signals

In Distributed Compressive Sensing (DCS), corre- lated sparse signals stand for an ensemble of signals characterized by presenting a sparse correlation. If one signal is known apriori, the remaining signals in the ensemble can be reconstructed using l1-minimization with far fewer measurements compared to separate CS reconstruction. Reconstruction of such correlated signals is possible via Modified-CS and Regularized-Modified-BP. However, these methods are greatly influenced by the support set of the known signal that includes locations irrelevant to the target signal. While recovering each signal, prior to Modified- CS or Regularized-Modified-BP, we propose an adaptation step to retain only the sparse locations significant to that signal. We call our proposed methods as Modified-Adaptive-BP and Regularized-Modified-Adaptive-BP. Theoretical guarantees and experimental results show that our proposed methods provide efficient recovery compared to that of the Modified-CS and its regularized version. Index Terms—Distributed Compressive Sensing, Correlated sparse signals, Adaptation, Modified-Adaptive-BP, Regularized- Modified-Adaptive-BP.

Modified adaptive basis pursuits for recovery of correlated sparse signals

This paper presents Interior search algorithm for solving economic load dispatch problem of a microgrid. ISA is a aesthetic seeking algorithm which is inspired from the interior design and decoration. The efficacy of ISA has been validated on a simple microgrid test system which comprises wind generators, fuel cells, diesel generators. Simulation results reveal that ISA appears to be a robust unconventional technique for elucidating the ELD problems in microgrid when compared with the other optimization techniques taking into consideration the superiority of the solution attained.

Economic load dispatch in a microgrid using Interior Search Algorithm

In this paper, we exploit the relationship between the translational image motion and the Compressive Sensing (CS) measurements in order to perform the camera motion estimation in the compressed domain. Various CS measurement matrices have been investigated, and most of them are random Gaussian/Bernoulli matrices. Data acquisition using such matrices becomes computationally expensive for real time applications. Data acquisition using circulant CS matrices can be efficiently implemented in hardware using shift registers. We propose to use a circulant CS matrix on image frames to obtain the CS measurements and then to perform motion estimation in the measurement domain. Experimental results show that our method guarantees high motion estimation accuracy with few measurements. Our proposed method finds its application in video shot segmentation and video tracking where fast camera motion estimation is needed.

Sathiya Narayanan

Papers

Greedy pursuits assisted basis pursuit for compressive sensing

Greedy Pursuits Assisted Basis Pursuit for reconstruction of joint-sparse signals

Modified adaptive basis pursuits for recovery of correlated sparse signals

Economic load dispatch in a microgrid using Interior Search Algorithm

Camera motion estimation using circulant compressive sensing matrices