Compress Sensing
A. Vakili; M. Shams Esfand Abadi; M. Kalantari
Abstract
Background and Objectives: In the realm of compressed sensing, most greedy sparse recovery algorithms necessitate former information about the signal's sparsity level, which may not be available in practical conditions. To address this, methods based on the Sparsity Adaptive Matching Pursuit (SAMP) algorithm ...
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Background and Objectives: In the realm of compressed sensing, most greedy sparse recovery algorithms necessitate former information about the signal's sparsity level, which may not be available in practical conditions. To address this, methods based on the Sparsity Adaptive Matching Pursuit (SAMP) algorithm have been developed to self-determine this parameter and recover the signal using only the sampling matrix and measurements. Determining a suitable Initial Value for the algorithm can greatly affect the performance of the algorithm.Methods: One of the latest sparsity adaptive methods is Correlation Calculation SAMP (CCSAMP), which relies on correlation calculations between the signals recovered from the support set and the candidate set. In this paper, we present a modified version of CCSAMP that incorporates a pre-estimation phase for determining the initial value of the sparsity level, as well as a modified acceptance criteria considering the variance of noise. Results: To validate the efficiency of the proposed algorithm over the previous approaches, random sparse test signals with various sparsity levels were generated, sampled at the compression ratio of 50%, and recovered with the proposed and previous methos. The results indicate that the suggested method needs, on average, 5 to 6 fewer iterations compared to the previous methods, just due to the pre-estimation of the initial guess for the sparsity level. Furthermore, as far as the least square technique is integrated in some parts of the algorithm, in presence of noise the modified acceptance criteria significantly improve the success rate while achieving a lower mean squared error (MSE) in the recovery process.Conclusion: The pre-estimation process makes it possible to recover signal with fewer iterations while keeping the recovery quality as before. The fewer the number of iterations, the faster the algorithm. By incorporating the noise variance into the accept criteria, the method achieves a higher success rate and a lower mean squared error (MSE) in the recovery process.
Digital Signal Processing
E. Heydari; M. Shams Esfand Abadi; S.M. Khademiyan
Abstract
Background and Objectives: In order to improve the performance of normalized subband adaptive filter algorithm (NSAF) for identifying the block-sparse (BS) systems, this paper introduces the novel adaptive algorithm which is called BSNSAF. In the following, an improved multiband structured subband adaptive ...
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Background and Objectives: In order to improve the performance of normalized subband adaptive filter algorithm (NSAF) for identifying the block-sparse (BS) systems, this paper introduces the novel adaptive algorithm which is called BSNSAF. In the following, an improved multiband structured subband adaptive filter (IMSAF) algorithms for BS system identification is also proposed. The BS-IMSAF has faster convergence speed than BS-NSAF. Since the computational complexity of BS-IMSAF is high, the selective regressor (SR) and dynamic selection (DS) approaches are utilized and BS-SR-IMSAF and BS-DS-IMSAF are introduced. Furthermore, the theoretical steady-state performance analysis of the presented algorithms is studied.Methods: All algorithms are established based on the 𝐿2,0-norm constraint to the proposed cost function and the method of Lagrange multipliers is used to optimize the cost function.Results: The good performance of the proposed algorithms is demonstrated through several simulation results in the system identification setup. The algorithms are justified and compared in various scenarios and optimum values of the parameters are obtained. Also, the computational complexity of different algorithms are studied. In addition, the theoretical steady state values of mean square error (MSE) values are compared with simulation values.Conclusion: The BS-NSAF algorithm has better performance than NSAF for BS system identification. The BSIMSAF algorithm has better convergence speed than BS-NSAF. To reduce the computational complexity, the BS-SR-IMSAF and BS-DSR-IMSAF
Compress Sensing
Z. Habibi; H. Zayyani; M. Shams Esfandabadi
Abstract
Background and Objectives: Compressive sensing (CS) theory has been widely used in various fields, such as wireless communications. One of the main issues in the wireless communication field in recent years is how to identify block-sparse systems. We can follow this issue, by using CS theory and block-sparse ...
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Background and Objectives: Compressive sensing (CS) theory has been widely used in various fields, such as wireless communications. One of the main issues in the wireless communication field in recent years is how to identify block-sparse systems. We can follow this issue, by using CS theory and block-sparse signal recovery algorithms.Methods: This paper presents a new block-sparse signal recovery algorithm for the adaptive block-sparse system identification scenario, named stochastic block normalized iterative hard thresholding (SBNIHT) algorithm. The proposed algorithm is a new block version of the SSR normalized iterative hard thresholding (NIHT) algorithm with an adaptive filter framework. It uses a search method to identify the blocks of the impulse response of the unknown block-sparse system that we wish to estimate. In addition, the necessary condition to guarantee the convergence for this algorithm is derived in this paper.Results: Simulation results show that the proposed SBNIHT algorithm has a better performance than other algorithms in the literature with respect to the convergence and tracking capability.Conclusion: In this study, one new greedy algorithm is suggested for the block-sparse system identification scenario. Although the proposed SBNIHT algorithm is more complex than other competing algorithms but has better convergence and tracking capability performance.
Electronics
J. Khosravi; Mohammad Shams Esfand Abadi; R. Ebrahimpour
Abstract
Background and Objectives: There are numerous applications for image registration (IR). The main purpose of the IR is to find a map between two different situation images. In this way, the main objective is to find this map to reconstruct the target image as optimum as possible. Methods: Needless to ...
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Background and Objectives: There are numerous applications for image registration (IR). The main purpose of the IR is to find a map between two different situation images. In this way, the main objective is to find this map to reconstruct the target image as optimum as possible. Methods: Needless to say, the IR task is an optimization problem. As the optimization method, although the evolutionary ones are sometimes more effective in escaping the local minima, their speed is not emulated the mathematical ones at all. In this paper, we employed a mathematical framework based on the Newton method. This framework is suitable for any efficient cost function. Yet we used the sum of square difference (SSD). We also provided an effective strategy in order to avoid sticking in the local minima. Results: The proposed newton method with SSD as a cost function expresses more decent speed and accuracy in comparison to Gradient descent and genetic algorithms methods based on presented criteria. By considering SSD as the model cost function, the proposed method is able to introduce, respectively, accurate and fast registration method which could be exploited by the relevant applications. Simulation results indicate the effectiveness of the proposed model. Conclusion: The proposed innovative method based on the Newton optimization technique on separate cost functions is able to outperform regular Gradient descent and genetic algorithms. The presented framework is not based on any specific cost function, so any innovative cost functions could be effectively employed by our approach. Whether the objective is to reach accurate or fast results, the proposed method could be investigated accordingly.======================================================================================================Copyrights©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.======================================================================================================
