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. ======================================================================================================Copyrights©2021 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.======================================================================================================
Electronics
J. Khosravi; M. Shams Esfandabadi; 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.======================================================================================================
