Nowadays, a large amount of information has to be transmitted or processed. This implies high-power processing, large memory density, and increased energy consumption. In several applications, such as imaging, radar, ...
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Nowadays, a large amount of information has to be transmitted or processed. This implies high-power processing, large memory density, and increased energy consumption. In several applications, such as imaging, radar, speech recognition, and data acquisition, the signals involved can be considered sparse or compressive in some domain. The compressive sensing theory could be a proper candidate to deal with these constraints. It can be used to recover sparse or compressive signals with fewer measurements than the traditional methods. Two problems must be addressed by compressive sensing theory: design of the measurement matrix and development of an efficient sparserecovery algorithm. These algorithms are usually classified into three categories: convex relaxation, non-convex optimization techniques, and greedy algorithms. This paper intends to supply a comprehensive study and a state-of-the-art review of these algorithms to researchers who wish to develop and use them. Moreover, a wide range of compressive sensing theory applications is summarized and some open research challenges are presented.
The distributed ISAR technique can form multiple virtual equivalent sensors to observe the target from multiple observation angles, and can obtain more spatial sampling data at the same time than monostatic ISAR, so i...
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The distributed ISAR technique can form multiple virtual equivalent sensors to observe the target from multiple observation angles, and can obtain more spatial sampling data at the same time than monostatic ISAR, so it has the potential to increase the cross-range resolution. In this paper, sparse signal recoveryalgorithms are proposed to obtain the cross-range image of the distributed ISAR when the conventional Fourier transform imaging method is not applicable due to (1) nonuniform rotation of the target (2) large echo gap and (3) low echo SNR. After obtaining the distributed ISAR echoes, the range migration of the scatterer in each equivalent sensor is analyzed, and a cross-range phase that causes a positioning error is compensated. Then, the sparse representation of the echo in each range bin is given and the homotopy L1L0 (HL1L0) method is introduced. Singular value decomposition (SVD) is used to improve the robustness of the algorithm. Simulation results show that the sparse recovery algorithms can achieve high cross-range resolution, and HL1L0 method is better than orthogonal matching pursuit (OMP) and smoothed L0 (SL0) under different echo gaps and SNRs according to the four proposed evaluation criteria. Real data experiment verifies the advantage of the distributed ISAR and the effectiveness of the proposed method.
A significant goal of the study of metagenomes obtained from an environment is to find the microbial diversity and the abundance of each organism in the community. Phylotyping and binning methods which address this pr...
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A significant goal of the study of metagenomes obtained from an environment is to find the microbial diversity and the abundance of each organism in the community. Phylotyping and binning methods which address this problem generally operate using either marker sequences or by classifying each genome fragment individually. However, these approaches might not use all the information contained in the metagenome. We propose an approach based on a Multiple Input Multiple Output (MIMO) communication system model. Results from two different implementations of this approach, one using DNA-DNA hybridization simulations and one using short read mapping are evaluated using simulated and actual metagenomes and compared with other methods of phylotyping. The proposed approaches generally performed better under different scenarios including pathogen detection tasks of community complexity and low and high sequencing coverage while being highly computationally effective. The resulting framework can be integrated to metagenome analysis pipelines for phylogenetic diversity estimation. The approach is modular so that techniques other than hybridization simulations and short read mapping may be integrated. We have observed that even for low coverage samples, the method provides accurate estimates. Therefore, the use of the proposed strategy could enable the task of exploring biodiversity with limited resources.
sparse recovery algorithms have been applied to the Space-time adaptive processing for reducing the requirement of samples over the past 15 years. However, many sparse recovery algorithms are not robust and need accur...
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sparse recovery algorithms have been applied to the Space-time adaptive processing for reducing the requirement of samples over the past 15 years. However, many sparse recovery algorithms are not robust and need accurate user parameters. Conventional sparse Bayesian learning (SBL) algorithms are insensitivity to user parameters but converge slowly. To remedy the limitation, two iterative reweighted algorithms are proposed based on SBL. In order to minimise the SBL penalty function, we construct its upper-bounding surrogate function via the concave conjugate function and apply iterative reweighted algorithms to minimise the surrogate function. Theoretical analysis and numerical experiments all exhibit great performance of the proposed algorithms.
BackgroundThe capacity of the current molecular testing convention does not allow high-throughput and community level scans of COVID-19 infections. The diameter in the current paradigm of shallow tracing is unlikely t...
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BackgroundThe capacity of the current molecular testing convention does not allow high-throughput and community level scans of COVID-19 infections. The diameter in the current paradigm of shallow tracing is unlikely to reach the silent clusters that might be as important as the symptomatic cases in the spread of the disease. Group testing is a feasible and promising approach when the resources are scarce and when a relatively low prevalence regime is observed on the *** employed group testing with a sparse random pooling scheme and conventional group test decoding algorithms both for exact and inexact *** simulations showed that significant reduction in per case test numbers (or expansion in total test numbers preserving the number of actual tests conducted) for very sparse prevalence regimes is available. Currently proposed COVID-19 group testing schemes offer a gain up to 15X-20X scale-up. There is a good probability that the required scale up to achieve massive scale testing might be greater in certain scenarios. We investigated if further improvement is available, especially in sparse prevalence occurrence where outbreaks are needed to be avoided by population *** simulations show that sparse random pooling can provide improved efficiency gains compared to conventional group testing or Reed-Solomon error correcting codes. Therefore, we propose that special designs for different scenarios could be available and it is possible to scale up testing capabilities significantly.
Compressed sensing (CS) is a pioneering sub-Nyquist sampling technique that reconstructs signals from far fewer measurements than traditional acquisition schemes. Considerable amount of research has been carried out o...
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Compressed sensing (CS) is a pioneering sub-Nyquist sampling technique that reconstructs signals from far fewer measurements than traditional acquisition schemes. Considerable amount of research has been carried out over the years to develop powerful algorithms that can accomplish optimal recovery. However, they vary considerably in their ease of implementation, speed of recovery and noise resilience. This work presents the Sparsity Independent Regularized Pursuit (SIRP) which achieves an admirable trade-off between these key features. Further, it requires no prior knowledge of exact sparsity level and possesses a regular structure, making it amenable to low cost hardware solutions. Experimental investigations reveal the competitiveness of SIRP with existing state-of-art in regard to successful recovery and significant speed-up. The algorithm also attains superior results on a considerably large image recovery problem, which demonstrates its suitability to real world applications. (C) 2020 Elsevier B.V. All rights reserved.
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