Current Issue : July - September Volume : 2017 Issue Number : 3 Articles : 5 Articles
This paper presents a variant of the iterative shrinkage-thresholding (IST) algorithm,\ncalled backtracking-based adaptive IST (BAIST), for image compressive sensing (CS) reconstruction.\nFor increasing iterations, IST usually yields a smoothing of the solution and runs into prematurity.\nTo add back more details, the BAIST method backtracks to the previous noisy image using L2 norm\nminimization, i.e., minimizing the Euclidean distance between the current solution and the previous\nones. Through this modification, the BAIST method achieves superior performance while maintaining\nthe low complexity of IST-type methods. Also, BAIST takes a nonlocal regularization with an adaptive\nregularizor to automatically detect the sparsity level of an image. Experimental results show that our\nalgorithm outperforms the original IST method and several excellent CS techniques....
Since frequent communication between applications takes place in high speed networks,\ndeep packet inspection (DPI) plays an important role in the network application awareness.\nThe signature-based network intrusion detection system (NIDS) contains a DPI technique that\nexamines the incoming packet payloads by employing a pattern matching algorithm that dominates\nthe overall inspection performance. Existing studies focused on implementing efficient pattern\nmatching algorithms by parallel programming on software platforms because of the advantages of\nlower cost and higher scalability. Either the central processing unit (CPU) or the graphic processing\nunit (GPU) were involved. Our studies focused on designing a pattern matching algorithm based on\nthe cooperation between both CPU and GPU. In this paper, we present an enhanced design for our\nprevious work, a length-bounded hybrid CPU/GPU pattern matching algorithm (LHPMA). In the\npreliminary experiment, the performance and comparison with the previous work are displayed,\nand the experimental results show that the LHPMA can achieve not only effective CPU/GPU\ncooperation but also higher throughput than the previous method....
For the problem ofmultiaircraft cooperative suppression interference array (MACSIA) against the enemy air defense radar network\nin electronic warfaremission planning, firstly, the concept of route planning security zone is proposed and the solution to get the\nminimum width of security zone based on mathematical morphology is put forward. Secondly, the minimum width of security\nzone and the sum of the distance between each jamming aircraft and the center of radar network are regarded as objective function,\nand the multiobjective optimization model of MACSIA is built, and then an improved multiobjective particle swarm optimization\nalgorithm is used to solve the model. The decomposition mechanism is adopted and the proportional distribution is used to\nmaintain diversity of the new found nondominated solutions. Finally, the Pareto optimal solutions are analyzed by simulation,\nand the optimal MACSIA schemes of each jamming aircraft suppression against the enemy air defense radar network are obtained\nand verify that the built multiobjective optimization model is corrected. It also shows that the improved multiobjective particle\nswarm optimization algorithm for solving the problem of MACSIA is feasible and effective....
In the absence of the upper bound of time-varying target acceleration, the finite-time-convergent guidance (FTCG) problem for\nmissile is addressed in this paper. Firstly, a novel adaptive finite-time disturbance observer (AFDO) is developed based on adaptivegain\nsuper twisting (ASTW) algorithm to estimate the unknown target acceleration. Subsequently, a new FTCG law is proposed\nby using the output of AFDO. The newly proposed FTCG law has several advantages over existing FTCG laws. First, for timevarying\ntarget acceleration, the proposed method can strictly guarantee the trajectory of the closed-loop system is driven onto\nthe sliding surface rather than a neighbourhood of sliding surface in the extended-state-observer-based FTCG (ESOFTCG) law.\nSecond, the proposed method requires no upper bound information on the target acceleration. Third, the chattering problem in\nthe conventional FTCG (CFTCG) law is completely avoided in this paper. Simulation result demonstrates the effectiveness of the\nproposed AFDO and the proposed FTCG law....
Selection of initial points, the number of clusters and finding proper clusters centers are\nstill the main challenge in clustering processes. In this paper, we suggest genetic algorithm\nbased method which searches several solution spaces simultaneously. The solution spaces are\npopulation groups consisting of elements with similar structure. Elements in a group have the\nsame size, while elements in different groups are of different sizes. The proposed algorithm\nprocesses the population in groups of chromosomes with one gene, two genes to k genes.\nThese genes hold corresponding information about the cluster centers. In the proposed method,\nthe crossover and mutation operators can accept parents with different sizes; this can lead to\nversatility in population and information transfer among sub-populations. We implemented the\nproposed method and evaluated its performance against some random datasets and the Ruspini\ndataset as well. The experimental results show that the proposed method could effectively\ndetermine the appropriate number of clusters and recognize their centers. Overall this research\nimplies that using heterogeneous population in the genetic algorithm can lead to better results....
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