Current Issue : April - June Volume : 2018 Issue Number : 2 Articles : 5 Articles
A novel detection algorithm for vision systems has been proposed based on combined fuzzy image processing and bacterial\nalgorithm. This combination aims to increase the detection efficiency and reduce the computational time. In addition, the proposed\nalgorithm has been tested through real-time robot navigation system, where it has been applied to detect the robot and obstacles\nin unstructured environment and generate 2D maps. These maps contain the starting and destination points in addition to current\npositions of the robot and obstacles. Moreover, the genetic algorithm (GA) has been modified and applied to produce time-based\ntrajectory for the optimal path. It is based on proposing and enhancing the searching ability of the robot to move towards the\noptimal path solution. Many scenarios have been adopted in indoor environment to verify the capability of the new algorithm in\nterms of detection efficiency and computational time....
Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic\nsigns have their unique features compared with traffic signs of other countries. Convolutional neural\nnetworks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in\ntraffic sign classification. In this paper, we present a Chinese traffic sign detection algorithm based\non a deep convolutional network. To achieve real-time Chinese traffic sign detection, we propose\nan end-to-end convolutional network inspired by YOLOv2. In view of the characteristics of traffic\nsigns, we take the multiple 1 Ã?â?? 1 convolutional layers in intermediate layers of the network and\ndecrease the convolutional layers in top layers to reduce the computational complexity. For effectively\ndetecting small traffic signs, we divide the input images into dense grids to obtain finer feature maps.\nMoreover, we expand the Chinese traffic sign dataset (CTSD) and improve the marker information,\nwhich is available online. All experimental results evaluated according to our expanded CTSD and\nGerman Traffic Sign Detection Benchmark (GTSDB) indicate that the proposed method is the faster\nand more robust. The fastest detection speed achieved was 0.017 s per image....
The decision tree algorithm is a core technology in data classification mining, and ID3\n(Iterative Dichotomiser 3) algorithm is a famous one, which has achieved good results in the field\nof classification mining. Nevertheless, there exist some disadvantages of ID3 such as attributes biasing\nmulti-values, high complexity, large scales, etc. In this paper, an improved ID3 algorithm is proposed\nthat combines the simplified information entropy based on different weights with coordination degree\nin rough set theory. The traditional ID3 algorithm and the proposed one are fairly compared by using\nthree common data samples as well as the decision tree classifiers. It is shown that the proposed\nalgorithm has a better performance in the running time and tree structure, but not in accuracy than\nthe ID3 algorithm, for the first two sample sets, which are small. For the third sample set that is large,\nthe proposed algorithm improves the ID3 algorithm for all of the running time, tree structure and\naccuracy. The experimental results show that the proposed algorithm is effective and viable....
Thesplit feasibility problem arises inmany fields in the realworld, such as signal processing, image reconstruction, and medical care.\nIn this paper, we present a solution algorithm called memory gradient projection method for solving the split feasibility problem,\nwhich employs a parameter and two previous iterations to get the next iteration, and its step size can be calculated directly. It not\nonly improves the flexibility of the algorithm, but also avoids computing the largest eigenvalue of the related matrix or estimating\nthe Lipschitz constant in each iteration. Theoretical convergence results are established under some suitable conditions....
Cloud computing environment provides several on-demand services and resource sharing for clients. Business processes are\nmanaged using the workflow technology over the cloud, which represents one of the challenges in using the resources in an efficient\nmanner due to the dependencies between the tasks. In this paper, a Hybrid GA-PSO algorithm is proposed to allocate tasks to\nthe resources efficiently. The Hybrid GA-PSO algorithm aims to reduce the makespan and the cost and balance the load of the\ndependent tasks over the heterogonous resources in cloud computing environments. The experiment results show that the GAPSO\nalgorithm decreases the total execution time of the workflow tasks, in comparison with GA, PSO, HSGA,WSGA, andMTCT\nalgorithms. Furthermore, it reduces the execution cost. In addition, it improves the load balancing of the workflow application over\nthe available resources. Finally, the obtained results also proved that the proposed algorithm converges to optimal solutions faster\nand with higher quality compared to other algorithms...
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