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Hierarchical Wireless Multimedia Sensor Networks for Collaborative Hybrid SemiSupervised Classifier Learning
, 2007
"... Abstract: Wireless multimedia sensor networks (WMSN) have recently emerged as one of the most important technologies, driven by the powerful multimedia signal acquisition and processing abilities. Target classification is an important research issue addressed in WMSN, which has strict requirement in ..."
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Abstract: Wireless multimedia sensor networks (WMSN) have recently emerged as one of the most important technologies, driven by the powerful multimedia signal acquisition and processing abilities. Target classification is an important research issue addressed in WMSN, which has strict requirement in robustness, quickness and accuracy. This paper proposes a collaborative semisupervised classifier learning algorithm to achieve durative online learning for support vector machine (SVM) based robust target classification. The proposed algorithm incrementally carries out the semisupervised classifier learning process in hierarchical WMSN, with the collaboration of multiple sensor nodes in a hybrid computing paradigm. For decreasing the energy consumption and improving the performance, some metrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes, and a sensor node selection strategy is also proposed to reduce the impact of inevitable missing detection and false detection. With the ant optimization routing, the learning process is implemented with the selected sensor nodes, which can decrease the energy consumption. Experimental results demonstrate that the collaborative hybrid semisupervised classifier learning algorithm can effectively implement target classification in hierarchical WMSN. It has outstanding performance in terms of energy efficiency and time cost, which verifies the effectiveness of the sensor nodes selection and ant optimization routing.
DNA Algorithm Employing Temperature Gradient for Multiple Traveling Salesperson Problem
"... The biological Deoxyribo Nucleic Acid (DNA) strand is found to be a promising computing unit. An attempt has been made to solve symmetric Multiple Travelling Salesperson Problem (MTSP) with single depot using DNA. In this paper, the thermodynamic properties of DNA have been utilized along with other ..."
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The biological Deoxyribo Nucleic Acid (DNA) strand is found to be a promising computing unit. An attempt has been made to solve symmetric Multiple Travelling Salesperson Problem (MTSP) with single depot using DNA. In this paper, the thermodynamic properties of DNA have been utilized along with other biochemical operations to obtain the optimal solution. Actual distance values are possible to be represented using the thermodynamic properties of DNA. Moreover, the proposed approach can be adopted in solving more reallife applications like Vehicle Routing problems and Scheduling problems, with necessary modifications. In this work, an instance with seven cities and three salespersons is solved using DNA computing. This method exhibits the ability to solve NPcomplete problems using molecular computing.
A Hybrid Evolutionary Approach for Multi Robot Path Exploration Problem
"... planning problem is one of the famous problems in robot’s offline decision making algorithms. In this paper, a hybrid approach is presented that combines clustering and Genetic Algorithm (GA) to solve the Multi Robot Path Exploration Problem. The aim is to find collision free path, which Robot can f ..."
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planning problem is one of the famous problems in robot’s offline decision making algorithms. In this paper, a hybrid approach is presented that combines clustering and Genetic Algorithm (GA) to solve the Multi Robot Path Exploration Problem. The aim is to find collision free path, which Robot can follow to reach the target from its starting position. Environment is considered as a complete weighted graph representing the locations or points in the world environment and Traveling Salesman Problem (TSP) solving approach, based on GA is tried to solve this problem. Clustering is used to group the points (land marks) in the environment and rendezvous point is selected where all the robots finally meet. Experimental results are presented to illustrate the performance of the proposed scheme.
Solving the Multiple Traveling Salesman Problem by a Novel Meta heuristic Algorithm
"... The multiple traveling salesman problem (MTSP) is a generalization of the famous traveling salesman problem (TSP), where more than one salesman is used in the solution. Although the MTSP is a typical kind of computationally complex combinatorial optimization problem, it can be extended to a wide var ..."
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The multiple traveling salesman problem (MTSP) is a generalization of the famous traveling salesman problem (TSP), where more than one salesman is used in the solution. Although the MTSP is a typical kind of computationally complex combinatorial optimization problem, it can be extended to a wide variety of routing problems. This paper presents an efficient and evolutionary optimization algorithm which has been developed through combining Modified Imperialist Competitive Algorithm and LinKernigan Algorithm (MICA) in order to solve the MTSP. In the proposed algorithm, an absorption function and several local search algorithms as a revolution operator are used. The performance of our algorithm was tested on several MTSP benchmark problems and the results confirmed that the MICA performs well and is quite competitive with other metaheuristic algorithms.