Current Issue : April - June Volume : 2014 Issue Number : 2 Articles : 5 Articles
3D gestural interaction provides a powerful and natural way to interact with computers using the hands and body for a variety of\r\ndifferent applications including video games, training and simulation, and medicine. However, accurately recognizing 3D gestures\r\nso that they can be reliably used in these applications poses many different research challenges. In this paper, we examine the state\r\nof the field of 3D gestural interfaces by presenting the latest strategies on how to collect the raw 3D gesture data from the user and\r\nhow to accurately analyze this raw data to correctly recognize 3D gestures users perform. In addition, we examine the latest in 3D\r\ngesture recognition performance in terms of accuracy and gesture set size and discuss how different applications are making use of\r\n3D gestural interaction. Finally, we present ideas for future research in this thriving and active research area....
This paper presents a comparative study between optimization-based andmarket-based approaches used for solving theMultirobot\r\ntask allocation (MRTA) problem that arises in the context of multirobot systems (MRS). The two proposed approaches are used\r\nto find the optimal allocation of a number of heterogeneous robots to a number of heterogeneous tasks. The two approaches\r\nwere extensively tested over a number of test scenarios in order to test their capability of handling complex heavily constrained\r\nMRS applications that include extended number of tasks and robots. Finally, a comparative study is implemented between the two\r\napproaches and the results show that the optimization-based approach outperforms themarket-based approach in terms of optimal\r\nallocation and computational time....
Multirobot systems (MRSs) are capable of solving task complexity, increasing performance in terms of maximizing spatial/\r\ntemporal/radio coverage or minimizing mission completion time. They are also more reliable than single-robot systems\r\nas robustness is increased through redundancy. Many applications such as rescue, reconnaissance, and surveillance and\r\ncommunication relaying require the MRS to be able to self-organize the team members in a decentralized way. Group formation is\r\none of the benchmark problems in MRS to study self-organization in these systems. This paper presents a hybrid approach to group\r\nformation problem in multi-robot systems. This approach combines the efficiency of the cellular automata as finite state machine,\r\nthe interconnectivity of the virtual grid and its bonding technique, and last but not least the decentralization of the adaptive dynamic\r\nleadership....
Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion algorithms\r\nreduce the uncertainty by combining data from several sources. However, if these several sources provide inconsistent data,\r\ncatastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of\r\neach of the individual sensor. This paper presents an approach tomultisensor data fusion in order to decrease data uncertainty with\r\nability to identify and handle inconsistency.Theproposed approach relies on combining a modified Bayesian fusion algorithm with\r\nKalman filtering. Three different approaches, namely, prefiltering, postfiltering and pre-postfiltering are described based on how\r\nfiltering is applied to the sensor data, to the fused data or both. A case study to find the position of a mobile robot by estimating its\r\nx and y coordinates using four sensors is presented. The simulations show that combining fusion with filtering helps in handling\r\nthe problem of uncertainty and inconsistency of the data....
It has been always critical and inevitable to select and assess the appropriate and efficient vendors for the companies such that all the\r\naspects and factors leading to the importance of the select process should be considered. This paper studies the process of selecting\r\nthe vendors simultaneously in three aspects of multiple criteria, random factors, and reaching efficient solutions with the objective\r\nof improvement. Thus, selecting the vendors is introduced in the form of a mixed integer multiobjective stochastic problem and\r\nfor the first time it is converted by CCGC (min-max) model to a mixed integer nonlinear single objective deterministic problem.\r\nAs the converted problem is nonlinear and solving it in large scale will be time-consuming then the artificial bee colony (ABC)\r\nalgorithm is used to solve it. Also, in order to better understand ABC efficiency, a comparison is performed between this algorithm\r\nand the particle swarmoptimization (PSO) and the imperialist competitive algorithm (ICA) and Lingo software output.The results\r\nobtained from a real example show that ABC offers more efficient solutions to the problem solving in large scale and PSO spends\r\nless time to solve the same problem....
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