29884226
Stochastic collision detection between deformable models using particle swarm optimization algorithm
We present an efficient algorithm for detecting collisions and self-collisions between highly deformable mass models, which is a combination of newly developed stochastic method and particle swarm optimization (PSO) algorithm. In stochastic collision detection, user can balance performance and detection quality by sampling primitive pairs within the models. To accelerate detecting process in the primitive pair space, we introduce PSO algorithm to complete the optimization for the first time. And in the end of this paper, we give the precision and efficiency evaluation about the algorithm and find it might be a reasonable choice for deformable models in collision detection
39977640
Image segmentation by aggregation graph-cuts
In this paper, we describe a fast semi-automatic segmentation algorithm using nodes aggregation and graph-cuts. The segmentation process is reliably computed automatically no additional users' efforts are required. It is convenient and efficient in practical applications. Experiments are given and outputs are encouraging
27639183
An Adaptive Stochastic Collision Detection Between Deformable Objects Using Particle Swarm Optimization
In this paper, we present an efficient method for detecting collisions between highly deformable objects, which is a combination of newly developed stochastic method and Particle Swarm Optimization (PSO) algorithm. Firstly, our algorithm samples primitive pairs within the models to construct a discrete binary search space for PSO, and in this way user can balance performance and detection quality. Besides a particle update process is added in every time step to handle the dynamic environments caused by deformations. Our algorithm is also very general that makes no assumptions about the input models and doesn’t need to store additional data structures either. In the end, we give the precision and efficiency evaluation about the algorithm and find it might be a reasonable choice for complex deformable models in collision detection systems.
5137060
Optimal Threshold for Channel Estimation in MIMO-OFDM System
In this paper the optimal threshold is proposed to derive channel impulse response on the multi-path taps and eliminate noise on the other taps. The choice of threshold, which is crucial for the accuracy of the estimator, is analyzed theoretically and the optimal threshold introducing the least MSE is derived. Simulation results show that the performance of our proposed channel estimation with the optimal threshold has significant improvement over the conventional channel estimation algorithm.
6122481
A Uniform Host Protocol Framework Planning to Change
As a substantial foundation of the Internet, traditional layered host protocol stack is found to be a framework "not planning to change". This framework is hard to accommodate new layers and new protocols, hence hard to provide new services including security and mobility. What's more, it is also hard to share information among layers, which is critical for cross-layer optimization in MANET implementation. A novel protocol stack framework, altitude based architecture (ABA), is presented in this paper. The proposed framework is cater to framework extensibility, and by negotiation mechanisms, a universal communication model is also presented for backward-compatibility. ABA, which is planning to change, aims to build a uniform host protocol framework for future Internet and MANET. Analyses and simulation illustrations are also given in the paper.
1395095
A Tabu Search Algorithm for Post-processing Multiple Sequence Alignment
Tabu search is a meta-heuristic approach that is proven to be useful in solving combinatorial optimization problems. We implement the adaptive memory features of tabu search to refine a multiple sequence alignment. Adaptive memory helps the search process to avoid local optima and explores the solution space economically and effectively without getting trapped into cycles. The algorithm is further enhanced by introducing extended tabu search features such as intensification and diversification. The neighborhoods of a solution are generated stochastically and a consistency-based objective function is employed to measure its quality. The algorithm is tested with the datasets from BAliBASE benchmarking database. We have observed through experiments that tabu search is able to improve the quality of multiple alignments generated by other software such as ClustalW and T-Coffee. The source code of our algorithm is available at http://www.bii.a-star.edu.sg/~tariq/tabu/.