Search

This paper proposes a concurrent implementation of a background subtraction algorithm suitable for monochromatic video sequences. In the training period, a set of frames is used to determine an estimate of the scene background, as well as background noise. In the test period, each new frame of the video sequence is compared to the background model, and foreground objects are obtained taking into account the background noise estimate. Shadows and highlights are also detected and removed. Concurrent programming tools and techniques, such as multithreading, are used to implement a concurrent solution for this problem, and combined with other optimization tools (such as destination word accumulation for morphological operators and the use of lookup tables for shadow detection/removal) to achieve the highest frame rate possible. Experimental results comprise a comparison of the performance including progressively each optimization tool, and they show that the fully optimized algorithm is much faster than a naive sequential implementation. Also, experiments performed on two-way processor and on dual-core processor computers indicate that the proposed algorithm is suited for the recent multi-core technology. [ABSTRACT FROM AUTHOR]

The optimization of many engineering systems is challenged by the solution over-fit to the data set used to evaluate potential solutions during the evolutionary process. The solution over-fit phenomenon is hard to detect and is especially prevalent in problems involving example-based training, such as pattern feature selection and pattern classifier design. For these applications, uncontrolled over-fit can lead to biased features being extracted and degraded classifier generalization abilities. This paper details the performance of a solution over-fit control strategy used in the multiobjective evolutionary optimization of a multileveled classification system. This control, embedded within a solution validation procedure, minimizes the over-fit effects without modifying the dominance relation used in the processing of candidate solutions. Extensive experimental analysis using multiobjective genetic and memetic algorithms demonstrates both the need and the efficiency of the proposed over-fit control for pattern classification systems optimization. [ABSTRACT FROM AUTHOR]

For constrained optimization problems, evolutionary algorithms often utilize a penalty function to deal with constraints, even if it is difficult to control the penalty parameters. To overcome this shortcoming, this paper presents a new penalty function which has no parameter and can effectively handle constraint first, after which a hybrid-fitness function integrating this penalty function into the objective function is designed. The new fitness function can properly evaluate not only feasible solution, but also infeasible one, and distinguish any feasible one from an infeasible one. Meanwhile, a new crossover operator based on simplex crossover operator and a new PSO mutation operator are also proposed, which can produce high quality offspring. Based on these, a new evolutionary algorithm for constrained optimization problems is proposed. The simulations are made on ten widely used benchmark problems, and the results indicate the proposed algorithm is effective. [ABSTRACT FROM AUTHOR]

A temporal flexible planning problem that involves contingent and requirement events can be formulated as a simple temporal network with uncertainty (STNU). An STNU is controllable when there is a strategy for executing the requirement events (or actions) in such a way that all the conditions involving contingent events can be satisfied in all situations. The most interesting and useful controllability property is dynamic controllability in which the remaining actions in an STNU can always be scheduled under all possible feasible durations of future contingent events when all the past contingent events are known. In this paper, we propose and study a novel problem of assigning bounds on the duration of each requirement link in order for the resulting STNU to be dynamically controllable and to minimize the total cost over the allowed durations of all requirement links. We first prove the NP hardness of the problem with a linear cost function. We then formulate the dynamic controllability of an STNU as the constraints in a nonlinear optimization problem. Finally, we present methods for reducing the number of constraints in order to make the problem tractable and to demonstrate the computational performance of our methods. [ABSTRACT FROM AUTHOR]

This paper deals with a new middle path approach developed for reducing alignment calculations in BLAST algorithm. This is a new step which is introduced in BLAST algorithm in between the ungapped and gapped alignments. This step of middle path approach between the ungapped and gapped alignments reduces the number of sequences going for gapped alignment. This results in the improvement in speed for alignment up to 30%. [ABSTRACT FROM AUTHOR]