To solve the problem that the depth-of-field limitation of optical imaging systems leads to the inability of a single image to capture the clarity of the entire scene, we propose FADFNet,a Focus Attention and Dual-Feature Adaptation Multi-Focus Image Fusion Network. Traditional fusion methods rely on handcrafted features such as gradients or contrasts, which usually make it difficult to distinguish between focused and defocus areas in complex scenes, while deep learning methods often produce artifacts or lose details in transition areas.FADFNet overcomes these challenges by integrating three key components: the Focus Attention (FA) block, which captures pixel-level global dependencies and dynamically adjusts feature importance through an adaptive attention mechanism; the Dual-Feature Adaptive (DA) block, which employs a bidirectional structure to globally weight and embed focused pixels, thereby enhancing focused region features; and the Feature Pyramid Network (FPN), which modulates near- and far-focus feature maps to produce a high-resolution fused images. The experimental results show that FADFNet is superior to existing methods in terms of fusion quality.

Monte-Carlo models are analyzed for multiple scattering channels in optical wireless communications. It is demonstrated that the system impulse response function can be obtained by Monte-Carlo integration model. The convergence performance for the Monte-Carlo integration model is analyzed and improved by introducing different sampling methods. The simulation results show that the gamma function model for channel impulse response function can only be applied to the cases where the common volume between the transmitted light beam and the receiving field-of-view is open. Numerical simulation suggests that for a three-order scattering case, the computation efficiency of the Monte-Carlo integration model based on partial importance sampling is about 12 times of the original Monte-Carlo integration model based on uniform sampling, and 5.6 times of the widely used Monte-Carlo simulation model. The numerical results also show that the Monte-Carlo integration model based on partial importance sampling has higher computation efficiency than the Monte-Carlo simulation model in a higher-order scattering communication scenario.

Runtime reconfigurations of component-based systems must be undertaken with careful considerations of dependency between components. The safer and less disruptive a reconfiguration strategy is, the more accurate dependency information it needs. This paper proposes to manage dynamic dependency between components with mealy machine automatically derived from the implementation of components. To maintain the current dependency information for a component at runtime, the corresponding machine is instrumented into the component implementation in such a way that it is always synchronized with the execution of the component. We implemented a prototypical tool for this approach and evaluated it with a realistic benchmark application. The results show that our approach achieves a high accuracy and keeps low overheads without introducing any manual work.

In this paper, the problem of partner selection for virtual enterprise is addressed. When one or more than one partner are to be selected for the same subtask in the virtual enterprise configuration, the problem is much complicated. A genetic algorithm is used to solve the problem in this paper. By this algorithm, the population evolves to produce the new population continuously by selection, crossover and mutation. In order to search for the optimal solution, we propose a selection method that combines truncation selection and roulette wheel selection. Crossover and mutation methods are used to adjust the parameters P/sub c/ and P/sub m/ automatically according to the searching range. Experiment results show that the proposed method is effective.