793200
The alignment problem of visual cryptography schemes
Pixel expansion is an important parameter for Visual Cryptography Schemes (VCS). However, most papers in the literature are dedicated to reduce pixel expansion on the pixel level, i.e. to reduce the number of subpixels that represent a pixel in the original secret image. It is quite insufficient since the final size of the transparencies of the VCS is affected not only by the number of the subpixels, but also by the size of the subpixels in the transparencies. However, reducing the size of the subpixels in the transparencies results in difficulties of alignment of the transparencies. In this paper, we consider the alignment problem in VCS, and prove that in order to visually recover the original secret image, it is not necessary to align the transparencies precisely. This study is restricted to the case when only one transparency is shifted.
14796999
Robust color-based tracking
Color as a distinct feature is widely used for object representation and tracking. However, color-based tracking is often influenced by clutter background and illumination variation. This paper presents a robust color-based tracking method, in which robust color feature is extracted for constructing the observation model under the modified particle filter tracking framework. The object is represented by its dominant color, and the weighted histogram with spatial information of the dominant color is used to optimize object models. In the particle filter framework, an extended iterated likelihood weighting scheme is employed to utilize more valuable particles. The experimental results show it is a real-time robust tracker, and it can obtain more than 30 fps with 2.4 G CPU and 512 MRAM.
34743400
An optimal weather condition dependent approach for emission planning in urban areas
Abstract The urban air quality strongly depends on the weather conditions. When unfavorable weather conditions are forecasted, it is desirable to reduce a certain amount of pollutant emissions to maintain a given level of air quality. In this paper, we propose and demonstrate a new approach to find the optimal reduction, and we contribute to the methodology of decision support of short-term emission control. This approach is based on weather forecasts and state-of-the-art 3-dimensional numerical air-quality prediction models by solving an optimal control problem with the emission cuts as the control variables. The objective of the optimal problem is to minimize the total cost due to the emission cuts, subject to feasibility constraints, system-governing model constraints, and target constraints. When a high-resolution numerical model is used as the state constraint, the problem can become a very high dimensional one. A practical approach to solving this problem with high dimension is proposed, based on the adjoint technique. The proposed approach is demonstrated with two computational test cases in Jinan, China for the control of sulfur dioxide. The results show the capability and computational efficiency of the method and suggest a promising potential for emission planning applications based on weather forecasts.
9484607
Visual-Quality Optimizing Super Resolution
In this paper, we propose a robust image super-resolution (SR) algorithm that aims to maximize the overall visual quality of SR results. We consider a good SR algorithm to be fidelity preserving, image detail enhancing and smooth. Accordingly, we define perception-based measures for these visual qualities. Based on these quality measures, we formulate image SR as an optimization problem aiming to maximize the overall quality. Since the quality measures are quadratic, the optimization can be solved efficiently. Experiments on a large image set and subjective user study demonstrate the effectiveness of the perception-based quality measures and the robustness and efficiency of the presented method.
7806615
A Novel Ordering-Based Greedy Bayesian Network Learning Algorithm on Limited Data
Existing algorithms for learning Bayesian network (BN) require a lot of computation on high dimensional itemsets, which affects accuracy especially on limited datasets and takes up a large amount of time. To alleviate the above problem, we propose a novel BN learning algorithm OM- RMRG, Ordering-based Max Relevance and Min Redun- dancy Greedy algorithm. OMRMRG presents an ordering- based greedy search method with a greedy pruning proce- dure, applies Max-Relevance and Min-Redundancy feature selection method, and proposes Local Bayesian Increment function according to Bayesian Information Criterion (BIC) formula and the likelihood property of overfitting. Exper- imental results show that OMRMRG algorithm has much better efficiency and accuracy than most of existing BN learning algorithms on limited datasets.
20485137
Multi-objective scheduling problems subjected to special process constraint
The problem of parallel machine multi-objective scheduling subjected to special process constraint in the textile industries, as one of the most important combinational optimization problems, is different from other parallel machine scheduling problems in the following characteristics. On one hand, processing machines are non-identical; on the other hand, the sort of job processed on every machine can be restricted Considering one of the multi-objective problems, either minimizing the maximum completion time among all the machines(makespan) or minimizing the total earliness/tardiness penalty of all the jobs has been cornerstone of most studies done so far. However, under special process constraint, taking them into account as a multi-objective problem has not been well studied Therefore, in this paper, a multi-objective model based on them is presented and a new parallel genetic algorithm based on a vector group coding method is also proposed in order to effectively solve this model. The algorithm shows the following advantages: the coding method is simple and can effectively reflect the virtual scheduling policy, which can vividly reflect the numbers and sequences of these processed jobs on every machine, and then enables the individuals generated by crossover and mutation to satisfy process constraint. Numerical experiments show that it is efficient, and is better than the common genetic algorithm, and has the better parallel efficiency. A much better prospect of application can be optimistically expected.
10413997
The Max-Relevance and Min-Redundancy Greedy Bayesian Network Learning Algorithm
Existing algorithms for learning Bayesian network require a lot of computation on high dimensional itemsets which affects reliability, robustness and accuracy of these algorithms and takes up a large amount of time. To address the above problem, we propose a new Bayesian network learning algorithm MRMRG, Max Relevance-Min Redundancy Greedy. MRMRG algorithm is a variant of K2 which is a well-known BN learning algorithm. We also analyze the time complexity of MRMRG. The experimental results show that MRMRG algorithm has much better efficiency. It is also shown that MRMRG algorithm has better accuracy than most of existing learning algorithms for limited sample datasets.
33185687
Supremum of Agent Number Needed in Analyzing Security Protocols Based on Horn Logic