ESSVCS: an enriched secret sharing visual cryptography

Visual Cryptography (VC) is a powerful technique that combines the notions of perfect ciphers and secret sharing in cryptography with that of raster graphics. A binary image can be divided into shares that are able to be stacked together so as to approximately recover the original image. VC is a unique technique in the sense that the encrypted message can be decrypted directly by the Human Visual System (HVS). The distinguishing characteristic of VC is the ability of secret restoration without the use of computation. However because of restrictions of the HVS, pixel expansion and alignment problems, a VC scheme perhaps can only be applied to share a small size of secret image. In this paper, we present an Enriched Secret Sharing Visual Cryptography Scheme (ESSVCS) to let the VC shares carry more secrets, the technique is to use cypher output of private-key systems as the input random numbers of VC scheme, meanwhile the encryption key could be shared, the shared keys could be associated with the VC shares. After this operation, VC scheme and secret sharing scheme are merged with the private-key system. Under this design, we implement a (k; t; n)-VC scheme. Compared to those existing schemes, our scheme could greatly enhance the ability of current VC schemes and could cope with pretty rich secrets

Development of new features of ant colony optimization for flowshop scheduling

Ant colony optimization (ACO) is a meta-heuristic based on the indirect communication of a colony of artificial ants mediated by pheromone trails with the collaboration and knowledge-sharing mechanism during their food-seeking process. In this study, we introduce two new features that are inspired from real ant behavior to develop a new ACO algorithm to produce better solutions. The proposed ACO algorithm is applied to two NP-hard flowshop scheduling problems. The first problem is to minimize the total completion time and the second is to minimize a combination of makespan and total completion time. Numerical results indicate that the proposed new features of ACO are very effective and the synergy of combining all the new features for the proposed ACO algorithm can solve the two problems to a certain scale by producing schedules of better quality.

Understanding the Pheromone System within Ant Colony Optimization

Ant Colony Optimization (ACO) is a collection of metaheuristics inspired by foraging in ant colonies, whose aim is to solve combinatorial optimization problems. We identify some principles behind the metaheuristics ’ rules; and we show that ensuring their application, as a correction to a published algorithm for the vertex cover problem, leads to a statistically significant improvement in empirical results

Synthesis of a reusable oxotungsten-containing SBA-15 mesoporous catalyst for the organic solvent-free conversion of cyclohexene to adipic acid

An oxotungsten-silica mesoporous structure (WSBA-15) has a hierarchical crystalline architecture in which the W dopants possess tetrahedral coordination geometries for the mixed-valence states W6+, W5+, and W4+. The WSBA-15 catalyst can be recycled-without any loss of activity-for the direct oxidation (30% H2O2,) of cyclohexene to colorless, crystalline adipic acid (55% yield) under organic solvent-free conditions. (C) 2006 Elsevier B.V. All rights reserved