摘要：A novel Discrete Wavelet Transform (DWT) based Hidden Markov Module (HMM) for face recognition is presented in this letter. To improve the accuracy of HMM based face recognition algorithm, DWT is used to replace Discrete Cosine Transform (DCT) for observation sequence ex- traction. Extensive experiments are conducted on two public databases and the results show that the proposed method can improve the accuracy significantly, especially when the face database is large and only few training images are available.

摘要：This paper describes an evolutionary art system, which explores the potential ability of evolutionary computation in Batik design. We investigate the use of Interactive Evolutionary Algorithm （IEA） in our system, with the goal of enhancing user＇s creativity to generate innovative Batik-like patterns. We focus mainly on two crucial aspects of the system. First, a new representation is proposed to capture the features in Batik and create innovative patterns through evolutionary processes. Second, an out-breeding mechanism is applied to our system, in order to sustain user＇s interest for a longer period. Our system can search a much larger design space than other systems and can avoid being trapped in a local optimum. We describe the system in detail and the methodology we have adopted in the system. ＂Our experimental results have shown that our newly developed system is effective and has great potentials in evolving novel Batik design.

摘要：There are a lot of security issues in block cipher *** analysis and enhanced design of a dynamic block cipher was ***,the safety of ciphertext was enhanced based on confusion substitution of S-box,thus disordering the internal structure of data blocks by four steps of matrix ***,the diffusivity of ciphertext was obtained by cyclic displacement of bytes using column ambiguity *** dynamic key was finally generated by using LFSR,which improved the stochastic characters of secret key in each of round of *** safety performance of proposed algorithm was analyzed by simulation *** results showed the proposed algorithm has a little effect on the speed of encryption and decryption while enhancing the ***,the proposed algorithm has highly scalability,the dimension of S-box and the number of register can be dynamically extended according to the security requirement.

摘要：Remote sensing cross-modal image-text retrieval(RSCIR)can flexibly and subjectively retrieve remote sensing images utilizing query text,which has received more researchers’attention ***,with the increasing volume of visual-language pre-training model parameters,direct transfer learning consumes a substantial amount of computational and storage ***,recently proposed parameter-efficient transfer learning methods mainly focus on the reconstruction of channel features,ignoring the spatial features which are vital for modeling key entity *** address these issues,we design an efficient transfer learning framework for RSCIR,which is based on spatial feature efficient reconstruction(SPER).A concise and efficient spatial adapter is introduced to enhance the extraction of spatial *** spatial adapter is able to spatially reconstruct the features in the backbone with few parameters while incorporating the prior information from the channel *** conduct quantitative and qualitative experiments on two different commonly used RSCIR *** with traditional methods,our approach achieves an improvement of 3%-11% in sumR *** with methods finetuning all parameters,our proposed method only trains less than 1% of the parameters,while maintaining an overall performance of about 96%.

摘要：This paper deals with the problem of designing a robust discrete output-feedback based repetitive-control system for a class of linear plants with periodic uncertainties. The periodicity of the repetitive-control system is exploited to establish a two-dimensional （2D） model that converts the design problem into a robust stabilization problem for a discrete 2D system. By employing Lyapunov stability theory and the singular-value decomposition of the output matrix, a linear-matrix-inequality （LMI） based stability condition is derived. The condition can be used directly to design the gains of the repetitive controller. Two tuning parameters in the LMI enable the preferential adjustment of control and learning. A numerical example illustrates the design procedure and demonstrates the validity of the method.

摘要：Role based access control is one of the widely used access control *** are investigations in the literature that use knowledge representation mechanisms such as formal concept analysis(FCA),description logics,and Ontology for representing access control ***,while using FCA,investigations reported in the literature so far work on the logic that transforms the three dimensional access control matrix into dyadic formal *** transformation is mainly to derive the formal concepts,lattice structure and implications to represent role hierarchy and constraints of *** this work,we propose a methodology that models RBAC using triadic FCA without transforming the triadic access control matrix into dyadic formal *** discussion is on two lines of *** present how triadic FCA can provide a suitable representation of RBAC policy and we demonstrate how this representation follows role hierarchy and constraints of RBAC on sample healthcare network available in the literature.

摘要：Since most ad hoc mobile devices today operate on batteries,the power consumption becomes an important *** paper proposes a cross-layer design of energy-aware ad hoc on-demand distance vector(CEAODV) routing protocol which adopts cross-layer mechanism and energy-aware metric to improve AODV routing protocol to reduce the energy consumption and then prolong the life of the whole *** CEAODV,the link layer and the routing layer work together to choose the optimized transmission power for nodes and the route for *** link layer provides the energy consumption information for the routing layer and the routing layer chooses route accordingly and conversely controls the link layer to adjust the transmission *** simulation result shows that CEAODV can outperform AODV to save more *** can reduce the consumed energy by about 8%over traditional energy-aware *** the performance is better when the traffic load is higher in the network.

摘要：Hardware/software partitioning is an essential step in hardware/software *** large size problems,it is difficult to consider both solution quality and *** paper presents an efficient GPU-based parallel tabu search algorithm(GPTS)for HW/SW partitioning.A single GPU kernel of compacting neighborhood is proposed to reduce the amount of GPU global memory accesses theoretically.A kernel fusion strategy is further proposed to reduce the amount of GPU global memory accesses of *** further minimize the transfer overhead of GPTS between CPU and GPU,an optimized transfer strategy for GPU-based tabu evaluation is proposed,which considers that all the candidates do not satisfy the given *** show that GPTS outperforms state-of-the-art work of tabu search and is competitive with other methods for HW/SW *** proposed parallelization is significant when considering the ordinary GPU platform.

摘要：Standard support vector machines （SVMs） train- ing algorithms have O（l3） computational and O（l2） space complexities, where l is the training set size. It is thus com- /putationally infeasible on very large data *** alleviate the Computational burden in SVM training, we propose an algo- rithm to train SVMs on a bound vectors set that is extracted based on Fisher projection. For linear separate problems, we use linear Fisher discriminant to compute the projection line, while for non-linear separate problems, we use kernel Fisher discriminant to compute the projection line. For each case, we select a certain ratio samples whose projections are adja- cent to those of the other class as bound vectors. Theoretical analysis shows that the proposed algorithm is with low com- putational and space *** experiments on several classification benchmarks demonstrate the effective- ness of our approach.

摘要：Marginal Fisher analysis （MFA） is a repre- sentative margin-based learning algorithm for face recognition. A major problem in MFA is how to select appropriate parameters, k1 and k2, to construct the respective intrinsic and penalty graphs. In this paper, we propose a novel method called nearest-neighbor （NN） classifier motivated marginal discriminant projections （NN-MDP）. Motivated by the NN classifier, NN-MDP seeks a few projection vectors to prevent data samples from being wrongly categorized. Like MFA, NN-MDP can characterize the compactness and separability of samples simultaneously. Moreover, in contrast to MFA, NN-MDP can actively construct the intrinsic graph and penalty graph without unknown parameters. Experimental results on the 0RL, Yale, and FERET face databases show that NN-MDP not only avoids the intractability, and high expense of neighborhood parameter selection, but is also more applicable to face recognition with NN classifier than other methods.