Aiming at the problem of the current common Convolutional Neural Network(CNN)in the expression and gender recognition tasks,that is training process is complicated,time-consuming,and poor in real-time performance,a realtime facial expression and gender recognition model based on depthwise separable convolutional neural network was proposed. Firstly,the Multi-Task Convolutional Neural Network(MTCNN)was used to detect faces in different scale input images,and the detected face positions were tracked by Kernelized Correlation Filter(KCF)to increase the detection speed. Then,the bottleneck layers of convolution kernels of different scales were set,the kernel convolution units were formed by the feature fusion method of channel combination,the diversified features were extracted by the depthwise separable convolutional neural network with residual blocks and separable convolution units,and the number of parameters was reduced to lightweight the model structure. Besides,real-time enabled backpropagation visualization was used to reveal the dynamic changes of the weights and characteristics of learning. Finally,the two networks of expression recognition and gender recognition were combined in parallel to realize real-time recognition of expression and gender. Experimental results show that the proposed network model has a recognition rate of 73. 8% on the FER-2013 dataset,a recognition rate of 96% on the CK+ dataset,the accuracy of gender classification on the IMDB dataset reaches 96%;and this model has the overall processing speed reached 70 frames per second,which is improved by 1. 5 times compared with the method of common convolutional neural network combined with support vector machine. Therefore,for datasets with large differences in quantity,resolution and size,the proposed network model has fast detection,short training time,simple feature extraction, and high recognition rate and real-time performance.

Erasure code technology is a typical data fault tolerance method in distributed storage systems. Compared with multi-copy technology, it can provide high data reliability with low storage overhead. However, the high repair cost limits the practical applications of erasure code technology. Aiming at problems of high repair cost, complex encoding and poor flexibility of existing erasure codes, a simple-encoding erasure code with low repair cost - Rotation Group Repairable Code (RGRC) was proposed. According to RGRC, multiple strips were combined into a strip set at first. After that, the association relationship between the strips was used to hierarchically rotate and encode the data blocks in the strip set to obtain the corresponding redundant blocks. RGRC greatly reduced the amount of data needed to be read and transmitted in the process of single-node repair, thus saving a lot of network bandwidth resources. Meanwhile, RGRC still retained high fault tolerance when solving the problem of high repair cost of a single node. And, in order to meet the different needs of distributed storage systems, RGRC was able to flexibly weigh the storage overhead and repair cost of the system. Comparison experiments were conducted on a distributed storage system, the experimental analysis shows that compared with RS (Reed-Solomon) codes, LRC (Locally Repairable Codes), basic-Pyramid, DLRC (Dynamic Local Reconstruction Codes), pLRC (proactive Locally Repairable Codes), GRC (Group Repairable Codes) and UFP-LRC (Unequal Failure Protection based Local Reconstruction Codes), RGRC can reduce the repair cost of single node repair by 14%-61% through adding a small amount of storage overhead, and reduces the repair time by 14%-58%.

Focusing on the problem that the existing car high-beam requires the driver to manually change the headlamp through his own judgment of the road condition, which may results in a traffic accident due to the illegal use of the high-beam, a matrix LED high-beam intelligent assistant control system which can automatically adjust the radiation way of high-beam according to the road condition and environment was designed and implemented. Firstly, according to the driving characteristics of vehicles and related traffic regulations, the intelligent control strategy of matrix LED high-beam assistant system was proposed for different road conditions. Then the hardware and software of the system were designed and implemented. In the hardware part, the device selection and circuit design of the modules like main controller, LED power driver and matrix switch controller were given, and the software part was composed of function modules like driving circuit control, matrix switch control and intelligent control strategy. Finally, a complete experiment system under laboratory conditions was built for functional test. The experiment test results indicate that the proposed method has accurate results and is steady, reliable, better in real-time and easy to realize, which achieves the expected goal.

Since the network booting time overhead is high in supercomputer system, the idea that the network boot distribution algorithm is one of the main factors affecting the network boot performance and the main direction of optimizing network boot performance was proposed. Firstly, the main factors affecting large-scale network boot performance were analyzed. Secondly, combined with a typical supercomputer system, the network boot data flow topologies of Supernode Cyclic Distribution Algorithm (SCDA) and Board Cyclic Distribution Algorithm (BCDA) were analyzed. Finally, the pressure of above two algorithms on each network path branch and the available network performance were quantitatively analyzed. It can be seen that the bandwidth performance of BCDA is 1-20 times of that of SCDA. Theoretical analysis and model deduction show that the finer-grained mapping algorithm between compute nodes and boot servers can make as many boot servers as possible be used while boot some resources, reducing the premature competition for partial network resources and improving network boot performance.

In view of poor performance of Coyote Optimization Algorithm (COA), a Best and Worst coyotes strengthened COA (BWCOA) was proposed. Firstly, for growth of the worst coyote in the group, a global optimal coyote guiding operation was introduced on the basis of the optimal coyote guidance to improve the social adaptability (local search ability) of the worst coyote. Then, a random perturbation operation was embedded in the growth process of the optimal coyote in the group, which means using the random perturbation between coyotes to promote the development of the coyotes and make full play of the initiative of each coyotes in the group to improve the diversity of the population and thus to enhance the global search ability, while the growing pattern of the other coyotes kept unchanged. BWCOA was applied to complex function optimization and Quadratic Assignment Problem (QAP) using hospital department layout as an example. Experimental results on CEC-2014 complex functions show that compared with COA and other state-of-the-art algorithms, BWCOA obtains 1.63 in the average ranking and 1.68 rank mean in the Friedman test, both of the results are the best. Experimental results on 6 QAP benchmark sets show that BWCOA obtains the best mean values for 5 times. These prove that BWCOA is more competitive.

Focusing on the problem of solving coupled Logistic fractional-order differential equation, a discretization method was introduced to solve it discretly. Firstly, a coupled Logistic integer-order differential equation was introduced into the fields of fractional-order calculus. Secondly, the corresponding coupled Logistic fractional-order differential equation with piecewise constant arguments was analyzed and the proposed discretization method was applied to solve the model numerically. Then, according to the fixed point theory, the stability of the fixed point of the synthetic dynamic system was discussed, and the boundary equation of the first bifurcation of the coupled Logistic fractional-order system in the parameter space was given. Finally, the model was numerically simulated by Matlab, and more complex dynamics phenomena of model were discussed with Lyapunov index, phase diagram, time series diagram and bifurcation diagram. The simulation results show that, the proposed method is successful in discretizing coupled Logistic fractional-order differential equation.

To solve the problems of fine-grained image classification methods, such as highly complex methods and difficulty of using deeper models, a Deep Model Transfer (DMT) method was proposed. Firstly, the deep model was pre-trained on the coarse-grained image dataset. Secondly, the pre-trained deep model classification layer was trained based on inexact supervised learning by using fine-grained image dataset and transferred to the feature distribution direction of the novel dataset. Finally, the trained model was exported and tested on the corresponding test sets. The experimental results show that the classification accuracy rates on the STANFORD DOGS, CUB-200-2011 and OXFORD FLOWER-102 fine-grained image datasets are 72.23%, 73.33%, and 96.27%, respectively, which proves the effectiveness of DMT method on fine-grained image classification.

In order to estimate the State-Of-Charge (SOC) of automobile power lithium-ion battery online, an Unscented Kalman Filtering (UKF) algorithm was proposed combined with neural network. First of all, Thevenin circuit was treated as an equivalent circuit, the state space representation of the battery model was established and the least square method was applied to identify the parameters of model. Then on this basis, the neural network algorithm was expected to fit the functional relationships between SOC of battery and model parameters respectively. After many experiments, the convergence curve of the neural network algorithm was determined. The proposed method was more accurate than the traditional curve fitting. In addition, the Extended Kalman Filtering (EKF) principle and the UKF principle were introduced separately and some tests were designed including the validation experiment of battery equivalent circuit model, the test experiment of SOC and the convergence experiment of the algorithms. The experimental results show that, the proposed method which can be used for SOC estimation online has higher estimation precision and stronger environmental adaptability than simple extended Kalman filtering algorithm under different conditions, its maximum error is less than 4%. Finally, the proposed algorithm combining UKF and neural network has better convergence and robustness, which can be used to solve the problems of inaccurate estimation of initial value and cumulative error effectively.

When the target apperance is influenced by the change of illumination, occlusion or attitude, the robustness and accuracy of target tracking system are usually frangible. In order to solving this problem, sparse representation was introduced into the particle filter framework for target tracking and a sparse cooperative model was proposed. Firstly, the target object was represented by intensity in the target motion positioning model. Secondly, the optimal classification features were extracted by training the positive template set and negative template set in the discriminant classification model, then the target was weighted by the histogram in the generative model. Subsequently, discriminant classification model and generative model were cooperated in a collaborative model, and the target was determined by the reconstruction error. Finally, every module was updated independently to mitigate the effects of changes in the appearance of the target. The experimental results show that the average center location error of the proposed model is only 7.5 pixels, meanwhile the model has good performance in anti-noise and real-time.

To solve the problem that traditional image classification methods deal with the whole image in a non-hierarchical way, an image classification method based on visual saliency detection was proposed. Firstly, the visual attention model was employed to generate the salient region. Secondly, the texture feature and time signature feature of the image were extracted by Gabor filter and pulse coupled neural network, respectively. Finally, the support vector machine was adopted to accomplish image classification according to the features of the salient region. The experimental results show that the image classification precision rates of the proposed method in SIMPLIcity and Caltech are 94.26% and 95.43%, respectively. Obviously, saliency detection and efficient image feature extraction are significant to image classification.

Prevalent human detection methods are usually applied in cases without rotation angle, and their detection rates are poor when rotation angle varies. In order to solve the issue, an algorithm which could identify human with variable rotation angle was proposed. Firstly, Radial Gradient Transform (RGT) method was adopted to obtain the rotation-invariance gradient. Then, adopting the method similar to the way that blocks were overlapped in the Histogram of Oriented Gradient(HOG) feature, a plurality of descriptors with rotation angle information were obtained and connected linearly into a descriptor group with rotation invariance feature, according to the descriptors' rotation angle. Finally, the human detection algorithm was conducted with the support of a two-level cascaded classifier based on Support Vector Machine (SVM). The recognition rate of the proposed algorithm achieves more than 86% for a human test set with 144 different rotation angles based on the INRIA pedestrian database. In the meantime, the false detection rate is less than 10% for a non-human test set with 144 different rotation angles. The experiments indicate that the proposed algorithm can be used for human detection in an image with arbitrary rotation angle.

To solve the problem in most of conventional multi-task learning algorithms which evaluate risk independently for single task and lack uniform constraint across all tasks, a new hyper-spherical multi-task learning algorithm with adaptive grouping was proposed in this paper. Based on Extreme Learning Machine (ELM) as basic framework, this algorithm introduced hyper-spherical loss function to evaluate the risks of all tasks uniformly, and got decision model via iterative reweighted least squares solution. Furthermore, considering the existence of relatedness between tasks, this paper also constructed regularizer with grouping structure based on the assumption that related tasks had more similar weight vector, which would make the tasks in same group be trained independently. Finally, the optimization object was transformed into a mixed 0-1 programming problem, and a multi-objective method was utilized to identify optimal grouping structure and get model parameters. The simulation results on toy data and cylindrical vibration signal data show that the proposed algorithm outperforms state-of-the-art methods in terms of generalization performance and the ability of identifying inner structure in tasks.

The paper firstly analyzed some security problems in Li-Niu's (LI X, NIU J W, KHAN M K, et al. An enhanced smart card based remote user password authentication scheme［J］. Journal of Network and Computer Applications, 2013, 36(5):1365-1371.) enhanced smart card based remote user password authentication scheme, and then proposed a novel smart-card-based scheme. In new scheme, a self-verified timestamp technique was combined with symmetric encryption methods to solve the problem of implementing clock synchronization in most typical smart-card-based schemes. Compared with Li-Niu's scheme, this scheme can not only provide the users' anonymity, but also resist the impersonation attacks and the privileged insider attacks. The scheme is more secure and efficient for the complicated network environment.

A new scheme for unlocking implementation of concurrent Binary Search Tree (BST) based on asynchronous shared memory systems was provided in this paper. This scheme possessed two outstanding advantages: The deletion is wait-free, and the insertion is lock-free. The experimental results show that this scheme is highly scalable and can produce high throughputs under heavy load.

To meet the fast and accurate requirement of image measurement in the auto-focusing system of image measurement instrument, gray contrast function was improved as following: by making use of the features that the focus image has smaller range of the gray scale transition than the defocused image and calculating the average change in gray value, focusing was reached with the number of changes in gray-scale. The comparison shows that the improved gray scale contrast function has simpler time computational complexity and higher focusing sensitivity. The improved function, with good stability, has faster focusing speed and higher focusing accuracy than other methods in the auto-focusing system of image measurement instrument.

A new identity-based proxy re-signcryption scheme was put forward on the basis of the signcryption with proxy re-encryption proposed by Chandrasekar S (CHANDRASEKAR S, AMBIKA K, RANGAN P C. Signcryption with proxy re-encryption . http://eprint.iacr.org/2008/276). The new scheme achieves a transparent conversion from one identity-based signcryption to another identity-based signcryption by using a semi-trusted proxy. And it realizes the complete conversion of signcryption, that is, it concurrently achieves the conversion of both confidentiality and verification. In the same time, it realizes the full public verifiability of the signcryption without the direct participation of the plaintext. Based on the Computation Bilinear Diffie-Hellman (CBDH) problem, it is proved to be IND-CCA2 secure in the Random Oracle Model (ROM). Through the analysis of its efficiency and function, the scheme resolves the problems of both failing to realize the conversion of the verification and the verifiability of signcryption needing the participation of plaintext in the scheme advanced by Chandrasekar S.