In order to solve the problem of low accuracy of some distortion types judgment by image single distortion type judgment algorithm, an image single distortion type judgment method based on two-channel Convolutional Neural Network (CNN) was proposed. Firstly, the fixed size image block was obtained by cropping the image, and the high-frequency information map was obtained by Haar wavelet transform of the image block. Then, the image block and the corresponding high-frequency information map were respectively input into the convolutional layers of different channels to extract the deep feature map, and the deep features were fused and input into the fully connected layer. Finally, the values of the last layer of the fully connected layer were input into the Softmax function classifier to obtain the probability distribution of the single distortion type of the image. Experimental results on LIVE database show that, the proposed method has the image single distortion type judgement accuracy up to 95.21%, and compared with five other image single distortion type judgment methods for comparison, the proposed method has the accuracies for judging JPEG2000 and fast fading distortions improved by at least 6.69 percentage points and 2.46 percentage points respectively. The proposed method can accurately identify the single distortion type in the image.

Deep convolutional neural networks are generally large in scale and complex in computation, which limits their application in high real-time and resource-constrained environments. Therefore, it is necessary to optimize the compression and acceleration of the existing structures of convolutional neural networks. In order to solve this problem, a hybrid compression method combining pruning and stream merging was proposed. In the method， the model was decompressed through different angles, further reducing the memory consumption and time consumption caused by parameter redundancy and structural redundancy. Firstly, the redundant parameters in each layer were cut off from the inside of the model. Then the non-essential layers were merged with the important layers from the structure of the model. Finally, the accuracy of the model was restored by retraining. The experimental results on the MNIST dataset show that the proposed hybrid compression method compresses LeNet-5 to 1/20 and improves its running speed by 8 times without reducing the accuracy of the model.

Focused on the issue that traditional machine learning algorithms still need to manually represent features, a protein subcellular localization algorithm based on the deep network of Stacked Denoising AutoEncoder (SDAE) was proposed. Firstly, the improved Pseudo-Amino Acid Composition (PseAAC), Pseudo Position Specific Scoring Matrix (PsePSSM) and Conjoint Traid (CT) were used to extract the features of the protein sequence respectively, and the feature vectors obtained by these three methods were fused to obtain a new feature expression model of protein sequence. Secondly, the fused feature vector was input into the SDAE deep network to automatically learn more effective feature representation. Thirdly, the Softmax regression classifier was adopted to make the classification and prediction of subcells, and leave-one-out cross validation was performed on Viral proteins and Plant proteins datasets. Finally, the results of the proposed algorithm were compared with those of the existing algorithms such as mGOASVM (multi-label protein subcellular localization based on Gene Ontology and Support Vector Machine) and HybridGO-Loc (mining Hybrid features on Gene Ontology for predicting subcellular Localization of multi-location proteins). Experimental results show that the new algorithm achieves 98.24% accuracy on Viral proteins dataset, which is 9.35 Percentage Points higher than that of mGOASVM algorithm. And the new algorithm achieves 97.63% accuracy on Plant proteins dataset, which is 10.21 percentage points and 4.07 percentage points higher than those of mGOASVM algorithm and HybridGO-Loc algorithm respectively. To sum up, it can be shown that the proposed new algorithm can effectively improve the accuracy of the prediction of protein subcellular localization.

Aiming at the problems of limited energy of nodes, short life cycle and low throughput of Wireless Sensor Network (WSN), a WSN Clustering Routing algorithm based on Genetic Algorithm (GA) and Fuzzy C-Means (FCM) clustering (GAFCMCR) was proposed, which adopted the method of centralized clustering and distributed cluster head election. Network clustering was performed by the base station using a FCM clustering algorithm optimized by GA during network initialization. The cluster head of the first round was the node closest to the center of the cluster. From the second round, the election of the cluster head was carried out by the cluster head of the previous round. The residual energy of candidate node, the distance from the node to the base station, and the mean distance from the node to other nodes in the cluster were considered in the election process, and the weights of these three factors were real-time adjusted according to network status. In the data transfer phase, the polling mechanism was introduced into intra-cluster communication. The simulation results show that, compared with the LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm and the K-means-based Uniform Clustering Routing (KUCR) algorithm, the life cycle of the network in GAFCMCR is prolonged by 105% and 20% respectively. GAFCMCR has good clustering effect, good energy balance and higher throughput.

Heterogeneous Multi-core Processors (HMPs) platform has become the mainstream solution for modern embedded system design, and online mapping or scheduling plays a vital role in making full use of the advantages of high performance and low power consumption. Aiming at the dynamic mapping problem of application tasks in HMPs, a mapping and scheduling approach based on machine learning prediction model was proposed. On the one hand, a machine learning model was constructed to predict and evaluate the performance of different mapping strategies rapidly and efficiently, so as to provide support for online scheduling. On the other hand, the machine learning model was integrated with genetic algorithm to find out the optimal resource allocation strategy efficiently. Finally, an Motion-Join Photographic Experts Group (M-JPEG) decoder was used to verify the effectiveness of the proposed approach. The experimental results show that, compared with the Round Robin Scheduler (RRS) and sampling scheduling approaches, the proposed online mapping/scheduling approach has reduced the average execution time by about 19% and 28% respectively.

Deep neural network has powerful feature self-learning ability, which can obtain the granularity features of different levels by multi-layer stepwise feature extraction. However, when the target subject of an image has strong correlation with the background color, the feature extraction will be "lazy", the extracted features are difficult to be discriminated with low abstraction level. To solve this problem, the intrinsic law of feature extraction of deep neural network was studied by experiments. It was found that there was correlation between feature extraction preference and background color of the image. Eliminating this correlation was able to help deep neural network ignore background interference and extract the features of the target subject directly. Therefore, a data enhancement algorithm was proposed and experiments were carried out on the self-built dataset. The experimental results show that the proposed algorithm can reduce the interference of background color on the extraction of target features, reduce over-fitting and improve classification effect.

The conventional Deep Belief Network (DBN) often utilizes the method of randomly initializing the weights and bias of Restricted Boltzmann Machine(RBM) to initialize the network. Although it could overcome the problems of local optimality and long training time to some extent, it is still difficult to further achieve higher accuracy and better learning efficiency owing to the huge difference between reconstruction and original input resulting from random initialization. In view of the above-mentioned problem, a kind of DBN model based on Sparse Denoising AutoEncoder (SDAE) was proposed. The advantage of the advocated model was the feature extraction by SDAE. Firstly, SDAE was trained, and then, the obtained weights and bias were utilized to initialize DBN. Finally, DBN was trained. Experiments were performed on card game data set of Poker hand and handwriting data sets of MNIST and USPS to verify the performance of the proposed model. In Poker hand data set, compared with the conventional DBN, the error rate of the proposed model is lowered by 46.4%, the accuracy rate and the recall rate are improved by 15.56% and 14.12% respectively. The results exhibit that the proposed method is superior to other existing methods in recognition performance.

In order to make the mobile device more convenient and faster decrypt the outsourcing data stored in the cloud, on the basis of Identity-Based Broadcast Encryption (IBBE) system and Identity-Based Encryption (IBE) system, using the technique of outsourcing the decryption proposed by Green et al. (GREEN M, HOHENBERGER S, WATERS B. Outsourcing the decryption of ABE ciphertexts. Proceedings of the 20th USENIX Conference on Security. Berkeley:USENIX Association, 2011:34), a Modified Asymmetric Cross-cryptosystem Proxy Re-Encryption (MACPRE) scheme across the encryption system was proposed. The proposed scheme is more suitable for mobile devices with limited computing power to securely share the data stored in the cloud. When the mobile user decrypts the re-encrypted data, the plaintext can be restored by performing one exponent operation and one bilinear pairing operation, which greatly improves the decryption efficiency of the mobile user and saves the power consumption of the mobile user. The security of this proposed scheme can be reduced to the security of the IBE and IBBE scheme. The theoretical analysis and experimental results show that, the proposed scheme can allow the mobile devices to decrypt data stored in the cloud by spending less time, and ease the problem of limited computing power of the mobile devices. The proposed scheme is more practical.

Focusing on the issue that the Oriented fast and Rotated Brief (ORB) algorithm does not have scale invariance, an improved algorithm based on Speed-Up Robust Feature (SURF) and ORB was proposed. First, the feature points were detected by Hessian matrix, which made the extracted feature points have scale invariance. Second, the feature descriptors were generated by the ORB. Then the K-nearest neighbor algorithm was used for rough matching. Finally, the ratio test, symmetry test, the Least Median Squares (LMedS) theorem was used for purification. When the scale changed, the proposed algorithm's matching precision was improved by 74.3 percentage points than the ORB and matching precision was improved by 4.8 percentage points than the SURF. When the rotation changed, the proposed algorithm's matching precision was improved by 6.6 percentage points than the ORB. The proposed algorithm's matching time was above the SURF, below the ORB. The experimental results show that the improved algorithm not only keeps the rotation invariance of ORB, but also has the scale invariance, and the matching accuracy is improved greatly without decreasing the speed.

In order to solve the security threat of resource sharing and privilege existed in cloud service environment, a new cloud trust model based on non-interference theory, namely NICTM, was proposed. The elements existed in cloud service such as domains, actions, situations, and outputs were abstracted to formally define the trusted domain in cloud services. Besides, the theorem of trusted user domain behavior was proved, and the user domain which followed the theorem could be proved to be trusted. Finally the prototype system was built on Xen virtualization platform, and the feasibility of the model was verified by experiments.

Concering that the traditional access control methods fail to support dynamic authorization and file evaluation, and suffer from malicious re-sharing issue, an Access Control Mechanism with Dynamic Authorization and File Evaluation (DAFE-AC) was proposed. DAFE-AC adopted a dynamic authorization mechanism to monitor authorized users in real-time and allowed users to supervise each other. The file evaluation mechanism in DAFE-AC could dynamically update the access threshold of files. Based on the Hash/index database, DAFE-AC can ensure the uniqueness of files in the system. In DAFE-AC, a user' authorization value can dynamically change with behaviors of other users, and users can perform file evaluation to eliminate malicious re-sharing of files.

Concerning the problems of the low accuracy of community detection in bipartite network and the strong dependence on additional parameters, depending on the original network topology, based on the idea of spectral clustering algorithm, a new community algorithm was proposed. The proposed algorithm mined community by mapping a bipartite network to a single network, substituted resource distribution matrix for traditional similarity matrix, effectively guaranteed the information of the original network, improved the input of spectral clustering algorithm and the accuracy of community detection. The modularity function was applied to clustering analysis, and the modularity was used to measure the quality of community mining, effectively solved the problem of automatically determining the clustering number. The experimental results on the actual network and artificial network show that, compared with ant colony optimization algorithm, edge clustering coefficient algorithm etc., the proposed algorithm can not only accurately identify the number of the communities of the bipartite network, but also obtain higher quality of community partitioning without previously known parameters. The proposed algorithm can be applied to the deep understanding of bipartite network, such as recommendation and influence analysis.

To ensure the integrity and accuracy of the meteorological data, combined with automatic weather station's daily average temperature data which contained discontinuous noise, three types of membership functions were submitted. A compensation algorithm of Fuzzy Support Vector Machine (FSVM) based on root-mean-square membership function was designed and the compensation model was established too. Finally, the FSVM method was compared with the traditional Support Vector Machine (SVM) method. The experimental results show that the proposed algorithm has good recognition capability for noise points. After interpolation, the data precision was 1.4℃, better than 1.6℃ of the traditional SVM method. Moreover, the whole data precision was 1.13℃, superior to 1.42℃ of the traditional SVM method.

Concerning the present situation that Quality of Service (QoS) evaluation methods ignore the implicit service quality assessment and lead to inaccurate results, a service evaluation method that comprehensively considered explicit and implicit quality attributes was put forward. Explicit quality attributes were expressed in vector form, using service quality assessment model, after quantization, normalization, then evaluation values were calculated; and implicit quality attributes were expressed according to the evaluation on similar users' recommendation. The users' credibility and difference between old and new users were considered in the evaluation process. Finally the explicit and implicit quality evaluation was regarded as the QoS evaluation results. The experiments were performed in comparison with three algorithms by using one million Web Service QoS data. The simulation results show that the proposed method has certain feasibility and accuracy.

The problem of NoC (Network on Chip) mapping for complex SoC (System on Chip) chip is urgently needed to be solved while most of the existing mapping schemes do not considered testing requirements. This paper proposed a novel NoC mapping algorithm optimized for testing, which considered the improvement of testability and the minimization of mapping cost together. Firstly, the partition algorithm was adopted to arrange all the IP cores into parallel testing groups, combined with the optimized test structure, so that the testing time was minimized. Then, based on traffic information between IP cores, genetic algorithm was applied to accomplish the NoC mapping, which was aimed to the minimum mapping cost. The experimental results on ITC02 benchmark circuits show that the testing time can be reduced by 12.67% on average and the mapping costs decreased by 24.5% on average compared with the random mapping.

Long coherent integration duration is needed for weak Global Positioning System (GPS) signal acquisition. However, it is limited in 10ms by the navigation data bit flipping, which is far from enough. To further improve the acquisition sensitivity, a bit-flipping prediction algorithm was proposed. Firstly, the 5ms signal with possible involve bit-flipping, which was detected through the comparison of the coherent integration results of the several blocks of data, was canceled. Then coherent integration was applied to its sub-block signal in rest 15ms and the results did differential coherent integration to overcome its bit-flip and reduced the square loss of non-coherence. At the same time, the summation operation was done ahead of coherent integration to depress its computational complexity. The theoretical research and simulation results show that the acquisition sensitivity and acquisition efficiency have been improved, and the algorithm even can capture the weak signal with Signal-to-Noise Ratio (SNR) less than -50dB.

In this paper, a matrix computing-based mathematic model was established for the feedforward discrete Madaline network with one hidden layer. By analyzing the matrix representing samples and the matrix representing attributes of the network, and combining the hyperplane division theory in high dimensional space, a batch learning method was proposed for Madaline network with the input of lower dimensional samples. This method can effectively solve the problem of two-category classification of discrete data.

A modified Artificial Bee Colony (ABC) algorithm was proposed for numerical function optimization in this paper, in order to solve the problems of slow convergence and low computational precision of conventional ABC algorithm. The modified ABC algorithm can adjust the step size of the selected neighbor food source position adaptively according to the objective function. On the other hand, the searching method based on a nonlinear adjustment of search range depending on the iteration was introduced for scout bees. The modified ABC algorithm can improve the exploitation, and avoids the premature convergence effectively. The experimental results on six benchmark functions show that, the modified ABC algorithm significantly improves the optimization ability. The modified ABC algorithm can achieve the global minimum values for numerous multimodal functions with high dimension. Compared to the other approaches, the proposed method not only obtains higher quality solutions, but also has a faster convergence speed.

To solve some existing problems in multi-modal function optimization, an improved Clonal Selection Algorithm (CSA) based on the backtracking mechanism, Backtracking Clonal Selection Algorithm (BCSA), was proposed in this paper. The global search capability could be enhanced by using the improved backtracking mechanism and the restraining operation of memory antibodies, which maintained the diversity of antibodies. In addition, in order to improve the convergence speed, the improved dynamic mutation, selection and crossover operation were adopted. The results tested on typical multi-modal functions show that BCSA has a powerful performance in global search.

In image based robot visual servo system， image Jacobian matrix is commonly used for calibration. Using on-line image Jacobian matrix estimation method， the complex system calibration can be avoided without knowing the accurate system models. In this paper， the author proposed to use the Unscented Kalman Filter (UKF) for on-line estimation of total Jacobian matrix for the sake of improving the tracking accuracy of the robots which is tracking a moving object. In order to evaluate the performance， three algorithms using Kalman Filter (KF), Particle Filter (PF), and UKF were used for total Jocobian matrix estimation in a 2-Degree Of Freedom (DOF) robot visual servo platform. The experimental results show that the UKF algorithm outperforms the other two in accuracy while its time cost is very much close to the KF algorithm.

A method based on a 3D model converter to simulate the development of virtual plant was presented. The converter was mainly used to import the plant organs which have fine details into the virtual plant developmental system, then communicating with the L-system to implement the simulation of the development of plants controlled by the physiology of plant. It improves the virtual effect produced by the former systems which only take account of the geometric model.

 Based upon the decoupling of end-system names from network addresses, FARA provides considerable generality, flexibility and security with a range of assurance levels by avoiding the introduction of a new global namespace. The new concept of naming and binding presents an all-important direction to new generation network architecture.