Autonomous Underwater Vehicle (AUV) task planning is the key technology that affects the level of cluster intelligence. In the existing task planning models, only the problem of homogeneous AUV cluster and single dive task planning are considered. Therefore, a multi-dive task planning model for AUV heterogeneous clusters was proposed. Firstly the model considered the energy constraints of AUV, the engineering cost of AUV multiple round-trip charging in mother ship, the efficiency difference between heterogeneous cluster individuals, and the diversity of tasks. Then in order to improve the efficiency of solving the problem model, an optimization algorithm based on discrete particle swarm was proposed. The algorithm introduced matrix coding for describing particle velocity and position and the task loss model for evaluating particle quality to improve the particle updating process, achieving efficient target optimization. Simulation experiments show that the algorithm not only solves the multi-dive task planning problem of heterogeneous AUV clusters, but also reduces the task loss by 11% compared with the task planning model using genetic algorithm.

In most existing multi-manifold face recognition algorithms, the original data with noise are directly processed, but the noisy data often have a negative impact on the accuracy of the algorithm. In order to solve the problem, a Robust Multi-Manifold Discriminant Local Graph Embedding algorithm based on the Maximum Margin Criterion (RMMDLGE/MMC) was proposed. Firstly, a denoising projection was introduced to process the original data for iterative noise reduction, and the purer data were extracted. Secondly, the data image was divided into blocks and a multi-manifold model was established. Thirdly, combined with the idea of maximum margin criterion, an optimal projection matrix was sought to maximize the sample distances on different manifolds while to minimize the sample distances on the same manifold. Finally, the distance from the test sample manifold to the training sample manifold was calculated for classification and identification. The experimental results show that, compared with Multi-Manifold Local Graph Embedding algorithm based on the Maximum Margin Criterion (MLGE/MMC) which performs well, the classification recognition rate of the proposed algorithm is improved by 1.04, 1.28 and 2.13 percentage points respectively on ORL, Yale and FERET database with noise and the classification effect is obviously improved.

To solve privacy leakage and other security problems in Radio-Frequency Identification (RFID) tag authentication and tag ownership transfer, and to simplify the design of tag ownership transfer protocol, an RFID authentication protocol enabling tag ownership transfer was proposed for those tags that support Elliptic Curve Cryptography (ECC). The structure of the protocol is similar to the structure of the Diffie-Hellman logarithm, and tag privacy of the protocol is based on complexity to solve the computational Diffie-Hellman problem. Analysis of tag privacy and other security properties of the protocol were given, and comparisons between recent ECC-based authentication protocols and the proposed protocol were also given. The results show that the proposed protocol achieves best performance, under a comprehensive evaluation of supporting tag ownership transfer, tag computation cost, communication cost and tag privacy protection. In addition, a simplified version that realizes tag authentication to reader and is suitable for secure environments was also given.

It is difficult for existing rumor detection methods to overcome the disadvantage of data collection and detection delay. To resolve this problem, a rumor detection method based on burst topic detection inspired by the momentum model and domain expert discovery was proposed. The dynamics theory in physics was introduced to model the topic features spreading among the Weibo platform, and dynamic physical quantities of the topic features were used to describe the burst characteristics and tendency of topic development. Then, emergent topics were extracted after feature clustering. Next, according to the domain relativity between the topic and the expert, domain experts for each emergent topic were selected within experts pool, which is responsible for identifying the credibility of the emergent topic. The experimental results show that the proposed method gets 13 percentage points improvement on accuracy comparing with the Weibo rumor identification method based merely on supervised machine learning, and the detection time is reduced to 20 hours compared with dominating manual methods, which means that the proposed method is applicable for real rumor detection situation.

Since the exsiting dictionary learning methods can not effectively construct discriminant structured dictionary, a discriminant dictionary learning method with discriminant and representative ability was proposed and applied in software defect detection. Firstly, sparse representation model was redesigned to train structured dictionary by adding the discriminant constraint term into the object function, which made the class-dictionary have strong representation ability for the corresponding class-samples but poor representation ability for the irrelevant class-samples. Secondly, the Fisher criterion discriminant term was added to make the representative coefficients have discriminant ability in different classes. Finally, the optimization of the designed dictionary learning model was solved to obtain strongly structured and sparsely representative dictionary. The NASA defect dataset was selected as the experiment data, and compared with Principal Component Analysis (PCA), Logistics Regression (LR), decision tree, Support Vector Machine (SVM) and the typical dictionary learning method, the accuracy and F-measure value of the proposed method were both increased. Experimental results indicate that the proposed method can increase detection accuracy with improving the classifier performance.

Due to user revocability, the new data manager needs to verify the integrity of the former data manager's management data stored in the cloud server, which is obviously inevitable in reality. In order to solve this issue, an efficient privacy-preserving public auditing scheme for cloud storage scheme was proposed. Firstly, in the proposed scheme based on unidirectional proxy re-signature, the proxy re-signature key was generated by the current data manager's private key and the former public key, which did not leak any information, to realize transferring of ownership data caused by the users revocability securely. Secondly, it was proved that the proposed scheme could protect any malicious cloud server from generating the forged response proof which could pass the verification to cheat the Third Party Auditor (TPA). Moreover, the random masking technique was employed to prevent the curious TPA from revealing the primitive data blocks. Compared with the Padna scheme, even though the proposed scheme adds the new functions but its communication overhead in the process of auditing and computational cost are also lower than Panda's.

The estimation of the low-dimensional subspace data may have serious deviation under lacking of the training samples. In order to solve the problem, a novel regularized neighborhood preserving embedding algorithm based on QR decomposition was proposed. Firstly, a local Laplace matrix was defined to preserve local structure of the original data. Secondly, the eigen spectrum space of within-class scatter matrix was divided into three subspaces, the new eigenvector space was obtained by inverse spectrum model defined weight function and then the preprocess of the high-dimensional data was achieved. Finally, a neighborhood preserving adjacency matrix was defined, the projection matrix obtained by QR decomposition and the nearest neighbor classifier were selected for face recognition. Compared with the Regularized Generalized Discriminant Locality Preserving Projection (RGDLPP) algorithm, the recognition accuracy rate of the proposed method was respectively increased by 2 percentage points, 1.5 percentage points, 1.5 percentage points and 2 percentage points on ORL, Yale, FERET and PIE database. The experimental results show that the proposed algorithm is easy to implement and has high recognition rate relatively under Small Sample Size (SSS).

In engineering practice, due to the influence of the factors such as manufacturing process, source design, and package technique, most commercially available optical sources have unique radiation characteristics which belong to the range of non-Lambertian emitters. However, the physical channel characterization of current wireless optical local area network is based on the Lambertian emitter which taking the started optical source as a standard. Aiming at this problem, the radiation characteristics of two typical non-Lambertian emitters were incorporated into the physical multipath channel characterization of wireless optical local area network. The focus was the induced effect on the coverage performance of indoor wireless optical local area network by the comparison with the traditional Lambertian emitter. The numerical results indicate that, the non-Lambertian emitters, especially the one with bowl-shaped radiation characteristic, are capable of significantly improving the spatial uniformity of optical path loss and the respective increase can be up to 0.5 dB. However, in the time delay characteristic of coverage domain, both the two non-Lambertian emitters can elevate the level of Root Mean Square (RMS) delay spread to different extent. Specifically, the respective elevations are 0.27 ns and 0.38 ns respectively.

The adaptability of light source array simplification in channel modeling of wireless optical access network was evaluated, the applicable performance of this simplification to channel characterization under different transmitter configuration, different Field Of View (FOV) and different source radiation pattern was discussed. Simulation results illustrate that the applicable performance has a strong dependence on the FOV, only when the FOV is no less than 60°, the induced deviation to optical path loss and root mean square delay spread are limited within 1.53 dBo and 0.77 ns, respectively.

To overcome the inadaptability of conventional single source-oriented modeling scheme in satisfying the need of Light Emitting Diode (LED) array-based wireless optical access network, two novel evolution editions of this scheme were proposed. The first one sufficiently covered the diffuse portion from all sources. Based on the first one, the second evolution edition more precisely included the relative delays of all diffuse components. The two proposed schemes were capable of avoiding the overestimation in baseband transmission characteristic by current evolution scheme. Numerical results indicate that, the overestimation in baseband transmission bandwidth and transmission gain can be up to 50 MHz and 15 dB respectively as diffuse path components of all sources are sufficiently characterized. Moreover, two novel schemes are capable of quantifying the correlation between one of receiver position, reflectance and Field Of View (FOV) of receiver and transmission characteristic which overcomes the limitation of current evolution edition in characterizing above correlation.

In three-dimensional sound reproduction with two speakers, Crosstalk Cancellation System (CCS) performance optimization often pay more attention to the effect independently by the factors such as inverse filter parameters design and loudspeaker configuration. A frequency-domain Least-Squares (LS) estimation approximation was proposed to use for the performance optimization. The relationship between these factors and their effect on CCS performance was evaluated systematically. To achieve the tradeoff of computing efficiency and system performance of crosstalk cancellation algorithm, this method obtained the optimization parameters. The effect of crosstalk cancellation was evaluated with Channel Separation (CS) and Performance Error (PE) index, and the simulation results indicate that these parameters can obtain good crosstalk cancellation effect.

Considering high dimensional data image in face recognition application and Euclidean distance cannot accurately reflect the similarity between samples, a Mahalanobis distance based Local Marginal Fisher Analysis (MLMFA) dimensionality reduction algorithm was proposed. A Mahalanobis distance could be ascertained from the existing samples. Then, the Mahalanobis distance was used to choose neighbors and to reduce the dimensionality of new samples. Meanwhile, to describe the intra-class compactness and the inter-class separability, intra-class “similarity” graph and inter-class “penalty” graph were constructed by using Mahalanobis distance, and local structure of data set was preserved well. With the proposed algorithm being conducted on YALE and FERET, MLMFA outperforms the algorithms based on traditional Euclidean distance with maximum average recognition rate by 1.03% and 6% respectively. The results demonstrate that the proposed algorithm has very good classification and recognition performance.

As indoor Optical Wireless Communications (OWC) is characterized by enormous spectrum source, lower power need, immunity to interference caused by other Radio Frequency (RF) wireless devices, it has attracted an increasing attention in the field. However, due to multipath transmission, it is hard to obtain satisfactory uniform signal quality at the receiving terminal even for locations within the same room. A genetic algorithm based optimization scheme was proposed as a candidate approach for OWC to reduce the variability of the received power. And the simulation results based upon the use of a commercially available detector with the field of view of 50 degree show that the dynamic range of received power can be reduced to 34.6% against the peak optical power from 50.3% while the impact on illumination function is negligible.

The existing indoor optical wireless communication systems can not provide good wireless coverage uniformity and are not suitable for commercial applications. Two distributed light design schemes were proposed to solve the above problems, which increased the group number of LED array, and then increased the uniformity of light distribution of every group. The simulation results indicate that with the same number of LED chips, the distributed light design scheme is superior to the traditional one in uniformity of wireless signal coverage and complexity of commercial implementation, and can avoid the coverage valley.

Based on the theory of kernel generalized optimal feature extracted mode,a new method for the corresponding mode was proposed.Firstly space transform method was used to transform initial kernel between class scatter matrix and kernel total scatter matrix,so the kernel total scatter matrix became positive definition. At the same time,by the means of kernel uncorrelated feature vectors extraction,the feature vectors got were statistical uncorrelated.To verify the effectiveness of this method,experiment was tested on ORL face databases and the result showed that the face recognition method proposed is more available than other methods such as kernel discriminant analysis.