Aiming at the tracking ambiguity of Alternate Binary Offset Carrier (AltBOC) (15, 10) signal caused by the multi-peak of autocorrelation function, an unambiguous tracking method based on combined correlation function was proposed. Firstly, two special local reference signals were designed in this method, then the correlation operations between the local reference signals and the received signals were realized through the code tracking loop to obtain two cross-correlation functions, and finally the ambiguity was eliminated through the unambiguous correlation function which is the multiplication of the two obtained cross-correlation functions. The tracking accuracy under thermal noise and anti-multipath interference performance of the proposed method were studied. The results show that:1) the proposed method realizes the unambiguous tracking; 2) the tracking accuracy of the proposed method is between Binary Phase Shift Keying Like (BPSK Like) method and Pseudo Correlation Function (PCF) method, and the tracking accuracy of the proposed method is nearly close to that of PCF method when the noise ratio is greater than 40 dB·Hz; 3) when the multipath delay is greater than 0.1 code chip, the proposed method has the multipath error significantly smaller than that of BPSK Like method and PCF method.

Binary Offset Carrier (BOC) modulation signal is a new type of satellite navigation signal with ingenious design. Eliminating the tracking ambiguity of signal is the premise of exploring its potentiality. Concerning the problem that the autocorrelation function of Cosine-phased BOC (CosBOC) signal is relatively more complex and difficult to express concisely and uniformly, as well as the lack of research on its tracking ambiguity, an unambiguous tracking method based on combined correlation function was proposed for CosBOC (10,5) signal in the E6 band of Galileo system. Firstly, a local reference signal of linear combination of as few local duplicated signals as possible and their shift weighted variants was designed, then through the code tracking loop, the reference signal was correlated with the received signal to obtain a correlation function without sub-peaks, eliminating the tracking ambiguity of code discriminator. Finally the code tracking error of the method was analyzed. The experimental results show that, compared with Binary Phase Shift Keying Like (BPSK Like) method, Bump Jump method and Pseudo Correlation Function (PCF) method, the proposed method has simple tracking loop structure, and it can suppress the tracking ambiguity completely and has better code tracking performance on the whole.

A group key management scheme based on distributed Path Computation Element (PCE) architecture was proposed aiming at the communication characteristics and key management requirement of multi-domain optical networks in PCE architecture. Firstly, the key relation of multi-domain optical network under distributed PCE architecture was modeled as a two-layer key hypergraph by using hypergraph theory. Then, the key management method based on self-authenticated public key cryptosystem and member filtering technique was adopted in the autonomous domain layer and the group key agreement method based on elliptic curve cryptosystem was adopted in the PCE layer. Finally, the generation, distribution, update and dynamic management of the key were completed, and the confidentiality problem of the private key of member and the impersonation problem of the third party node were well solved. At the same time, the computational overhead of key update was reduced. The performance analysis shows that the proposed scheme has forward security, backward security, private key confidentiality and is against collusion attack. Compared with the typical decentralized scheme, the proposed scheme achieves better performance in terms of key storage capacity, encryption/decryption times and communication overhead.

In view of the application scenario of Message Passing Interface (MPI) in the field of high performance computing, in order to optimize the existing data centralized management model of MPI and enhance its processing capability for big data, a set of MPI Data Storage Plug-in (MPI-DSP) for large data processing was developed and designed by using the idea of parallel and distributed systems. Firstly, the interface function was created to achieve the design goal of "calculation to storage migration" in a way of minimizing the impact on MPI system. The file allocation and calculation were separated to make the MPI break through the bottleneck of network transmission when reading large data files. Then, the design goal, operation mechanism and implementation strategy were analyzed and elaborated. The design concept was verified by describing the application of interface function MPI_Open in MPI environment. By comparing the time performance of using MPI-DSP component with that of original MPI in data file processing through Wordcount experiment, the feasibility of MPI "computation to storage migration" mode was preliminarily validated, which enables that it has the large data processing capability in high performance application scenarios. At the same time, the applicable environment and limitations of MPI-DSP were analyzed, and its application scope was defined.

Aiming at the problems of the current self-localization algorithms for indoor mobile robot, such as the low positioning accuracy, increasing positioning error with time, the signal's multipath effect and non-line-of-sight effect, a new mobile robot self-localization method based on Monte Carlo Localization (MCL) was proposed. Firstly, through analyzing the mobile robot self-localization system based on Radio Frequency IDentification (RFID), the robot motion model was established. Secondly, through the analysis of the mobile robot positioning system based on Received Signal Strength Indicator (RSSI), the observation model was put forward. Finally, in order to improve the computing efficiency of particle filter, the particle culling strategy and particle weight strategy considering orientation of the particles were given, to enhance the positioning accuracy and the execution efficiency of the new positioning system. The position errors of the new algorithm were about 3 cm in both the X direction and the Y direction, while position error of the traditional localization algorithm in the X direction and the Y direction were both about 6 cm. Simulation results show that the new algorithm doubles the positioning accuracy, and has good robustness.

On the basis of the Fast Sparse Coding (FSC) model, considering the maximum sparse distribution of feature coefficients and the orthogonality of feature bases of an image, a Modified FSC (MFSC) model was proposed in this paper. This FSC algorithm was based on iteratively solving two convex optimization problems: L ₁-norm based regularized least square problem and L ₂-norm based constrained least square problem, and it can realize the learning of complete bases and overcomplete bases, as well as efficiently extract the features of images. Moreover, the convergence speed of FSC is quicker than that of Basic Sparse Coding (BSC). The images of natural scene and palmprint were used to test the property of FSC algorithm proposed by the authors in feature extraction, and then the extracted features were utilized to image reconstruction. Compared with reconstructed images obtained by BSC, the experimental results verify the validity of the modified FSC in quickly extracting image features.

Concerning the inefficient time performance and lower clustering accuracy revealed by the traditional density-based algorithms of DBSCAN and DBRS, this paper proposed an improved density-based clustering algorithm called DBLRS, which is combined with limited regional sampling technique. The algorithm used the parameter Eps to search for the neighborhood and expanded points of a core point without increasing time and space complexity, and implemented data sampling in a limited area (Eps,2Eps). The experimental results confirm that DBLRS can reduce the probability of large clusters' splitting and improve the algorithmic efficiency and clustering accuracy by selecting representative points to expand a cluster.

The application of traditional software prediction model has poor generalized performance. This paper put forward a software reliability prediction model based on Learning Vector Quantization (LVQ) neural network. First, this paper analyzed the structure characteristics of LVQ neural network and its relation with software reliability prediction. Then the network was used to predict the software reliability. In the end, the authors confirmed the algorithm through multiple simulation experiments under the Matlab environment and the data from Metrics Data Program (MDP) database of National Aeronautics and Space Administration （NASA） of USA. The experimental results indicate that the method is feasible and has a higher prediction precision than the traditional software prediction method.

For the problem of integrity authentication about compressed video streams, this paper proposed a video watermarking algorithm based on Compressive Sensing (CS). The H.264 compressed video stream was taken as the research object. By conducting random projection on the I frames of video sequences, a slight amount of measurements were obtained. Through quantization and replacement encryption, the measurements were embedded into the macro block which had the maximum amplitude in the motion vector in P frame. While authenticating, the watermark was extracted from the P frame that contains watermarked video sequences, and the similarly random CS projection on the I frames was conducted. By comparing the difference between the measurements, the integrity of video could be authenticated. The simulation results show that the intended video watermarking algorithm ensures high video quality, and little effect on the encoding rate, and it has strong robustness against attacks such as frame deletion and frame insertion.

The restriction of computing and storage of Trusted Platform Module （TPM） and authentication complexity of Direct Anonymous Attestation （DAA） results in that DAA mechanism is for single-domain. In order to overcome this limitation, a DAA for cross-domain was proposed in this paper, considering paralleling, high-performance and co-processing in distributed network. The mechanism introduced a trusted third party-Certificate Arbitration Center(CAC) to verify the reality of requester's platform certificate, which issues the cross-domain certificate to the requester with real TPM. The certificate is issued once for repeated usage, which prevents CAC becoming system bottleneck. The mechanism reaches the controllable anonymity, and the analysis with universally composable security shows that the mechanism realizes the cross-domain anonymous attestation securely.