Erasure coding is the underlying implementation technology of double fault tolerance for Redundant Array of Independent Disks-6 （RAID-6）， and the performance of erasure code is one of the important factors affecting the performance of RAID-6. Aiming at the problems of I/O imbalance and slow data recovery of array erasure codes commonly used in RAID-6， an Exclusive OR （XOR） based hybrid array code was proposed， namely J-code. A new parity check generation rule was adopted by J-code. Firstly， two-dimensional array constructed from the original data was used to calculate the diagonal parity bits and construct a new array. Then， the positional relationship between the data blocks in the new array was used to calculate the anti-diagonal parity bits. Besides， the original data and part of the parity bits were stored by J-code on the same disk， which reduced the number of XOR operations in the process of encoding and decoding as well as the number of data blocks read in the recovery process of a single disk， thereby reducing the complexity of encoding and decoding as well as the I/O cost for repairing a single disk， and alleviating the phenomenon of disk hotspot concentration. Simulation results show that compared with array codes such as RDP （Row-Diagonal Parity） and EaR （Endurance-aware RAID-6）， J-code has the encoding time reduced by 0.30% to 28.70%， the single disk failure repair time reduced by 2.23% to 31.62%， and the double disk failure repair time reduced by 0.39% to 36.00%.

Aiming at the problems that the deep spectral clustering models perform poorly in training stability and generalization capability， a Deep Spectral Clustering algorithm with L1 Regularization （DSCLR） was proposed. Firstly， L1 regularization was introduced into the objective function of deep spectral clustering to sparsify the eigen vectors of the Laplacian matrix generated by the deep neural network model. And the generalization capability of the model was enhanced. Secondly， the network structure of the spectral clustering algorithm based on deep neural network was improved by using the Parametric Rectified Linear Unit activation function （PReLU） to solve the problems of model training instability and underfitting. Experimental results on MNIST dataset show that the proposed algorithm improves Clustering Accuracy （CA）， Normalized Mutual Information （NMI） index， and Adjusted Rand Index （ARI） by 11.85， 7.75， and 17.19 percentage points compared to the deep spectral clustering algorithm， respectively. Furthermore， the proposed algorithm also significantly improves the three evaluation metrics， CA， NMI and ARI， compared to algorithms such as Deep Embedded Clustering （DEC） and Deep Spectral Clustering using Dual Autoencoder Network （DSCDAN）.

Aiming at the problems of inaccurate feature extraction and high complexity of traditional ElectroCardioGram (ECG) detection algorithms based on morphological features, an improved Long Short-Term Memory (LSTM) neural network was proposed. Based on the advantage of traditional LSTM model in time series data processing, the proposed model added reverse and depth calculations which avoids extraction of waveform features artificially and strengthens learning ability of the network. And supervised learning was performed in the model according to the given heart beat sequences and category labels, realizing the arrhythmia detection of unknown heart beats. The experimental results on the arrhythmia datasets in MIT-BIH database show that the overall accuracy of the proposed method reaches 98.34%. Compared with support vector machine, the accuracy and F1 value of the model are both improved.

A scheduling mechanism based on Service Level Objective (SLO) in multi-tenant cluster, including a preference scheduling algorithm and a resource preemption algorithm, was proposed to solve the problem of the inability to guarantee the SLOs of jobs in multi-tenant clusters. The preference scheduling algorithm considered the users who overused resources above their quota and the users who did not, then assigned a higher priority to the jobs of the latter users, under this condition, the job with highest priority was preferentially allocated resources. When the resources was limited, the resource preemption algorithm preempted the resource for the jobs whose urgency was above the threshold, and chose the jobs with the lowest urgency in the corresponding range of the running jobs according to the resource usages of tenants.The experimental results show that, compared with the current multi-tenant scheduler named Capacity Scheduler, the proposed mechanism can significantly improve the deadline guarantee rate of jobs and SLO with guaranteeing the job execution efficiency and the equity among tenants at the same time.

In response to the issue of security and privacy-preserving in mobile cloud computing, an anonymous mechanism using cloud storage was proposed. Zero-knowledge proofs and the digital signature technology were introduced into anonymous registration to simplify the steps of key authentication, building upon which the third party was used to bind users and their identity certificates that avoid legitimate cloud services for malicious purposes. The focus of data sharing is on how to take advantage of account parameters of sharers so as to solve the security issues due to secret key loss. Theoretical analysis shows that the proposed identity certificate and shared key generation schemes contribute to users' privacy.

In order to overcome the weakness of direction and the poorness of purpose in multidimensional search and the premature convergence, this paper presented an improved particle swarm optimization algorithm. For both the best and the worst information of the cognitive part and the best and the worst information of the social part, the improved algorithm respectively assigned different learning factors, and the algorithm has a greater ability to learn. Each particle associatively memorized the best information and the worst information in its history, and then found the optimal position in accordance with the principle of chasing the best and avoiding the worst. Associative memory overcomes the weakness of direction and the poorness of purpose in multidimensional search. The principle of chasing the best and avoiding the worst keeps the diversity of population, helps to improve the convergence speed, and overcomes the premature convergence. Simulation test of the benchmark function has verified the validity of the algorithm.

Concerning the problem of tracking interested moving target inaccurately because of complex background, a robust tracking algorithm based on adaptive multi-feature fusion was proposed. First, the algorithm obtained the weighted color distribution model of interested moving target in the HSV color space. Then invariant moment was used to eliminate the interference of the similar background color and illumination changes. The algorithm fused the two features in the particle filter by adjusting their weights and updating particle weights adaptively. Thus, the algorithm can track the moving target accurately and stably. The experimental results show that the algorithm can track interested moving target accurately when moving target is tracked under complex background such as translation, variant posture, and be blocked of the moving object, varying illumination and the interference of the similar background color. The algorithm has strong robustness to background interference.

Concerning the copyright protection of binary document images, a zero watermark algorithm was proposed. This algorithm constructed the texture image based on Local Binary Pattern (LBP), and then zero watermark information was constructed from the texture spectral histograms of the texture image. This method had a better invisibility compared to other text image watermarking, and the original image information would not be changed. Watermark attacks including image cropping, adding noise and rotation operators were tested. The experimental results show that the proposed zero watermark algorithm has a good performance in robustness. And these attack operators have little impact on zero watermark information, and the algorithm is of stability with the lowest standard correlation above 0.85.

The dynamics, autonomy and anonymity of the P2P network cause many security problems. The traditional trust model in structured P2P network can not satisfy the P2P environment commendably. In this paper, a trust model named DrTrust in unstructured P2P network was proposed, which was based on direct and recommended trust scheme. It takes advantage of the combination of the direct trust and recommended trust to calculate the trust accurately, using distributed way to store trust value and incentive and punishment mechanism to update trust value. The experimental results prove that DrTrust outperforms the current trust models in accurate trust computation and the inhibition to the malicious peers.

Embedded Ethernet was applied to solving the bottleneck of data transfer in distributed seismic data acquisition system. A relay topology was put forward based on the features of seismic acquisition system, and the key point of network protocol based on this architecture was also given, in which the generation of route table and dynamic IP address assign were discussed with emphasis.