Constructing the transition probability matrix for outlier detection by using traditional graph-based methods requires the use of the overall distribution of the data， and the local information of the data is easily ignored， resulting in the problem of low detection accuracy， and using the local information of the data may lead to “suspended link” problem. Aiming at these problems， an Outlier Detection algorithm based on Hologram Stationary Distribution Factor （HSDFOD） was proposed. Firstly， a local information graph was constructed by adaptively obtaining the set of neighbors of each data point through the similarity matrix. Then， a global information graph was constructed by the minimum spanning tree. Finally， the local information graph and the global information graph were integrated into a hologram to construct a transition probability matrix for Markov random walk， and the outliers were detected through the generated stationary distribution. On the synthetic datasets A1 to A4， HDFSOD has higher precision than SOD （Outlier Detection in axis-parallel Subspaces of high dimensional data）， SUOD （accelerating large-Scale Unsupervised heterogeneous Outlier Detection）， IForest （Isolation Forest） and HBOS （Histogram-Based Outlier Score）； and AUC （Area Under Curve） also better than the four comparison algorithms generally. On the real datasets， the precision of HSDFOD is higher than 80%， and the AUC of HSDFOD is higher than those of SOD， SUOD， IForest and HBOS. It can be seen that the proposed algorithm has a good application prospect in outlier detection.

Aiming at the shortcomings of Equilibrium Optimizer （EO） such as low optimization accuracy， slow convergence and being easy to fall into local optimum， a new EO in consideration with distance factor and Elite Evolutionary Strategy （EES） named E-SFDBEO was proposed. Firstly， the distance factor was introduced to select the candidate solutions of the equilibrium pool， and the adaptive weight was used to balance the fitness value and distance， thereby adjusting the exploration and development capabilities of the algorithm in different iterations. Secondly， the EES was introduced to improve the convergence speed and accuracy of the algorithm by both elite natural evolution and elite random mutation. Finally， the adaptive t-distribution mutation strategy was used to perturb some individuals， and the individuals were retained with greedy strategy， so that the algorithm was able to jump out of the local optimum effectively. In the simulation experiment， the proposed algorithm was compared with 4 basic algorithms and 2 improved algorithms based on 10 benchmark test functions and Wilcoxon rank sum test was performed to the algorithms. The results show that the proposed algorithm has better convergence and higher solution accuracy.

At present， each consensus node of the parallel chain needs to send its own consensus transaction to the main chain to participate in the consensus. As a result， a large number of consensus transactions occupy the block capacity of the main chain seriously and waste transaction fees. In order to solve the above problems， an optimization scheme of parallel chain consensus algorithm based on BLS （Boneh-Lynn-Shacham） aggregate signature technology was proposed by combining bilinear map technology with the characteristics of the same consensus data and different signatures of consensus trades on parallel chain. Firstly， the transaction data was signed by the consensus node. Then， the consensus transaction was broadcasted by each node of the parallel chain and the message was synchronized internally through P2P （Peer-to-Peer） network. Finally， the consensus transactions were counted by Leader node. When the number of consensus transactions was greater than 2/3， the corresponding BLS signature data was aggregated and the transaction aggregate signature was sent to the main chain for verification. Experimental results show that compared with the original parallel chain consensus algorithm， the proposed scheme can effectively solve the problem of consensus nodes on the parallel chain repeatedly sending consensus transactions to the main chain， save transaction fees with reducing the occupancy of the storage space of the main chain， only occupy 4 KB of the storage space of the main chain and only generate a transaction fee of 0.01 BiT Yuan （BTY）.

Aiming at the problem of spectrum allocation based on maximizing network benefit in cognitive radio and the fact that Manta Ray Foraging Optimization （MRFO） algorithm is difficult to solve the problem of spectrum allocation， a Discrete Manta Ray Foraging Optimization （DMRFO） algorithm was proposed.Considering the pro-1 characteristic of spectrum allocation problem in engineering， firstly， MRFO algorithm was discretely binarized based on the Sigmoid Function （SF） discrete method. Secondly， the XOR operator and velocity adjustment factor were used to guide the manta rays to adaptively adjust the position of next time to the optimal solution according to the current velocity. Then， the binary spiral foraging was carried out near the global optimal solution to avoid the algorithm from falling into the local optimum. Finally， the proposed DMRFO algorithm was applied to solve the spectrum allocation problem. Simulation results show that the convergence mean and standard deviation of the network benefit when using DMRFO algorithm to allocate spectrum are 362.60 and 4.14 respectively， which are significantly better than those of Discrete Artificial Bee Colony （DABC） algorithm， Binary Particle Swarm Optimization （BPSO） algorithm and Improved Binary Particle Swarm Optimization （IBPSO） algorithm.

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.

To deal with the problem that the process of Gas Chromatography-Mass Spectrography (GC-MS) data is complex and time consuming which delays the whole experimental progress, taking the alignment of multiple samples as an example, a parallel framework for processing GC-MS data on Sector/Sphere was proposed, and an algorithm of aligning multiple samples in parallel was implemented. First, the similarity matrix of all the samples was computed, then the sample set was divided into small sample sets according to hierarchical clustering and samples in each set were aligned respectively, finally the results of each set were merged according to the average sample of the set. The experimental results show that the error rate of the parallel alignment algorithm is 2.9% and the speedup ratio reaches 3.29 using the cluster with 4 PC, which can speed up the process at a high accuracy, and handle the problem that the processing time is too long.

The time series of Pulse Coupling Neural Network (PCNN) is widely used in the image retrieval and identification, but it cannot embody the shape and characteristics of the image, which results in the failure of image evaluation. In this paper, the local time series of cross visual cortex was proposed to solve the problem. Fist, the image was divided into blocks; then, the time series of each block was extracted; last, the local time series were linked to global time series. The proposed algorithm was compared with the basic time series and the time series added with edges information. The experimental results demonstrate that the proposed method can effectively and efficiently solve the problems existing in the basic time series.