Short-term traffic flow prediction is not only related to historical data， but also affected by the traffic of adjacent areas. Since the trend and spatial correlation of traffic flow are ignored by traditional Time Series Decomposition （TSD） models， a time series processing model based on the combination of Time Series Decomposition and Spatio-Temporal features （TSD-ST） was proposed. Firstly， the trend component and periodic component were obtained by using Empirical Mode Decomposition （EMD） and Discrete Fourier Transform （DFT）， the Spatio-Temporal （ST） correlation of the fluctuation component was mined by Mutual Information algorithm （MI）， and the state vector was reconstructed on the basis of the above. Then， the fluctuation component was predicted by using the state vector through Long Short-Term Memory （LSTM） network. Finally， the final predicted value was obtained by reconstructing the prediction results of the three parts of the sequence. The validity of the model was verified on the real data of Interstate I090 in Washington State， USA. Experimental results show that the Root Mean Square Error （RMSE） of the proposed model TSD-ST-LSTM is reduced by 16.5%， 34.0%， and 36.6% compared with that of Support Vector Regression （SVR）， Gradient Boosting Regression Tree （GBRT） and LSTM， respectively. It can be seen that the proposed model is very effective in improving prediction accuracy.

Since the space for saving cipher texts is more than that for saving plaintexts in elliptic curve cryptography encrypting process, a method of generating cipher texts which utilized elliptic curve cryptography based on plaintext length was proposed. Firstly, by analyzing encrypting operation process of elliptic curve encryption, it was deduced that the space for cipher texts of elliptic curve points was decided by the number of plaintexts in elliptic curve points. Secondly, by fusing the encrypting patterns based on segmentation and combination plaintexts, an encrypting model was constructed, and plaintext segmentation algorithm and plaintext combination algorithm were put forward to generate the minimum number of elliptic curve points. Finally, the demanded space for saving cipher texts in elliptic curve points was calculated, and the solutions for reducing the number of cipher texts in elliptic curve points were given. By the analysis and calculation, it is shown that the space of cipher text elliptic curve points decreases 88.2% by segmentation plaintexts and decreases 90.2% by combination plaintexts. The results show the method can decrease the number of cipher texts in elliptic curve points and the storage space demand for hardware.

To evaluate the connectivity of parallel and distributed computer systems which take 3-ary n-cubes as underlying topologies, by constructing the extra 2-cut of 3-ary n-cubes, the second-extraconnectivity of 3-ary n-cubes was proved to be equal to 6n-7 for an arbitrary integer n no less than 2. The result shows that for any two nodes of the parallel and distributed system which take the 3-ary n-cube as underlying topology, there is at least a fault-free path connecting them if the number of the faulty nodes in the system does not exceed 6n-8 and each connected branch still has at least three healthy nodes.

Recognition of hand-written ballot symbols is crucial in vote-counting system based on image understanding. To improve the accuracy of ballot symbol recognition, a symbol recognition approach based on run feature was proposed. Firstly, the concept of run was presented and a discriminant model was established based on run features of ballot symbols. Secondly, the relative positions of runs were described with a ternary tree. In addition, the main run regions were extracted in noisy environment by merging run regions, and an approach of recognizing ambiguous symbols was also given. Finally, the experimental results show that the run features can accurately reflect the geometrical features of ballot symbols. The proposed approach achieves high accuracy, and its accuracy is 6.07% higher than that of template matching algorithm.

To deal with broadband signal processing, a new algorithm for the Direction Of Arrival (DOA) estimation of wideband Linear Frequency Modulation (LFM) signal was introduced. In this paper, the broadband LFM signal was transformed into a narrowband signal by Discrete Polynomial-phase Transform (DPT). After that, the wideband LFM signal could be transformed into a single sinusoidal signal and the new noise. And the time-varying direction vector in time domain was changed to the time-invariant vector. Then, the conventional narrow band signal processing method, Multiple Signal Classification (MUSIC) algorithm, was used to estimate the DOA. The theoretical analysis and simulation results show that the proposed method can accurately estimate DOA of the signal. The scheme is easy to implement with less computation, and it has good estimation performance.

In the image resizing algorithm based on mesh deformation, the mesh quality is crucial. A new local-global based triangular mesh optimization algorithm was proposed to improve the quality of the triangular mesh representing the image being resized. In the local step, the equilateral triangle, which is most similar to each triangle in the mesh, was obtained using custom rules, and a set of objectives affine transformation function was got. While in the global step, the optimal position of each node was solved by least-square method based on as rigid as possible method to minimize the value of the deformation energy function. Simultaneously, constrained control was added in optimization process to protect the critical areas of the grid from changing. The experimental results demonstrate that the quality of the planar triangular mesh is greatly improved.