The traditional Convolutional Neural Network (CNN) can only extract local features for human behaviors and actions, which leads to low recognition accuracy for similar behaviors. To resolve this problem, a two-stream Non-Local Residual Network (NL-ResNet) based behavior recognition method was proposed. First, the RGB (Red-Green-Blue) frame and the dense optical flow graph of the video were extracted, which were used as the inputs of spatial and temporal flow networks, respectively, and a pre-processing method combining corner cropping and multiple scales was used to perform data enhancement. Second, the residual blocks of the residual network were used to extract local appearance features and motion features of the video respectively, then the global information of the video was extracted by the non-local CNN module connected after the residual block, so as to achieve the crossover extraction of local and global features of the network. Finally, the two branch networks were classified more accurately by A-softmax loss function, and the recognition results after weighted fusion were output. The method makes full use of global and local features to improve the representation capability of the model. On UCF101 dataset, NL-ResNet achieves a recognition accuracy of 93.5%, which is 5.5 percentage points higher compared to the original two-stream network. Experimental results show that the proposed model can better extract behavior features, and effectively improve the behavior recognition accuracy.

A time series trend prediction algorithm at multiple time scales based on novel feature model was proposed to solve the trend prediction problem of stock and fund time series data. Firstly, a feature tree with multiple time scales of features was extracted from original time series, which described time series with the characteristics of the series in each level and relationship between levels. Then, the hidden states in feature sequences were extracted by clustering. Finally, a Multiple Time Scaled Trend Prediction Algorithm (MTSTPA) was designed by using Hidden Markov Model (HMM) to simultaneously predict the trend and length of the trends at different scales. In the experiments on real stock datasets, the prediction accuracy at every scale are more than 60%. Compared with the algorithm without using feature tree, the model using the feature tree is more efficient, and the accuracy is up to 10 percentage points higher at a certain scale. At the same time, compared with the classical Auto-Regressive Moving Average (ARMA) model and pattern-based Hidden Markov Model (PHMM), MTSTPA performs better, verifying the validity of MTSTPA.

To solve the problems of local or global sparse representation based image saliency detection methods, such as incomplete object extracted, unsmooth boundary and residual noise, an image saliency detection algorithm based on adaptive fusion of local sparse representation and global sparse representation was proposed. Firstly, the original image was divided into a set of image blocks, and these blocks were used to substitute the image pixels, which may decrease the computational complexity. Secondly, the blocked image was represented via local sparse representation. Specifically, for each image block, an overcomplete dictionary was generated by using its surrounding image blocks, and based on such dictionary the image block was sparsely reconstructed. As a result, an initial local saliency map which may effectively extract the edges of the salient objects could be gotten. Thirdly, the blocked image was represented by global sparse representation. The procedures were similar to the above steps. The difference was that, for each image block, the overcomplete dictionary was constructed by using the image blocks from the four margins of the input image. According to this, an initial global saliency map which could effectively detect the inner areas of the salient objects was obtained. Finally, the initial local and global saliency maps were adaptively fused together to compute the final saliency map. Experimental results demonstrate that compared with several classical saliency detection methods, the proposed algorithm significantly improves the precision, recall and F-measure.

The single frequency modulation or phase modulation waveform based on chaotic sequence has low waveform complexity, which limits predictive probability of chaotic signal, radar intercept probability and anti-interference performance. In order to solve the problems, joint frequency-phase modulation based on chaotic sequence in radar waveform was proposed. Firstly, the radar signal was carried out for the chaotic frequency encoding, which was that a pulse was divided into a series of sub-pulses and different frequency modulation was carried out for different sub-pulses. At the same time, in each frequency encoding sub-pulse, the random initial phase was used in each cycle of waveform. The simulation results show that the maximum value of autocorrelation sidelobe peak of joint frequency-phase modulation based on chaotic radar signal achieved -24.71 dB. Compared with the frequency modulation or phase modulation based on chaotic signal, the correlation performance of the proposed joint frequency-phase modulation has improved. The experimental results show that, the joint frequency-phase modulation chaotic radar waveform combines the advantages of phase modulation and frequency modulation and is an ideal detection signal with the flat power spectrum characteristic of phase modulation and anti-noise-interference ability of frequency modulation.

Because the base stations can only be placed at the border of the monitored area, the border placement problem was formally defined. For the goal to place the minimum number of base stations to cover as much as possible the monitored areas, an improved placement algorithm with polynomial time was proposed. The coverage percentage of initial algorithm was analyzed first. When initial coverage percentage is larger than guaranteed coverage percentage, it is possible to reduce the size of initial placement set. Finally, placement set was gradually improved to achieve the minimun of placement set. The results indicate that the coverage percentage and placement set of the proposed algorithm are superior to random algorithm in different test environments.

Coverage problem is one of the most fundamental problems in wireless sensor networks since it reflects the sensing quality. The present studys mostly concentrates in Omni-directional sensors which is not suitable in many applications such as video surveillance systems consisting of directional video sensors. This paper present a new (k,ω)-angle coverage problem which study directional sensors deployment. The goal is to deploy minimal number of sensors to k-angle cover all the targets. It present a greedy algorithm to solve this problem. For this algorithm, it define three contribution functions to determine the location to deploy sensor. The proposed method greedily selects a maximal contribution location to deploy a sensor until the entire targets are k-angle covered. Simulation results exhibit the characteristic and performance of this algorithm.