Most existing air quality prediction methods focus on simple time series data for trend prediction， and ignore the pollutant transport and diffusion laws and corresponding classified pattern features. In order to solve the above problem， a PM_2.5 diffusion characteristic extraction method based on Candlestick Pattern Matching （CPM） was proposed. Firstly， the basic periodic candlestick charts from a large number of historical PM_2.5 sequences were generated by using the convolution idea of Convolutional Neural Network （CNN）. Then， the concentration patterns of different candlestick chart feature vectors were clustered and analyzed by using the distance formula. Finally， combining the unique advantages of CNN in image recognition， a hybrid model integrating graphical features and time series features sequences was formed， and the trend reversal that would be caused by candlestick charts with reversal signals was judged. Experimental results on the monitoring time series dataset of Guilin air quality online monitoring stations show that compared with the VGG （Visual Geometry Group）-based method which uses the single time series data， the accuracy of the CPM-based method is improved by 1.9 percentage points. It can be seen that the CPM-based method can effectively extract the trend features of PM_2.5 and be used for predicting the periodic change of pollutant concentration in the future.

In order to further improve the precision of navigation signal, band efficiency and the reliability performance for navigation systems, a new signal system adopted Minimum Shift Keying (MSK) with Binary Offset Carrier (BOC) based on Low Density Party Check (LDPC) codes was presented, which was called LDPC-MSK-BOC signal system. The navigation performances of the BOC and MSK-BOC were evaluated based on the parameters of Compass and GPS typical signals, which were scaled with power spectral density, code tracking error, multipath error envelope, bit error rate, anti-narrowband jamming merit factor and anti-matched spectrum jamming merit factor for demodulation processing, anti-narrowband jamming merit factor and anti-matched spectrum jamming merit factor for code tracking processing, and spectral separation coefficient. The theoretical analysis and simulation show that the proposed system has better performance in the field of code tracking precision and anti-multipath compared with BOC signal system under the limited spectrum resource condition. Meanwhile, the signal structure can further improve system reliability and band efficiency.

The existing dimensionality reduction methods based on manifold learning are sensitive to the selection of local neighbors, and the reduced data do not have good separability. This paper proposed a dimensionality reduction method with data separability based on adaptive neighborhood selection, which adaptively selected the neighborhood at each sample point based on estimated intrinsic dimensionality of data and local tangent orientation. Meanwhile, it clustered the similar sample points by using clustering information when mapping data, which guaranteed good separability for the reduced data and achieved better dimensionality reduction results. The experimental results show that the new method derives a better embedding result on the artificially generated data sets. In addition, it can get expected result on face visualization classification and image retrieval.