In massive Multiple-Input-Multiple-Output (MIMO) system, as the increse of antenna number, traditional detection algorithms have lower performance, higher complexity, and they are not suitable for high order modulation. To solve the problem, based on the idea of shadow domain, a search tree detection algorithm combining Quadratic Programming (QP) and Branch and Bound (BB) algorithm was proposed. Firstly, with QP model constructed, the unreliable symbols from solution vector of first-order QP algorithm were extracted; then, BB search tree algorithm was applied to the unreliable symbols for the optimal solution; meanwhile three pruning strategies were proposed to reach a compromise between complexity and performance. The simulation results show that the proposed algorithm increases 20 dB performance gain compared with the traditional QP algorithm in 64 Quadrature Amplitude Modulation (QAM) and increases 21 dB performance gain compared with QP algorithm in 256 QAM. Meanwhile, applying the same pruning strategies, the complexity of the proposed algorithm is reduced by about 50 percentage points compared with the traditional search tree algorithm.

Most existing forensic methods for digital speech aim at detecting a specific operation, which means that these methods can not identify various operations at a time. To solve the problem, a universal forensic algorithm for simultaneously detecting various operations, such as pitch modification, low-pass filtering, high-pass filtering, and noise adding was proposed. Firstly, the statistical moments of Mel-Frequency Cepstral Coefficients (MFCC) were calculated, and cepstrum mean and variance normalization were applied to the moments. Then, a multi-class classifier based on multiple two-class classifiers was constructed. Finally, the classifier was used to identify various types of speech operations. The experimental results on TIMIT and UME speech datasets show that the proposed universal features achieve detection accuracy over 97% for various speech operations. And the detection accuracy in the test of MP3 compression robustness is still above 96%.

The kernels of retest on existing railway are orbit line-shape segmentation and line-shape parameter optimization. Based on statistics of inertial angle measurement, an algorithm for line-shape segmentation of orbit plane and optimal line-shape parameter estimation was proposed. On the basis of change laws of orbit line-shape, the combined iterative method was utilized to calculate the optimal line-shape parameter of the orbit. The option of orbit plane line-shape was modeled as an optimization issue. Firstly, the orbit was roughly segmented by least square fitted slope change of fixed-curvature curves. Then, the orbit line-shape was fitted based on the measured data. Finally, the combined iterative method was applied to achieve precise segmentation and establish the optimal line-shape parameter. The simulation examples indicate that the proposed algorithm surpasses the existing artificial estimation algorithm which obtains the line-shape parameter fitting results based on two sets of different segmentation points, and has less differences from the results yielded by the exhaustion method. The Root Mean Square Error (RMSE) of the proposed algorithm is only higher than the exhaustion method by 4.93%, while the computational complexity is just 0.02% of that of the exhaustion method. The actual measurements on Xi'an metro line No.3 also have convinced the availability of the proposed algorithm.