Focused on the problems of low accuracy and long time cost of manual detection of breast cancer lymph node metastasis, a neural network detection model based on residual network structure and with Octave convolution method to design convolution layers was proposed. Firstly, based on the convolution layer of residual network, the input and output eigenvectors in the convolution layer were divided into high frequencies and low frequencies, and the channel width and height of the low-frequencies were reduced to half of those of the high frequencies. Then, the convolution operation between the low-frequency vector and the high-frequency vector was realized by up-sampling the low-frequency vector with the reduction by half, and the convolution operation between the high-frequency vector and the low-frequency vector was realized by average pooling of the high-frequency vector. Finally, the convolutions between high-frequency vectors and between high-frequency vector and low-frequency vector were added to obtain the high-frequency output, and the convolutions between low-frequency vectors and between low-frequency vector and high-frequency vector were added to obtain the low-frequency output. In this way, Octave convolution layer was constructed, and all convolution layers in residual network were replaced by Octave convolution layers to construct the detection model. In theory, the amount of computation of convolution in Octave convolution layer was reduced by 75%, effectively speeding up the training of the model. On the cloud server with maximum memory of 13 GB and free disk size of 4.9 GB, the PCam (PatchCamelyon) dataset was used for testing. The results show that the model has the recognition accuracy of 95.1%, the memory occupied of 8.7 GB, the disk occupied of 356.4 MB, and the average single training time of 4 minutes 42 seconds. Compared with the ResNet50, this model has the accuracy reduced by 0.6%, the memory saved by 0.6 GB, the disk saved by 105.9 MB, and the single training time shortened by 1 minute. The experimental results demonstrate that the proposed model has high recognition accuracy, short training time and small memory consumption, which reduces the requirement of computing resources under the background of big data era, making the model have application value.

In order to solve the problems in Chinese sentence grouping domain, including the lack of computational linguistics data and the joint makers in a discourse, this paper proposed an automatic Chinese sentence grouping method based on Multiple Discriminant Analysis (MDA). Moreover, sentences group was rarely considered as a grammar unit. An annotated evaluation corpus for Chinese sentence group was constructed based on Chinese sentence group theory. And then, a group of evaluation functions J was designed based on the MDA method to realize automatic Chinese sentence grouping. The experimental results show that the length of a segmented unit and one discourse's joint makers contribute to the performance of Chinese sentence group. And the Skip-Gram model has a better effect than the traditional Vector Space Model (VSM). The evaluation parameter P_μ reaches to 85.37% and WindowDiff reduces to 24.08% respectively. The proposed method has better grouping performance than that of the original MDA method.

An adaptive filter control method based on Recursive Least Squares (RLS) was proposed for solving the low convergence speed of Filtered-X Least Mean Square (FXLMS) and Filtered-U Least Mean Square (FULMS) algorithms. It was roughly composed of two parts: Infinite Impulse Response (IIR) filter and RLS algorithm. IIR filter was as the main frame of the whole algorithm and adjusted the filter weights in real-time to realize the adaptive filter control. Seen from the analysis and comparison, the algorithm has higher convergence speed and the overall vibration response of the controlled object drops by about 65%, which full proves the validation and feasibility of the algorithm.