Aiming at the problem of excessive time overhead of the Extended Isolation Forest (EIF) algorithm, a new algorithm named Extended Isolation Forest based on Random Subspace (RS-EIF) was proposed. Firstly, multiple random subspaces were determined in the original data space. Then, in each random subspace, the extended isolated tree was constructed by calculating the intercept vector and slope of each node, and multiple extended isolated trees were integrated into a subspace extended isolation forest. Finally, the average traversal depth of data point in the extended isolation forest was calculated to determine whether the data point was abnormal. Experimental results on 9 real datasets in Outliter Detection DataSet (ODDS) and 7 synthetic datasets with multivariate distribution show that, the RS-EIF algorithm is sensitive to local anomalies and reduces the time overhead by about 60% compared with the EIF algorithm; on the ODDS datasets with many samples, its recognition accuracy is 2 percentage points to 12 percentage points higher than those of the isolation Forest (iForest) algorithm, Lightweight On-line Detection of Anomalies (LODA) algorithm and COPula-based Outlier Detection (COPOD) algorithm. The RS-EIF algorithm has the higher recognition efficiency in the dataset with a large number of samples.

Aiming at the difference between the clustering effects of wavelet clustering algorithm under different grid division scales, an improved method based on peak grid was proposed. The algorithm mainly aimed at improving the detection method of connected regions in wavelet clustering. First, the spatial grids after wavelet transform were sorted according to the grid values; then, the breadth-first-search method was used to traverse each spatial grid to detect the peak connected regions in the data after wavelet transform; finally, the connected regions were marked and mapped to the original data space to obtain the clustering result. Experimental results of 8 synthetic datasets(4 convex datasets and 4 non-convex datasets) and 2 real datasets in the UCI database showed that the improved algorithm had good performance at low grid division scales, and compared with the original wavelet clustering algorithm, this algorithm had the requirement for grid division scale reduced by 25% to 60%, and the clustering time reduced by 14% under the same clustering effect.

In order to improve the speech enhancement effect of small microphone array, a better method was proposed for small-array speech enhancement by combining the Array Crosstalk Resistant Adaptive Noise Cancellation (ACRANC) method with the BeamForming (BF) method. Firstly, ACRANC subsystems were constructed to obtain multiple channels of enhanced speech signals. Then, the proposed Adaptive Mode Control (AMC) algorithm and the Delay And Sum (DAS) beamforming method were applied to the enhanced speech signals for further improving the enhancement effect of multi-channel speech signals. The computational complexity of the proposed method was estimated, and it was verified that the proposed method was able to be realized in real-time with common chips. Experimental results in actual environments show that the speech enhancement effect of the proposed method is higher than that of the ACRANC method and thus the method has some advantages.

In order to improve the denoising performance of dual mini micro-array speech enhancement system in multi-noise environment, an improved generalized sidelobe canceller speech enhancement algorithm for dual mini micro-array was proposed. According to the structure characteristics of the dual mini micro-array, firstly, an improved coherent filtering algorithm based on noise cross-power spectrum estimation was used to eliminate the weak correlation noise between microphones with long distances. Secondly, the strong correlation noise between microphones with short distances was eliminated by using a generalized sidelobe cancelling algorithm. Finally, the minima-controlled recursive averaging based sub-band spectrum subtraction was used to eliminate the residual noise in different spectrum bands purposefully. Experimental results show that the proposed algorithm achieves better score in perceptual evaluation of speech quality than existing dual mini micro-array speech enhancement algorithms under multi-noise environment, and improves the suppression effect of dual mini micro-array speech enhancement system on complex noise to a certain extent.

Since traditional filter methods to solve Simultaneous Localization And Mapping (SLAM) problems will accumulate errors, a three-dimensional SLAM algorithm based on Bag-Of-Words (BOW) algorithm which can effectively solves the problem of accumulating errors was proposed. Compared to the common algorithms like random selection and k-Dimensional Tree (Kd-Tree), a tree structure visual bag of words loop detection algorithm was designed which could greatly increase the speed of similar scene detection. Firstly, a GPU based feature extraction algorithm was adopted. Through using cross matching and k-Nearest Neighbor (kNN) algorithm, robust inliers were got. Secondly, Random Sample Consensus Singular Value Decomposition (RANSAC SVD) algorithm was used to calculate the initial transformation between two frames. And then a Generalized-Iterative Closest Point (G-ICP) algorithm was used to optimize the transformation to get precise transformation. At last, incremental Smoothing And Mapping (iSAM) Graph optimization algorithm was used to calculate the camera pose and the point cloud map and trajectory were created. The test results on the standard dataset show that the algorithm can achieve good robustness and precision under complex environment.

In order to get better speech enhancement effect for hearing aids when used in the environment with non-stationary or multiple noise, which will lead a sharp decline effect of user experience, a Coherent Filter Generalized Sidelobe Canceller (CF-GSC) speech enhancement algorithm based on small size microphone array was proposed. Aiming at the weak correlation noise which caused by the waves, fans and other approximate white noise, as well as the strong correlation noise caused by the point or other competitive sources, coherent filtering and traditional Generalized Sidelobe Canceller (GSC) structure were utilized to remove weak correlation and strong correlation noise separately, the Voice Activity Detection (VAD) algorithm was also applied during this process. The simulation results show that the proposed algorithm can obtain enhancement effect by almost 2 dB compared with the improved coherent filter and traditional generalized sidelobe canceller method under the environment of a variety of noise, meanwhile, the speech intelligibility also gets obviously improved.