The fake speech produced by modern speech synthesis and timbre conversion systems poses a serious threat to the automatic speaker recognition system. Most of the existing fake speech detection systems perform well for the known attack types in the training process， but degrades significantly in detecting unknown attack types in practical applications. Therefore， combined with the recently proposed Dual?Path Res2Net （DP?Res2Net）， a semi?supervised end?to?end fake speech detection method based on time?domain waveforms was proposed. Firstly， semi?supervised learning was adopted for domain transfer to reduce the difference of data distribution between training set and test set. Then， for feature engineering， time-domain sampling points were input into DP?Res2Net directly， which increased the local multi?scale information and made full use of the dependence between audio segments. Finally， the embedded tensors were obtained to judge fake speech from natural speech after the input features going through the shallow convolution module， feature fusion module and global average pooling module. The performance of the proposed method was evaluated on the publicly available ASVspoof 2021 Speech Deep Fake evaluation set as well as the dataset VCC （Voice Conversion Challenge）. Experimental results show that the Equal Error Rate （EER） of the proposed method is 19.97%， which is 10.8% less than that of the official optimal baseline system， verifying that the semi?supervised end?to?end fake speech detection method based on time?domain waveforms is effective when recognizing unknown attacks and has higher generalization capability.

In order to realize the accurate detection and classification of muscle fatigue states, a new complete muscle fatigue detection and classification system based on human surface ElectroMyoGraphy (sEMG) signals was proposed. Firstly, human sEMG signals were collected through AgCl surface patch electrode and high-precision analog front-end device ADS1299. The time-domain and frequency-domain features of sEMG signals reflecting human muscle fatigue states were extracted after the denoising preprocessing using wavelet transformation. Then, on the basis of the common features such as Integrated ElectroMyoGraphy (IEMG), Root Mean Square (RMS), Median Frequency (MF), Mean Power Frequency (MPF), in order to depict the fatigue states of human muscle more finely, the Band Spectral Entropy (BSE) of frequency domain features of sEMG signals were introduced. In order to compensate the weakness of Fourier transform in dealing with non-stationary signals, the time-frequency feature of the sEMG signals, named mean instantaneous frequency based on Ensemble Empirical Mode Decomposition-Hilbert transform (EEMD-HT), was introduced. Finally, in order to improve the classification accuracy of muscle non-fatigue and fatigue states, the Support Vector Machine optimized by Particle Swarm Optimization algorithm (PSO-SVM) with mutation was used for the classification of sEMG signals to realize the detection of human muscle fatigue states. Fifteen healthy young men were recruited to carry out sEMG signal acquisition experiments, and a sEMG signal database was established to extract features for classification. The experimental results show that, the proposed system can realize high-accuracy sEMG signal acquisition and high-accuracy classification of muscle fatigue states, and its accuracy rate of classification is above 90%.

Due to the limited angle of photography, some natural images are oscured by fence-like occlusion such as barbed wire, fence and glass joints. A novel fence-like occlusion detection algorithm was proposed to repair such images. Firstly, aiming at the drawbacks of the existing fence detection algorithms using single pixel color feature and fixed shape feature, the image was divided into super pixels and a joint feature of color and texture was introduced to describe the super pixel blocks. Thus, the classification of a pixel classification problem was converted to a super pixel classification problem, which inhibited the misclassification caused by local color changes. Secondly, the super-pixel blocks were classified by using the graph cuts algorithm to extend the mesh structure along the smooth edges without being restricted by the fixed shape, which improved the detection accuracy of the special-shaped fence structure and avoided the manual input required by the algorithm proposed by Farid et al. (FARID M S, MAHMOOD A, GRANGETTO M. Image de-fencing framework with hybrid inpainting algorithm. Signal, Image and Video Processing, 2016, 10(7):1193-1201) Then, new joint features were used to train the Support Vector Machine (SVM) classifier and classify all non-classified super-pixel blocks to obtain an accurate fence mask. Finally, the SAIST (Spatially Adaptive Iterative Singular-value Thresholding) inpainting algorithm was used to repair the image. In the experiment, the obtained fence mask retained more detail than that of the algorithm proposed by Farid et al., meanwhile using the same repair algorithm, the image restoration effect was significantly improved. Using the same fence mask, restored images by using the SAIST algorithm are 3.06 and 0.02 higher than that by using the algorithm proposed by Farid et al., respectively, in Peak Signal-to-Noise Rate (PSNR) and Structural SIMilarity (SSIM). The overall repair results were significantly improved compared to the algorithm proposed by Farid et al. and the algorithm proposed by Liu et al. (LIU Y Y, BELKINA T, HAYS J H, et al. Image de-fencing. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2008:1-8) when using the SAIST inpainting algorithm combined with the proposed fence detection algorithm. The experimental results show that the proposed algorithm improves the detection accuracy of the fence mask, thus yields better fence removed image reconstruction.

Aiming at the problem of impure simplicity and too simple feature extraction of training samples in the existing saliency detection model based on guided learning, an improved algorithm based on Boosting was proposed to detect saliency, which improve the accuracy of the training sample set and improve the way of feature extraction to achieve the improvement of learning effect. Firstly, the coarse sample map was generated from the bottom-up model for saliency detection, and the coarse sample map was quickly and effectively optimized by the cellular automata to establish the reliable Boosting samples. The training samples were set up to mark the original images. Then, the color and texture features were extracted from the training set. Finally, Support Vector Machine (SVM) weak classifiers with different feature and different kernel were used to generate a strong classifier based on Boosting, and the foreground and background of each pixel of the image was classified, and a saliency map was obtained. On the ASD database and the SED1 database, the experimental results show that the proposed algorithm can produce complete clear and salient maps for complex and simple images, with good AUC (Area Under Curve) evaluation value for accuracy-recall curve. Because of its accuracy, the proposed algorithm can be applied in pre-processing stage of computer vision.

An algorithm based on random Dijkstra algorithm and applying genetic algorithm to solve SPDRGS(Shortest Path problem on Dynamic Route Guidance System) was proposed. By applying random Dijkstra algorithm, the algorithm cleared out the biggest obstruction between the genetic algorithm and SPDRGS, which is how to get the initial generation of GA(Genetic algorithm). According to DRGS’s (Dynamic Route Guidance System) demand for time complexity and network constraint condition of route guidance algorithms, this algorithm can quickly find the excellent path and does not need any network constraint condition, which also can solve the problems on continuously and discrete dynamic networks. So the algorithm proposed can satisfy the demand of DRGS.