Since current available sleep electroencephalogram data sets for sleep staging are all class imbalanced small data sets, it is hard to achieve ideal staging result by directly migration application of deep learning models. A deep automatic sleep staging model for class imbalanced small data sets was proposed, from the aspect of data oversampling and model training optimization. Firstly, a Modified Synthetic Minority Oversampling TEchnique (MSMOTE) was improved from the perspective of reducing the decision region, and the new technique was applied to generate the minority class samples in the original data sets. Then, the reconstructed class balanced data sets were used to pre-activate the sleep staging model. The 15-fold cross-validation experiment showed the overall classification accuracy was 86.73% and the macro-averaged F1-score was 81.70%. The value of F1 for the minimum class increased from 45.16% to 53.64% by using the data sets reconstructed by improved MSMOTE, to pre-activate the model. In conclusion, the model can realize the end-to-end learning for raw sleep electroencephalogram signals. It has a higher classification efficiency by comparison with recent advanced research and is suitable for the portable sleep monitors that work in conjunction with remote servers.

Due to critical security vulnerabilities of the protocols for Short Message Service (SMS), SMS spam come to the fore through numerous malicious pseudo base-stations, to spread fraud message or illegal advertisements. Nowadays, SMS spam negatively affects daily lives of the masses, even influences the stability of society. However, with respect to the properties as mobility and concealment of pseudo base-stations, exploring the trajectory and activity of pseudo base-stations is a difficult task. To solve this problem, a visual analytics scheme was proposed to trail pseudo base-stations via multi-users' SMS spam collected by mobile service provider. Multi-visualized views and a visual analytics system were designed based upon the proposed scheme. Moreover, a case study was presented to validate the proposed method and system, with the aid of dataset provided by the ChinaVis'2017 Challenge I. The result verifies the feasibility and effectiveness of the proposed method.

Many traditional machine learning methods tend to get biased classifier which leads to lower classification precision for minor class in sequential imbalanced data. To improve the classification accuracy of minor class, a new hybrid sampling online extreme learning machine on sequential imbalanced data was proposed. This algorithm could improve the classification accuracy of minor class as well as reduce the loss of classification accuracy of major class, which contained two stages. In offline stage, the principal curve was introduced to model the confidence regions of minor class and major class respectively based on the strategy of balanced samples. Over-sampling of minority and under-sampling of majority was achieved based on confidence region. Then the initial model was established. In online stage, only the most valuable samples of major class were chosen according to the sample importance, and then the network weight was updated dynamically. The proposed algorithm had upper bound of the information loss through the theoretical proof. The experiment was taken on two UCI datasets and the real-world air pollutant forecasting dataset of Macao. The experimental results show that, compared with the existing methods such as Online Sequential Extreme Learning Machine (OS-ELM), Extreme Learning Machine (ELM) and Meta-Cognitive Online Sequential Extreme Learning Machine (MCOS-ELM), the proposed method has higher prediction precision and better numerical stability.

Based on open source softwares of Computer Haptics, visualizAtion and Interactive in 3D (CHAI 3D) and Open Graphic Library (OpenGL), a virtual surgical system was designed for reduction of maxillary fracture. The virtual simulation scenario was constructed with real patients' CT data. A geomagic force feedback device was used to manipulate the virtual 3D models and output haptic feedback. On the basis of the original single finger-proxy algorithm, a multi-proxy collision algorithm was proposed to solve the problem that the tools might stab into the virtual organs during the simulation. In the virtual surgical system, the operator could use the force feedback device to choose, move and rotate the virtual skull model to simulate the movement and placement in real operation. The proposed system can be used to train medical students and for preoperative planning of complicated surgeries.

Many traditional machine learning methods tend to get biased classifier which leads to low classification precision for minor class in imbalanced online sequential data. To improve the classification accuracy of minor class, a new weighted online sequential extreme learning machine based on imbalanced sample-reconstruction was proposed. The algorithm started from exploiting distributed characteristics of online sequential data, and contained two stages. In offline stage, the principal curve was introduced to construct the confidence region, where over-sampling was achieved for minor class to construct the equilibrium sample set which was consistent with the sample distribution trend, and then the initial model was established. In online stage, a new weighted method was proposed to update sample weight dynamically, where the value of weight was related to training error. The proposed method was evaluated on UCI dataset and Macao meteorological data. Compared with the existing methods, such as Online Sequential-Extreme Learning Machine (OS-ELM), Extreme Learning Machine (ELM)and Meta-Cognitive Online Sequential- Extreme Learning Machine (MCOS-ELM), the experimental results show that the proposed method can identify the minor class with a higher ability. Moreover, the training time of the proposed method has not much difference compared with the others, which shows that the proposed method can greatly increase the minor prediction accuracy without affecting the complexity of algorithm.

Inspired by prior knowledge of face images' approximate symmetry, an algorithm based on symmetric Gabor features and sparse representation was proposed, which was successfully applied into face recognition in the paper. At first, mirror transform was performed on face images to get their mirror images, with which the face images could be decomposed into odd-even symmetric faces. Then, Gabor features were extracted from both odd faces and even faces to get the Gabor odd-even symmetric features，which could be fused via a weighting factor to generate the new features. At last, the newly obtained features were combined to form an over-complete dictionary which was used by sparse representation to classify the faces. The experimental results on AR and FERET face databases show that the new method can achieve high accuracy even when face images are under expression, pose and illumination variations.

In order to improve the quality of the solution in solving Quadratic Assignment Problem (QAP), an effective Max-Min Ant System (MMAS) was designed. Firstly, by using optimal iteration idea, the location and its corresponding task were selected randomly from the current optimal tour as the initial value of next iteration, so as to enhance the effectiveness of each searching in MMAS. Secondly, in order to increase the purpose of the search in every step, the incremental value of target function after adding new task was used as the heuristic factor to guide effectively the state transition. Then, the pheromone was updated by using the multi-elitist strategy so that it could increase the diversity of the solution. And an effective double-mutation technique was designed to improve the quality of solution and accelerate the algorithm convergence speed. Finally, a large number of data sets from QAPLIB were experimented. The experimental result shows that the proposed algorithm is significantly better than other algorithms in accuracy and stability on solving quadratic assignment problem.

The accuracy of the speech endpoint detection has a direct impact on the precision of speech recognition, synthesis, enhancement, etc. To improve the effectiveness of speech endpoint detection, an algorithm based on critical band and energy entropy was proposed. It took full advantage of the frequency distribution of human auditory characteristics, and divided the speech signals according to critical bands. Combined with the different distribution of energy entropy of each critical band of the signals respectively in the speech segments and noise segments, speech endpoint detection under different background noises was completed. The experimental results indicate that the average accuracy of the newly proposed algorithm is 1.6% higher than the traditional short-time energy algorithm. The proposed method can achieve the detection of speech endpoint under various noise environment of low Signal to Noise Ratio (SNR).