Aiming at the problems of medical ultrasound images such as many noisy points, fuzzy boundaries, and difficulty in defining the cardiac contours, a new Spatial Frequency Divided Attention Network for ultrasound image segmentation (SFDA-Net) was proposed. Firstly, with the help of Octave convolution, the high and low-frequency parallel processing of image in the entire network was realized to obtain more diverse information. Then, the Convolutional Block Attention Module (CBAM) was added for paying more attention to the effective information when image feature recovered, so as to reduce the loss of segmenting the entire target area. Finally Focal Tversky Loss was considered as the objective function to reduce the weights of simple samples and pay more attention on difficult samples, as well as decrease the errors introduced by pixel misjudgment between the categories. Through multiple sets of comparative experiments,it can be seen that with the parameter number lower than that of the original UNet++, SFDA-Net has the segmentation accuracy increased by 6.2 percentage points, Dice sore risen by 8.76 percentage points, mean Pixel Accuracy (mPA) improved to 84.09%, and mean Intersection Over Union (mIoU) increased to 75.79%. SFDA-Net steadily improves the network performance while reducing parameters, and makes the echocardiographic segmentation more accurate.

In order to solve the problem that the traditional cascaded Convolutional Neural Network (CNN) has low accuracy of feature point localization in left ventricular ultrasound image, an improved cascaded CNN with region extracted by Faster Region-based CNN (Faster-RCNN) model was proposed to locate the left ventricular endocardial and epicardial feature points in ultrasound images. Firstly, the traditional cascaded CNN was improved by a structure of two-stage cascaded. In the first stage, an improved convolutional network was used to roughly locate the endocardial and epicardial joint feature points. In the second stage, four improved convolutional networks were used to fine-tune the endocardial feature points and the epicardial feature points separately. After that, the positions of joint contour feature points were output. Secondly, the improved cascaded CNN was merged with target region extraction, which means that the target region containing the left ventricle was extracted by the Faster-RCNN model and then was sent into the improved cascaded CNN. Finally, the left ventricular contour feature points were located from coarse to fine. Experimental results show that compared with the traditional cascaded CNN, the proposed method is much more accurate in left ventricle feature point localization, and its prediction points are closer to the actual values. Under the root mean square error evaluation standard, the accuracy of feature point localization is improved by 32.6 percentage points.

In order to effectively protect video information, according to the characteristics of H.265/HEVC (High Efficiency Video Coding), a scheme combining transform coefficient scrambling and syntax element encryption was proposed. For Transform Unit (TU), the TU with the size of 4×4 was scrambled by Arnold transform. At the same time, a shift cipher was designed, and the cipher was initialized according to the approximate distribution rule of the Direct Current (DC) coefficient of the TU, and the DC coefficients of TU with the size of 8×8, 16×16 and 32×32 were shifting encrypted using encryption map generated by Arnold transform. For some of the syntax elements with bypass coding used in the entropy coding process, the Logistic chaotic sequence was used for encryption. After encryption, the Peak Signal-to-Noise Ratio (PSNR) and Structual Similarity (SSIM) of the video were decreased by 26.1 dB and 0.51 respectively on average, while the compression ratio was only decreased by 1.126% and the coding time was only increased by 0.17%. Experimental results show that under the premise of ensuring better encryption effect and less impact on bit rate, the proposed scheme has less extra coding overhead and is suitable for real-time video applications.

Due to the high complexity of intra encoding in High Efficiency Video Coding (HEVC), an efficient intra encoding algorithm combining coding unit segmentation and intra mode selection based on texture feature was proposed. The strength of dominant direction of each depth layer was used to decide whether the Coding Unit (CU) need segmentation, and to reduce the number of intra modes. Firstly, the variance of pixels was used in the coding unit, and the strength of dominant direction based on pixel units to was calculated determine its texture direction complexity, and the final depth was derived by means of the strategy of threshold. Secondly, the relation of vertical complexity and horizontal complexity and the probability of selected intra model were used to choose a subset of prediction modes, and the encoding complexity was further reduced. Compared to HM15.0, the proposed algorithm saves 51.997% encoding time on average, while the Bjontegaard Delta Peak Signal-to-Noise Rate (BDPSNR) only decreases by 0.059 dB and the Bjontegaard Delta Bit Rate (BDBR) increases by 1.018%. The experimental results show that the method can reduce the encoding complexity in the premise of negligible RD performance loss, which is beneficial to real-time video applications of HEVC standard.

Aiming at the problems of limited hiding capacity and obvious increasing bit rate in the existing hiding solutions, an intra-frame video information hiding algorithm based on diamond coding was proposed. Firstly, based on High Efficiency Video Coding (HEVC), two prediction models of adjacent 4×4 blocks were combined into a pattern pair, then the improved diamond coding algorithm was used to guide pattern modulation and information embedding. Next, the embedding coding for hidden informtion was done for second time with keeping the optimal coding division, thus ensuring the embedding quantity and eliminating intra frame distortion drift. The experimental results show that the Peak Signal-to-Noise Ratio (PSNR) is reduced by less than 0.03dB and the bit rate is increased by less than 0.53% by using the proposed algorithm, while the embedding capacity is greatly improved, and both the subjective and objective qualities of the video are well guaranteed.

Focusing on the issue that most of the existing evaluation methods transform images into different coordinate domain, a spatial Natural Scene Statistics (NSS) based model of no reference stereoscopic image quality assessment method was proposed. Among the stereoscopic image quality assessment, in order to better combine with the binocular visual features of human beings, left and right images were fused to construct a cyclopean map. Firstly, via statistical distribution of the Cyclopean Mean Subtracted Contrast Normalized (CMSCN) coefficients, the natural scene statistical characteristics were extracted in spatial domain from the cyclopean map. Secondly, by getting statistical distribution of the Disparity Mean Subtracted Contrast Normalized (DMSCN) coefficients, and the same characteristics were extracted from the disparity map obtained by optical flow model. Finally, Support Vector Regression (SVR) was performed to predict the objective scores of stereoscopic images by establishing the relationship between the stereoscopic image feature information and the Difference Mean Opinion Score (DMOS). The experimental results show that compared with other methods, the Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank-Order Correlation Coefficient (SROCC) indicators reach 0.94 on symmetric stereoscopic image database, and the PLCC indicator reaches 0.91 and the SROCC indicator reaches 0.93 on asymmetric stereoscopic image database, which indicate the proposed method can achieve higher consistency with subjective assessment of stereoscopic images.

During the automatic segmentation of cardiac structures in echocardiographic sequences within a cardiac cycle, the contour with weak edges can not be extracted effectively. A new approach combining Speeded Up Robust Feature (SURF) and Chan-Vese model was proposed to resolve this problem. Firstly, the weak boundary of heart chamber in the first frame was marked manually. Then, the SURF points around the boundary were extracted to build Delaunay triangulation. The positions of weak boundaries of subsequent frames were predicted using feature points matching between adjacent frames. The coarse contour was extracted using Chan-Vese model, and the fine contour of object could be acquired by region growing algorithm. The experiment proves that the proposed algorithm can effectively extract the contour of heart chamber with weak edges, and the result is similar to that by manual segmentation.

The existing error concealment algorithms for I frame are difficult to balance the recovery image quality and the algorithm complexity. To solve the problem, an efficient intra-fame partitioning error concealment method was proposed. Firstly, according to the motion correlation between video frames, the lost macro blocks were divided into motion blocks and static blocks. For static blocks, frame copy error concealment method was used to conceal lost blocks. For motion blocks, they were divided into smooth blocks and texture blocks by the texture information of the correctly decoded macro blocks. Then, the bilinear interpolation method was adopted to restore the smooth blocks and more delicate Weighted Template matching with Exponentially distributed weights (WTE) method was used to conceal texture blocks. The experimental results show that, compared with the WTE method, the proposed method has improved the Peak Signal-to-Noise Ratio (PSNR) by the average of 2.6 dB and decreased the computation complexity averagely by 90%. As for video sequences with different features and resolutions in continuous scene, the proposed method achieves certain applicability.

In view of the problem that high definition stereoscopic video sequences have high resolution, less information of macro block, and network transmission error, an end-to-end transmission distortion model was proposed. Considering error diffusion between frames caused by packet loss and the characteristics of spatial and temporal correlation, the recursive algorithm could estimate distortion accurately. And the error concealment method of copying the previous one of the lost frame was mainly used in the model, reducing the dependencies of the decoder. The simulation results show that the average prediction error of the distortion model can be controlled within 6%, and this model can be adapted to estimate transmission distortion for stereo video sequences with different features and resolutions under different network environments.

To solve the problem of small fault detection of instruments in process industry, independent components were extracted by Independent Component Analysis (ICA) from instruments recorded data. And independent component subspaces were established according to the contribution matrix. Fault detection model was constructed in each independent component subspace with statistical variables. A proper ensemble strategy was chosen by combining all the fault detection results. Finally, the instrument with fault was located by contribution algorithm. The simulation results with TE (Tennessee Eastman) process show that this method has higher precision on small fault detection and more flexibility with proper ensemble strategy.

High pressure piston diaphragm pump is the most important power source of the pipeline transportation. To solve the problem of on-line monitoring on the fault of internal piston, the authors put forward a detection method based on acoustic emission signal's wavelet packet frequency and Kernel Principal Component Analysis (KPCA). Firstly, the author adopted wavelet packet to deal with the acoustic emission data to get each frequency band energy value. Secondly, the authors used KPCA to decompose the energy in high dimensional space to find the feature model, and made use of statistics SPE and T2 in feature model to make detection on fault signal. Finally, the authors conducted experiments to verify the statistics of acoustic emission of GEHO diaphragm pump's check valve. In comparison with the PCA method, the proposed method can make on-line monitoring on fault of internal piston fast and accurate, so it has good application prospect on the domain of the high pressure piston diaphragm pump's non-destructive fault detection.

To resolve the problem of poor robustness when reaching the Terminal sliding mode control, a time-varying sliding mode control method was proposed. A nonlinear time-varying sliding mode surface was designed after analyzing the influences of designed parameters of sliding mode surface to the performances of system. To deal with the disturbances of a class of Multi-Input Multi-Output (MIMO) nonlinear system, a disturbance observer system was constructed. According to the disturbance observer system, the external disturbances were approached on-line by adjusting the weights. The simulation results show that, the settle-time of the proposed scheme is less than that of PID control by 80%. The proposed method has no overshoots. The simulation results demonstrate that the proposed design can be used on the control of MIMO nonlinear system.