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.

Speech bandwidth expansion is used to enhance the auditory quality by artificially recovering the lost components in the high-band spectrum of narrow-band speech. Aiming at the problem of excitation expansion in speech source-filter extension model, a piecewise extension method was proposed. The higher spectrum part in the narrow-band excitation source and the white noise with the equivalent narrow-band excitation frame energy were used as the excitation sources for the lower and upper part of the extension band respectively. At last, the wideband excitation signal was composed of the above two and the original narrow band one. Experimental results of the wide band speech reconstruction with Hidden Markov Model (HMM) based spectrum envelope estimation show that the proposed method is superior to spectrum shift excitation expansion method.

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 limitation of conventional static Functional Connectivity (FC) techniques in investigating the dynamic functional brain states, an effective method based on whole-brain Dynamic Functional Connectivity (DFC) was proposed to characterize the time-varying brain states. First, the Diffusion Tensor Imaging (DTI) data were used to construct individual whole-brain networks with high accuracy and the functional Magnetic Resonance Imaging (fMRI) data of motor-related task was projected to the corresponding DTI space to extract the fMRI signals of each node for each subject. Then, one kind of sliding time window approach was applied to calculate the time-varying whole-brain functional connectivity strength matrix, and the corresponding Dynamic Functional Connectivity Vector (DFCV) samples were further extracted and collected. Finally, the DFCV samples were learned and classified by one sparse representation based method called Fisher Discriminative Dictionary Learning (FDDL). Total eight different whole-brain functional connectome patterns representing the dynamic brain states were obtained from this motor-related task experiment. The spatial distributions of functional connectivity strength showed obvious variance within different patterns. The pattern #1, pattern #2 and pattern #3 covered most of the samples (77.6%) and the similarities between each of them and the average static whole-brain functional connectivity strength matrix were obviously higher than other five patterns. Furthermore, the brain states were found to transfer from one pattern to another according to certain rules. The experimental results show that the proposed analysis method combining whole-brain DFC and FDDL learning is effective for describing and characterizing the dynamic brain states during task brain activity. It provides a foundation for exploring the dynamic information processing mechanism of the brain.

To improve the prediction ability of image memorability, a method for automatically predicting the memorability of an image was proposed by using visual saliency entropy and improved Object Bank feature. The proposed method improved the traditional Object Bank feature and extracted the visual saliency entropy feature. Then a prediction model of image memorability was constructed by using Support Vector Regression (SVR). The experimental results show that the correlation coefficiency of the proposed method is three percentage higher than the state-of-the-art method. The proposed model can be used in image memorability prediction, image retrieval ranking and advertisement assessment analysis.

The (t,n) threshold ring signature could be generated by any t entities of n entities group on behalf of the whole group, while the actual signers remain anonymous. In order to design the threshold ring signature scheme with constant size, this paper presented an identity-based threshold ring signature scheme without random oracle by using bilinear pairing technique. In the end, the authors prove this scheme satisfy the unconditional signer ambiguity and existential unforgeability against selective identity, selective chosen message attack in terms of the hardness of Diffie-Hellman Inversion （DHI） problem.

In the particle filter framework, estimation accuracy strongly depends on the choice of proposal distribution. The traditional particle filter uses system transition probability as the proposal distribution without considering the new observing information; therefore, they cannot give accurate estimation. A new tracking framework applied with particle filter algorithm was proposed, which used Galerkin's method to construct proposal distribution. This proposal distribution enhanced the estimation accuracy compared to traditional filters. In the proposed framework, color model and shape model were adaptively fused, and a new model update scheme was also proposed to improve the stability of the object tracking. The experimental results demonstrate the availability of the proposed algorithm.

To enhance the detection performance of statistical model-based Voice Activity Detection (VAD) using likelihood ratio test, an improved VAD was proposed by utilizing the correlation between tandem speech frames. First a priori Signal-to-Noise Ratio (SNR) was estimated using recursive estimation method based on the result of the previous speech frame instead of the traditional decision-directed method. Secondly double thresholds were designed by depending on the previous frame's detention result. Finally a detection rule was presented based on two-state Hidden Markov Model (HMM) coupled with double thresholds. The experimental results show that the inter-frame correlation based VAD scheme gets better performance than the Sohn's VAD.

The wireless heterogeneous networks require efficient resource allocation between different communication systems with different modes. To gain effective wireless resource utilization between different systems, a resource allocation strategy based on XG system architecture was proposed. And then a multidimensional resource container was presented, which can meet the traffic demands of different communication systems. A two-level resource allocation strategy and its corresponding resource allocation algorithm were utilized to match the multidimensional resource container with diversified traffic demands between different communication systems. The simulation and performance analysis show that the proposed strategy can improve wireless frequency resource utilization and satisfy the traffic demands between different communication systems.

To remove the noise of optical images while preserving its fine details, the extant object scale was upgraded to the decimal object scale for reflecting the size of local object structure more accurately, and a hybrid image filter which contains two parts was proposed. The first part was an adaptive Gaussian filter based on decimal object scale, the scale of the Gaussian kernel and the mask size of filtering were controlled adaptively by the decimal object scale. The second part was an adaptive median filter based on decimal object scale, and the impulse noise points which were selected adaptively by the decimal object scale were filtered. The weakness of the first part in suppressing the impulse noise was remedied by the second part. Both theory analysis and simulation results show that the presented method can suppress various point-like noise and it is superior to several traditional methods in preserving the fine details and signal to noise ratio.

It is a hard job to track the moving objects in video sequence.Considering disadvantages of watershed transform,a novel moving object tracking algorithm was proposed based on Hausdorff distance.In the algorithm,firstly Canny edge detector was adopted for the image boundary generation,and multi-scale watershed transform was used to initialize contour of the objects.Then the matching of the objects was judged by partial Hausdorff distance.Finally,multi-scale watershed transform was reused to update the object model.Experiment results show that the proposed algorithm can efficiently track more than one non-rigid moving object simultaneously.

Shot cut detection is one of the challenging problems in video auto-index and retrieval. A multi-scale algorithm of shot cut detection on MPEG compressed domain which analyzed the video stream on the scales of GOP, slot and B frames was proposed in this paper. The I frames in two adjacent GOP was examined to find if there were shot cut whitin the GOP; the area of the cut was located by analyzing the slot and the exact frame where the shot cut occurred was found by examining B frames between two reference frames.

A novel motion segmentation algorithm based on multi-model robust estimation was proprosed. Fisrtly, the split and merge technique based on quad-tree was proposed to obtain the initial motion numbers of robust estimation and corresponding initial parameters of motion model. Then the parameters were updated by using estimating parameters, and the motion models were merged based on the motion consistency. Lastly small motion objects could be detected by using small-object detection techniques. Experimental results show that the method can obtain promising results.