Advanced Materials Research Vols. 989-994

Abstract: In order to enables the video to be displayed on a multitude of different terminals, Multi-view video coding (MVC) demands high compression rates as well as view scalability. A new the inter-view prediction structure with flexibility, MVC compatibility and view scalability is proposed. The proposed scheme first correct views. It then uses the position of the cameras and the relation of inter-views to divide all views into base view and enhancement views. Finally, the views quire by the viewers use scalable Multi-view Video Coding (SMVC). The main bitstream is the same as a H.264/AVC mono-sequence bitstream for H.264/AVC compatibility. The auxiliary bitstream come from enhancement views coding. We proposed SMVC scheme is tested with two Multi-view sequences to determine its flexibly view scalability, high coding efficiency and random access performance.

Abstract: To remove the noise of weak signals detected in the underwater environment, the existing de-noising algorithms cannot perform satisfactorily. This paper aims to developing a new method to solve the problem, where the compressed sensing theory, wavelet transformation based filtering techniques and sparse signal reconstruction algorithm are employed. The simulation results show that the proposed algorithm performs favorably and has good potential to be used in some engineering applications.

Abstract: The uniform circular planar array (UCA) has lots of advantages what linear arrays and other surface arrays don't have. However, because of its limitations, such as the high side-lobe level and low zero depth, the application on controlling array space direction by beam forming has been limited. Considering the characteristics of UCA, the receiving UCA's signal model is established in this paper. The influence factors of UCA's beam will be found out on the basis of theoretical derivation. A new method, that can improve the performance of the UCA beam directivity by amplitude weighting, is proposed in this paper. The theoretical derivation results and simulation results improved that the method could have a better ability in side-lobe suppression, and the new method could make the UCA to have a good application prospect.

Abstract: The telemetry signal is often interfered with impulse noise, which results in difficulty in time domain and frequency domain analysis results. Hereby a new impulse noise suppression method based on wavelet transform and median filtering technique was proposed. The received signal is decomposed into detailed components and approximate components, and then the median filtering is carried on the wavelet decomposition components with vary filtering window size according to the wavelet transform scale respectively. This method can suppress the impulse noise effectively and keep the detail information of the signal from the loss at the same time. The simulation and experimental results prove the effectiveness of the method.

Abstract: In order to realize the accurate identification of the image color vegetable diseases in Greenhouse, In this paper, by using computer image processing technology in the intelligent recognition of vegetable diseases in greenhouse leaf, First, the moving average method can effectively remove the noise by the image of R,G, B three channel gray level, Then separate three channel image using Sobel-operator and the 4 template based on the direction of gradient magnitude ,and then integrate the three channel edge ,it can overcome the shortcoming of traditional gray image information, and make up the deficiency of the traditional Sobel operator edge thinning geometric features.

Abstract: Image fusion can be effectively utilized to obtain image redundant information from sensors, hereby improving the accuracy and reliability of information. Based on multi-resolution decomposition of the traditional image fusion method is vulnerable to high frequency noise, fusion is often ineffective. An improved image fusion algorithm has been studied based on the wavelet multi-resolution decomposition. The principle of the algorithm is regional energy maximum for low frequency decomposition image, and the bivariate statistical model for high frequency part. Experimental results show that the bivariate statistical model for the high frequency band is robust to noise based on the joint probability of wavelet coefficient in the conditions of Daubechies wavelet basis function with decomposing level 5 multi-resolution decomposition. Simultaneously, the regional energy maximum for low frequency band can be effective on the high frequency band based on the bivariate statistical model. Fusion image have a larger contrast, the preferred details and the higher gray level resolution.

Abstract: In order to extract the fault information from rolling element bearing, combined with Kurtosis criteria and Hessian matrix. An improved rolling element bearing fault signal extraction method is proposed. Kurtosis is the cost function. The method is according to the construction principles of blind source separation (BSS), and it uses an analytically derived Hessian matrix in the maximization process of the cost function used. Then the impact signal is extracted successfully. The effectiveness of the method is demonstrated on simulated signal.

Abstract: The performance of FCM for image segmentation directly subjects to the initialized membership matrix. This paper proposed twice FCM method to solve the membership matrix initiation problem. The image is spared to a blurred image at first, and then uses the FCM for the blurred image to obtain an iterative result, in which the membership matrix is taken as the initialized membership function of the FCM for the original image processing. This method overcomes the random membership initialization method cannot convergence to the optimum point of the objective function of FCM for the image segmentation at some extend, furthermore, it can obtain better results than the traditional FCM method.

Abstract: Because of the weakness of traditional Apriori algorithm, this paper presents an improved algorithm for mining frequent itemsets, which constructs bit vector and graph, the algorithm deletes node and the adjacent edges according to the number of node’s edges, which need traverse graph to generate candidate itemsets and verify candidate itemset by bit vector. Experimental results show that the improved algorithm has better efficiency than Apriori algorithm.

Abstract: Otsu segmentation algorithm is one of the most successful algorithms in the field of image processing,However, it has drawbacks such as it is poor in dividing small object in images. This paper propose a improved Otsu algorithm based on weighted smooth histogram which can ensure the location of threshold point is more near to the valley point of the image and at the same time ensure the ensure between-class variance maximum.The results show that the improved Otsu algorithm can effectively accomplish segmentation for multimodal image, and get better segmentation results for images added Gaussian noise.