B-mode cardiac ultrasound image segmentation is a fundamental step before cardiac functional parameters estimation. Aiming at the problem that the accuracy of segmentation is low because of the low resolution of ultrasound image, and the model based image segmentation algorithms need a large number of training sets, an image segmentation algorithm based on pixel clustering was proposed combined with prior knowledge of B-mode cardiac ultrasound images. Firstly, anisotropic diffusion was used to preprocess the image. Secondly, one-dimensional K-means was used to cluster the pixels. Finally, every pixel value of the image was assigned to the pixel value of its best cluster center according to cluster results and prior knowledge. The theoretical analysis shows that the proposed algorithm can get the maximum Peak Signal-to-Noise Ratio (PSNR) of ultrasound image; the experimental results show that the proposed algorithm performs better than Otsu algorithm, and its PSNR is increased by 11.5% compared with Otsu algorithm. The proposed algorithm can still work even for a single ultrasound image and can be suitble for ultrasound image segmentation of any shapes, so it is conducive to estimate cardiac functional parameters more accurately.

It is hard to obtain a satisfactory reconstructive quality while compressing time-varying signals monitored by Wireless Sensor Network (WSN) using Compressive Sensing (CS), therefore a novel dynamic sampling method based on data prediction and sampling rate feedback control was proposed. Firstly, the sink node acquired the changing trend by analyzing the liner degree differences between current reconstructed data and last reconstructed data. Then the sink node calculated the suitable sampling rate according to the changing trend and fed back the result to sensors to dynamically adjust their sampling process. The experimental results show that the proposed dynamic sampling method can acquire higher reconstructed data accuracy than the CS data gathering method based on static sampling rate for WSN.

In traditional Non-Local Means (NLM) algorithm, the weighted Euclidean norm can not truly reflect the similarity between two neighborhoods under large noise standard deviation. To address this problem, a new NLM denoising algorithm combined with similarity weight was proposed. Firstly, the noise image was decomposed by using the advantages of stationary wavelet transform, and the filtering function was used to predenoise each detailed subband data. Secondly, according to the refined image, the similarity reference factor between the patches was calculated, and it was used to replace Gauss kernel function of the traditional NLM algorithm. Finally, to make the similarity weights more in line with the characteristics of Human Visual System (HVS), the block Singular Value Decomposition (SVD) method based on image structure perception was used to define neighborhood similarity measure, which can more accurately reflect the similarity between neighborhoods compared with the traditional NLM. The experimental results demonstrate that the hybrid similarity weighted NLM algorithm performs better than the traditional NLM in retaining the texture details and edge information, and the Structural SIMilarity (SSIM) index measurement values is also improved in comparison with the traditional NLM algorithm. When the noise standard deviation is large enough, the proposed approach is of effectiveness and robustness.

In order to enrich and improve the ability of the existing models for reasoning and predicting with 3D cardinal direction relations and enhance the usability of the existing models, and then better meet the demands of real applications for complex 3D spatial data, the inverse reasoning of 3D cardinal direction relations was studied. After deeply studying the theory of n-dimensional block algebra, an algorithm for computing the inverse of the basic 3D cardinal direction relations on the basis of 3D block algebra was devised. Theoretical analysis and the results of the example show that the proposed algorithm is correct and complete. This work can better enhance the power of intelligent analysis and processing for the complex 3D direction relations of the spatial database.

Aiming at the problems that the traditional Support Vector Machine (SVM) classifier is sensitive to outliers and has the large number of Support Vectors (SV) and the parameter of its separating hyperplane is not sparse, the Truncated hinge loss SVM with Smoothly Clipped Absolute Deviation (SCAD) penalty (SCAD-TSVM) was put forward and was used for constructing the financial early-warning model. At the same time, an iterative updating algorithm was proposed to solve the SCAD-TSVM model. Experiments were implemented on the financial data of A-share manufacturing listed companies of the Shanghai and Shenzhen stock markets. Compared to the T-2 and T-3 models constructed by SVM with L1 norm penalty (L1-SVM), SVM with SCAD penalty (SCAD-SVM) and Truncated hinge loss SVM (TSVM), the T-2 and T-3 model constructed by the SCAD-TSVM had the best sparseness and the highest accuracy of prediction, and its average accuracies of prediction with different number of training samples were higher than those of the L1-SVM, SCAD-SVM and TSVM algorithms.

Concerning the classification problems of complex data distribution of scientific practice, economic life and many other fields, the correlation between variables could not be well described by using traditional Support Vector Machine (SVM), which would influence the classification performance. For this situation, Q-Gaussian function that was a parametric generalization of Gaussian function was put forward as the kernel function of SVM, and a financial early-warning model based on SVM with Q-Gaussian kernel was presented. Based on the financial data of A-share manufacturing listed companies of the Shanghai and Shenzhen stock markets, T-2 and T-3 financial early-warning model were constructed in experiments, the significance test was used to select some suitable indicators and the Cross Validation (CV) was used to determine model parameters. Compared to SVM model with Gaussian kernel, the forecasting accuracies of T-2 and T-3 model constructed by SVM with Q-Gaussian kernel were improved about 3%, and high-cost type I errors were reduced by at most 14.29%.

To overcome the shortcomings of current methods in chromatic scan map vectorization, an interactive vectorization method was proposed. It used the color distance and line width as characteristics, and adopted the strategies such as fuzzy point selection, adjustable tracking direction and changeable tracking mode. Experiment results show that it can vectorized the chromatic scan map rapidly and interactively.