The SAR image is classified by using the feature vector which is composed of wavelet texture energy features, the gray-level co-occurrence matrix features and the tone of filtered SAR image with tree wavelet.

Through the development of the multidelaytime 4th order accumulation slice structure of Gas noise sine signal and Hankel matrix features composed of these accumulation, the highresolution sine signal evaluation method is proposed from the accumulation of delay time.

If an (N×P) matrix W is the geological data matrix, where N is the number of samples and P the number of variables, then the matrix features an N much greater then P. Previously, an (N×N) matrix, which is usually a high dimension matrix, must be formed in Q-mode factor analysis.

In the aspect of imagery processing, the author propose a method of using GLCM(Gray-Level Co-occurrence Matrix) to calculate the texture characteristic of SAR images. And the SAR image is classified by using the feature vector that is composed of the Gray Level Co-occurrence matrix features and gray of pixels.

Simulations show that this method can efficiently segment multi-texture images into several regions according to their different texture properties, and the segmentation error ratio is lower than the method of combining co-occurrence matrix features extraction with K-means clustering.

Low temperature plasma technique with fluorocarbon compounds atmosphere is the latest development for surface modification of various materials. Through introducing fluoro group to the surface of the matrix by plasma treatment, the matrix features low surface energy and many special properties.

150 sidescan sonar images for mud,sand and rock seafloors are classified using the presented three-dimensional feature vector,and recognition rates of maximum 96.7% and minimum 90.7% are achieved. These same 150 seafloor images are also classified using the conventional gray level co-occurence matrix features,and a recognition rate of 87.3% is achieved,which shows that the presented seafloor classification method has better classification performance.

Forx>amp;gt;0.25 up to 0.7 the structural matrix features are almost similar to those of crystalline TlAsSe2.

If an (N×P) matrix W is the geological data matrix, where N is the number of samples and P the number of variables, then the matrix features an N much greater then P. Previously, an (N×N) matrix, which is usually a high dimension matrix, must be formed in Q-mode factor analysis. Therefore it has only a restricted application owing to the high dimension matrix. The R-Q mode factor analysis, however, only needs to form a ( P × P ) matrix W'W(usually P<50). On the basis of W'W, we...

If an (N×P) matrix W is the geological data matrix, where N is the number of samples and P the number of variables, then the matrix features an N much greater then P. Previously, an (N×N) matrix, which is usually a high dimension matrix, must be formed in Q-mode factor analysis. Therefore it has only a restricted application owing to the high dimension matrix. The R-Q mode factor analysis, however, only needs to form a ( P × P ) matrix W'W(usually P<50). On the basis of W'W, we can extract both R-mode and Q-mode factors simultaneously by invoking the Eckart - young theorem, which makes the actual application of Q-mode factor possible. Another advantage of the R-Q mode factor analysis is that Q-mode factor and R- mode factor can be shown simultaneously on a plan. Then we can observe the relationship between the variables, between the samples, between the variables and factors and between the samples and variables on the plan.

By employing the elastic and elastic plastic finite element method(FEM), the effects of matrix feature on the stress transfer mechanisms of short fiber composites are studied. In the calculation, the variations in matrix modulus, yield strength and hardening modulus are considered. It is concluded that large deformation of matrix is harmful to the improvement of the mechanical performances of the composites.

This paper suggests a texture classification algorithm based on the feature symbol random field (FSRF). FSRF is a 2 D representation of the image's bank information. In FSRF, symbol value of a pixel gives out its structure function in texture region and is more valuable than the gray value for texture classification. Meanwhile, several co occurrence matrix features are driven from the FSRF which are more powerful than energy features. This paper also suggests a Hierarchical scheme which leads to...

This paper suggests a texture classification algorithm based on the feature symbol random field (FSRF). FSRF is a 2 D representation of the image's bank information. In FSRF, symbol value of a pixel gives out its structure function in texture region and is more valuable than the gray value for texture classification. Meanwhile, several co occurrence matrix features are driven from the FSRF which are more powerful than energy features. This paper also suggests a Hierarchical scheme which leads to 96% correct ratio in the included experiments, while the gray value based method 67.5% and bank energies based method 75%.