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Each pixel in the hyperspectral unmixing process is modeled as a linear combination of endmembers, which can be expressed in the form of linear combinations of a number of pure spectral signatures that are known in advance. However, the limitation of Gaussian random variables on its computational complexity or sparsity affects the efficiency and accuracy. This paper proposes a novel approach for the optimization of measurement matrix in compressive sensing (CS) theory for hyperspectral unmixing. Firstly, a new Toeplitz-structured chaotic measurement matrix (TSCMM) is formed by pseudo-random chaotic elements, which can be implemented by a simple hardware; secondly, rank revealing QR factorization with eigenvalue decomposition is presented to speed up the measurement time; finally, orthogonal gradient descent method for measurement matrix optimization is used to achieve optimal incoherence. Experimental results demonstrate that the proposed approach can lead to better CS reconstruction performance with low extra computational cost in hyperspectral unmixing.

Recent research has shown the benefits of large margin framework for feature selection. In this paper, we propose a novel feature selection algorithm, termed as Large Margin Subspace Learning (LMSL), which seeks a projection matrix to maximize the margin of a given sample, defined as the distance between the nearest missing (the nearest neighbor with the different label) and the nearest hit (the nearest neighbor with the same label) of the given sample. Instead of calculating the nearest neighbor of the given sample directly, we treat each sample with different (same) labels with the given sample as a potential nearest missing (hint), with the probability estimated by kernel density estimation. By this way, the nearest missing (hint) is calculated as an expectation of all different (same) class samples. In order to perform feature selection, an @?"2","1-norm is imposed on the projection matrix to enforce row-sparsity. An efficient algorithm is then proposed to solve the resultant optimization problem. Comprehensive experiments are conducted to compare the performance of the proposed algorithm with the other five state-of-the-art algorithms RFS, SPFS, mRMR, TR and LLFS, it achieves better performance than the former four. Compared with the algorithm LLFS, the proposed algorithm has a competitive performance with however a significantly faster computational.

The article describes a hypotenuse-height deviation compression method of magnanimous point cloud data based on hierarchy. Firstly the point cloud is stratified into several hierarchies which turn into linear point clouds, then compressed by using hypotenuse-height deviation threshold. By experiment, the compression effect of the method brought up is compared with that of several traditional point cloud compression methods, which verify the capability of realizing high-efficiency compression with abundant detailed features retained.

Wavelet transform has shown a strong capability to filter out noise that existed in images. The main problem to improve denoising performance lies in the fact that it is difficult to select suitable wavelet transform and related thresholds. In this paper, we completely explore characteristics of dyadic wavelet transform and its possible use to image denoising. An improved threshold method is elaborately designed to establish the self-adjusted layer threshold function and wavelet reconstruction. Theoretical analysis shows that denoising precision can be improved by way of adopting different thresholds according to the different scales of the wavelet coefficients of image and noise. Experiment results demonstrated that our method is obviously superior to that of fixed threshold regarding the effect of image denoising.

For a saturated soil, a saturated poro-elastic model is more realistic than the linear elastic or the viscoelastic one. Based on the Fourier transform method in the frequency wave number domain, the expression of the equivalent stiffness of the saturated poro-elastic half space interacting with an infinite beam to harmonic moving loads is obtained. The time domain solutions for the beam and the saturated poro-elastic half space are derived by means of the inverse Fourier transform method. Also, the influences of the load speed, frequency and material parameters of the poroelastic half space on the responses of the beam are investigated. Numerical results show that the frequency corresponding to the maximum deflection and bending moment increases with increasing load speed. Also, the values of the maximum bending moment increases up to the critical speed. The load frequency corresponding to the maximum bending moment depends on the value of the load speed. With increasing load speed, spacing between frequencies increases. Keywordsmoving loads, saturated poro-elastic half space, Biot’s theory, infinite beam, Fourier transform method

Taking the approximate equations for long waves in shallow water as example, the quasi-wavelet discrete scheme is proposed for obtaining numerical solution of the (1+1) dimension nonlinear partial differential equation. In the method, the quasi-wavelet discrete scheme is adopted to discretize the spatial derivative discrete and the ordinary differential equation about time is obtained. Then the fourth order Rung-Katta method is employed to discretize the temporal derivative. Finally the quasi-wavelet solution is compared with the analytical solution, and the computations are validated.

The methods of conventional encryption cannot be applicable to images for the resistance to statistic attack, differential attack and grey code attack. In this paper, the confusion is improved in terms of chaotic permutation with ergodic matrix, and the diffusion is implemented through a new chaotic dynamic system incorporated with a S-box algebraic operation and a 'XOR plus mod' operation, which greatly enhances the practical security of the system with a little computational expense, and a key scheme is also proposed. Experimental and theoretical results also show that our scheme is efficient and very secure