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There is information redundancy in both spatial and spectral aspects of hyperspectral images. Considering a fixed proportion in sequential forward method may not find the optimal bands, we modify a band selection (BS) method by introducing a parameter to adjust the proportion of standard deviation to correlation, which may select key bands quickly and accurately. In addition, slant Haar type orthogonal transforms (SHTOT) have slant base vectors suitable to express the image brightness with gradual change. However, SHTOT have attracted little attention of scholars'. This paper introduces SHTOT with fast algorithm and varied parameters to the further compression of band images from a space point of view. Comparative experiments were performed with other BS strategies and state-of-the-art orthogonal transforms, such as DCT, DWT, Walsh, slant transform, Haar type orthogonal transforms. Final experimental results achieved on the commonly used data sets validate that the proposed approach has a faster speed, high compression ratio and good image quality. Additionally, it is more appropriate to choose different SHTOT for the specific application. The sparse SHTOT generated by certain parameter values are more suitable for the high requirement in image quality, but the compression ratio isn't very high, while dense SHTOT produced by another parameter values are fitter for the opposite cases. [ABSTRACT FROM AUTHOR]

It is important for nonlinear hyperbolic conservation laws (NHCL) to own a simulation scheme with high order accuracy, simple computation, and non-oscillatory character. In this paper, a unified and novel lattice Boltzmann model is presented for solving n-dimensional NHCL with the source term. By introducing the high order source term of explicit lattice Boltzmann method (LBM) and the optimum dimensionless relaxation time varied with the specific issues, the effects of space and time resolutions on the accuracy and stability of the model are investigated for the different problems in one to three dimensions. Both the theoretical analysis and numerical simulation validate that the results by the proposed LBM have second-order accuracy in both space and time, which agree well with the analytical solutions.

Based on the chaotic oscillator, a method for weak signal detection using information fusion technology is proposed in this paper. On the one hand, various methods are employed to the amplitude detection of the same weak periodic signal, then the detection outcomes are fused by the adaptive weighted fusion method. On the other hand, during the detection course, information entropy, statistic distance, and Walsh transform are, respectively, used in the state recognition of chaotic oscillator from the viewpoint of time domain or frequency domain, then the recognition results are fused by the k/l fusion method. Numerical results show that the proposed approach detects signal more precisely, identifies state more accurately, and represents information more completely compared with traditional methods.