Hyperspectral image compression based on lapped transform and Tucker decomposition

In this paper, we present a hyperspectral image compression system based on the lapped transform and Tucker decomposition (LT-TD). In the proposed method, each band of a hyperspectral image is first decorrelated by a lapped transform. The transformed coefficients of different frequencies are rearranged into three-dimensional (3D) wavelet sub-band structures. The 3D sub-bands are viewed as third-order tensors. Then they are decomposed by Tucker decomposition into a core tensor and three factor matrices. The core tensor preserves most of the energy of the original tensor, and it is encoded using a bit-plane coding algorithm into bit-streams. Comparison experiments have been performed and provided, as well as an analysis regarding the contributing factors for the compression performance, such as the rank of the core tensor and quantization of the factor matrices.     

基于LP与小波包的复杂机电系统监测序列混沌降噪方法

针对流程工业生产系统监测数据存在强噪声和混沌性的特点,提出了一种局部投影方法(Local Projection Method)与小波包方法相结合的信号降噪方法。该方法先利用局部投影方法从动力学系统嵌入流形的角度进行多次迭代降噪,并根据关联维数来判定迭代终止;再利用小波包方法从频率的角度进行降噪,抑制高频噪声的干扰,取得了较好的降噪效果。用Lorenz时间序列进行仿真验证,对仿真时间序列加入不同程度的噪声,对比分析小波包、局部投影与该方法降噪后的相空间、SNR值和最大Lyapunov指数,证明了该方法对于中高强度噪声具有更好的降噪效果。并将该方法用于某压缩机组的实际监测数据降噪,评估三种方法的降噪效果,进一步验证了该方法的优越性。     

光学显微定位系统定位精度分析

为了提高光学显微定位系统对细胞微生物识别定位的精度：一方面，必须改进手眼标定方法；另一方面，需要提高全局图像识别的准确性，因此，提出一种两步法对系统进行手眼标定。首先，通过标定固定靶标来确定系统原点，并得到视觉模块相对于系统原点的转换关系；然后，根据每次拍照的起始点位置、拍照的数量和移动的步长求解出全局图像相对于系统原点的转换关系；最后，为了进一步提高全局转换关系的准确度，提出一种基于傅里叶变换的误差矫正方法，利用傅里叶变换求解出视觉模块在移动过程中的误差，并加入系统进行补偿。实验结果表明，误差补偿之后，系统X轴方向的误差均值从10.23 μm降为-0.002 μm，Y轴方向的误差均值从6.9 μm降为-0.50 μm，显微定位系统的平均定位精度达到了99%以上。结果表明，所提方法可很好地用于光学显微定位系统对细胞微生物进行高精度的自动化抓取。     

基于反馈Hough变换的管道点云检测和识别

管道是流程工厂的主体，其几何形状表现为圆柱体或圆台。针对现有管道检测识别方法对噪声较为敏感、检测精度不高、检测结果难以验证等问题，本文提出一种具有轴向反馈修正的三维霍夫变换方法。首先，根据八叉树划分点云空间，计算点云法向量并生成圆柱体或圆台高斯映射图，利用三维霍夫变换法估计初始轴向；然后，基于初始轴向计算圆柱体横截面或圆台投影轮廓，并建立轴向优化目标函数，通过迭代优化获得最终的轴向；最后通过霍夫变换法拟合圆柱体或圆台轴心位置并计算半径值。实验结果表明，该方法可有效提高管道轴向、半径等参数的估算准确度，同时，本文提出的优化目标函数也为检测结果提供了一种新的评价方法。     

Variable-dimensional vector modulation for perceptual-based DWT blind audio watermarking with adjustable payload capacity

A variable-dimensional vector modulation (VDVM) scheme is introduced to maximize the efficiency of the norm-space DWT-based blind audio watermarking technique. This flexible scheme allows the watermarking algorithm to reach a balance between robustness and capacity, while the imperceptivity is always ensured. The imperfection of applying quantization index modulation in the open-loop case has been rectified. The effectiveness of the proposed scheme is proven using the perceptual evaluation of audio quality (PEAQ) and bit error rates of recovered watermarks under various signal processing attacks. Experimental results show that the proposed VDVM scheme is comparable to other recently developed methods in robustness and imperceptivity even at a capacity as high as 301.46 bps. Such a capacity can be further doubled by halving the dimension of the involved DWT vector, while the robustness is still maintained at a satisfactory level.     

Compressively sampled light field reconstruction using orthogonal frequency selection and refinement

This paper considers the compressive sensing framework as a way of overcoming the spatio-angular trade-off inherent to light field acquisition devices. We present a novel method to reconstruct a full 4D light field from a sparse set of data samples or measurements. The approach relies on the assumption that sparse models in the 4D Fourier domain can efficiently represent light fields. The proposed algorithm reconstructs light fields by selecting the frequencies of the Fourier basis functions that best approximate the available samples in 4D hyper-blocks. The performance of the reconstruction algorithm is further improved by enforcing orthogonality of the approximation residue at each iteration, i.e. for each selected basis function. Since sparsity is better preserved in the continuous Fourier domain, we propose to refine the selected frequencies by searching for neighboring non-integer frequency values. Experiments show that the proposed algorithm yields performance improvements of more than 1 dB compared to state-of-the-art compressive light field reconstruction methods. The frequency refinement step also significantly enhances the visual quality of reconstruction results of our method by a 1.8 dB average.     

Mixed convective peristaltic flow of Eyring‐Prandtl fluid with chemical reaction and variable electrical conductivity in a tapered asymmetric channel

The influence of inconstant electrical conductivity and chemical reaction on the peristaltic motion of non‐Newtonian Eyring‐Prandtl fluid inside a tapered asymmetric channel is investigated. The system is concerned by a uniform external magnetic field. The heat and mass transfer are considered. The problem is controlled mathematically by a system of nonlinear partial differential equations which describe the velocity, temperature, and nanoparticle concentration of the fluid. By means of long wavelength and low Reynolds numbers, our system is simplified. It is explained by using the multi‐step differential transform method as a semi‐analytical technique. The distributions of velocity, temperature, nanoparticle concentration, as well as pressure gradient and pressure rise are obtained as a function of the physical parameters of the problem. The effects of these parameters on these distributions are deliberated numerically and illustrated graphically through a set of figures. The results indicate that the parameters play a significant role in controlling the velocity, temperature, nanoparticle concentration, pressure gradient, and pressure rise.     

The Investigation of the Fractional-View Dynamics of Helmholtz Equations Within Caputo Operator

It is eminent that partial differential equations are extensively meaningful in physics, mathematics and engineering. Natural phenomena are formulated with partial differential equations and are solved analytically or numerically to interrogate the system’s dynamical behavior. In the present research, mathematical modeling is extended and the modeling solutions Helmholtz equations are discussed in the fractional view of derivatives. First, the Helmholtz equations are presented in Caputo’s fractional derivative. Then Natural transformation, along with the decomposition method, is used to attain the series form solutions of the suggested problems. For justification of the proposed technique, it is applied to several numerical examples. The graphical representation of the solutions shows that the suggested technique is an accurate and effective technique with a high convergence rate than other methods. The less calculation and higher rate of convergence have confirmed the present technique’s reliability and applicability to solve partial differential equations and their systems in a fractional framework.     

Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line

To maintain the efficient and reliable operation of power systems, it is extremely important that the transmission line faults need to be detected and located in a reliable and accurate manner. A number of mathematical and intelligent techniques are available in the literature for estimating the fault location. However, the results are not satisfactory due to the wide variation in operating conditions such as system loading level, fault inception instance, fault resistance and dc offset and harmonics contents in the transient signal of the faulted transmission line. Keeping in view of aforesaid, a new approach based on generalized neural network (GNN) with wavelet transform is presented for fault location estimation. Wavelet transform is used to extract the features of faulty current signals in terms of standard deviation. Obtained features are used as an input to the GNN model for estimating the location of fault in a given transmission systems. Results obtained from GNN model are compared with ANN and well established mathematical models and found more accurate.     

Defect detection in magnetic tile images based on stationary wavelet transform

A novel approach using stationary wavelet transform (SWT) is proposed for automatically detecting low-contrast defects under various light conditions in magnetic tile images. In this method, the uneven background was removed by Sobel operation. Then the index low-pass filtering and the nonlinear enhancement were respectively used to eliminate the interference and enhance the target in subbands produced by SWT. To verify the validity of the proposed algorithm, extensive experiments were conducted in a novel machine vision based system. As the result shows, the proposed method achieves an accuracy rate of 92.86% in detecting various defects in magnetic tile surfaces with the average operation time of 0.5190 s, and is superior to traditional methods in terms of the high reliability and accuracy.