关于热方程U_t—c(x)(k(x)U_x)_x=0确定未知系数k(x)的一个反问题

本文讨论了热方程U_1—c(x)k(x)U_x)_x=0在区域x>0,t>0上确定未知系数k(x)的反问题,文中给出了局部解的存在,唯一性。     

基于原子范数最小化的步进频率ISAR一维高分辨距离成像方法

针对传统离散化压缩感知方法在网格失配条件下步进频率(SF)?ISAR?1维距离成像估计性能下降的问题,该文提出一种基于原子范数最小化(ANM)的高分辨距离成像方法.首先,构建基于原子范数的无网格SF?IS-AR距离向稀疏表示模型,将1维距离成像问题转化为原子系数以及频率估计问题.然后,利用原子范数半正定性质,将原子范数最小化问题转化为半正定规划问题,并基于交替方向乘子法实现快速求解.最后,利用Vander-monde分解得到最终的1维高分辨距离成像结果.由于避免了网格离散化处理,因此可以实现网格失配、低量测值条件下的高分辨距离成像,且保持了高的距离分辨能力.理论分析与仿真实验验证了所提方法的有效性.     

Electrically enhanced dewatering (EED) of particulate suspensions

Electrically enhanced dewatering has been characterised from first principles using model kaolinite suspensions in both the coagulated and dispersed state and sludge from a potable water treatment plant. The dewatering properties, namely the compressive yield stress or extent of dewatering (quantified as the applied pressure at an equilibrium solids concentration) and the hindered settling function or the resistance of fluid flow (quantified as the rate of fluid expression at an equilibrium solids concentration), have been measured as a function of electric field strength. For both the dispersed and coagulated kaolinite suspensions, the rate of dewatering was found to improve at all applied pressures and with increasing applied electrical field strength, up to 1250 V m⁻¹. Improvements in the extent of dewatering were also observed but only for the coagulated suspension. The greatest improvements in dewatering were observed at the lowest applied pressures. Improvement in suspension compressibility is only predicted for the specific case where the feed to the process is both coagulated and at a neutral or low pH. For the potable water sludge, improvements in the rate of dewatering were observed at all pressures above 10 kPa. At lower pressures, the low particle surface charge caused a slow onset of electro-osmotic effects. In general, the results indicate that the application of an electric field in situ during dewatering primarily helps to increase the rate of dewatering and the benefit of this methodology is predicted to be for suspensions that exhibit low permeability at low solids concentrations in applications where the pressure is low or the process is gravity driven.     

Feature fusion via dual-resolution compressive measurement matrix analysis for spectral image classification

In the compressive spectral imaging (CSI) framework, different architectures have been proposed to recover high-resolution spectral images from compressive measurements. Since CSI architectures compactly capture the relevant information of the spectral image, various methods that extract classification features from compressive samples have been recently proposed. However, these techniques require a feature extraction procedure that reorders measurements using the information embedded in the coded aperture patterns. In this paper, a method that fuses features directly from dual-resolution compressive measurements is proposed for spectral image classification. More precisely, the fusion method is formulated as an inverse problem that estimates high-spatial-resolution and low-dimensional feature bands from compressive measurements. To this end, the decimation matrices that describe the compressive measurements as degraded versions of the fused features are mathematically modeled using the information embedded in the coded aperture patterns. Furthermore, we include both a sparsity-promoting and a total-variation (TV) regularization terms to the fusion problem in order to consider the correlations between neighbor pixels, and therefore, improve the accuracy of pixel-based classifiers. To solve the fusion problem, we describe an algorithm based on the accelerated variant of the alternating direction method of multipliers (accelerated-ADMM). Additionally, a classification approach that includes the developed fusion method and a multilayer neural network is introduced. Finally, the proposed approach is evaluated on three remote sensing spectral images and a set of compressive measurements captured in the laboratory. Extensive simulations show that the proposed classification approach outperforms other approaches under various performance metrics.     

Isotropic and anisotropic filtering norm-minimization: A generalization of the TV and TGV minimizations using NESTA

Compressive sensing (CS) allows for the reconstruction of sparse signals based on measurements acquired at sub-Nyquist sampling rates. Amongst the common CS methods, total variation (TV) minimization is a common approach used to reconstruct images that are approximately piece-wise constant. The discrete gradient operation in TV can be described as a filtering operation using the horizontal and vertical finite differences filters. In this paper, we generalize the TV minimization procedure for any set of digital filters. The proposed method allows one to reconstruct signals that are sparse when filtered with some set of filters, other than the finite difference operators. Our implementation is based on a fast and accurate first-order optimization algorithm which is called NESTA, in a reference to the Nesterov’s algorithm. We incorporate isotropic and anisotropic filtering combinations to the original TV minimization method implemented in the original NESTA algorithm. We also propose 3 forms of the second order total generalized variation (TGV) when using first and second order filters. In order to evaluate the method, we perform a systematic set of experiments using synthetic and real magnetic resonance images, with several sets of filters and cost functions and under different undersampling factors and noise levels. A statistical analysis of the results shows that the best configurations of our method provide a significantly better image quality when compared to the TV and TGV for MRI reconstruction.     

基于光谱稀疏模型的高光谱压缩感知重构

提出了一种基于光谱稀疏化的压缩感知采样与重构模型,通过从训练样本中构建光谱稀疏字典提升光谱稀疏化效果,同时在重构时兼顾空间图像的全变分约束进一步提升重构精度.对200波段AVIRIS高光谱场景进行压缩感知重构的实验表明,利用构建的光谱稀疏字典与传统的DCT字典和Haar小波字典相比光谱稀疏化效果明显提升,同时在25%采样下基于光谱稀疏字典几乎无差别重构出了高光谱图像,同样条件下在空间和光谱的精度与现有常用方法相比有较大的提升.     

一种改进的长时间压缩感知跟踪算法

压缩感知跟踪(CT)算法具有简单、高效、实时的优点,但是却存在着跟踪窗口尺寸不能自适应变化,无法有效处理遮挡以及跟踪失败后的目标再发现等问题.为了解决上述问题,提出了一种改进的长时间压缩感知跟踪算法.所提出的算法采用多尺度的目标外观再匹配方法,使得跟踪窗口大小能够适应目标尺寸变化.此外,通过分析滑动窗口内跟踪窗口图像的整体特征变化来判定目标是否发生遮挡.为了解决跟踪器漂移问题,采用Haar特征在线生成检测器,实现目标的再发现.实验结果表明提出的算法相比原CT算法具有更好的鲁棒性和准确性.     

Interactions between silica sand and sodium silicate solution during consolidation process

This work is based on the knowledge of the consolidation of silica sand with an alkaline solution in order to determine the mechanisms that occur during the drying of sand and various alkaline solution mixtures. The investigations concern effects of sand distribution size, dilution of sodium silicate solutions and drying temperature of the mixtures on consolidation behaviour. The thermal analysis performed on fresh mixtures show a release of free-water from diluted silicate solution during the consolidation. SEM observations and compressive strength tests results indicate that interactions between sodium silicate binder and silica sand depend on drying temperature. Consequently two consolidation mechanisms are proposed: at low drying temperature (70 °C), sodium silicate acts as a thin layer of glue covering sand grains and bind them to each other, while at high temperature (150 °C), dissolution-precipitation reaction occurred in the mixture consolidating more strongly the granular system. The increase of Si/Na molar ratio in a sodium silicate solution containing silica sand is in accordance with the proposed mechanism.     

An improved sparsity adaptive matching pursuit algorithm for compressive sensing based on regularized backtracking

Sparsity Adaptive Matching Pursuit (SAMP) algorithm is a widely used reconstruction algorithm for compressive sensing in the case that the sparsity is unknown. In order to match the sparsity more accurately, we presented an improved SAMP algorithm based on Regularized Backtracking (SAMP-RB). By adapting a regularized backtracking step to SAMP algorithm in each iteration stage, the proposed algorithm can flexibly remove the inappropriate atoms. The experimental results show that SAMP-RB reconstruction algorithm greatly improves SAMP algorithm both in reconstruction quality and computational time. It has better reconstruction efficiency than most of the available matching pursuit algorithms.