Direct raycasting of unstructured cell-centered data by discontinuity Roe-average computation

In the field of computational fluid dynamics (CFD), the upwind finite volume method (FVM) is widely applied to solve 3D flows with discontinuity phenomena (e.g., shock waves). It produces unstructured data at the center of each cell (cell-centered data) with the flow discontinuity constraint on the inner-face between face-neighboring cells. For visualization, existing approaches with interpolation usually pre-extrapolate cell-centered data into cell-vertexed data (data values given at cell vertices) and only handle cell-vertexed data during actual rendering, which unconsciously depress the rendering accuracy and violate the discontinuity constraint. In this paper, we propose a novel method to visualize cell-centered data directly avoiding extrapolation and keep the discontinuity in the rendering data on the framework of multi-pass raycasting. During resampling, the field is reconstructed using the original cell-centered data value and the cell-gradient estimated by Green–Gauss theorem. To keep the discontinuity, we reconstruct the field at an inner-face resampled point using both the face-adjacencies and get two discontinuous field values. Then the field is obtained by computing Roe-average of the two. The analysis and experiments demonstrate that our approach gains a high-accuracy reconstruction and leads to a high-quality image.     

Application-Driven Compression for Visualizing Large-Scale Time-Varying Data

The authors present an application-driven approach to compressing large-scale time-varying volume data. Their approach identifies a reference feature to partition the data into space-time blocks, which are compressed with various precisions depending on their association to the feature. Runtime decompression is performed with bit-wise texture packing and deferred filtering. This method achieves high compression rates and interactive rendering while preserving fine details surrounding regions of interest. Such an application-driven approach could help computational scientists cope with the large-data problem.     

Optimal Error Estimates of the Legendre Tau Method for Second-Order Differential Equations

In this paper, we prove that the Legendre tau method has the optimal rate of convergence in L ²-norm, H ¹-norm and H ²-norm for one-dimensional second-order steady differential equations with three kinds of boundary conditions and in C([0,T];L ²(I))-norm for the corresponding evolution equation with the Dirichlet boundary condition. For the generalized Burgers equation, we develop a Legendre tau-Chebyshev collocation method, which can also be optimally convergent in C([0,T];L ²(I))-norm. Finally, we give some numerical examples.     

Dynamically modulated intensity interrogation scheme using waveguide coupled surface plasmon resonance sensors

An electro-optically modulated intensity interrogation method based on tunable waveguide coupled surface plasmon resonance sensors has been proposed. It has been theoretically and experimentally demonstrated that the proposed scheme can enable sensitive measurement of measurand variations. By modulating the refractive index in the waveguide layer, this interrogation method yields modulated signal whose amplitude is related to measurand's refractive index. This amplitude modulated signal offers a higher signal to noise ratio and eliminates additive noise in the sensor system. A preliminary investigation using saline buffers with different NaCl concentrations shows a resolution of 2.3 × 10^?6 refractive index unit by our approach. Resolution can be controlled by the amplitude of the applied modulation voltage and can be further enhanced by optimizing the device structure or improving the electro-optical (E-O) coefficient of the E-O material. This approach is simple, stable, and promising for low-cost or multi-channel SPR biosensor applications.     

Ordered mesoporous Pd/SnO2 synthesized by a nanocasting route for high hydrogen sensing performance

Ordered mesoporous SnO₂ and mesoporous Pd/SnO₂ have been successfully synthesized via nanocasting method using the hexagonal mesoporous SBA-15 as template. Two different procedures, impregnation technique and direct synthesis, were utilized for the doping of Pd in the mesoporous SnO₂. The results of small angle X-ray diffraction (SAXD), nitrogen adsorption–desorption and transmission electron microscopy (TEM) demonstrate that the SnO₂ and Pd/SnO₂ display ordered mesoporous structures and high surface areas. Wide angle X-ray diffraction (WAXD) and X-ray photoelectron spectroscopy (XPS) reveal tetragonal structure of SnO₂ and the existence of Pd element. The sensing properties of mesoporous SnO₂ and mesoporous Pd/SnO₂ for H₂ were detected. The sensor utilizing mesoporous Pd/SnO₂ via direct synthesis method exhibits excellent response and recovery behavior and much higher sensitivity to H₂, compared to those using mesoporous SnO₂ and mesoporous Pd/SnO₂ via impregnation technique. It is believed that its high gas sensing performance is derived from the large surface area, high activity and well dispersion of Pd additive, as well as high porosity, which lead to highly effective surface interaction between the target gas molecules and the surface active sites.     

Dual graph regularized NMF model for social event detection from Flickr data

In this work, we aim to discover real-world events from Flickr data by devising a three-stage event detection framework. In the first stage, a multimodal fusion (MF) model is designed to deal with the heterogeneous feature modalities possessed by the user-shared data, which is advantageous in computation complexity. In the second stage, a dual graph regularized non-negative matrix factorization (DGNMF) model is proposed to learn compact feature representations. DGNMF incorporates Laplacian regularization terms for the data graph and base graph into the objective, keeping the geometry structures underlying the data samples and dictionary bases simultaneously. In the third stage, hybrid clustering algorithms are applied seamlessly to discover event clusters. Extensive experiments conducted on the real-world dataset reveal the MF-DGNMF-based approaches outperform the baselines.     

Microstructural topology optimization of structural-acoustic coupled systems for minimizing sound pressure level

The aim of this paper is to present a microstructural topology optimization methodology for the structural-acoustic coupled system. In the structural-acoustic system, the structure is considered to be a thin composite plate composed of periodic uniform microstructures. The discrete design variables are used in the microstructural topology optimization, and the constitutive matrix is interpolated by the power-law scheme at the micro scale. The equivalent macro material properties of the microstructure are computed through the homogenization method. The design objective is to minimize the sound pressure level (SPL) in an interior acoustic medium. The sensitivities of the SPL with respect to design variables are derived. The bi-directional evolutionary structural optimization (BESO) method is extended to solve the structural-acoustic coupled optimization problem to find the optimal material distribution of the microstructure. Numerical examples of a hexahedral box and an automobile passenger compartment are given to demonstrate the efficiency of the presented microstructural topology optimization method.     

基于NS2的802.11效果异常现象仿真研究

在802.11 WLAN(Wireless Local Network)中,当一些无线节点的传输速率明显低于其他节点与AP(Access Point)间的传输速率时,将会使区域内所有节点的传输性能降低,这种现象称为效果异常(Performance Anomaly),Hyogon K等人在文献[6-7]中提出用改变封包容量和竞争窗口大小的方式来解决这个问题.文章通过在NS2网络仿真环境下,设计实验拓扑,编写性能分析脚本,对效果异常现象进行仿真并验证了上述两种方案能够有效缓解该现象并保持系统传输性能稳定.