Parallel Fully-Implicit Computation of Magnetohydrodynamics Acceleration Experiments

A three-dimensional MHD solver is described in the paper. The solver simulates reacting flows with nonequilibrium between translational-rotational, vibrational and electron translational modes. The conservation equations are discretized with implicit time marching and the second-order modified Steger-Warming scheme, and the resulted linear system is solved iteratively with Newton-Krylov-Schwarz method that is implemented by PETS,: package. The results of convergence tests arc plotted, which show good scalability and convergence around twice faster when compared with the DPLR method. Then five test runs are conducted simulating the experiments done at the NASA Ames MHD channel, and the calculated pressures, temperatures, electrical conductivity, back EMF, load factors and flow accelerations are shown to agree with the experimental data. Our computation shows that the electrical conductivity distribution is not uniform in the powered section of the MHD channel, and that it is important to include Joule heating in order to calculate the correct conductivity and the MHD acceleration.

Droplet Image Feedback Control System in Evaporation Experiment

In order to realize the steady-state droplet evaporation, image feedback control system is designed based on DSP. The system has three main functions: to capture and store droplet images during the experiment; to calculate droplet geometrical and physical parameters such as volume, surface area, surface tension and evaporation velocity at a high-precision level; to keep the droplet volume constant. The DSP can drive an injection controller with the PID control to inject liquid so as to keep the droplet volume constant. The evaporation velocity of droplet can be calculated by measuring the injected volume during the evaporation. The structure of hardware and software of the control system, key processing methods such as contour fitting and experimental results are described.

Micro Insert: A Prototype Full-Ring PET Device for Improving the Image Resolution of a Small-Animal PET Scanner

A full-ring PET insert device should be able to enhance the image resolution of existing small-animal PET scanners. Methods: The device consists of 18 high-resolution PET detectors in a cylindric enclosure. Each detector contains a cerium-doped lutetium oxyorthosilicate array (12 x 12 crystals, 0.72 x 1.51 x 3.75 mm each) coupled to a position-sensitive photomultiplier tube via an optical fiber bundle made of 8 x 16 square multiclad fibers. Signals from the insert detectors are connected to the scanner through the electronics of the disabled first ring of detectors, which permits coincidence detection between the 2 systems. Energy resolution of a detector was measured using a Ge-68 point source, and a calibrated 68Ge point source stepped across the axial field of view (FOV) provided the sensitivity profile of the system. A Na-22 point source imaged at different offsets from the center characterized the in-plane resolution of the insert system. Imaging was then performed with a Derenzo phantom filled with 19.5 MBq of F-18-fluoride and imaged for 2 h; a 24.3-g mouse injected with 129.5 MBq of F-18-fluoride and imaged in 5 bed positions at 3.5 h after injection; and a 22.8-g mouse injected with 14.3 MBq of F-18-FDG and imaged for 2 h with electrocardiogram gating. Results: The energy resolution of a typical detector module at 511 keV is 19.0% +/- 3.1 %. The peak sensitivity of the system is approximately 2.67%. The image resolution of the system ranges from 1.0- to 1.8-mm full width at half maximum near the center of the FOV, depending on the type of coincidence events used for image reconstruction. Derenzo phantom and mouse bone images showed significant improvement in transaxial image resolution using the insert device. Mouse heart images demonstrated the gated imaging capability of the device. Conclusion: We have built a prototype full-ring insert device for a small-animal PET scanner to provide higher-resolution PET images within a reduced imaging FOV. Development of additional correction techniques are needed to achieve quantitative imaging with such an insert.

低能He~(2+)入射He原子转移电离实验中出射电子成像研究；Momentum image of emission electrons in transfer ionization process of slow He~(2+) colliding on He

利用反应显微谱仪对70keV He2+-He转移电离过程中的出射电子进行了成像,研究了出射电子的空间速度分布特征.结果表明:电子主要集中在散射平面内;在散射平面内,电子速度分布介于零与入射离子速度Vp之间(即前向出射)且在散射离子和靶核核间轴处有一极小值,呈现出典型的双峰结构.出射电子的上述分布特征可由出射电子波函数σ振幅和π振幅的干涉进行定性解释,σ振幅和π振幅对出射电子波函数的贡献与碰撞参数相关.在小碰撞参数下,π振幅的贡献更加明显;而在大碰撞参数下,σ振幅的贡献更加显著.

Local histogram based geometric invariant image watermarking

Compared with other existing methods, the feature point-based image watermarking schemes can resist to global geometric attacks and local geometric attacks, especially cropping and random bending attacks (RBAs), by binding watermark synchronization with salient image characteristics. However, the watermark detection rate remains low in the current feature point-based watermarking schemes. The main reason is that both of feature point extraction and watermark embedding are more or less related to the pixel position, which is seriously distorted by the interpolation error and the shift problem during geometric attacks. In view of these facts, this paper proposes a geometrically robust image watermarking scheme based on local histogram. Our scheme mainly consists of three components: (1) feature points extraction and local circular regions (LCRs) construction are conducted by using Harris-Laplace detector; (2) a mechanism of grapy theoretical clustering-based feature selection is used to choose a set of non-overlapped LCRs, then geometrically invariant LCRs are completely formed through dominant orientation normalization; and (3) the histogram and mean statistically independent of the pixel position are calculated over the selected LCRs and utilized to embed watermarks. Experimental results demonstrate that the proposed scheme can provide sufficient robustness against geometric attacks as well as common image processing operations. (C) 2010 Elsevier B.V. All rights reserved.

Partially Supervised Neighbor Embedding for Example-Based Image Super-Resolution

Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding.

An integrated aurora image retrieval system: AuroraEye

With the digital all-sky imager (ASI) emergence in aurora research, millions of images are captured annually. However, only a fraction of which can be actually used. To address the problem incurred by low efficient manual processing, an integrated image analysis and retrieval system is developed. For precisely representing aurora image, macroscopic and microscopic features are combined to describe aurora texture. To reduce the feature dimensionality of the huge dataset, a modified local binary pattern (LBP) called ALBP is proposed to depict the microscopic texture, and scale-invariant Gabor and orientation-invariant Gabor are employed to extract the macroscopic texture. A physical property of aurora is inducted as region features to bridge the gap between the low-level visual features and high-level semantic description. The experiments results demonstrate that the ALBP method achieves high classification rate and low computational complexity. The retrieval simulation results show that the developed retrieval system is efficient for huge dataset. (c) 2010 Elsevier Inc. All rights reserved.

A Unified Tensor Level Set for Image Segmentation

This paper presents a new region-based unified tensor level set model for image segmentation. This model introduces a three-order tensor to comprehensively depict features of pixels, e.g., gray value and the local geometrical features, such as orientation and gradient, and then, by defining a weighted distance, we generalized the representative region-based level set method from scalar to tensor. The proposed model has four main advantages compared with the traditional representative method as follows. First, involving the Gaussian filter bank, the model is robust against noise, particularly the salt-and pepper-type noise. Second, considering the local geometrical features, e. g., orientation and gradient, the model pays more attention to boundaries and makes the evolving curve stop more easily at the boundary location. Third, due to the unified tensor pixel representation representing the pixels, the model segments images more accurately and naturally. Fourth, based on a weighted distance definition, the model possesses the capacity to cope with data varying from scalar to vector, then to high-order tensor. We apply the proposed method to synthetic, medical, and natural images, and the result suggests that the proposed method is superior to the available representative region-based level set method.