A unified analysis of exact methods of inverting the 2-D exponential radon transform, with implications for noise control in SPECT

Exact methods of inverting the two-dimensional (2-D) exponential Radon transform have been proposed by Bellini et al. (1979) and by Inouye et al. (1989), both of whom worked in the spatial-frequency domain to estimate the 2-D Fourier transform of the unattenuated sinogram; by Hawkins et al. (1988), who worked with circularly harmonic Bessel transforms; and by Tretiak and Metz (1980), who followed filtering of appropriately-modified projections by exponentially-weighted backprojection. With perfect sampling, all four of these methods are exact in the absence of projection-data noise, but empirical studies have shown that they propagate noise differently, and no underlying theoretical relationship among the methods has been evident. Here, an analysis of the 2-D Fourier transform of the modified sinogram reveals that all previously-proposed linear methods can be interpreted as special cases of a broad class of methods, and that each method in the class can be implemented, in principle, by any one of four distinct techniques. Moreover, the analysis suggests a new member of the class that Is predicted to have noise properties better than those of previously-proposed members.     

粘合体系对天然橡胶复合材料性能的影响

本文以木质纤维素/蒙脱土、炭黑为增强材料,制备了天然橡胶复合材料,探讨了以间苯二酚(R-80)、六甲氧基甲基三聚氰胺(HMMM)和新癸酸钴为主要成分的粘合体系对天然橡胶复合材料性能的影响。结果表明:间-甲-钴粘合体系大幅提高了天然橡胶复合材料的焦烧时间(T_(10))和正硫化时间(T_(90)),降低了其硫化速度(V_(C1));力学测试显示,粘合体系对复合材料的拉伸强度、撕裂强度和断裂伸长率的影响相当大;提高了复合材料的邵氏A硬度、密度和耐磨性。     

Neural-network method applied to the stereo image correspondence problem in three-component particle image velocimetry

The successful application of a recurrent neural network of the Hopfield type to the solution of the stereo image-pair reconciliation problem in stereoscopic particle image velocimetry (PIV) in the tracking mode is described. The results of applying the network to both virtual-flow and physical-flow PIV data sets are presented, and the usefulness of this novel approach to PIV stereo image analysis is demonstrated. A partner-particle image-pair density (PPID) parameter is defined as the average number of potential particle image-pair candidates in the search window in the second view corresponding to a single image pair in the first view. A quantitative assessment of the performance of the method is then made from groups of 100 synthetic flow images at various values of the PPID. The successful pairing of complementary image points is shown to vary from 100% at a PPID of 1 and to remain greater than 97% successful for PPID's up to 5. The application of the method to a hydraulic flow is also described, with in-line stereo images presented, and the application of the neural-matching method is demonstrated for a typical data set.     

Protein crystal growth in microgravity using a liquid/liquid diffusion method

Crystallization of proteins by liquid liquid diffusion method was performed in microgravity using the MDA Minilab aboard the US Space Shuttle. Three proteins, namely lysozyme, trichosanthin, and a new lechin, were crystallized in the space experiment. In contrast to the results of space experiments with a tube-like vapor diffusion method, the crystallization conditions for growing better crystals in space are remarkably different from the conditions optimized on earth. This may be due to difficulties in ground optimization, which are caused by gravity-dependent phenomena, in particular the specific convective flow occurring with liquid liquid diffusion.     

Acceleration and filtering in the generalized Landweber iteration using a variable shaping matrix

The generalized Landweber iteration with a variable shaping matrix is used to solve the large linear system of equations arising in the image reconstruction problem of emission tomography. The method is based on the property that once a spatial frequency image component is almost recovered within in in the generalized Landweber iteration, this component will still stay within in during subsequent iterations with a different shaping matrix, as long as this shaping matrix satisfies the convergence criterion for the component. Two different shaping matrices are used: the first recovers low-frequency image components; and the second may be used either to accelerate the reconstruction of high-frequency image components, or to attenuate these components to filter the image. The variable shaping matrix gives results similar to truncated inverse filtering, but requires much less computation and memory, since it does not rely on the singular value decomposition.     

Tracking mobile users in wireless networks via semi-supervised colocalization

Recent years have witnessed the growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labeled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters or access points. To solve this problem, we have developed a novel machine learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile users' locations and the locations of access points. Our framework exploits both labeled and unlabeled data from mobile devices and access points. In our two-phase solution, we first build a manifold-based model from a batch of labeled and unlabeled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase colocalization to an online and incremental model that can deal with labeled and unlabeled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed on three different testbeds.     

S-Band 1mm SiC MESFET with 2W Output on Semi-Insulated SiC Substrate

介绍了用热壁反应炉在50mm SiC半绝缘衬底上制备的SiC MESFET外延材料.其沟道层厚度约为0.35μm,掺杂浓度约为1.7×1017cm-3.沟道和衬底之间的缓冲层为非有意掺杂的弱n型.欧姆接触用的帽层掺杂浓度约1019cm-3.器件制备采用了ICP刻蚀等技术.微波测试结果表明,1mm栅宽功率器件封装后在2GHz下输出功率达到了2W.