利用氟化气体和氧气的混和物进行SiC薄膜的反应离子刻蚀

本文研究了在ＳＦ６，ＣＢｒＦ３和ＣＨＦ３与氧相混合的几种氟化气体等离子体中，ＳｉＣ薄膜反应离子刻蚀（ＲＩＥ）的深度。通过监测射频等离子体的光发射谱及产生等离子体的直流偏压来研究刻蚀机理，为了更精确地定量分析刻蚀工艺，使用氩光能测定技术使等离子发射强度转换为相应的等离子物质浓度。为获得选择性的ＳｉＣ－Ｓｉ刻蚀及ＳｉＣ薄膜的各向异性图形，对等离子体条件，如气体混合物的构成，压力和功率进行了研究。首次采     

Protein design to understand peptide ligand recognition by tetratricopeptide repeat proteins

Protein design aims to understand the fundamentals of protein structure by creating novel proteins with pre-specified folds. An equally important goal is to understand protein function by creating novel proteins with pre-specified activities. Here we describe the design and characterization of a tetratricopeptide (TPR) protein, which binds to the C-terminal peptide of the eukaryotic chaperone Hsp90. The design emphasizes the importance of both direct, short-range protein-peptide interactions and of long-range electrostatic optimization. We demonstrate that the designed protein binds specifically to the desired peptide and discriminates between it and the similar C-terminal peptide of Hsp70.     

Polychromatic target identification with a color liquid-crystal-TV-based joint-transform correlator

A real-time polychromatic target-detection technique that uses a computer-addressable color liquid-crystal- TV-based joint-transform correlator (JTC) is presented. One of the major features of the polychromatic JTC is that the spectra content of the object can be exploited. Since the liquid-crystal-TV panel is computer addressable, the JTC system can be made adaptive, by which the joint-transform power spectrum can be manipulated. Experimental demonstration of the proposed polychromatic JTC is provided, in which we show that the spatial and the spectral content of targets can be detected.     

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.     

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.