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Recently, AOTF infrared spectrometer attracts more and more attention, and the speed of obtaining spectrum curve depends on not only the number of spectral sampling point, but also the spectral sampling rate. This paper introduces a circuit design method, in which a precision low noise amplified circuit has been used. Compared with the traditional lock-in circuit, this method has an advantage of fast sampling speed, which could be up to 10us orders of magnitude in theory. The experiment was done according to the above theory. The result shows that sampling speed could reach 10 2 us magnitudes.

In order to realize a compact instrument for detection of explosive at trace levels, LIBS was applied on residues from different explosives and potentially interfering materials. The residues were simply placed on aluminum support and the measurements were performed in air. Spectral line intensities from the characteristic atoms/molecules and their ratios, are strongly varying from one sampling point to another. The reasons for such variations were studied and explained, allowing establishing a suitable procedure for material recognition. Correct classification was always obtained for five types of explosives, while for TATP, nitroglycerine, DNT and EGDN this occurred only for very thin residues. In all the cases, the estimated detection threshold is between 0.1 ng and 1 ng.

As is known to all, the LAMOST active optics wavefront test is realized by Shack-Hartmann Wavefront Sensor. But because of the characteristics and difficulties of the LAMOST, it is the key problem in the design of LAMOST Shack-Hartmann Wavefront Sensor to select and decide the sampling point number corresponding to one segment of LAMOST. In this paper we mainly discuss the sampling point number experiment by simulating different numbers of Shack-Hartmann sampling points from one LAMOST segment based on the Large Aperture Active Optics Experiment Telescope Device, which is briefly introduced with LAMOST. After the introduction, the main contents of this article are given including the following three experimental analysis parts. The first experimental analysis is the active optics tests, active optics close loop corrections and comparisons among experiments with different sampling point numbers. The second is the fitting and correction of the low-frequency aberration items existing in the system with about twenty sampling points. The last is that the low-frequency aberrations such as astigmatism and defocus are generated and then corrected with only twenty sampling points after close loop correction with all sampling points. Finally some primary conclusions of LAMOST Shack-Hartmann Wavefront Sensor sampling point number are reached and given.

This paper describes a series of experiments in data fusion of remotely sensed multispectral satellite imagery, in-situ physical measurement data (temperature, pH, salinity), and implicitly encoded knowledge (contained in location and season) to predict values and classified levels of chlorophyll-a using an artificial neural net (ANN). ANNs inherently fuse data inputs and discover relationships to provide a fused interpretation of the inputs. The experiments investigated the effects of fusing data and knowledge from the three different types of sources: non-contact, physical contact, and implicit. The results indicate that fusing the three source types improved prediction of chlorophyll-a values and classification levels, and that the multisource ANN fusion approach might improve or augment present periodic sample point monitoring methods for chlorophyll-a.

To facilitate early detection and removal of colonic polyps, we have developed a method to present the whole colon surface in a single 2D image. To improve detection sensitivity and lower false positives while using the 2D image, we utilize curvature-vector pair and curvature-images to construct a candidate-point image to indicate possible locations of polyps in the 2D image. Planes perpendicular to the centerline are generated. On each plane, equiangularly spaced radial rays are generated, which intersect the colon surface, generating sample points. The centerline index of the planes and the angular index of the radial rays on each plane define the x and y coordinates of a 2D array, respectively. At each sample point, four curves are constructed along the x, the y, and the two diagonal directions, for which four curvatures are calculated. The curvature vectors which pointing into the lumen are set to zero. Then, at each sample point, the values of the maximum and minimum curvature are assigned to the corresponding element in the 2D array. Using this 2D array and a two-step thresholding method, a so-called candidate-point image, which indicates possible locations of polyps, is generated. In the two-step thresholding method, the thresholds are determined by using a five training colon-data sets. This candidate-point image can be used in conjunction with other analyses in polyp detection.