4 resultados para time-resolved Kerr rotation

em Massachusetts Institute of Technology


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IntraCavity Laser Absorption Spectroscopy (ICLAS) is a high-resolution, high sensitivity spectroscopic method capable of measuring line positions, linewidths, lineshapes, and absolute line intensities with a sensitivity that far exceeds that of a traditional multiple pass absorption cell or Fourier Transform spectrometer. From the fundamental knowledge obtained through these measurements, information about the underlying spectroscopy, dynamics, and kinetics of the species interrogated can be derived. The construction of an ICLA Spectrometer will be detailed, and the measurements utilizing ICLAS will be discussed, as well as the theory of operation and modifications of the experimental apparatus. Results include: i) Line intensities and collision-broadening coefficients of the A band of oxygen and previously unobserved, high J, rotational transitions of the A band, hot-band transitions, and transitions of isotopically substituted species. ii) High-resolution (0.013 cm-1) spectra of the second overtone of the OH stretch of trans-nitrous acid recorded between 10,230 and 10,350 cm-1. The spectra were analyzed to yield a complete set of rotational parameters and an absolute band intensity, and two groups of anharmonic perturbations were observed and analyzed. These findings are discussed in the context of the contribution of overtone-mediated processes to OH radical production in the lower atmosphere.

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In this report, a face recognition system that is capable of detecting and recognizing frontal and rotated faces was developed. Two face recognition methods focusing on the aspect of pose invariance are presented and evaluated - the whole face approach and the component-based approach. The main challenge of this project is to develop a system that is able to identify faces under different viewing angles in realtime. The development of such a system will enhance the capability and robustness of current face recognition technology. The whole-face approach recognizes faces by classifying a single feature vector consisting of the gray values of the whole face image. The component-based approach first locates the facial components and extracts them. These components are normalized and combined into a single feature vector for classification. The Support Vector Machine (SVM) is used as the classifier for both approaches. Extensive tests with respect to the robustness against pose changes are performed on a database that includes faces rotated up to about 40 degrees in depth. The component-based approach clearly outperforms the whole-face approach on all tests. Although this approach isproven to be more reliable, it is still too slow for real-time applications. That is the reason why a real-time face recognition system using the whole-face approach is implemented to recognize people in color video sequences.

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In the first part of this paper we show that a new technique exploiting 1D correlation of 2D or even 1D patches between successive frames may be sufficient to compute a satisfactory estimation of the optical flow field. The algorithm is well-suited to VLSI implementations. The sparse measurements provided by the technique can be used to compute qualitative properties of the flow for a number of different visual tsks. In particular, the second part of the paper shows how to combine our 1D correlation technique with a scheme for detecting expansion or rotation ([5]) in a simple algorithm which also suggests interesting biological implications. The algorithm provides a rough estimate of time-to-crash. It was tested on real image sequences. We show its performance and compare the results to previous approaches.

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Cyclic changes in the shape of a quasi-rigid body on a curved manifold can lead to net translation and/or rotation of the body in the manifold. Presuming space-time is a curved manifold as portrayed by general relativity, translation in space can be accomplished simply by cyclic changes in the shape of a body, without any thrust or external forces.