The pumped system of the XeCl excimer laser has been designed and applied to analyze UV XeC1 laser spectroscopy characteristics. Under the proportion of mixed gas of HCI：Xe：He = 0.1%： 1% ：98.9%, excimer laser ligh...The pumped system of the XeCl excimer laser has been designed and applied to analyze UV XeC1 laser spectroscopy characteristics. Under the proportion of mixed gas of HCI：Xe：He = 0.1%： 1% ：98.9%, excimer laser light was generated by stable glow discharge process. The laser spectrum, pulse duration, and laser power properties were obtained. The result shows that this XeCI excimer laser exhibits unique spectral properties, with two peaks between 307.7 nm and 308.5 nm at high pressure with a pumped power of 1.3959 MW/cm^3. The transition relies on the strongest transitions between B-X and C-X. The maximum-intensity transition of spectroscopy is B to X energy levels. The laser parameters are as follows： minimal duration of 15.42 ns, a repetition rate from 0.5 Hz to 5 Hz, single pulse stable power of 400 m J, and beam divergence angle of 3 mrad. The laser can be used to study UV spectroscopy, laser ablation sampling and sputtered pinnate form.展开更多
This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accu...This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accuracy of the hypotheses using AdaBoost and Probabilistic Decision-Based Neural Network (PDBNN) classifiers, which exploit local and global features of vehicles, respectively. The combination of 2 classifiers can be used to learn the complementary relationship between local and global features, and it gains an extremely low false positive rate while maintaining a high detection rate. For the MIT Center for Biological & Computational Learning (CBCL) database, a 96.3% detection rate leads to a false alarm rate of approximately 0.0013%. The objective of this study is to extract the characteristic of vehicles in both local- and global-orientation, and model the implicit invariance of vehicles. This boosted approach provides a more effective solution to handle the problems encountered by conventional background-based detection systems. The experimental results of this study prove that the proposed system achieves good performance in detecting vehicles without background information. The implemented system also extract useful traffic information that can be used for further processing, such as tracking, counting, classification, and recognition.展开更多
基金supported by the National Science foundation of China (No.30400101, 60171043)the National Science Foundation of Shannxi Province under Grant No.2001C21the Science Foundation of Xian Jiaotong University of No.xjj2004013
文摘The pumped system of the XeCl excimer laser has been designed and applied to analyze UV XeC1 laser spectroscopy characteristics. Under the proportion of mixed gas of HCI：Xe：He = 0.1%： 1% ：98.9%, excimer laser light was generated by stable glow discharge process. The laser spectrum, pulse duration, and laser power properties were obtained. The result shows that this XeCI excimer laser exhibits unique spectral properties, with two peaks between 307.7 nm and 308.5 nm at high pressure with a pumped power of 1.3959 MW/cm^3. The transition relies on the strongest transitions between B-X and C-X. The maximum-intensity transition of spectroscopy is B to X energy levels. The laser parameters are as follows： minimal duration of 15.42 ns, a repetition rate from 0.5 Hz to 5 Hz, single pulse stable power of 400 m J, and beam divergence angle of 3 mrad. The laser can be used to study UV spectroscopy, laser ablation sampling and sputtered pinnate form.
文摘This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accuracy of the hypotheses using AdaBoost and Probabilistic Decision-Based Neural Network (PDBNN) classifiers, which exploit local and global features of vehicles, respectively. The combination of 2 classifiers can be used to learn the complementary relationship between local and global features, and it gains an extremely low false positive rate while maintaining a high detection rate. For the MIT Center for Biological & Computational Learning (CBCL) database, a 96.3% detection rate leads to a false alarm rate of approximately 0.0013%. The objective of this study is to extract the characteristic of vehicles in both local- and global-orientation, and model the implicit invariance of vehicles. This boosted approach provides a more effective solution to handle the problems encountered by conventional background-based detection systems. The experimental results of this study prove that the proposed system achieves good performance in detecting vehicles without background information. The implemented system also extract useful traffic information that can be used for further processing, such as tracking, counting, classification, and recognition.
