Local environment of Er3+ in Er-doped Si nanoclusters embedded in SiO2 films

The local environment of Er3+ in heavily Er-doped (Er, 2.5 at. %) Si nanoclusters embedded in SiO2 films annealed at various temperatures was investigated by using the fluorescence-extended x-ray absorption fine structure spectroscopy. The results show that annealing caused a large effect on the local environment of Er3+ surrounded by O atoms and the 1.54 mu m photoluminescence intensity. The correlation between the local environment around Er3+ and the corresponding 1.54 mu m photoluminescence was discussed. (c) 2006 American Institute of Physics.

Accurate vibration detection of a rough surface - art. no. 683118

Based on the semiconductor laser whose spectral line with width is compressed to be less than 1.2Mhz, a system was designed to measure and improve the amplitude and frequency of the real-time microvibration with sinusoidal modulation. real-time microvibration measurement was executed without alignment problem in the interferometry; and low-frequency disturbance of environment could be eliminated. Suggestions were also given to consummate the system. The system also has resistance against the low frequency disturbance of the environment.

Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.

用改进的ART-2网络建立移动机器人环境模型的研究

本文应用自适应共振理论中ART-2神经网络进行移动机器人环境障碍模式识别。ART-2神经网络在处理单方向渐变的模式输入时具有模式漂移的特点，机器人在静态环境中运动依赖这种特点，但在动态环境中模式漂移的特点却会对机器人的安全造成威胁。为此，设计了一种改进的ART-2神经网络，使得移动机器人同时适应在静态和动态环境中安全运动。