Considering the low efficiency during the process of traditional calibration for digital-display vibrometers, an automatic calibration system for vibrometers based on machine vision is developed. First, an automatic vibration control system is established on the basis of a personal computer, and the output of a vibration exciter on which a digital-display vibrometer to be calibrated is installed, is automatically adjusted to vibrate at a preset vibration level and a preset frequency. Then the display of the vibrometer is captured by a digital camera and identified by means of image recognition. According to the vibration level of the exciter measured by a laser interferometer and the recognized display of the vibrometer, the properties of the vibrometer are calculated and output by the computer. Image recognition algorithms for the display of the vibrometer with a high recognition rate are presented, and the recognition for vibrating digits and alternating digits is especially analyzed in detail. Experimental results on the built-up system show that the prposed image recognition methods are very effective and the system could liberate operators from boring and intense calibration work for digital-display vibrometers
The three-dimensional (3D) coordinate measurement of radio frequency identification (RFID) multi-tag networks is one of the important issues in the field of RFID, which affects the reading performance of RFID multi-tag networks. In this paper, a novel method for 3D coordinate measurement of RFID multitag networks is proposed. A dual-CCD system (vertical and horizontal cameras) is used to obtain images of RFID multi-tag networks from different angles. The iterative threshold segmentation and the morphological filtering method are used to process the images. The template matching method is respectively used to determine the two-dimensional (2D) coordinate and the vertical coordinate of each tag. After that, the 3D coordinate of each tag is obtained. Finally, a back-propagation (BP) neural network is used to model the nonlinear relationship between the RFID multi-tag network and the corresponding reading distance. The BP neural network can predict the reading distances of unknown tag groups and find out the optimal distribution structure of the tag groups corresponding to the maximum reading distance. In the future work, the corresponding in-depth research on the neural network to adjust the distribution of tags will be done.