Zhao Ziran

A position resolution MRPC for muon tomography

Muon tomography is a promising method in the detection and imaging of high Z material. In general, considering the quality of track reconstruction in imaging, a detector of good position resolution, high efficiency and large area is required. This paper presents the design and study of a prototype of position sensitive MRPC with 0.15 mm narrow gas gap and 2.54 mm strip readout. Through a cosmic-ray experiment, the performance of MRPC module is carefully observed and each channel is calibrated. Through an X ray experiment with a narrow slit, the position resolution is studied. The results show that the time resolution of the module can reach 61ps and the spatial resolution can reach 0.36 mm.

Automatic target recognition method for millimeter-wave body scanner

Automatic target recognition is a significant function of millimeter-wave body scanner. Based on the principle of saliency and sparse coding, this paper proposes a new method for millimeter-wave body image processing and recognition. According to the characteristics such as shape, texture and gray level of the millimeter wave image, the method realizes automatic recognition for concealed objects. A large number of experiments verify the accuracy of the method on detecting objects concealed in different position of human body.

A Synthetic Targets Detection Method for Human Millimeter-Wave Holographic Imaging System

Automatic Target Recognition (ATR) technology is of great significance in security inspection, while traditional object detection methods are proved not efficient in human body millimeter-wave images. In this paper, we propose a synthetic objection detection method for millimeter-wave images. We choose saliency, SIFT and HOG features to form image descriptors. According to sparse representation, the features are encoded again and fed to a linear SVM for target/non-target classification. Previous works proved that the amount of training samples would influence the efficiency of SVM classifiers. Thus, we utilize several simulating methods for data augmentation, aiming to increase the number of training samples before training linear SVM classifiers. The experimental results show that our approach is efficient in target detection of human body millimeter-wave images. Moreover, classifiers trained on larger sets with simulated samples have better performance in classification on our testing dataset.