Prof. Zhong Liu Profile

Prof. Zhong Liu

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Proceedings Article | 29 December 2023 Paper

Simulation of terahertz signal reception detection based on field enhancement

Zhonggang Xiong, Deqing Liu, Liping Shang, Zhong Liu

Proceedings Volume 12975, 1297506 (2023) https://doi.org/10.1117/12.3014291

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Simulations of terahertz signals in the time or frequency domain focus on the photoconductive antenna (PCA). However, they lack a simpler and more accurate simulation technology for PCA, which plays a crucial role in designing and optimizing detectors. Such work is essential in terahertz imaging and time-domain spectroscopy (THz-TDS). This work simulates an incident terahertz wave by introducing a three-dimensional (3-D) finite-difference time-domain (FDTD) simulation in the form of a total field to scattering field. This wave is pretreated as a plane wave that is incident on the receiver. The equation of carrier dynamics with semiconductor charge and transport is solved by using the 3-D full-wave FDTD method. A center surface current method is used to calculate the time-varying conductivity. A sampling electric field was used to evaluate the photocurrent. They were obtained by convolving the main time-varying photoconductivity of the photoexcited carrier distribution on the cross-section in the middle of the gap of a PCA receiver. Then, we compare these simulations with previously reported data from an incident terahertz signal. In addition, we simulated the detection characteristics with other results in the literature. Our simulation tool can accurately reproduce these data sets. These simulations can be used to design and optimize the receiving performance of different PCAs structures before costly fabrication has commenced.

SPIE Journal Paper | 29 April 2022 Open Access

Image matching via the local neighborhood for low inlier ratio

Weiqing Wang, Yongrong Sun, Zhong Liu, Zhantian Qin, Can Wang, Jinchang Qin

JEI, Vol. 31, Issue 02, 023039, (April 2022) https://doi.org/10.1117/12.10.1117/1.JEI.31.2.023039

KEYWORDS: Statistical analysis, Machine vision, Computer vision technology, Visibility, Motion measurement, Image retrieval, Image registration, Image quality, Image compression, Feature extraction

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Establishing reliable correspondences in an image pair is a prerequisite and crucial in computer vision. It remains a difficult topic to separate true and false matches in the given putative dataset with a low inlier ratio. To address this problem, an image matching method is proposed, via the local neighborhood of feature points. Grid-based motion statistics are initially engaged to preprocess the putative dataset, especially in which the inlier ratio is low. The local neighborhood distributions of feature points are then collected as the quality function of correspondences. Progressive sample consensus is next employed to estimate a global deformation for removing false matches. Robust experiments on nine typical image pairs with low inlier ratios demonstrate the superiority of our proposed method over five state-of-the-art methods. The comparison experiments on the Oxford dataset illustrate that our method outperforms the other five image matching methods.

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