Zongkai Yang

Monostatic RCS measurement from sparse near-field sampling based on reciprocity theorem

Changyu Lou, Jingcheng Zhao, Zongkai Yang, Yang Yang et autres

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Removing target-background coupling via Freeman-Durden polarimetric decomposition and phase-preserving reconstruction

Zongkai Yang, Yijia Chen, Weikang Si

Abstract A phase-preserving polarimetric methodology for coupling suppression in radar cross-section (RCS) measurement is presented. For each pixel/bin, a Freeman-Durden three-component decomposition calculates surface, double-bounce, and volume powers using linear polarization; co-polar responses are subsequently reconstructed using explicit double-bounce nulling while preserving …

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Is the Required Truncation Region the Same for Arbitrary Targets? — Insights from Near-Field Circular STMR Measurements

Changyu Lou, Yuchen Zhang, Jingcheng Zhao, X. Wu et autres

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Toward an AI-Empowered Ecosystem in Special Education: A Commentary on AI-Enhanced Adaptive Intervention for Individuals with Autism

Zongkai Yang

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Assessment and intervention with virtual reality technology for children aged 3–12 years with autism spectrum disorders: A scoping review

Kai Zhang, Jingying Chen, Zongkai Yang

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Modified Linear Aperture Imaging Technique for Concave Structures in Near-Field Measurement Applications

Weikang Si, Weilong Wang, Xiao Liu, Wenli Lu et autres

Abstract This manuscript addresses the challenge of imaging artifacts in millimeter-wave (MMW) systems caused by concave structures due to multiple internal reflections. We introduce an modified linear aperture imaging technique specifically designed for concave structures, which effectively suppresses high-order scattering artifacts and …

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High-Precision Calibration of Probe Frequency Response for Near-Field Wideband Measurements

Weikang Si, Weilong Wang, Xiaohu Liu, Weiming Lü et autres

Abstract Nonlinear calibration of probe frequency response is a common problem in near-field measurement. In this paper, we present an over-the-air(OTA) calibration technique for near-field measurements, predicated on our proposed measurement model. A detailed discussion is provided on the use of metal …

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Reducing Background-Target Coupling through Three-Dimensional Imaging for RCS Inversion

Zongkai Yang, Yijia Chen, Changyu Lou, Weikang Si

Abstract Accurate radar cross section (RCS) measurements are challenged by background-target coupling, such as interactions with the ground or support structures. This paper introduces a method using three-dimensional (3D) imaging to isolate and measure target scattering characteristics. By utilizing high-resolution 3D imaging …

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Reducing Target-Ground Coupling in Near-Field Scattering Measurements by Elevation Adjustments

Zongkai Yang, Jingcheng Zhao, Yijia Chen, Changyu Lou et autres

The measurement of the scattering of big targets is difficult, particularly in indoor near-field conditions where there are many interactions between the target and the backdrop. Scattering clutter caused by the ground has a major impact on Radar Cross Section (RCS) measurements. …

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The Optimal Sample Region Required to Predict Monostatic RCS from Finite Near-Field Data

Changyu Lou, Jingcheng Zhao, Zongkai Yang, Jungang Miao

During near-field measurements, truncating the sampled angular domain can cause "spectral leakage" in the spatial domain, leading to measurement errors in Radar Cross Section (RCS). In this paper, we apply triangular window weighting to the sampled scattering field. By modifying the sampling …

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Automated Generation of Geometry Proof Problems Based on Point Geometry Identity

Lei Li, Zongkai Yang, Mao Chen, Xicheng Peng et autres

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Automated discovery of symbolic laws governing skill acquisition from naturally occurring data

Sannyuya Liu, Qing Li, Xiaoxuan Shen, Jianwen Sun et autres

Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes. The laws discovered under experimental paradigms are controversial and lack generalizability. This paper aims to unearth the laws of skill learning from large-scale training log …