Search Results for author: Jianjun Ma

Found 5 papers, 0 papers with code

Terahertz Channels in Atmospheric Conditions: Propagation Characteristics and Security Performance

no code implementations28 Aug 2024 Jianjun Ma, Yuheng Song, Mingxia Zhang, Guohao Liu, Weiming Li, John F. Federici, Daniel M. Mittleman

With the growing demand for higher wireless data rates, the interest in extending the carrier frequency of wireless links to the terahertz (THz) range has significantly increased.

UAV-Assisted Weather Radar Calibration: A Theoretical Model for Wind Influence on Metal Sphere Reflectivity

no code implementations20 Jun 2024 Jiabiao Zhao, Da Li, Jiayuan Cui, Houjun Sun, Jianjun Ma

The calibration of weather radar for detecting meteorological phenomena has advanced rapidly, aiming to enhance accuracy.

Position

Ground-to-UAV sub-Terahertz channel measurement and modeling

no code implementations3 Apr 2024 Da Li, Peian Li, Jiabiao Zhao, Jianjian Liang, Jiacheng Liu, Guohao Liu, Yuanshuai Lei, Wenbo Liu, Jianqin Deng, Fuyong Liu, Jianjun Ma

Employing experimental measurements through an unmodulated channel setup and a geometry-based stochastic model (GBSM) that integrates three-dimensional positional coordinates and beamwidth, this work evaluates the impact of UAV dynamic movements and antenna orientation on channel performance.

Eavesdropping Risk Evaluation on Terahertz Wireless Channels in Atmospheric Turbulence

no code implementations2 Sep 2020 Yu Mei, Jianping An, Jianjun Ma, Lothar Moeller, John F. Federici

Wireless networks operating at terahertz (THz) frequencies have been proposed as a promising candidate to support the ever-increasing capacity demand, which cannot be satisfied with existing radio-frequency (RF) technology.

Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation

no code implementations10 Mar 2018 Liangfu Chen, Zeng Yang, Jianjun Ma, Zheng Luo

As the demand for enabling high-level autonomous driving has increased in recent years and visual perception is one of the critical features to enable fully autonomous driving, in this paper, we introduce an efficient approach for simultaneous object detection, depth estimation and pixel-level semantic segmentation using a shared convolutional architecture.

Autonomous Driving Depth Estimation +6

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