4 citations · 10 across the 7 of their papers we have counts for
7 papers
Semantic-driven Wireless Environment Knowledge Representation for Efficiency-Accuracy Balanced Beam Prediction in Vehicular Networks
Jialin Wang, Jianhua Zhang, Yu Li +2
The rapid evolution of the internet of vehicles demands ultra-reliable low-latency communication in high-mobility environments, where conventional beam prediction methods suffer fr…
Electromagnetic Wave Property Inspired Radio Environment Knowledge Construction and AI-based Verification for 6G Digital Twin Channel
Jialin Wang, Jianhua Zhang, Yutong Sun +3
As the underlying foundation of a digital twin network (DTN), a digital twin channel (DTC) can accurately depict the process of radio propagation in the air interface to support th…
Lagrangian Covector Fluid with Free Surface
Zhiqi Li, Barnabás Börcsök, Duowen Chen +3
This paper introduces a novel Lagrangian fluid solver based on covector flow maps. We aim to address the challenges of establishing a robust flow-map solver for incompressible flui…
Towards 6G Digital Twin Channel Using Radio Environment Knowledge Pool
Jialin Wang, Jianhua Zhang, Yuxiang Zhang +6
The digital twin channel (DTC) is crucial for 6G wireless autonomous networks as it replicates the wireless channel fading states in 6G air interface transmissions. It is well know…
How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction
Yutong Sun, Jianhua Zhang, Li Yu +2
In view of the propagation environment directly determining the channel fading, the application tasks can also be solved with the aid of the environment information. Inspired by ta…
Skill requirements in job advertisements: A comparison of skill-categorization methods based on explanatory power in wage regressions
Ziqiao Ao, Gergely Horvath, Chunyuan Sheng +2
In this paper, we compare different methods to extract skill requirements from job advertisements. We consider three top-down methods that are based on expert-created dictionaries…
Implicit Saliency in Deep Neural Networks
Yutong Sun, Mohit Prabhushankar, Ghassan AlRegib
In this paper, we show that existing recognition and localization deep architectures, that have not been exposed to eye tracking data or any saliency datasets, are capable of predi…