Search Results for author: Hongtao Zhang

Found 7 papers, 0 papers with code

IVLMap: Instance-Aware Visual Language Grounding for Consumer Robot Navigation

no code implementations28 Mar 2024 Jiacui Huang, Hongtao Zhang, Mingbo Zhao, Zhou Wu

To address this challenge, we propose a new method, namely, Instance-aware Visual Language Map (IVLMap), to empower the robot with instance-level and attribute-level semantic mapping, where it is autonomously constructed by fusing the RGBD video data collected from the robot agent with special-designed natural language map indexing in the bird's-in-eye view.

Attribute Language Modelling +4

Discrete-Time Modeling and Handover Analysis of Intelligent Reflecting Surface-Assisted Networks

no code implementations12 Mar 2024 Hongtao Zhang, Haoyan Wei

This paper proposes a discrete-time model to explicitly track the HO process with variations in IRS connections, where IRS connections and HO process are discretized as finite states by measurement intervals, and transitions between states are modeled as stochastic processes.

Analysis of Intelligent Reflecting Surface-Enhanced Mobility Through a Line-of-Sight State Transition Model

no code implementations12 Mar 2024 Hongtao Zhang, Haoyan Wei

Rapid signal fluctuations due to blockage effects cause excessive handovers (HOs) and degrade mobility performance.

Blocking

PLCNet: Patch-wise Lane Correction Network for Automatic Lane Correction in High-definition Maps

no code implementations25 Jan 2024 Haiyang Peng, Yi Zhan, Benkang Wang, Hongtao Zhang

Vision lane detection with LiDAR position assignment is a prevalent method to acquire initial lanes for HD maps.

Lane Detection

A Scalable Arrangement Method for Aperiodic Array Antennas to Reduce Peak Sidelobe Level

no code implementations4 Jul 2023 Jiao Zhang, Hongtao Zhang, Xuelei Chen, Fengquan Wu, Yufeng Liu, Wenmei Zhang

Peak sidelobe level reduction (PSLR) is crucial in the application of large-scale array antenna, which directly determines the radiation performance of array antenna.

Position

Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination

no code implementations8 Nov 2021 Omar Alfarisi, Aikifa Raza, Hongtao Zhang, Djamel Ozzane, Mohamed Sassi, Tiejun Zhang

Notably, IROGA and MLDGRF have produced porosity results with an accuracy of 96. 2% and 97. 1% on the training set and 91. 7% and 94. 4% on blind test validation, respectively, in comparison with the three experimental measurements.

BIG-bench Machine Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.