Picture for Lu Yang

Lu Yang

RO-MAP: Real-Time Multi-Object Mapping with Neural Radiance Fields

Apr 12, 2023
Xiao Han, Houxuan Liu, Yunchao Ding, Lu Yang

Figure 1 for RO-MAP: Real-Time Multi-Object Mapping with Neural Radiance Fields

Figure 2 for RO-MAP: Real-Time Multi-Object Mapping with Neural Radiance Fields

Figure 3 for RO-MAP: Real-Time Multi-Object Mapping with Neural Radiance Fields

Figure 4 for RO-MAP: Real-Time Multi-Object Mapping with Neural Radiance Fields

* The code and dataset will be available at: https://github.com/XiaoHan-Git/RO-MAP

Access Paper or Ask Questions

On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels

Feb 27, 2023
Lu Yang, Seyit Camtepe, Yansong Gao, Vicky Liu, Dhammika Jayalath

Figure 1 for On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels

Figure 2 for On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels

Figure 3 for On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels

Figure 4 for On the Use of Power Amplifier Nonlinearity Quotient to Improve Radio Frequency Fingerprint Identification in Time-Varying Channels

* 7 pages, 7 figures, first preprint

Access Paper or Ask Questions

Deep Learning Technique for Human Parsing: A Survey and Outlook

Jan 01, 2023
Lu Yang, Wenhe Jia, Shan Li, Qing Song

Figure 1 for Deep Learning Technique for Human Parsing: A Survey and Outlook

Figure 2 for Deep Learning Technique for Human Parsing: A Survey and Outlook

Figure 3 for Deep Learning Technique for Human Parsing: A Survey and Outlook

Figure 4 for Deep Learning Technique for Human Parsing: A Survey and Outlook

* Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (under review)

Access Paper or Ask Questions

TIVE: A Toolbox for Identifying Video Instance Segmentation Errors

Oct 17, 2022
Wenhe Jia, Lu Yang, Zilong Jia, Wenyi Zhao, Yilin Zhou, Qing Song

Figure 1 for TIVE: A Toolbox for Identifying Video Instance Segmentation Errors

Figure 2 for TIVE: A Toolbox for Identifying Video Instance Segmentation Errors

Figure 3 for TIVE: A Toolbox for Identifying Video Instance Segmentation Errors

Figure 4 for TIVE: A Toolbox for Identifying Video Instance Segmentation Errors

* 11pages, 6 figures

Access Paper or Ask Questions

SQ-SLAM: Monocular Semantic SLAM Based on Superquadric Object Representation

Sep 22, 2022
Xiao Han, Lu Yang

Figure 1 for SQ-SLAM: Monocular Semantic SLAM Based on Superquadric Object Representation

Figure 2 for SQ-SLAM: Monocular Semantic SLAM Based on Superquadric Object Representation

Figure 3 for SQ-SLAM: Monocular Semantic SLAM Based on Superquadric Object Representation

Figure 4 for SQ-SLAM: Monocular Semantic SLAM Based on Superquadric Object Representation

* Submitted to ICRA 2023

Access Paper or Ask Questions

TOSE: A Fast Capacity Estimation Algorithm Based on Spike Approximations

Sep 02, 2022
Dandan Jiang, Han Hao, Lu Yang, Rui Wang

Figure 1 for TOSE: A Fast Capacity Estimation Algorithm Based on Spike Approximations

Figure 2 for TOSE: A Fast Capacity Estimation Algorithm Based on Spike Approximations

Figure 3 for TOSE: A Fast Capacity Estimation Algorithm Based on Spike Approximations

Figure 4 for TOSE: A Fast Capacity Estimation Algorithm Based on Spike Approximations

* IEEE 96th Vehicular Technology Conference: VTC2022-Fall
* 6 pages, 4 figures. arXiv admin note: text overlap with arXiv:2204.03393

Access Paper or Ask Questions

POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study

Aug 23, 2022
Lu Yang, Sheng Wang, Russ B. Altman

Figure 1 for POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study

Figure 2 for POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study

Figure 3 for POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study

Figure 4 for POPDx: An Automated Framework for Patient Phenotyping across 392,246 Individuals in the UK Biobank Study

* 44 pages, 6 main figures, 2 main tables

Access Paper or Ask Questions