Litao Huang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Litao Huang has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
- Yugen Yi
Jiangxi Normal University, Nanchang, China
4 shared publications - Xinping Rao
Jiangxi Normal University
3 shared publications - Jingkai Guo2 shared publications
- Zehui Zhang2 shared publications
- Celimuge Wu
University of Electro-Communications, Tokyo, Japan
1 shared publication Furong Lin
Jiangxi Normal University
1 shared publication
Latest Publications
- 27 March 2026
- Published by Elsevier in Knowledge-Based Systems
- Vol. 342
Dual-Branch representation alignment framework for deep multi-view clustering
- 27 March 2026
- Published by Elsevier in Knowledge-Based Systems
- Vol. 342
Dual-Branch representation alignment framework for deep multi-view clustering
- 11 December 2025
- Published by Elsevier in Knowledge-Based Systems
- Vol. 334
RCLEAF: Reliable contrastive learning-driven efficient adaptive fusion for multi-view clustering
- 11 December 2025
- Published by Elsevier in Knowledge-Based Systems
- Vol. 334
RCLEAF: Reliable contrastive learning-driven efficient adaptive fusion for multi-view clustering
- 15 July 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Communications Letters
- Vol. 28 (9) , 2076-2080
WiFi-Based Device-Free Passive Multi-Targets Localization Using Multi-Label Learning
In this letter, we propose MTFLoc, a novel device-free passive fingerprinting localization system based on CSI, leveraging multi-label learning to overcome challenges in multi-target localization. The MTFLoc system divides the localization area into multiple training point areas, each representing a unique class or label. This formulation transforms the multi-target localization problem into a multi-label classification problem. It is crucial to obtain high richness and resolution fingerprint features. MTFLoc uses novel data pre-processing techniques to obtain high-resolution fused multi-target fingerprint features (FMTF) from CSI amplitude and phase information, improving localization accuracy and quality. Finally, the FMTFs are inputted into a deep learning-based multi-label classification framework for parameter training and location estimation. Experimental results clearly demonstrate the outstanding performance of MTFLoc compared to existing multi-target localization approaches.
- 15 July 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Communications Letters
- Vol. 28 (9) , 2076-2080
WiFi-Based Device-Free Passive Multi-Targets Localization Using Multi-Label Learning
In this letter, we propose MTFLoc, a novel device-free passive fingerprinting localization system based on CSI, leveraging multi-label learning to overcome challenges in multi-target localization. The MTFLoc system divides the localization area into multiple training point areas, each representing a unique class or label. This formulation transforms the multi-target localization problem into a multi-label classification problem. It is crucial to obtain high richness and resolution fingerprint features. MTFLoc uses novel data pre-processing techniques to obtain high-resolution fused multi-target fingerprint features (FMTF) from CSI amplitude and phase information, improving localization accuracy and quality. Finally, the FMTFs are inputted into a deep learning-based multi-label classification framework for parameter training and location estimation. Experimental results clearly demonstrate the outstanding performance of MTFLoc compared to existing multi-target localization approaches.