JLJunda Li
Junda Li
*This is an algorithmically generated author record based on publicly available information.
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Junda Li has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Weijia Wen
The Hong Kong University of Science and Technology
2 shared publications - Xiao Luo
Department of Physics, The Hong Kong University of Science and Technology, Hong Kong 999077, China
2 shared publications - Beini Zhang
Faculty of Engineering The University of Hong Kong Hong Kong China
1 shared publication - Chung Ting Justine Hui
The University of Auckland
1 shared publication - Handong Tan
Department of Individualized Interdisciplinary Program (Advanced Materials), The Hong Kong University of Science and Technology, Hong Kong 999077, China
1 shared publication - Jiahao Jiang
Advanced Materials Thrust, The Hong Kong University of Science and Technology, Guangzhou 511400, China
1 shared publication
Latest Publications
- 1 March 2026
- Published by Elsevier in Neural Networks
- Vol. 200, 108794
Freezing pre-trained parameters of encoders for denoisers: Expanding pixel involvement and filtering out high-frequency noise
- 1 March 2026
- Published by Elsevier in Neural Networks
- Vol. 200, 108794
Freezing pre-trained parameters of encoders for denoisers: Expanding pixel involvement and filtering out high-frequency noise
- Open Access
- 13 December 2025
- Published by Association for Computing Machinery (ACM)
- p. 1-7
Audiovisual Congruence in Virtual Reality: Effects on Speech Intelligibility for Native and Non-Native Listeners
- 13 December 2025
- Published by Association for Computing Machinery (ACM)
- p. 1-7
Open AccessAudiovisual Congruence in Virtual Reality: Effects on Speech Intelligibility for Native and Non-Native Listeners
- Open Access
Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning
In diverse realms of research, such as holographic optical tweezer mechanical measurements, colloidal particle motion state examinations, cell tracking, and drug delivery, the localization and analysis of particle motion command paramount significance. Algorithms ranging from conventional numerical methods to advanced deep-learning networks mark substantial strides in the sphere of particle orientation analysis. However, the need for datasets has hindered the application of deep learning in particle tracking. In this work, we elucidated an efficacious methodology pivoted toward generating synthetic datasets conducive to this domain that resonates with robustness and precision when applied to real-world data of tracking 3D particles. We developed a 3D real-time particle positioning network based on the CenterNet network. After conducting experiments, our network has achieved a horizontal positioning error of 0.0478 μm and a -axis positioning error of 0.1990 μm. It shows the capability to handle real-time tracking of particles, diverse in dimensions, near the focal plane with high precision. In addition, we have rendered all datasets cultivated during this investigation accessible.Open AccessReal-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning
In diverse realms of research, such as holographic optical tweezer mechanical measurements, colloidal particle motion state examinations, cell tracking, and drug delivery, the localization and analysis of particle motion command paramount significance. Algorithms ranging from conventional numerical methods to advanced deep-learning networks mark substantial strides in the sphere of particle orientation analysis. However, the need for datasets has hindered the application of deep learning in particle tracking. In this work, we elucidated an efficacious methodology pivoted toward generating synthetic datasets conducive to this domain that resonates with robustness and precision when applied to real-world data of tracking 3D particles. We developed a 3D real-time particle positioning network based on the CenterNet network. After conducting experiments, our network has achieved a horizontal positioning error of 0.0478 μm and a -axis positioning error of 0.1990 μm. It shows the capability to handle real-time tracking of particles, diverse in dimensions, near the focal plane with high precision. In addition, we have rendered all datasets cultivated during this investigation accessible.