Intelligent Multimedia & Advanced Computing Lab
Introduction
The Intelligent Multimedia & Advanced Computing (IMAC) Laboratory fosters innovation at the intersection of intelligent computing and multimedia technologies. We focus on developing cutting-edge solutions by leveraging the power of machine learning (ML) and deep learning (DL) for a wide range of applications.
Our core research directions include:
Machine Learning and Deep Learning for Radio Signal Processing and Wireless Communications: We explore the application of ML and DL techniques to enhance the efficiency and reliability of wireless communication systems. This includes research on channel modeling, signal detection, interference mitigation, and resource allocation, all powered by intelligent algorithms.
Image and Video Analysis for Multimedia Applications: The lab investigates advanced algorithms for image and video understanding. This encompasses object recognition, image/video segmentation, activity analysis, and content-based retrieval. These techniques can be applied to various domains, including medical imaging, autonomous vehicles, security surveillance, and human-computer interaction.
Master candidate
Quang-Thai Le
Computer Science & Engineering
Year: 2023 - 2024
Topic: Road-Scene Semantic Segmentation Using 3D LiDAR Point Cloud
Publications:
Quang-Thai Le, Quoc-Hung Tran, Thien Huynh-The, "Strategic Improvements of SqueezeSegV2 for Road-Scene Semantic Segmentation Using 3D LiDAR Point Cloud," SoICT 2023, Ho Chi Minh, 2023 (link).
Ham-Thuat Vo
Computer Science & Engineering
Year: 2023 - 2024
Topic: TBD
Undergraduate candidate
Gia-Vuong Nguyen
Computer Science & Engineering
Year: 2020- 2024
Topic: Spectrum Sensing for 5G Signals Identification
Publications:
Gia-Vuong Nguyen, Thien Huynh-The, "Accurate Spectrum Sensing with Improved DeepLabV3+ for 5G-LTE Signals Identification," SoICT 2023, Ho Chi Minh, 2023 (link).
Quoc-Hung Tran
Computer Science & Engineering
Year: 2020 - 2024
Topic: LiDAR Point Cloud based Semantic Segmentation
Publications:
Quang-Thai Le, Quoc-Hung Tran, Thien Huynh-The, "Strategic Improvements of SqueezeSegV2 for Road-Scene Semantic Segmentation Using 3D LiDAR Point Cloud," SoICT 2023, Ho Chi Minh, 2023 (link).
Anh-Kiet Vo
Computer Science & Engineering
Year: 2020 - 2024
Topic: LiDAR Point Cloud based Object Detection
Publications:
Anh-Kiet Vo, Thien Huynh-The, "LiPoSeg: A Lightweight Encoder-Decoder Network for LiDAR-based Road-Object Semantic Segmentation," SoICT 2023, Ho Chi Minh, 2023 (link).
Minh-Khoi Tran
Computer Science & Engineering
Year: 2020 - 2024
Topic: Deep Learning for Aerial Lidar Semantic Segmentation
Publications:
Thanh-Lam Mai, Minh-Khoi Tran, Thien Huynh-The, "PointGANet: A Lightweight 3D Point Cloud Learning Architecture for Semantic Segmentation," SoICT 2023, Ho Chi Minh, 2023 (link).
Thanh-Lam Mai
Computer Science & Engineering
Year: 2020 - 2024
Topic: Deep Learning for Aerial Lidar Semantic Segmentation
Publications:
Thanh-Lam Mai, Minh-Khoi Tran, Thien Huynh-The, "PointGANet: A Lightweight 3D Point Cloud Learning Architecture for Semantic Segmentation," SoICT 2023, Ho Chi Minh, 2023 (link).