Edited by Elysee Chung

The winners of the National Science and Technology Council's (NSTC) “2024 Future Tech Award” have been announced, with a total of 82 technologies receiving recognition this year. Among them, 13 technologies from National Cheng Kung University (NCKU) were awarded. This year's evaluation continued the two main review criteria of “scientific breakthroughs” and “industrial applications.” The awarded technologies are highly innovative and have strong potential for future commercial development, whether in terms of technological breakthroughs or subsequent business growth.

The purpose of the Future Tech Award is to highlight cutting-edge scientific research achievements and showcase the nation's technological strength. It encourages research outcomes to enter global markets and strengthens international connections. Applications were widely invited from projects funded by the NSTC, Academia Sinica, the Ministry of Education, and the Ministry of Health and Welfare, among others. The submitted technologies were categorized into seven major areas: (1) Chemical Engineering and Materials, (2) AIoT and Smart Living Applications, (3) Green Energy, Environmental Protection, and Net-Zero Technology, (4) Electronics and Optoelectronics, (5) Biotechnology and New Pharmaceuticals, (6) Medical Devices, and (7) Humanities, Sports Technology, and Technological Arts.

Among the 13 award-winning technologies from NCKU, 5 technologies from EECS. 3 of them are in the field of Humanity and Technology, and 2 in AIoT and Smart Applications.

• Humanity and Technology Field:

AI Blended Training Programs and Learning Performance Evaluation System for Badminton Skills Learning and Improvement
Project PI: Professor Jeen-Shing Wang (王振興), Department of Electrical Engineering

This system integrates cloud computing and AI technology, using data analysis, digital assessment, and blended learning to assist in badminton training. It adjusts training based on each individual’s learning progress to provide personalized training, enhancing learning outcomes and motivation. 

Multi-View MultiPlayer Tracking Technology

Project Leader: Professor James Jenn-Jier Lien (連震杰)、(Co-PI) Professor Wei-Ta Chu (朱威達) , Department of Computer Science and Information Engineering

This system uses multiple cameras to capture synchronized multi-angle footage of the court. Pose detection technology is used to identify each player's position and body joints. A specially designed multi-view, multi-dimensional trajectory correlation algorithm correlates detection results from different angles and calculates 3D coordinates through triangulation for 3D tracking. This generates the players' 3D movement trajectories. The technology can be applied to match analysis, tactical assessment, and player performance evaluation, providing valuable scientific data to players and coaches.

• AIoT and Smart Applications Field:

A Pioneer Novel Weakly-supervised Multi-instance Learning Framework for Genetic Expression Recognition and Survival Prediction in Digital Pathology Images

Project Leader: Professor Jung-Hsien Chiang (蔣榮先), Department of Computer Science and Information Engineering

The team successfully addressed the challenges of processing massive pixel images with current AI hardware and the issue of insufficient manual annotations. This allows digital pathology, after training with deep learning models, to better assist doctors in making more precise decisions. The research also validated that specific gene expression features and patient prognosis can be directly identified and predicted from digital pathology images.

A Multispectral Light Source Based Miniaturized Tissue Oxygenation Imaging System for Telemedicine Wound Healing Phases Recognition

Project Leader: Professor Chih-Lung Lin (林志隆), Department of Electrical Engineering

This technology focuses on real-time monitoring and healing assessment in chronic wound care using multispectral imaging to provide blood oxygen saturation levels, which are not visible to the naked eye. The system trains an AI algorithm using clinical patient data collected at NCKU Hospital to identify wound tissues and assess healing progress. By integrating miniaturized systems and IoT technology, it allows healthcare professionals and patients to use it in various medical settings and at home, achieving continuous wound tracking and remote medical care.

Provider: NCKU News Center

Source: https://ennews-secr.ncku.edu.tw/p/406-1038-274375,r614.php?Lang=en