Computational Intelligence and IoT Laboratory
Research Field
Dr. Chao-Chun Chen is a Professor at the Institute of Manufacturing Information and Systems and the Department of Computer Science and Information Engineering at National Cheng Kung University (NCKU). Since joining NCKU, he has held significant leadership positions, including Director of the Institute of Manufacturing Information and Systems and Deputy Chair of the Department of CSIE (2019–2023). Prior to his current roles, he gained extensive academic experience serving as an Associate Professor and Assistant Professor at NCKU, and previously held faculty positions at Southern Taiwan University of Science and Technology and Shih Chien University.
The Computational Intelligence and IoT Laboratory (CIoT Lab) is dedicated to bridging the gap between theoretical intelligent algorithms and real-world IoT applications.
Our research is organized into three core pillars:
- Smart Agriculture & Livestock, focusing on UAV-based ranging for autonomous pollination and automated livestock behavior analysis;
- Intelligent Healthcare & Sports, developing computer vision systems for rehabilitation and sports motion assessment;
- LLM-driven Multimodal Game Content Generation, utilizing Large Language Models (LLMs) and Cyber-Physical Systems (CPS) to develop innovative service platforms for automated game content creation.
Through an integrated framework encompassing sensing, data analysis, and application deployment, CIoT Lab transforms complex data into actionable intelligence for the next generation of smart living and manufacturing.
Topic 1: AIoT-based Smart Livestock Surveillance and Behavior Analysis
This project involves developing lightweight deep learning models for real-time monitoring of livestock (e.g., swine). We focus on pose classification and automated feeding systems to enable real-time decision-making and enhance precision farming efficiency. (Ref: Automation 2023 Best Paper Honorable Mention).
Topic 2: Human Motion Analysis for Smart Healthcare and Sports
We develop AI systems for rehabilitation assessment and sports performance (e.g., golf swing analysis). This research utilizes computer vision to track human joints and provide feedback for physical therapy and athletic training.
Topic 3: AIoT-based Smart Campus Safety and Energy-Efficient Lighting
This project develops an AIoT platform for smart campuses to enhance safety and energy efficiency. By integrating YOLO-based computer vision, real-time data visualization, and edge computing, the system enables adaptive street lighting based on pedestrian flow and ambient brightness. It also performs real-time detection of abnormal events (e.g., bicycle accidents, stray animal incidents, or potential threats) and triggers automatic alerts, providing comprehensive campus security and supporting preventive infrastructure maintenance.
Topic 4: PigHub: An AI and Big Data-Driven Cross-Domain Integration Platform for Smart Livestock Farming
PigHub establishes a research-oriented AI infrastructure for commercial swine operations, integrating Edge-AI and multi-modal sensors for 24/7 intelligent monitoring. Key capabilities include automated anomaly detection, lifecycle health analytics, and non-invasive growth assessment to enhance animal welfare and operational efficiency. Serving as a unified data platform, PigHub supports international collaboration with SUT (Poland), PCCU, and NIU to advance precision livestock farming through large-scale data sharing.
- Best Paper Contest Honorable Mention, The 20th International Conference on Automation Technology (Automation 2023).
- Stellar Judge, NASA Space Apps Challenge (2022).
- Best Student Paper Award, ACIIDS (2019).
- Best Paper Award In Automation Finalist, IEEE ICRA (2018).
- Excellent Poster Award, NSTC Industrial-Academic Matchmaking Event (2021).
- Technical Consultant, Taiwan Hope Innovation Co., Ltd (2020–Present).
National Cheng Kung University, Taiwan (1998.09 – 2004.06)
Ph.D. in Computer Science and Information Engineering
Job Description
Typical tasks may include:
- Optimize lightweight DL models (e.g., YOLO) for efficient real-time inference on edge devices.
- Maintain high-concurrency TCP/WebSocket servers and design efficient binary communication protocols.
- Orchestrate async task queues (Celery/RabbitMQ) to optimize video and pose analysis pipelines.
- Plan L2/L3 network architectures and perform deep packet analysis to ensure system stability.
- Develop FastAPI microservices and integrate real-time streaming (FFmpeg) with SQL/NoSQL databases.
- Build reproducible research pipelines including version control, configuration, and experiment logging.
- Prepare technical summaries, visualizations, and results for academic papers and project reports.
Preferred Intern Educational Level
- Master's Student
- Ph.D. Candidate
Skill sets or Qualities
- Proficient in Python/PyTorch; experienced in CV, ML, 3D Geometry, and GPU programming. Familiar with Linux edge platforms (e.g., Jetson).
- Hands-on experience in Unity for VR/AR development.
- Familiar with network protocols, async task processing (Celery/FastAPI), and database design.
- Strong problem-solving skills; focus on reproducibility and technical English writing.
Job Description
Typical tasks may include:
- Optimize lightweight DL models (e.g., YOLO) for efficient real-time inference on edge devices.
- Maintain high-concurrency TCP/WebSocket servers and design efficient binary communication protocols.
- Orchestrate async task queues (Celery/RabbitMQ) to optimize video and pose analysis pipelines.
- Plan L2/L3 network architectures and perform deep packet analysis to ensure system stability.
- Develop FastAPI microservices and integrate real-time streaming (FFmpeg) with SQL/NoSQL databases.
- Build reproducible research pipelines including version control, configuration, and experiment logging.
- Prepare technical summaries, visualizations, and results for academic papers and project reports.
Preferred Intern Educational Level
- Master's Student
- Ph.D. Candidate
Skill sets or Qualities
- Proficient in Python/PyTorch; experienced in CV, ML, 3D Geometry, and GPU programming. Familiar with Linux edge platforms (e.g., Jetson).
- Hands-on experience in Unity for VR/AR development.
- Familiar with network protocols, async task processing (Celery/FastAPI), and database design.
- Strong problem-solving skills; focus on reproducibility and technical English writing.