網路實驗室
Research Field
Ph.D, ECE, North Carolina State University, USA, 1990
Advisory Programmer, IBM, RTP, NC, USA, 1990-1992
Associate Professor, CSE, Yuan Ze University, 1992-2010
Professor, CSE, Yuan Ze Unversity, 2010~ now
Director of International Bachelor Program in Informatics
Dean of International Academy
Established at 1993, in CSE Department, Yuan Ze University.
Facility: AI servers, PCs, IoT related devices.
Welcome to the Networking Lab at Yuan Ze University. In this dynamic and innovative environment, we embark on a journey at the intersection of cutting-edge technologies, exploring the realms of autonomous vehicles, deep learning, and anomaly detection. This lab serves as a hub for exploration, experimentation, and innovation, where we delve into the transformative potential of networking technologies in shaping the future of transportation and safety.
Autonomous Vehicles: At the core of our lab's focus lies the realm of autonomous vehicles – a groundbreaking domain that is revolutionizing the way we perceive mobility. We delve into the intricate world of self-driving cars, exploring the fusion of networking technologies with sensors, actuators, and control systems. Our mission is to contribute to the advancement of autonomous vehicles by developing networking solutions that enable seamless communication, real-time data sharing, and cooperative decision-making among vehicles and infrastructure.
Deep Learning: As we navigate the era of artificial intelligence, deep learning emerges as a pivotal force driving the advancement of various applications. In this lab, we harness the power of deep learning techniques to unlock the potential of autonomous vehicles. We explore neural networks, convolutional networks, recurrent networks, and beyond, seeking to optimize perception, decision-making, and control systems in autonomous vehicles. Through data-driven approaches, we strive to enhance the capabilities of these vehicles to understand their environment and respond intelligently.
Anomaly Detection: Ensuring the safety and reliability of autonomous vehicles is paramount. This is where our emphasis on anomaly detection comes into play. We develop and deploy cutting-edge algorithms that can identify and respond to unexpected situations – a critical aspect in the deployment of autonomous systems. By leveraging networking principles, data fusion, and deep learning, we equip autonomous vehicles with the ability to detect anomalies and adapt their behavior accordingly, ensuring the safety of passengers, pedestrians, and other road users.
Collaborative Learning Environment: The Networking Lab is not just a physical space; it's a collaborative ecosystem that fosters interdisciplinary discussions, fosters creativity, and encourages hands-on exploration. Our team of researchers, engineers, and students work together to push the boundaries of what's possible in the world of autonomous vehicles and intelligent transportation systems. Whether you're an aspiring researcher, a curious student, or a passionate innovator, you'll find a welcoming space to contribute and learn.
Incremental Learning: Incremental learning involves developing algorithms and techniques that allow systems to adapt and learn from new data as it arrives over time. This is particularly useful when dealing with evolving environments, where models need to continuously improve their performance and adapt to changing conditions without forgetting the previously acquired knowledge.
2D/3D Object Detection: Object detection is the process of identifying and locating specific objects within images or videos. In the case of 2D object detection, this involves recognizing objects in 2D images, while 3D object detection extends this to capturing spatial information for objects in a 3D scene. This technology has applications in areas such as autonomous driving, robotics, and surveillance.
Medical Image Processing: Medical image processing focuses on the analysis, enhancement, and interpretation of images from medical imaging modalities such as X-rays, MRIs, and CT scans. This field is crucial for aiding medical professionals in diagnosing and treating various medical conditions accurately and non-invasively.
Anomaly Detection: Anomaly detection involves identifying patterns or instances that deviate significantly from the norm in a given dataset. This technique is valuable in various contexts, such as detecting fraud in financial transactions, identifying faults in industrial processes, and even spotting anomalies in medical images to diagnose rare conditions.
Autonomous Vehicles: Autonomous vehicles represent a transformative technology that aims to enable vehicles to navigate and make decisions without human intervention. This involves a fusion of technologies, including computer vision, machine learning, sensor integration, and real-time decision-making, to create safe and efficient self-driving systems.
Distinguished papers in international conferences
Ph.D., ECE，North Carolina State University, USA