Photoelectric signal detection

Development of AI Platform for Smart Drone - Intelligent Flight: Due to its high mobility and the ability to fly in the sky, the drone has inspired more and more innovative applications/services in recent years. The goal of this project is to resolve the problem of blindly flying an unmanned aerial vehicle (UAV, which a drone in our case) when it is out of human sight or the range of wireless communication, and three major research and development directions will be considered in this project. Three artificial intelligence (AI) technologies, namely, smart sensing, smart control, and smart simulation, are applied in this project. Smart sensing - a flight system is developed, which can avoid the obstacles, complete a flight mission, and land safely. Smart control - an intelligence flight control system and a light-weighted somatosensory vest are developed. Smart simulation - a cost-effective training system and a 3D model simplification method are designed.

Our PEDD has been used to detect the HNE level in both tear and wound fluids of 9 patients, and the limitations of detection were about 0.76 μg/mL and 0.63 μg/mL respectively. Our device requires only 15 minutes, 3 μL of clinical sample for each test, and offers superior sensitivity compared to other current HNE detection methods. The HNE concentrations in the patient samples have shown that the elevated HNE levels might suggest an abnormal healing of either acute or chronic wounds, consistent with previous studies that showed excess protease levels in all non-healing wounds.

PANCREASaver contains a “Pancreas Cancer (PC) automatic segmentation model” (image segmentation) and a “Pancreas Cancer (PC) analysis AI model” (image classification) that can read the DICOM format of postcontrast CT images directly for the automatic analysis process. After conducting prep-processing with image processing algorithms, C2FNAS is employed to illustrate the tumor position prior to the diagnosis conducted by CNN. The results can be provided to the physician for diagnostic reference so as to reduce early omissions and increase the detection rate of pancreatic cancer.