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.

Combined with millimeter wave radar detection of chest undulation breathing mode and heart rate, continuous blood oxygen detection, active disease record of chat robot, and mobile phone analysis of 30 second sitting and standing alternate activity frequency mode, a set of personalized respiratory capacity benchmark is established through AI modeling, which can be applied to zero-contact respiratory physiological monitoring and useful for infectious disease ward, epidemic prevention hotels, centralized quarantine centers.

For video anomaly detection, we apply pretrained models to obtain the foreground and the optical flow as ground truth. Then our model estimates the information by taking only a single frame as input. For human behaviors, we take the human poses as input and use a GCN-based model to predict the future poses. Both the anomaly scores of these two works are given by the error of the estimation. For defect detection, our model takes patches of the image as input and learns to extract features. The anomaly score of each patch is given by the distance between the patch and all the training patches.

The dialogue system is the main subsystem of the visually impaired navigation system, which provides destinations for the navigation system through multiple dialogues. We use the knowledge graph as the basis for reasoning. In terms of close-range navigation, deep learning technology is used to develop RGB camera detection depth algorithm, indoor semantic cutting algorithm, integrated detection depth estimation and indoor semantic cutting in indoor obstacle avoidance, etc. The whole system uses the CellS software design framework to integrate distributed AIoT systems.

We provides a wearable device for the visually impaired to walk outdoors. By the deep learning network, the system can recognize and guide the visually impaired to walk on safe areas such as sidewalks and crosswalk. In addition, it can recognize the types of common obstacles and guide the visually impaired to avoid it in advance. Finally, we can convert the Google Maps route into easy-to-understand voice prompts instruction to guide the visually impaired to move in the right direction.

The IHC staining intensity grading algorithm combines a series of complementary processes of deep learning and image processing in order to overcome the difficulties in cell boundary segmentation and grade definition, and provides a cell-based IHC staining intensity grading and quantification capability. The proposed method serves as a useful assistive tool for physicians in performing accurate staining quantification in tissue microscopic images.

Drone delivery is a popular and emerging application at present. However, existing drone delivery systems can only deliver to outdoor open spaces via GPS, and cannot directly to the interior of recipient＇s building. In the era of covid-19 pandemic, we aim to reduce human contact and propose a drone delivery system that can deliver packages to the doorstep or the interior of buildings, and to achieve fully automatic control of the drone by developing visual positioning technique.

This research team develops unmanned 3D intelligent marine aquaculture technology, using omni AIoT aquaculture technology, especially strengthening the integration of gyroplane, ROV, unmanned ship and central control room, and using long-distance intelligent control to make employees shift from traditional field work to base and central control room. Due to the huge cross domain system of omni AIoT, it modularizes it, At the same time, edge computing technology is developed to facilitate commercial promotion.

AI農情調查之UAV群眾協作平台後台支援自動鑲嵌建模，更具備四項突破技術：(1)巨量影像格化技術；(2)平行運算技術；(3)任務規格標準化；(4)UAV任務媒合。致力於打造空中UBER協作服務，未來能應用於農作物分佈調查、大範圍災情調查、農業保險、農地違法使用調查與休耕補助調查等面向。

This project integrates clinical physical and mental medical practice, medical engineering, information engineering and social welfare units, adopts cross-fields and uses AI technology, and builds an innovative artificial intelligence auxiliary evaluation and treatment system for dementia care based on cultural context. Establish an early intervention mechanism for early detection of dementia, and establish measures to improve cognitive function, emotional state, social and daily behavioral functions.