The system consists of a racket sensor, a smartphone App, and the IoT shuttlecock serving machines. Data of the 3-axis inertial sensor will be sent to the smartphone via Bluetooth for analysis. The App can recognize 7 stroke activities, including clear, cut, drive, lob, rush, net play, and smash using machine learning algorithms, while the speed, force, and stroke times are recorded at the same time. The App also enables the user to set parameters of the serving machines for assigning shot placements and serving schedules.

The Air/Ground cooperation for optimal rice harvesting model is established to provide a visual harvesting decision service on a cloud platform. Drones and mobile devices are employing to estimate grain moisture and forecast the variation of harvest moisture content (HMC) in the coming days by huge amounts of imagery data, deep learning algorithms, and weather forecasts. This model can benefit in several aspects, such as setting an accurate and comprehensive optimal harvest schedule, reducing the cost of agricultural apparatus and barn ovens, ensuring the rice quality, and maximizing farmers' benefits. The potential value of the model practice could be more than a billion in Taiwan.

Our IoT of food allergen detection system has high sensitivityspecificity by magnetic nano-beads with advanced material coating. This advanced material successful inhibit biofouling in biosensor system. Other than this, Our system combine nanotechnology, biotechnology, IoTAI model. Device is small any easily carry to anywhere.

Currently, the calculation program built into smart phones is not convenient for blind people. The invention patent allows the blind to view the touch screen as a large buttonconstruct a computer program using the finite state machine theory. The blind person can inputcomplete all four operations by continuously clicking the touch screen.