A highly sensitivelinear ultrasonic radar was realized using digital self-injection-locking (SIL) technique. It can remotely monitor the vital signs of a human in motion, which will be demonstrated in this expo by upgrading a low-cost ultrasonic radar for reverse parking to support high-value telemedical applications. In the future it will further combine phased-array technology to implement non-contact 4D ultrasound system, which will bring disruptive innovation to medical technologies.

The purpose of this software is to implement real-time radar monitoring technique in meteorological service through industry-academia cooperation. The design transforms professional weather webpage into APP software, which is available for mobile devices, it also provides notification service by using GPS positioning system. This is an experimental product which integrates radar observational data in Taiwan with the latest observation data from FORMOSAT-7. By using data assimilation method and radar reflectivity extrapolation method, it develops real-time early warning technique and provides probability of precipitation information in future 1 hour.

This study proposes a 5G C-V2I (Cellular Vehicle-to-Infrastructure) enabled intelligent trajectory prediction and warning system, which can be implemented in a framework including RSUs (Road Side Units) with radar detection ability and 5G edge computing servers. This study exploits artificial intelligence to predict instant trajectories of vehicles at crossroads. The resulting augmented-awareness navigation information is then broadcasted to road users through 5G C-V2I with low latency. In practical applications, road users can obtain real-time dynamics of surrounding vehicles so that their level of safety can be effectively enhanced.

Based on deep learning camera/radar object detectiontracking technology, the proposed road side unit (RSU) system has achieved over 95 vehicle detection accuracy within 100m detection range in the processing performance of 10fps under nVidia Jetson Xavier platform. Compared to the 32-beam lidar based RSU, the proposed RSU achieves 97 reduction of sensor cost that exhibits high competitiveness in deployment cost. The proposed RSU system has been verified in fieldswe are now cooperating with an industry partner to deploy the RSU system in both TainanTao-Yuan cities.

The proposed indoor radar sensing system includes two enabling technology, activity recognitionrespiration rate estimation. In the former, the proposed framework consists of four major components: denosing, enhanced voxelization, data augmentation,dual-view machine-learning to lead to accurateefficient human-activity recognition. In the latter, the proposed system leverages the variation of the phase information of a specific frequency bin of the range profiles,proposes a dynamic adaptive respiration waveform filtering algorithm to improve accuracy.

"WISSDOM uses radial winds observed by multiple Doppler weather radars to synthesize the 3D wind fields in the atmosphere. Using variational approach to make the retrieved winds satisfy the following mathematicalphysical laws : (1)The geometric relationship between the retrieved windsthe radial winds observed by each individual radar (2)anelastic continuity equation (3)vertical vorticity equation (4) background wind field (5) Laplacian smoothing term. Special treatment is made allowing direct wind retrieval over complex terrain, which is necessary for weather analysis in Taiwan."

Combined with millimeter wave radar detection of chest undulation breathing modeheart rate, continuous blood oxygen detection, active disease record of chat robot,mobile phone analysis of 30 second sittingstanding 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 monitoringuseful for infectious disease ward, epidemic prevention hotels, centralized quarantine centers.

A 3D wireless tracking system for minimally invasive spine surgery navigation is developed based on the mm-wave radar technique. It has the benefit of multi-target trackingsmall-sized transponder. With the cooperation of radar systemtriangulation algorithm, the maximum error of 3D positioning is less than 1.8 mm in the distance of 1.2 m

An IOT sensor was presented to detect human motionvital signs with Wi-Fi signals. It mainly combines MIMO beamforming, injection-locking radarmachine learning techniques to recognizetrack physicalphysiological activities for health status monitoring. As a proof of concept for pandemic prevention, the presented sensor aims to inspect whether someone is exhibiting signssymptoms of COVID-19 infection within the Wi-Fi coverage area, such as fatigue, cough, shortness of breathirregular heart rhythm,even to detect sudden death.