Atmospheric motion vectors (AMVs) from satellite measurements serve as critical indicators of atmospheric dynamics, playing an essential role in enhancing the prediction precision of numerical weather prediction (NWP) models through data assimilation (DA). The implementation of finer satellite-derived vector products has the potential to significantly augment the accuracy of atmospheric flow field data in high-resolution regional NWP model simulations, thereby fulfilling the burgeoning requirements of operational weather nowcasting and forecasting. This study is focused on the development of mesoscale AMV (MAMV) products, which are distinguished by their exceptional quality and spatiotemporal resolution, leveraging data from the geostationary high-speed imager aboard the Fengyun-4B geostationary meteorological satellite (FY-4B/GHI). MAMVs of FY-4B/GHI feature an enhanced horizontal resolution of 3 km, enabling more accurate identification and monitoring of nongeostrophic flow patterns of mesoscale weather systems, as well as their fast-evolving dynamical structures and characteristics. Furthermore, a comparative analysis with radiosonde measurements highlights the precision of MAMV products, as evidenced by a speed bias (SB) of 0.37 m/s, a speed root mean square error (sRMSE) of 4.68 m/s, and a direction root mean square error (dRMSE) of 26.35°. The prospects of high-resolution satellite wind field data hold great potential for propelling scientific advancement and enriching our comprehension of atmospheric dynamics. This is particularly valuable in the context of typhoon monitoring and forecasting, where such data can lead to significant improvements in predictive capabilities.

Cardio-pulmonary resuscitation (CPR) is an effective first aid measure to deal with cardiac arrest and is the cornerstone of saving patients ’ lives. Chest compression is the most important part of CPR. This work proposed a non-contact CPR compression action quality detection system based on the depth camera in the first time. The RGB camera probe in the system identifies and tracks the position of the rescuer ’s hand in the RGB image and maps it to the depth image to obtain the CPR compression curve. Then the CPR parameters such as compression depth and compression frequency are obtained by solving the compression curve. Experiments with different camera deflection angles, different camera measurement distances and different compression frequencies were carried out to evaluate the measurement error of the system. The results showed that under appropriate conditions, the system performance is ideal, and the compression depth and frequency of CPR can be tracked stably.

This work presents a technique for measuring blood pressure waveform using a lightweight, stretchable, and wearable ultrasound patch. A system containing ultrasound transceiver hardware and signal processing software was developed for blood pressure waveform evaluation. By acquiring echo frames from 3 to 4 adjacent arterial locations and employing peak tracking techniques, stable arterial distension waveforms were evaluated. The time delay between channels was utilized to determine local pulse wave velocity (PWV) and arterial compliance, followed by the calculation of arterial blood pressure waveform based on arterial diameter. This technology has been validated on the carotid arteries of 10 human subjects, and the blood pressure waveform measured by the ultrasound patch demonstrated good consistency when compared with the arterial tonometer. The current results indicate that the wearable ultrasound patch can measure blood pressure waveform at central artery sites.

Identifying users’ sleep posture is significant in reducing sleep apnea events and avoiding postoperative pressure sores. Past studies have identified sleep posture by installing cameras, installing sensors on mattresses, or letting users wear wearable devices. However, the camera-based method is usually affected by light intensity or coverage and can invade users’ privacy. The method based on contact sensors will affect the comfort of users’ sleep. The use of radar can solve the problem of cameras and contact sensors, but previous studies need to collect data from new users for calibration to maintain high performance. In this work, we use FMCW radar to estimate the position image of the user in space. We propose a multi-task learning sleep posture recognition model based on a neural network, which uses the radar position image to estimate the user’s sleep posture. In addition, we use mix-up data enhancement to improve the model’s generalization. We collected data from 17 subjects to train and test our model. The proposed method can achieve 0.935 sleep posture recognition F1 score without collecting new user calibration data.

Complicated and regionally representative diurnal cycle characteristics of clouds may introduce some errors in the cloud mask (CLM) algorithm of the Geostationary (GEO) meteorological satellite imaging system, which are very difficult to be assessed by using analogous products of fixed-passing polar-orbiting satellites. In this investigation, the diurnal cycle of the performance of the CLM algorithm of the Advanced Geosynchronous Radiation Imager onboard the China Fengyun-4A satellite (FY-4A/AGRI) is validated by using manually observed cloud covers (CC) at 25 ground-based stations in China. The results indicate that the CCs calculated by the FY-4A/AGRI CLM algorithm are overestimated at 11:00 BJT (Beijing Time) and 14:00 BJT (around noon) and underestimated at 08:00 BJT and 20:00 BJT (in the morning and evening) at most stations. In summer, compared with other seasons, the CCs obtained from the FY-4A/AGRI over northern China and the Tibetan Plateau are much better, consistent with the manual observations, but the situation is the opposite in southern China. The CC results retrieved at the vegetation surface by FY-4A/AGRI, however, show the best and stable performance. Because of that, the two independent cloud tests induce most of the overestimations, and some sensitivity experiments for the CLM algorithm are conducted. The results show that the best improvement effect is achieved after only closing one cloud test using the$3.8\mu \text{m}$band. Many extremely overestimated CC samples (about 56.3%) are eliminated. After that, the FY-4A/AGRI CLM product is more reasonable compared with the corresponding infrared and visible imageries.