The surface water and ocean topography (SWOT) mission is currently operating in scientific orbit. The Ka-band radar interferometer (KaRIn) altimeter represents a significant departure from nadir altimeters, offering abundant observations that would greatly advance oceanographic research. However, the accuracy of altimetry data is crucial to the validity of research results and must be evaluated using crossover discrepancies to obtain prior estimations. Traditional methods are not fully suited to the wide-swath data and tend to be inefficient when processing massive amounts of data. In this study, we propose an improved latitude difference method for calculating crossover discrepancies. The reliability of this algorithm is verified using both along-track and across-track split data. The results indicate that the accuracy of crossover discrepancies for SWOT is comparable to those of conventional altimetry satellites, confirming the performance of the SWOT low-rate L2 KaRIn product. The standard deviation of crossover discrepancies between SWOT and other satellites in the South China Sea (SCS) is about 8 cm, around 6 cm in the Indian Ocean (southern) (IOS), and approximately 8 cm in the Gulf of Mexico (GOM), further proving the accuracy of the SWOT data. Analyzing the discrepancies beyond 50 km offshore, the results demonstrate that the KaRIn altimeter is influenced by the coastline, while its performance improves in the open ocean. By leveraging the vectorization algorithm, the efficiency has been improved significantly. The computation speed of crossover discrepancies using the improved latitude difference method is around 0.006 s per point, faster than the traditional method, demonstrating that the algorithm is efficient.

With the rising of demands for novel Human-Computer Interaction (HCI) approaches in the 3D space, a number of intelligent approaches have been proposed to achieve the HCI by tracking the translation and rotation of the target devices. In this paper, we propose to realize a light-weight, battery-free, 3D motion tracking solution by leveraging a spinning linearly polarized antenna to track a passive RFID tag array. Instead of using the fixed antennas, which can only receive stable signal in some specific environments due to the unpredictable multipath effect, we propose to mitigate the multipath effect and the ambient interference by continuously spinning a linearly polarized antenna, and then extract the most distinctive features based on the optimal reading conditions of the spinning antenna. In particular, because the phase variation around the matching direction is more stable while the RSSI variation around the mismatching direction is more distinctive, we leverage such matching/mismatching property of the linearly polarized antenna to extract the most distinctive features for motion tracking. To depict the property, we build a theoretical model to explain the RSSI and the phase variation of the RFID tag along with the spinning of the antenna, and further extend the model from a single RFID tag to an RFID tag array. Based on the model, we can extract the distinctive RSSI features for the rotation tracking and the stable phase features for the translation tracking. Moreover, to tackle the low rate of feature extraction due to the spinning of antenna, we further propose to enhance the unstable phase features based on the overall trend of other tags with interpolation, such that the sampling rate can be efficiently improved. Finally, we propose a LSTM (Long Short Term Memory)-based network to track the 3D motion based on the signal features extracted based on the polarization model. The experimental results show that our system can achieve an average error of 10.45 cm in the translation tracking, and an average error of$6.02^\circ$in the rotation tracking in the 3D space.

The Shapley value is widely regarded as a trustworthy attribution metric. However, when people use Shapley values to explain the attribution of input variables of a deep neural network (DNN), it usually requires a very high computational cost to approximate relatively accurate Shapley values in real-world applications. Therefore, we propose a novel network architecture, the HarsanyiNet, which makes inferences on the input sample and simultaneously computes the exact Shapley values of the input variables in a single forward propagation. The HarsanyiNet is designed on the theoretical foundation that the Shapley value can be reformulated as the redistribution of Harsanyi interactions encoded by the network.

Nowadays, the growing demand for the 3D human-computer interaction (HCI) has brought about a number of novel approaches, which achieve the HCI by tracking the motion of different devices, including the translation and the rotation. In this paper, we propose to use a spinning linearly polarized antenna to track the 3D motion of a specified object attached with the passive RFID tag array. Different from the fixed antenna-based solutions, which suffer from the unavoidable signal interferences at some specific positions/orientations, and only achieve the good performance in some feasible sensing conditions, our spinning antenna-based solution seeks to sufficiently suppress the ambient signal interferences and extracts the most distinctive features, by actively spinning the antenna to create the optimal sensing condition. Moreover, by leveraging the matching/mismatching property of the linearly polarized antenna, i.e., in comparison to the circularly polarized antenna, the phase variation around the matching direction is more stable, and the RSSI variation in the mismatching direction is more distinctive, we are able to find more distinctive features to estimate the position and the orientation. We build a model to investigate the RSSI and the phase variation of the RFID tag along with the spinning of the antenna, and further extend the model from a single RFID tag to an RFID tag array. Furthermore, we design corresponding solutions to extract the distinctive RSSI and phase values from the RF-signal variation. Our solution tracks the translation of the tag array based on the phase features, and the rotation of the tag array based on the RSSI variation. The experimental results show that our system can achieve an average error of 13. 6cm in the translation tracking, and an average error of 8.3° in the rotation tracking in the 3D space.

Infusion monitoring is in great demand for the hospital. In this demo, we propose RF-iCare, an RFID-based approach for monitoring the infusion status, including the liquid level and the drop speed. With a tag array attached on the infusion bottle, we design an RSSI-based signal match model to estimate the liquid level. With a tag attached on the Murphy's dropper, we leverage the phase variation of the tag to estimate the drop speed. We implement RF-iCare with a COTS RFID system and evaluate it in the real-world hospitals. Our experiments demonstrate that RF-iCare can accurately monitor the completion of the infusion over 91% tests, and estimate the liquid level with the mean accuracy of 0.8 cm as well as the drop speed with the error rate less than 3%.