Device-free handwriting recognition, which can be widely applied to Human–Computer Interaction (HCI), is expected to monitor the handwriting change of entities without actively involving any physical devices. The key intuition is that different handwriting movements induce different multi-path distortions in WiFi signals and generate distinct patterns in the time-series of Channel State Information (CSI). In this paper, a proposed WiFi-based system namely Wima, by means of which, we are in pursuit of an advanced solution to recognize letters written by hand in the air. Unlike existing WiFi-based handwriting recognition systems, Wima extracts features directly from the original CSI and then transmit them to subsequent classifiers immediately, a deep-learning-based method is firstly introduced in our system to automatically extract high-level features. More specifically, Wima transforms CSI characters like amplitude and phase into images, which are afterwards delivered to extract features by adopting Convolutional Neural Networks (CNN) respectively. The final simulation with commercial IEEE 802.11n NICs shows that Wima can achieve air-writing recognition with a better performance when compared with some existing routine approaches.

This paper presents a new navigation scheme for the global navigation satellite system and inertial navigation system (GNSS/INS) by integrating the particle filter a nd the extended Kalman filter. In the proposed scheme, t he particle filter (PF) is used to estimate the object position and its velocity while the extended Kalman filter (EKF) is applied to predict the orientation, accelerometer error gyroscope error. In addition, the adaptive Gaussian particle swarm optimization (AGPSO) algorithm is involved into the PF to further improve the estimation accuracy. Experimental results show that the positioning and velocity errors using the proposed method are greatly reduced compared to the standard particle filter estimation method. The positioning accuracy can achieve sub-meter level, which satisfies the requirements of high-precision positioning and navigation. Finally, we analyze the sensitivity of the proposed algorithm on the number of particles, and we show that increasing the number of particles within a limited range is effective for improving the accuracy of the proposed algorithm.

Federated edge learning (FEEL) is an innovative approach that facilitates collaborative training among numerous distributed edge devices while eliminating the need to transfer sensitive information. However, the practical deployment of FEEL faces significant constraints, owing to the limited and asymmetric computational and communication resources of these devices, along with their energy availability. To this end, we propose a novel asymmetry-tolerant training approach for FEEL, enabled via simultaneous wireless information and power transfer (SWIPT). This framework leverages SWIPT to offer sustainable energy support for devices while enabling them to train models with varying intensities. Given a limited energy budget, we highlight the critical trade-off between heterogeneous local training intensities and the quality of wireless transmission, suggesting that the design of local training and wireless transmission should be closely integrated, rather than treated as separate entities. To elucidate this perspective, we rigorously derive a new explicit upper bound that captures the combined impact of local training accuracy and the mean square error of wireless aggregation on the convergence performance of FEEL. To maximize overall system performance, we formulate two key optimization problems: the first aims to maximize the energy harvesting capability among all devices, while the second addresses the joint learning–communication optimization under the optimal energy harvesting solution. Comprehensive experiments demonstrate that our proposed framework achieves significant performance improvements compared to existing baselines.