Ye Wang

Security and privacy have become pressing concerns in low-altitude network (LAN), mainly due to its high mobility, and the critical reliance on line of sight (LoS) communication. To address these threats, covert communication is introduced into LAN to further enhance the transmission security by protecting the transmission behavior from being detected. In this paper, we introduced the unmanned aerial vehicle-mounted simultaneous transmitting and reflecting reconfigurable intelligent surfaces (UAV-STAR-RIS) into the covert multicasting LAN. Specifically, Alice transmits covert signals to Bobs via the UAV-STAR-RIS against two wardens. With the object to maximize the minimum achievable covert rate, we jointly optimize the transmit beamforming of Alice, the reflection and transmission coefficients of the STAR-RIS, and the trajectory of the UAV-STAR-RIS. Firstly, an iterative algorithmic framework combining alternating optimization (AO), successive convex approximation (SCA), and penalty-based semi-definite relaxation (SDR) is developed to optimize transmit beamforming and STAR-RIS coefficients under fixed UAV-STAR-RIS positions. Then, the trajectory of the UAV-STAR-RIS is optimized by utilizing the proximal policy optimization (PPO) algorithm. Numerical results indicate that our proposed scheme shows superiority in covert communication performance compared with benchmarks.

Image deblurring is a technique employed to reduce or eliminate degradation resulting from the effects of impulse response and atmospheric turbulence during remote sensing image acquisition. However, the issue of low recovery accuracy due to inadequate point spread function (PSF) matching is a significant challenge in current deblurring methods. In this paper, a remote sensing image deblurring method based on the differential Lorentzian point spread function model is proposed. Firstly, a differential Lorentzian point spread function model is proposed, which is able to model the line spread function (LSF) of a real image as a series of pulse width variable Lorentzian functions and their differential function combinations. Subsequently, a model parameter estimation algorithm based on the improved zeroing filter is proposed. This algorithm is able to reconstruct the target parameters by utilising the edge information of the actual image, with the objective of eliminating the matching error of the point spread function model in the actual complex scene and improving the parameter estimation accuracy. Finally, a remote sensing image deblurring method is proposed by using the differential Lorenz point diffusion function model combined with the L-R algorithm. The results of the simulation experiments demonstrate that the method proposed in this paper is more effective than existing remote sensing image deblurring methods in terms of image recovery accuracy.

This article presents a novel under-sampling method for ECG signals, aimed at reducing the sampling rate and power consumption in IoT-based ECG wearable devices. The key contribution addresses the common issue of model mismatch in existing methods, which negatively impacts signal reconstruction accuracy. Initially, the ECG signal is modeled as a linear combination of several Gaussian second-order derivative functions, which can be efficiently represented with only a few parameters, thus mitigating the problem of large model matching errors. To further enhance reconstruction accuracy, an improved two-channel finite rate of innovation sampling framework is introduced, effectively addressing the nonideal effects caused by the low-pass filter during sampling. Additionally, a modified annihilating filter reconstruction algorithm is proposed, allowing high-precision signal reconstruction using a small number of sampling points to estimate parameters. The validity of the proposed method is confirmed through simulations with real ECG signals from the MIT-BIH arrhythmia database, and a hardware platform is developed to verify its feasibility in a practical system. Experimental results demonstrate that, compared to the existing methods, the proposed approach significantly reduces reconstruction error (achieving a PRD as low as 2.29% and an SRR of 11.77 dB), and exhibits better robustness in noisy environments.

Unmanned aerial vehicles (UAVs) have been emerged as cost-effective platforms to extend the coverage of mobile edge computing (MEC) system. However, the broadcast and line-of-sight (LoS) channels in UAV communications create opportunities for malicious eavesdroppers to intercept the offloaded information from ground users, posing a serious challenge to both communication and computing security. In this correspondence, we investigate the problem of secure transmission in UAV-aided MEC systems. Our goal is to maximize the average secure computing capacity by jointly designing the UAV trajectory, time allocation and offloading decision strategy. To this end, we propose a novel double-deep Q-learning (DDQN) based trajectory optimization and resource allocation scheme. Furthermore, the size of the original action space is reduced to boost the convergence of the proposed DDQN-based scheme. Additionally, we design a reward function to navigate the UAV towards its intended destination. Simulation results demonstrate that the proposed DDQN-based scheme outperforms the baselines in terms of average secure computing capacity.