To establish stable vertical communication links for Non-Terrestrial Link (NTN), such as between ground stations and satellites, it is essential to mitigate the impact of weather conditions—such as clouds and rainfall—on signal propagation in NTN links. As a countermeasure in satellite optical communications, site diversity techniques are employed to avoid the effects of cloud coverage. This paper presents the results of an investigation into the impact of weather on optical communication, based on observational data collected at ground level and meteorological data obtained from satellites.
Electric bicycles (e-bikes) have gained significant popularity due to their eco-friendliness, affordability, and accessibility. Moreover, e-bikes serve as an inclusive transportation solution for individuals with limited physical stamina or mobility constraints, enabling longer and more frequent travel than traditional bicycles.This paper introduces an Advanced Rider Assistance System (ARAS) designed to enhance cyclist safety on smart e-bikes. The system is implemented on a prototype smart e-bike platform developed at the German University in Cairo (GUC), equipped with a resource-constrained Raspberry Pi 4. It leverages real-time embedded vision to detect road hazards.The proposed ARAS builds upon previous work in pothole detection, speed bump recognition (both marked and unmarked), cyclist alerting mechanisms, and traffic sign interpretation. A custom dataset was collected to train and evaluate several state-of-the-art object detection architectures, including YOLOv11, YOLOv12, RT-DETRv2, and D-FINE. Our findings show that the transformer-based D-FINE-N model provides the optimal performance-speed trade-off, demonstrating a superior balance of precision (88.7%) and recall (87.5%) and a leading F1-score of 88.1% and mAP@50-95 of 49.3%. Moreover, after optimization and conversion to the ONNX format, D-FINE-N achieved the highest inference speed, reaching 2.75 FPS on the target hardware. This research demonstrates the viability of deploying modern deep learning models for ARAS applications on low-power edge devices and highlights the effectiveness of D-FINE-N for real-time safety systems.
The electromagnetic effective degrees of freedom (EM EDOF) provide an insightful measure for characterizing the performance of multiple-input-multiple-output (MIMO) wireless communication systems. The dyadic Green’s function (DGF) is widely used as a benchmark tool for modeling ideal EM propagation due to its analytically tractable closed-form solution. This paper proposes a scatterer-parameterized EM channel model based on the DGF to characterize the EM EDOF in MIMO systems. The proposed EM model is first validated by comparison with results obtained using the method of moments (MoM). Then, the impact of distinct polarizations on the EM EDOF is investigated, highlighting the advantage of full polarization over transverse polarization in a scattering environment. Finally, the EM EDOF is analyzed through a sensitivity analysis of EM model parameters and further evaluated under varying scatterer-antenna distances. Numerical results show that near-field spatial modulation induced by scatterers increases the EM EDOF, with the outer scatterer regions contributing more prominently, while the effect weakens and converges to the free-space value in the far field. The proposed model offers an efficient tool for analyzing and optimizing MIMO capacity and spatial DOF.
Visual odometry is a crucial technology in mobile robotics, enabling the estimation of motion through continuous image observations. Traditional algorithms use geometric elements like point and line features to establish correspondences between frames for real-time pose estimation. While point features are simple but susceptible to external factors, line features offer stability but suffer from extraction speed and distribution issues. Effectively merge line feature into odometry becomes a highly promising direction for improving accuracy. To this end, this paper proposes HS-VO, a homogenized structural visual odometry. It incorporates quick line extraction and stable homogenization schemes, introducing Manhattan Axes (MA) for scene geometric constraints. Additionally, to effectively extract MA, we design a Breadth First Search (BFS)-based method for plane normal vector extraction. To verify the performance of HS-VO, we conduct experiments on public datasets. The results show that HS-VO significantly improves both speed and accuracy compared to other approaches.
Radar communication integration based on OFDM (Orthogonal Frequency Division Multiplexing) waveform is a promising approach to achieve dual functions of broadband wireless communications and radar sensing. DFTs-OFDM (Discrete Fourier Transform spreading-OFDM) radar using Zadoff-Chu (ZC) sequence is proposed because of ideal autocorrelation of ZC sequence and low PAPR (Peak to Average Power Ratio). However, DFTs-OFDM radar using ZC sequence has drawbacks of large range sidelobe which degrades multiple target detection performance and signal processing complexity in conventional time-domain based correlation detection. To solve these problems, this paper proposes novel frequency domain signal processing for DFT-OFDM radar using ZC sequence. It describes principle of the proposed frequency domain signal processing for DFTs-OFDM radar. It also evaluates range profile of DFTs-OFDM radar for various design parameters by computer simulations and demonstrates that the range sidelobe can be reduced to less than -60 dB and signal processing complexity is also reduced to less than 1/300.
