Impact of RIS Orientation on Throughput in UAV-Assisted Wireless Systems

This paper investigates the impact of Reconfigurable Intelligent Surface (RIS) orientation on the throughput performance of Unmanned Aerial Vehicle (UAV)-assisted wireless communication systems. Specifically, we study how physical rotation of the RIS, through controlled azimuth and elevation adjustments, influences the effective channel and data rate. A UAV-mounted RIS enables directional alignment to serve ground users in scenarios where the direct Base Station (BS)-to-user path is blocked. Using the SimRIS channel simulator, we analyze the system under various rotation angles and present performance heatmaps that highlight optimal RIS orientations. The study shows that RIS alignment has a substantial effect on achievable rates, thereby motivating orientation-aware optimization in practical deployments.

SDR-Based Metal Classification using Spectrogram Images from Micro-Doppler Signatures

Metallic materials such as brass, copper, and aluminum are used in numerous applications, including industrial manufacturing. The vibration characteristics of these objects are unique and can be used to identify these objects from a distance. This research presents a methodology for detecting and classifying these metallic objects using the vibration dynamics induced by their micro-Doppler signatures. The proposed approach utilizes image processing techniques to extract pivotal features from spectrograms. These spectrograms originate from micro-Doppler signatures of data collected during controlled laboratory experiments where signals were transmitted towards vibrating metal sheets, and the ensuing reflections were recorded using a software-defined radio (SDR). The spectrogram data was augmented using geometric transformation to train a convolutional neural network (CNN) based machine learning model for object classification. The results indicate that the proposed CNN model achieved an accuracy of more than 95% in classifying metals into brass, copper, and aluminum. This research could be used to understand the foundations of classifying spectrogram images using micro-Doppler signatures for its applications towards enhancing the sensing capabilities in industrial and defense applications.