Machine Learning

This dataset contains raw FMCW radar signals collected for human localization and activity monitoring in indoor environments. The data was recorded using mmWave radar sensors across two different laboratory settings, designed to simulate real-life scenarios for human detection and localization tasks.

The ORL and WarpPIE datasets consist of grayscale face images, with each sample representing a single individual captured under various lighting conditions, facial expressions, and occlusions. The COIL20 dataset contains grayscale images of 20 distinct objects, with each object represented by 72 images taken from different rotational perspectives. The MNIST and USPS datasets comprise handwritten digits ranging from 0 to 9. 

This study presents a deep learning-based framework for detecting vehicle deceleration patterns using Ultra-Wideband (UWB) Channel Impulse Response (CIR) analysis. Unlike traditional GPS or IMU-based systems, which struggle in GPS-denied environments such as tunnels, the proposed method leverages UWB CIR signal variations to classify two key driving behaviors: rapid deceleration and gradual deceleration. All data were collected from real-world experiments using UWB devices installed on actual vehicles at a professional highway testing site.

This dataset provides packet traces captured in a realistic 5G Vehicle-to-Everything (5G-V2X) environment, encompassing both legitimate vehicular communications and Distributed Denial of Service (DDoS) attacks. By deploying four user equipments (UEs) under multiple attacker configurations, the collected captures reflect various DDoS types (TCP SYN, UDP, and mixed) and reveal their impact on 5G-V2X networks. The dataset is further enriched with Argus files and CSV feature tables, facilitating data-driven approaches such as Machine Learning (ML)-based detection agents.