Artificial Intelligence

This LTE_RFFI project sets up an LTE device radio frequency fingerprint identification system using deep learning techniques. The LTE uplink signals are collected from ten different LTE devices using a USRP N210 in different locations. The sampling rate of the USRP is 25 MHz. The received signal is resampled to 30.72 MHz in Matlab and is saved in the MAT file form. The corresponding processed signals are included in the dataset. More details about the datasets can be found in the README document.

Weather radar echo extrapolation is an important approach for convective nowcasting, which predicts the evolution of convective systems in a short term. In recent years, radar echo extrapolation approaches based on deep learning have made significant progress and have been widely applied for radar echo extrapolation.

To thoroughly investigate the non-overlapping registration problem, we created our own datasets: Pokemon-Zero for zero overlap and Pokemon-Neg for negative overlap. In this section, we describe the process of dataset creation. 

SYPHAXAR dataset is a dataset for Arabic text detection in the wild. It was collected from Tunisia in “Sfax” city, the second largest Tunisian city after the capital. A total of 3078 images were gathered through manual collection one by one, with each image energizing text detection challenges in nature according to real existing complexity of 15 different routes along with ring roads, intersections and roundabouts. These annotated images consist of more than 31000 objects, each of which is enclosed within a bounding box.