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This article presents the details of the Cardinal RF (CardRF) dataset. CardRF is acquired to foster research in RF- based UAV detection and identification or RF fingerprinting. RF signals were collected from UAV controllers, UAV, Bluetooth, and Wi-Fi devices. Signals are collected at both visual line-of-sight and beyond-line-of-sight. The assumptions and procedure for the data acquisition are presented. A detailed explanation of how the data can be utilized is discussed. CardRF is over 65 GB in storage memory.


This project investigates bias in automatic facial recognition (FR). Specifically, subjects are grouped into predefined subgroups based on gender, ethnicity, and age. We propose a novel image collection called Balanced Faces in the Wild (BFW), which is balanced across eight subgroups (i.e., 800 face images of 100 subjects, each with 25 face samples).


The C3I Thermal Automotive Dataset provides > 35,000 distinct frames along with annotated thermal frames for the development of smart thermal perception system/ object detection system that will enable the automotive industry and researchers to develop safer and more efficient ADAS and self-driving car systems. The overall dataset is acquired, processed, and open-sourced in challenging weather and environmental scenarios. The dataset is recorded from a lost-cost yet effective 640x480 uncooled LWIR thermal camera.


COVID-19 tracing data are utilized to form two dataset networks, one is based on the virus transition between the world countries, as the dataset consists of 36 countries and 75 relationships between them. Whereas the other dataset is an attributed network based on the virus transition among the contact tracing in the Kingdom of Bahrain. This type of networks that is concerned in tracking a disease or virus was not formed based on COVID-19 virus transmission.


Researchers are becoming increasingly interested in dorsal hand vein biometrics because of their characteristics. These characteristics can be summarized as follows: it does not need contact with the capture device, cannot be forged, does not change over time, and provides high accuracy. Recognition systems of the dorsal hand rely on how the collected images captured from the device are good. Near-infrared (NIR) light is used to distinguish veins from the back of the hand.


Datasets for image and video aesthetics

1. Video Dataset : 107 videos 

This dataset has videos that can be framed into images.

Color contrast,Depth of Field[DoF],Rule of Third[RoT] attributes

that affect aesthetics can be extracted from the video datasets.


2.Slow videos and Fast videos can be assessed for motion

affecting aesthetics