This dataset was initially collected by Mrs Athira P K  with the help of  teachers and students of Rahmania school for handicapped, Kozhikode, Kerala, India. Later the dataset was extended by many other BTech and MTech students with the help of their friends.

MUDRA NITC dataset consists of videos of static and dynamic gestures of Indian sign language. In static gestures mainly static alphabets videos and  preprocessed image frames are included.

To illustrate the impact of the obstacles, we consider indoor and outdoor scenarios. We consider the Department of Computer Science and Engineering, IIT(BHU) buildings as indoor buildings and the railway platform as an outdoor scenario. Here, we use single-channel LG in our experiment. The distance between LNs and LG varies from 5 to 50 meters. The floor map illustrates the walls, doors, and windows between LNs and LG. We consider railway stations for the outdoor environment. The outdoor environment did not consist of obstacles between LNs and LG.

With the increasing use of drones for surveillance and monitoring purposes, there is a growing need for reliable and efficient object detection algorithms that can detect and track objects in aerial images and videos. To develop and test such algorithms,  datasets of aerial videos captured from drones are essential.

(a) The sample stage lifts the sample up to the tip of the cantilever until the tip-sample distance is about 2 mm or less, which facilitates safe pre-approach between the tip and the sample. (b) The laser-cantilever is aligned by moving the laser, while detecting the tip and the sample and measuring their positions. (c) The laser beam reflected from the cantilever head is aligned with the center of PSPD based on the electrical signals of PSPD.