Rupesh Kumar

Rupesh Kumar, Ashutosh Bajpai, Ayush Sinha

This research paper describes a realtime system for identifying American Sign Language (ASL) movements that employs modern computer vision and machine learning approaches. The suggested method makes use of the Mediapipe library for feature extraction and a Convolutional Neural Network (CNN) for ASL gesture classification. The testing results show that the suggested system can detect all ASL alphabets with an accuracy of 99.95%, indicating its potential for use in communication devices for people with hearing impairments. The proposed approach can also be applied to additional sign languages with similar hand motions, potentially increasing the quality of life for people with hearing loss. Overall, the study demonstrates the effectiveness of using Mediapipe and CNN for real-time sign language recognition, making a significant contribution to the field of computer vision and machine learning.

Rupesh Kumar, Ayush Sinha, Ashutosh Bajpai et al.

Sign language is a visual language that enhances communication between people and is frequently used as the primary form of communication by people with hearing loss. Even so, not many people with hearing loss use sign language, and they frequently experience social isolation. Therefore, it is necessary to create human-computer interface systems that can offer hearing-impaired people a social platform. Most commercial sign language translation systems now on the market are sensor-based, pricey, and challenging to use. Although vision-based systems are desperately needed, they must first overcome several challenges. Earlier continuous sign language recognition techniques used hidden Markov models, which have a limited ability to include temporal information. To get over these restrictions, several machine learning approaches are being applied to transform hand and sign language motions into spoken or written language. In this study, we compare various deep learning techniques for recognising sign language. Our survey aims to provide a comprehensive overview of the most recent approaches and challenges in this field.

Hao Qin, Rupesh Kumar, Vadim Makarov et al.

We propose an efficient strategy to attack a continuous-variable quantum key distribution (CV-QKD) system, that we call homodyne detector blinding. This attack strategy takes advantage of a generic vulnerability of homodyne receivers: a bright light pulse sent on the signal port can lead to a saturation of the detector electronics. While detector saturation has already been proposed to attack CV-QKD, the attack we study in this paper has the additional advantage of not requiring an eavesdropper to be phase locked with the homodyne receiver. We show that under certain conditions, an attacker can use a simple laser, incoherent with the homodyne receiver, to generate bright pulses and bias the excess noise to arbitrary small values, fully comprising CV-QKD security. These results highlight the feasibility and the impact of the detector blinding attack. We finally discuss how to design countermeasures in order to protect against this attack.