Research and Supervision

PROJECT ABSTRACT: 

Driver drowsiness poses a major threat to road safety and leads to numerous accidents. This project aims to develop a driver drowsiness Shield (DDSh). Drivers often feel drowsy during long journeys, which can reduce their concentration and reaction time. Additionally, stress and lack of sleep can cause driver fatigue, making the problem even worse. To mitigate these risks, the proposed system uses Convolutional Neural Networks (CNN) to create an efficient detection engine. Additionally, it employed computer vision technology in order to monitor the driver's eyes in real time via a webcam. This project involves continuously tracking a driver's face by capturing images from a webcam in real time using OpenCV. These images are then fed into a deep learning model that is trained to classify the driver's eye state as "open" or "closed." When it detects that the eyes are closed for a certain period of time, it activates a warning mechanism to notify the driver, thereby reducing the risk of accidents caused by drowsy driving. Through the integration of advanced technologies, the system will provide critical functions such as real-time driver fatigue monitoring and timely warnings to prevent potential accidents. The benefits of this system will extend not only to individual safety but also to society's contribution to reducing road accidents and deaths. Recognizing the signs of drowsiness early promotes safe driving and helps build a culture of road safety. 

 PROJECT ABSTRACT: 

This project focuses on developing a cutting-edge language translator, emphasizing a research-driven methodology for enhanced performance. The research component involves exploring state-of-the-art algorithms and methodologies in natural language processing (NLP), with a particular focus on neural machine translation advancements. We plan to leverage existing translation APIs such as Google Cloud Translation and incorporate open-source machine translation models for a robust foundation. The outcome of this research-driven approach is expected to result in a highly accurate and context-aware language translator, catering to a wide range of user needs. 

PROJECT ABSTRACT:

The proposed project aims to develop a versatile system for remote monitoring and control, where mobile phones serve as clients, connecting to other devices running server-side applications. The project addresses the need for efficient status monitoring and management of remote devices, catering to diverse applications across IoT, home automation, and industrial sectors. The primary problem to be addressed is the lack of a unified solution for monitoring the status of distributed devices from a centralized interface. By leveraging mobile phones as client devices, users can conveniently access real-time status information of server-side devices, including CPU usage, temperature, battery health, and system health. Functionalities of the system include:

• Development of server-side applications running on devices such as sensors, PCs, or other IoT devices, capable of gathering and providing status updates.

• Creation of mobile applications for Android and iOS platforms, serving as client interfaces to connect to server-side applications.

• Implementation of secure communication protocols to ensure data integrity and confidentiality between clients and servers.

• Provision of real-time updates and notifications to notify users of critical status changes or threshold alerts.

• Cross-platform compatibility of client applications to ensure seamless user experiences across different mobile platforms.