A VR/AR based learning platform for students to experience education in a 3-dimensional environment.A conceptual basis for educational applications of virtual reality can be following:
• Immersive VR furnishes first-person non-symbolic experiences that are specifically designed to help students learn better.
• These experiences cannot be obtained in any other way in formal education.
• This kind of experience makes up the bulk of our daily interaction with the world, though schools tend to promote third-person symbolic experiences.
• Constructivism provides the best theory on which to develop educational applications of VR.
• The convergence of theories of knowledge construction with VR technology allows learning to be enhanced by the manipulation of the relative size of objects in virtual worlds, by the transduction of otherwise imperceptible sources and by the visualization of abstract ideas that have so far defied representation.
We are creating an environment of virtual reality to better the traditional means of providing education by allowing students to learn in an immersive and 3D learning environment.
StudyShack is an Online Tuition Finder. Which is a location based application bridging the gap between student and teachers.
This project is an Online Tuition Finder, which uses location from google maps. With the help of this application, one can find the desired tuition, ONE TAP AWAY!
The expectations from the StudyShack is to bridge the gap between teacher and students. With the help of the StudyShack, students can find reliable teachers sitting at their home and teachers can find tuitions nearby to their home.
An embedded system that’s is intended to provide rural medical care. The basic objective of making such a system is to facilitate patients who sometime lack in getting proper medical attention when it comes to their whereabouts from remote areas.
Road accidents are suspected to be a primary concern nowadays and the key cause is the fatigue level of the driver.The system will track driver’s facial features like fast blinking, eye closures, head poses and yawning. We will try to achieve accuracy during the day, at night and under illuminated conditions through night vision camera.
Classifiers like Haar Cascade Classifiers or HOG + linear SVM will be chosen depending on their accuracy and shortest time of computation. EAR (eye aspect ratio) and facial expressions will be used to sense the tiredness of the car driver which will stimulate the system to ring alarms and send warnings consequently. The technical facets of the project will rely on OpenCV and python for facial recognition with Raspberry pi for hardware port connections.
The project “Driver’s Fatigue Level Detection System (DFLD)” was designed and developed to easily facilitate, monitor and help detect when a driver may be exhibiting diminished. Our system keeps an eye on the driver’s behaviour to identify any signs that the driver is losing concentration to the point where safety is being compromised. Our rationale was to create a system in a way to avoid accidents risks which are caused due to long hours, shift work, lack of sleep or sleep apnoea that may exhaust the driver and cause him/her to sleep while driving. Our aim was to make our system efficient and less costly.
The expectation from our project was to reduce road accidents caused by fatigueness, , especially the ones at night when the drivers are very sleepy and have to go for long deliveries. We believed that it would help save people’s lives. With this system , drivers could get a buzzer alarm as a warning to wake up incase they’re about to fall asleep. As a conclusion we could say that we were able to implement all of the aspects which include head tilts, yawns and eye closures. Also we were able to integrate a night vision camera. We had also planned on introducing a water spray to sprinkle water if the system detects eye closures, however after taking feedback from FYP-1 , we planned not to do that because it would have caused an accident if the driver spent time rubbing his eyes which had water, especially the ones who had to clean their glasses if they wore spectacles.
With the aim to make day to day tasks easier and accomplishable for the visually challenged individuals, Blind Sight is an android application that targets to be available to all kinds of users that need it.
A mobile application built using Image Processing and Artificial Intelligence to make daily tasks easier for visually challenged people.
In today’s world where technology is evolving at an extremely fast rate, it has made a large amount of our daily tasks easy for us. Technology has impacted not only our corporate and professional lives but our daily lives as well. From simple and complex task management tools to Ecommerce shopping platforms, we see technology everywhere.
We believe that while technology has impacted a wide variety of users and people positively, its major impact should be towards the group of people who are challenged physically, mentally or emotionally.
Hence we decided to work on a product that solves the daily problems faced by visually challenged individuals. Our product is an android application that is targeted to be available to a wide range of visually challenged individuals regardless of their social status.
A deep learning solution to better diagnose oral disease, to help a dentist make more accurate and faster diagnosis.
Radiographs in dentistry can help dental practitioners diagnose a number of oral and maxillofacial diseases and prescribe appropriate treatments and interventions based on that diagnosis. However, radiographs are also often difficult to read and present a number of challenges to dentists and diagnosticians when determining abnormalities.
Our project aims to use Deep Learning techniques with Convolutional Neural Networks to build classifiers that can accurately detect certain oral and maxillofacial diseases. The diseases include caries, cysts and tumors as well as diagnosing incorrect root canal treatments. We will also apply various other image processing steps to annotate the test OPG with teeth numbering, gender detection etc
This project explores the intersection between Security and Data Science. The objective is to improve the current implementation of Intrusion Detection Systems (IDS) and make them “intelligent.” By using the power of data, insights gained from network traffic, machine learning algorithms would enable IDSs to catch new and unknown attacks without any previous knowledge, to flag malicious activity when it sees it, all without the use of human intervention.
Campus Assistant(CampA) is a campus solution aims to provide features to facilitate daily campus activities, event marketing, news feed, calendar and indoor and outdoor maps to find venues.
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