Application

Jackal Robot: Converting Reality to Virtuality through Surrounding Rendering and Object Detection Best Capstone project Award

Nishank Singhal (Capstone project) Mentor: Dr. Christelle Scharff Pace University, Seidenberg School of CSIS

—The objective of this capstone project is to use a Jackal Robot to convert real-world environments into virtual worlds through object detection and rendering techniques. Deep learning algorithms such as Yolo, PlFuHD, and posenet-model are being implemented to accurately recognize and classify objects in the environment. The system is rendering a person from an image in a virtual space based on data gathered by the robot's sensors. The project aims to evaluate the accuracy and efficiency of the system and its potential applications in various fields. Although the project had to be ended prematurely due to a disaster, the system successfully detects and converts persons and chairs to virtual representations. Future work would involve implementing style-gan to handle texture in 3D models. The project seeks to advance the fields of robotics and virtual reality by providing the Jackal Robot with the capability to convert reality into virtuality.

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Application

Application of Convolutional Neural Network to Classify Sitting and Standing Postures - Best Paper Award

Nishank Singhal, Srishti, and V. Kalaichelvi

—The paper aims at identifying whether a sitting or standing posture of a person is correct or incorrect using image processing and deep learning approach. The approach includes: (i) to check whether a person is present or not in the image read (ii) if present, then detect whether the person is sitting or standing (iii) in case of sitting, identify whether the posture is correct or incorrect (iv) in case of standing, identify whether the posture is correct or incorrect. In accomplishing the task, an overall accuracy of 91.3% is achieved. The method has been evaluated by testing it with a real time video feed thereby demonstrating the efficiency of the model and the wonderfulpower of Convolutional Neural Network (CNN).

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Application

Advances in Engineering and Information Technology A Level 4 Autonomy Self Driving Car Protocol for the UAE - Best Paper Award

Mr. Indraneel Patil, Mr. Juzar Gulamali, Mr. Nishank Singhal,Shivnarain Ravichandran, Dr. Abdul Razak

Roy Amara’s eponymous law famously states, “we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” In line with Dubai’s Autonomous Transportation Strategy, Dubai, Abu Dhabi and the rest of the United Arab Emirates is making proactive efforts to make 25% of the total transportation autonomous. The current self driving car (Autonomy Level 3) technology allows the Driver much comfort on the road through a combination of various AI and Machine Learning based algorithms on Park Assist, Auto Pilot and Cruise Control and advanced sensors for simultaneous localisation and mapping. As the world is gearing towards a Level 4 Autonomy Car, a lot of issues in the Cyber Security of the car, Intelligence and unpredictable driving scenarios on the road remain unaddressed. Our team through this research paper has tried to propose threenovel solutions namely Dynamic Region of Interest, Round-about Central Unit and Thermal Imaging Cameras for enhancement in the existing technology and then we implemented them to test their efficiency with our very own miniature prototype of a Level 4 Autonomy Self Driving Cars called Maverick. Based on our experience through this project and our knowledge of sensors and SLAM we have tried to extrapolate the technology used in the Maverick to the real world streets of the UAE

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Application

Oil spills detection using drown

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