BTech Projectshttp://repository.iiitd.edu.in/xmlui/handle/123456789/452026-09-20T13:02:41Z2026-09-20T13:02:41ZMulti-modal sensor fusion for geometric and semantic scene understanding with graphsRao, NavvratAnand, Saket (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/21712026-09-18T22:00:47Z2024-04-26T00:00:00ZMulti-modal sensor fusion for geometric and semantic scene understanding with graphs
Rao, Navvrat; Anand, Saket (Advisor)
This thesis project, titled Multi-Modal Sensor Fusion for Geometric and Semantic Scene Understanding with Graphs, aims to highlight the constraints inherent in sensors such as LiDAR and cameras and highlight current tools and algorithms for clear interpretation of the environment to occupancy grid. We propose a solution using late fusion for these sen sors, then apply the Kalman filter to the fused data to estimate the detected obstacle’s state. The information derived from this process will then be integrated into the occupancy grid for enhanced accuracy and navigational utility.
2024-04-26T00:00:00ZMultimodal stream processing for accident detectionSingh, VishalShah, Rajiv Ratn (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/21702026-09-18T22:00:43Z2024-11-26T00:00:00ZMultimodal stream processing for accident detection
Singh, Vishal; Shah, Rajiv Ratn (Advisor)
This report presents a novel architecture for accident anticipation and localization in autonomous driving, integrating monocular depth-enhanced 3D modeling with large language models (LLMs). The system leverages advanced feature extraction techniques, such as MobileNetv2 and Cascade R-CNN, combined with attention mechanisms to process real-time dashcam video inputs. A dual vision attention mechanism enhances feature representation, while a dynamic object attention mechanism prioritizes high-risk objects. The architecture incorporates a GRU-based accident anticipation module and an attention-driven accident localization module to predict accident probabilities and identify hazardous objects. Finally, the model generates real-time verbal alerts using LLMs, offering contextually relevant warnings to passengers, enhancing safety and aware ness. This hybrid approach aims to improve both accident prediction accuracy and anticipation time, making it suitable for deployment in real-world autonomous driving systems. The model’s effectiveness is evaluated using metrics like average precision (AP) and mean Time to Accident (mTTA).
2024-11-26T00:00:00ZDevelopment of large language models and tools to study patents and develop insightsVyas, MadhavGrover, Anuj (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/21692026-09-18T22:00:46Z2024-11-30T00:00:00ZDevelopment of large language models and tools to study patents and develop insights
Vyas, Madhav; Grover, Anuj (Advisor)
The development and evolution of Static Random Access Memory (SRAM) has been a cornerstone in the advancement of semiconductor technology, largely due to its ability to deliver high-speed performance while consuming minimal power. SRAM is widely utilized in applications requiring fast and efficient memory access, such as microprocessors, cache memory, and embedded systems. As the demandfor morepowerful, energy-efficient, and scalable memory solutions grows, understanding the underlying innovations and trends within SRAM technology has become increasingly vital. This project presents a novel approach by combining the power of Large Language Models (LLMs) with a chatbot designed specifically for the analysis of SRAM-related patents. The mainobjective of this project is to harness the capabilities of LLMs to create a tool that can provide comprehensive, context-sensitive, and insightful information from SRAM patents. By systematically classifying and analyzing a wide array of patent data, this system is able to cover key aspects of SRAM technology, such as technical specifications, design innovations, architectural variations, performance optimization techniques, manufacturing advancements, and emerging trends in the field. The rich variety of data available in patent documents presents a unique opportunity to identify cutting-edge developments, breakthroughs, and potential challenges facing the SRAMindustry.
2024-11-30T00:00:00ZProject ROBIFYJain, AryanGrover, Anuj (Advisor)http://repository.iiitd.edu.in/xmlui/handle/123456789/21682026-09-18T22:00:35Z2025-07-20T00:00:00ZProject ROBIFY
Jain, Aryan; Grover, Anuj (Advisor)
This report details the B.Tech Project (BTP) undertaken by Aryan Jain under the Entrepreneur ship Track at IIIT Delhi, focusing on the development of STEM Toy Kits and an IOT Activity Scoring Platform for the startup Robify. The project aims to revolutionise STEM education for school students aged 7 to 10 by creating engaging, hands-on learning experiences. The STEM Toy Kits, including a sound-responsive crawling spider and a human-sensing dog bot car, intro duce concepts like sensor technology and robotics. The IOT platform enhances these kits by automating activity scoring, providing real-time feedback, and fostering competition through leaderboards. Despite challenges such as hardware integration and performance optimisation, the project achieved functional prototypes and a robust digital platform. This report outlines the design, development, integration, challenges, and future directions of Robify’s innovative approach to STEM education.
2025-07-20T00:00:00Z