Projects

Tackled advanced process memory management in the xv6 operating system, implementing dynamic on-demand paging and the innovative Copy-On-Write (CoW) Optimization. Features include dynamic On-Demand Binary Loading, a robust Page Fault Handler ensuring continuous process operation, efficient On-Demand Heap Memory loading, and a strategic Page Swapping to Disk mechanism to tackle memory constraints. Enhanced the fork() system call conserving memory by creating separate yet identical spaces for parent-child processes using CoW. Key technologies- C Programming, OS Concepts, Memory Management, Page Fault Handling, and CoW Optimization.

Delved into the OS boot process using the QEMU Emulator, creating a custom bootloader for the xv6 OS kernel. Utilized GDB Debugger for Boot ROM inspection and crafted a bootloader using Linker Scripts and Assembler Programming (Assembly). Enabled C code execution through stack setup and introduced user flexibility to boot different OSs. Enhanced security using RISC-V’s PMP Configuration and a custom Secure Boot Implementation. Technologies involved- QEMU, GDB, RISC-V, Assembly, C Programming, Linker Scripts, PMP, and Secure Boot

Undertook the implementation of user-level threads within xv6 processes by developing a comprehensive User-Level Threading Library (ULTLib). This library allowed for the segmentation of kernel-backed threads into multiple user-level threads with efficient management. Key tasks included ULTLib Initialization, dynamic Thread Creation with precise context management, intelligent Thread Switching and Scheduling incorporating multiple algorithms like round-robin and priority scheduling, and advanced Thread Yielding and Destruction capabilities. Additionally, crafted a new xv6 system call, ctime, tapping into the RISC-V register for accurate timing information. Core technologies and tools encompassed- C Programming, Assembly Language (RISC-V), OS Concepts, Thread Management, and System Calls.

Developed a hybrid cloud application utilizing advanced face-recognition techniques with Python. The system processes classroom videos, identifies students’ faces, and retrieves academic records from DynamoDB. Integrated AWS and OpenStack, enabling seamless scalability and cost-effectiveness by utilizing both public and private cloud resources. An extension of Project 2, this approach enhances control and flexibility in triggering AWS Lambda functions through OpenStack VMs.

Developed a cloud-based application using Python’s face-recognition library to integrate advanced face-recognition techniques. The system processes classroom videos, identifies students’ faces, and retrieves their academic records from DynamoDB. Implemented on AWS Lambda PaaS for efficient, accurate, and cost-effective student recognition and academic record management.

The service will take in images in the PNG format from users, use the deep learning model to perform image recognition, and return the top prediction from the model to the user as plain text. The importance of this service is to provide a scalable and efficient solution for image recognition that can be accessed remotely by users, without the need for them to train their own models or have access to specialized hardware.

Developed a self-driving car simulation using 28k images from cameras mounted on the vehicle, utilizing Udacity’s simulator. Trained a CNN model with multi-task learning to predict angle and throttle based on the simulated images.

Utilized context-aware summarization to improve the performance of relation extraction on Electronic Health Records (EHR) which are known to be long and pose a challenge for state-of-the-art NLP systems. I have Utilized the 2010 Relations Dataset for the research.