My Projects/Thesis
Master's Thesis - Innovative Rehabilitation Approach for Upper Limb Neurologic Conditions Using Mixed-Reality Simulation and EEG/EMG Biofeedback
My research focused on using Augmented Reality (AR), Mixed Reality (MR), Electroencephalogram (EEG), and Electromyogram (EMG) to assist stroke patients with upper limb weakness. Part of the "Smart NeuroRehab Ecosystem," in collaboration with the University of Washington Bothell, University of Washington Seattle, and Harborview Medical Center. Developed an interactive mobile-based AR/MR rehabilitation environment to enhance patient engagement. EEG and EMG data were processed in MATLAB with CNN, FNN, RNN, SVM, and LSTM models in PyTorch and Tensorflow for motor task classification.
Game Flow 1 Demo
Game Flow 2 Demo
Enhanced Vocabulary Trees for Real-Time Object Recognition in Image and Video Streams
This project explores k-Means clustering trees in computer vision, building on Nister & Stewenius (CVPR 2006). It evaluates their impact on visual recognition accuracy, structure, and efficiency. Implementation includes ORB feature detection, k-means clustering, max voting, and score calculations, tested on public datasets. Future work includes larger datasets (COCO) and alternative feature detectors to improve accuracy, advancing research in the Bag of Words framework.
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CUDA Accelerated K-Means Clustering
This project implements an efficient k-means clustering algorithm on CUDA, leveraging GPU parallel computing for large-scale data clustering. A novel data partitioning technique accelerates key computations, particularly distance computation. Experiments show significant speedups over CPU-based implementations, making this approach promising for large-scale applications.
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Searching Kitty
A web-based straightforward Word Search game written in Python and JS. Using React as the frontend client and Flask as the backend server.
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INKredible
A SwiftUI iOS application for OCR(Optical Character Recognition) with the feature to download the translated file into pdf, word or text format. This app also has a donations page.
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Real-time Particle Simulation with CUDA
This project leverages CUDA to accelerate a particle system simulation, enabling real-time visualization of fluid dynamics or physics simulations. Particles are simulated with gravity, elasticity, and spread parameters, and visualized in 3D using Python and OpenGL. The system can handle a large number of particles and frames, and simulates complex behavior like waterfall effects, while providing real-time interaction and performance optimization.
Real-time Audio Feature Extraction with CUDA for Speech Recognition
This project uses CUDA to accelerate the extraction of MFCCs (Mel-frequency cepstral coefficients) from live audio streams for real-time speech recognition. By leveraging GPU parallelism, it speeds up the processing, reducing latency compared to CPU methods. The system extracts features from audio, which are then used by a speech-to-text model for real-time transcription, making it suitable for applications like virtual assistants and transcription services.
Real-time Traffic Analysis with CUDA Object Detection
This project uses CUDA for real-time traffic analysis, focusing on vehicle detection and speed estimation. The system processes a sample video input, detects vehicles, calculates speeds, and visualizes results in real time. It includes key visualizations to understand vehicle speed distributions, variations, and trends. By leveraging GPU acceleration, the system demonstrates an efficient and scalable approach to real-time traffic monitoring and analysis.
Chatbot Response Acceleration with CUDA LLM Inference
This project accelerates chatbot response times by using CUDA with ONNX Runtime to run language model inference locally. It enables faster, real-time interactions for customer service and other applications by converting models like GPT-2 into an optimized ONNX format for GPU execution.
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POC on Youtube Data(Big Data Analysis)
Developed proof-of-concept proposal for analyzing large YouTube’s datasets using Hadoop, HDFS, Map-Reduce, Hive, Pig, and other technologies to curate top 10 lists across various categories.
Advanced AI Transformers for Computer Vision
This project, part of the "Advanced AI: Transformers for Computer Vision" LinkedIn Learning certification, leverages advanced AI transformer models for computer vision tasks. It focuses on implementing transformer-based architectures for image classification and segmentation tasks, enhancing performance in visual recognition. The project involved hands-on practice with the latest AI transformers, improving efficiency and accuracy in computer vision applications.
ConvoSphere: 3D GPT Chat with Avatars
ConvoSphere is a Unity-based 3D multiplayer chat app where users interact using customizable avatars. Powered by GPT-3.5, avatars respond intelligently in real time with text-based replies and emotive animations. Built with Photon PUN, it offers immersive AI-driven social interactions in a virtual environment.
