This project leverages Random Fourier Features (RFF) to tackle complex image processing related tasks. This includes innovative techniques for image completion, reconstruction, and super-resolution. It also provides an in-depth analysis of the performance of RFF in comparison to traditional methods in this specfic task. This project is also source of learning for working on projects under limited computational resources (GPU).

This project discovers how different types of gradient descent work. Our project compares standard and momentum-based methods, showcasing how momentum accelerates convergence and enhances stability. Dive into our Jupyter notebooks and explore the behavior of Full Batch and Stochastic Gradient Descent—both with and without momentum.

This project implements decision trees for both classification and regression tasks! A decision tree algorithm is a supervised learning algorithm that uses a tree-like structure to classify data or predict outcomes. This versatile tool supports various input-output configurations, offering accurate predictions and insightful visualizations for data-driven decisions.

Dive into the world of algorithmic problem-solving with this collection of advanced puzzle solvers and game strategies. From the 8-Puzzle and the 2x2x2 Rubik’s Cube to graph-based games like Sim, this project showcases efficient search algorithms, graph traversal techniques, advanced applications of data structures and featured implementations in C++ and C.

The Smart Bicycle Safety Monitoring System uses the power of your smartphone’s sensors to detect falls, overspeeding, and boundary crossing. Parents receive instant notifications, real-time location tracking, and, in case of emergencies, audio and video recordings directly to their phone. Whether your child is speeding or crossing an unsafe zone, you’ll always stay connected. Powered by Simulink.

This project models traffic flow using car-following behavior. The simulation predicts how vehicles accelerate and adjust their speeds in response to the vehicle ahead, using advanced numerical methods like Runge-Kutta and Euler’s Method. This helps us develop a better understanding of how microscopic traffic models can revolutionize smart city planning by optimizing road safety and traffic efficiency!