TinyWL based Window Manager for Linux (December 2024 - Present)

Advisor: Prof. Balgopal Komarath

Description: Developing a custom window manager, TinyWL, in C, focused on adding advanced features such as dual-window display, stacking, tiling, and window merging (snapping). This project incorporates the implementation of efficient window management techniques, enhances user interaction, and contributes to the Linux Society.

Technologies Used: C, Linux

GitHub Repository: The repository will be private until the project completion

Automated Room Occupancy Detection and Lighting Control (Jan 2024 - April 2024)

Advisor: Prof. Arup Lal Chakraborty

Description: Designed and implemented an automated room occupancy detection system using laser modules and photodiodes to monitor entry and exit events. Integrated the system with an Arduino Uno microcontroller to control lighting based on real-time occupancy data. The system also logged energy consumption and achieved a 15 percent reduction in energy usage compared to manual control systems. Tested and refined the system under various scenarios, ensuring over 90 percent detection accuracy and near-instantaneous response times.

Technologies Used: Arduino IDE, C++ for Embedded systems , Tinkercad, Arduino Uno,Laser Technology, Signal Processing

Documentation:For detailed analysis and documentation, please refer to the linked paper: Automated Room Occupancy Detection and Lighting Control Paper

GitHub Repository: Link to repo

Otsu's Thresholding and Noise Analysis (Jan 2024 - April 2024)

Advisor: Prof. Shanmuganathan Raman

Description: Implemented Otsu's Thresholding method to binarize grayscale images, segmenting foreground and background based on optimal intensity thresholding. Added Gaussian noise with varying variances to assess the robustness of the method under different noise conditions. Visualized and analyzed thresholding results using Matplotlib, comparing the impact of noise on segmentation quality. Developed a fully customized implementation instead of relying on existing sample codes, ensuring a deep understanding of the algorithm.

Technologies Used: OpenCV, NumPy, Streamlit, Plotly, Matplotlib

Web App Link: Otsu's Thresholding Web App

Repo Link: Github Repository

Uniform Photo Styling via Histogram Matching (Jan 2024 - April 2024)

Advisor: Prof. Shanmuganathan Raman

Description: Developed a Photo Style Consistency App that uses histogram matching to transfer the tonal characteristics from a target style image to one or more source images. The app processes images in the YCrCb color space to maintain accurate color representation while adjusting brightness, contrast, and pixel intensity distribution. Interactive visualizations with Plotly and adjustable blending parameters provide real-time feedback on the transformation, ensuring a consistent look across image collections. A fully customized implementation was created to ensure a deep understanding of the histogram matching algorithm.

Technologies Used: OpenCV, NumPy, Streamlit, Plotly, Matplotlib

Web App Link: Photo Style App

Repo Link: Github Repository