YOLOv8 Real-Time Rat Detection and Tracking
This project uses a custom-trained YOLOv8 model to detect and track rats in video streams, compute the distance traveled, and generate visual analytics such as path plots and heatmaps.
Jul 15, 2025
STM32 Diversity Gain Engine Communication
This code implements a sophisticated signal processing system on an STM32 microcontroller, designed to optimize signal reception and communication through advanced combining techniques.
Jan 2, 2023
Explainable AI (XAI) Hybrid CNN-LSTM with Attention
A machine learning project utilizing a hybrid CNN-LSTM architecture with an attention mechanism for explainable AI.
Jan 1, 2023
Vision Transformer (ViT) Implementation for Spoofing Detection
This comprehensive code implements a deep learning solution for spoofing detection using a Vision Transformer (ViT) architecture. The system leverages transfer learning by fine-tuning a pre-trained ViT model for binary classification, distinguishing between genuine and spoofed images in a specialized security dataset.
Jan 2, 2022
Train-MobileNet for TinyML
This code is a script for training an image classification model MobileNet using PyTorch. The code is structured to facilitate easy training, evaluation, and monitoring of a deep learning model for image classification.
Jan 2, 2022
STM32-VLC: Visible Light Communication Module
Design and implementation of a VLC communication module to transmit data using the STM32 microcontroller platform.
Jan 2, 2022
School Management System
The School Management System is a Django-based web application designed to streamline day-to-day academic administration. The project includes a simple, responsive static front-end, built with plain HTML, CSS and JavaScript, served from Django’s static/ directory.
Jan 2, 2022
Training pipeline of LSTM-CNN-RNN-MLP-InceptionTime
Fundamental files to train and evaluate a simple LSTM, MLP, CNN, and RNN model which can be trained on a time-series dataset composed of n input features and m outputs classes.
Jan 1, 2022
Hybrid CNN-LSTM Model with Attention for Time Series Classification
This documents the training and evaluation of a Hybrid CNN-LSTM Attention model for time series classification in a dataset. The model combines convolutional neural networks (CNNs) for feature extraction, long short-term memory (LSTM) networks for sequential modeling, and attention mechanisms to focus on important parts of the sequence.
Jan 1, 2022