Developed a static website that tracks all the objects in the space as a part of NASA Space Apps Challenge 2021. The challenge is named Mapping Space Trash in Real Time.
Use a TLE API to track all the known satellites and space debris.
Autodirs is a short for “Automatic Directories”. It is a python package that is used to automatically create multiple directories automatically by the name of the list provided in a text file.
It also takes an python list and generate the directories present in that list.
The package is open source and available on PyPI.
Computer vision and image processing are playing a vital role in the design of mobile robots and autonomous driving vehicle. This project aims at using computer vision to detect the lanes on the road.
The project was designed in Python using the computer vision and image processing library OpenCV. The code was tested on different types of images and finally incorporated on the video. The key features of the project are the use of different image processing techniques and the use of ‘region of interest’ (ROI) to concentrate on the image of the road and to reduce the noise in the image.
The student marks system, a primary data management system, is used to manage the student data. It is used to store student information, unit information, and student marks record from different units. This project is the foundation of the data management system with several functions to perform various operations on the data. Moreover, this project aims at providing a user-friendly interface to enhance the user experience.
Conducted research on possible methods to analyse and create a predictive model on the available data.
Key Features of the project:
Predictive analysis of the blast-induced vibration on the data procured by YIN Zuoming et al. (2015).
Introduced Back Propagation algorithm to train the Neural Network in MATLAB Neural Network Toolbox to reduce the percentage of average error of prediction.
Designed a preliminary system to automate the process of inspection of a car entering a garage.
Key features of the project:
Designed a licence plate recognition system using computer vision and machine learning to identify the licence plate of the car. Also, tested the algorithm on the prototype of the system.
Designed a control system to automatically capture the image of the car with three camera modules triggered by the sensors integrated with the microcontroller.
Created an automatic storage system of the images of the car from different camera modules and store them in a directory referenced with the license plate number.