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Name: SURANJIT KOSTA

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SURANJIT KOSTA's Projects

lumazoid

Firmware for the Lumazoid realtime music visualizer board

machine-learning-books

this is a fork of collection of books for machine learning.

machine-learning-tutorials

machine learning and deep learning tutorials, articles and other resources

machine_learning_examples

A collection of machine learning examples and tutorials.

non-intrusive-attendance-marking-system-using-ai

The project that we worked on this summer internship falls in the domain of research in IoT (Internet of Things). Initially, the mentor asked us to find real-life problems, which we would attempt to solve by using the tools of Information Technology. We were allowed to discuss and work in a group of three. We picked the problem of devising an attendance monitoring system, which would mark the presence of the students in a big room, in a non-intrusive manner using image recognition, for e.g. an auditorium or our college’s lecture theatre. Our project was divided into two phases, which would be illustrated in the subsequent passages. The first phase involved doing a literature survey on the tools and technologies through various authentic research papers and the existing libraries, which would enable us to devise a backend structure for our project. We, then developed a flowchart, which comprised of two modules of processes, through which the procedure would pass through. The first module involves the initial training of a machine learning based classifier by training it with the various images of a specific person. The second module involves the testing part in the real environment, which involves face detection and face recognition. A camera would take the frames/image of a live audience. Then, these frames would be pre-processed (involves grey-scaling and image resizing) for achieving better performance in the subsequent face detection module. The face-detection algorithm would detect all the faces present in the frame, and would crop the detected faces, and would pass them to the face recognition classifier for testing. The classifier would classify the cropped images and would mark the attendance accordingly. The libraries used for face-detection were that of OpenCV, and a convolutional neural network was trained for the image recognition part. The libraries which were used for training the convolutional neural network was Keras. The second phase involved the implementation part, where we had to gather the data for training the neural network, and find out the parameters of the image, for which we are getting better accuracy performance. We trained the neural network with the images of about 64 students, with about 20 images per student, covering different angles and brightness levels. We trained the network with 70 percent of the image corpus, and used the remaining 30 percent for testing. We got an accuracy of 93 percent. For testing the face detection part, we took a video of a classroom of about 40 students. Then, we generated frames from the video and passed it to the face detection algorithm. We extrapolated that the accuracy of an individual frame was not that high, but if we consider all the detected members in all the frames, we are covering almost every student. Hence, considering multiple frames for testing is crucial to get a high detection accuracy. We are currently trying to figure out the camera and its mounting position, which would be conducive for the algorithm, to give us accurate results.

part-ia-examplespapers-python

Python exercises from examples papers in Part IA of the Engineering Tripos

partia-computing-michaelmas

Activities and exercises for the Part IA computing course in Michaelmas Term

python

All Algorithms implemented in Python

python-machine-learning-book-2nd-edition

The "Python Machine Learning (2nd edition)" book code repository and info resource

python-programming-exercises

100+ Python challenging programming exercises

python-random-quote

A file-based quote bot written in Python

pythondatasciencecourse

Exercises of Udemy Jose Portillo Python for Data Science Course

raspberry-pi-tutorials

面向软件工程师的树莓派入门教程

raspberryturk

The Raspberry Turk is a robot that can play chess—it's entirely open source, based on Raspberry Pi, and inspired by the 18th century chess playing machine, the Mechanical Turk.

rfid-based-attendance

IOT Based attendance system with the help of RFID

robot-blueberry

Raspberry pi based humanoid robot.

scikit-learn-videos

Jupyter notebooks from the scikit-learn video series

se2017

Smart Attendance Management System - SE2017

selfdriving-robot-car

Raspberry Pi based robot car which is capable of autonomous driving using Deep Neural network.

smart-attendance-management

Intelligent System Designed For Monitoring And Marking Presence Of People.

smart-attendance-system

Smart Attendance System Using Face Recognition

smart-attendance-system-1

This is an end to end project using face recognition, open CV and python. It's an entire mechanism, a raspberrypi system identifies the person and the data would be updated in a database which can be viewed on a website.

smart-attendance-system-using-tensorflow

Smart Attendance System using Tensorflow

smart-meter-using-esp32-lora-heltec

This paper presents the design and development of a prototype for measuring electrical quantities in which assists the customer in managing the electricity consumption of their home. The system developed is a voltage and alternating current meter, whose core is an ESP32 module, which uses advantage of features native to the Arduino IDE platform. The functionalities of the prototype are the measurements of the fundamental electrical quantities, the calculation of the active power in real time, the calculation of the monthly energy consumption and the monthly cost of the residence, in order to present reliable values, the results are determined through data redundancy systems. Furthermore, another analysis is the transmission of data over long distances using a communication protocol that allows files transfer, such as LoraWan, a technology which uses radio frequency and allows long distance communication with minimal power consumption. In addition, the goal is to develop an application, on iOS and Android systems, with communication to the power meter, where the company responsible for the power supply has the access to perform the appropriate technical analysis of energy supply and for the costumer evaluate energy consumption expenses at different periods. This entire system is interactive as it allows the consumer to set expense goals and receive messages about missed goals or if power failure detection in the grid.

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