Name: Rajat Chhabra
Type: User
Company: Technical University, Munich
Bio: Master Student in Computer Science at TU Munich, who likes to cultivate coding habits. Maths Fan. Chess, sometimes. Open Source contributor!
Location: Munich, Germany
Rajat Chhabra's Projects
ESP8266 core for Arduino
This is a machine learning based project which trains a neural network using back-propagation algorithm. The UCI database for breast cancer classification is used and MATLAB nntool is used for constructing the neural network.
dummy repository to see the annotation collaboration feature
PID controller tuning in discrete and continuous domain in MATLAB.
Simple file handling functions
A fuzzy controller designed for a second order system and compared with a PID Controller tuned using Ziegler-Nichols (ZN) technique. See alongside attached report for more information.
The code for our work in localization with a generative model.
A collection of easy-to-understand guides to programming tools
This is a demo GitHub Pages and Jekyll site. See README for more info.
A simple program to be run on arduino IDE (version tested on : 1.6.6) and implemented using a hash table.
Simplex Lap winding design of a machine using InkScape vector graphics.
Neural network based controller for DC Motor Position control. Thereafter, comparison with The Ziegler–Nichols PID tuning method is done: to see the improvement.
Pole Placement technique implemented on DC Motor Position Transfer Function using MATLAB.
A project I (along with 3 other team mates) undertook in a typical "48 hours innovation challenge" at Innothon at India Smart Grid Week (ISGW) which took place in NSIT, 2017. The LED on a conventional electronic meter blinks at a specific rate. Arduino takes input, calculates frequency, and predicts energy consumption.
Takes in data from sensors using Serial Communication from other arduinos connected in series. P10 modules are used using DMD Library (opensource) from Freetronics. This master arduino would accept string in format "S1" followed by a number between 0 to 255.
TensorFlow Tutorials with YouTube Videos
Tracking and Detection in Computer Vision: Task 1 is to compute HOG, Task 2 is to train Decision Tree, Random Forest; Task 3 is object detection and classification, doing non maxmum suppression, followed by precision and recall value calculation.