rishikumarjp2k3's starred repositories
xerox-automation
A solution on automating VIT Xerox shop queues.
Xerox-Shop-Automation
A helpful web application for Xerox shop owners to automate payments and printing
Android-App-HawkEye
Developed an Android Application that notified the users regarding the entry and exit of their vehicles. Using Video and Image Processing(Tesseract for number plate recognition and YOLO for number plate detection) libraries the car is identified and it’s registration plate would be verified with the data in Firebase.
Mini-Project
PRABOT is a number plate detection system that tracks the entry and exit of vehicles of an institution. The system is to detect the number plate or license plate of vehicles using or passing through a gate. Verification of the validity of the vehicle is carried out and then access to the gate is either granted or denied
College_Gate
Entry & Exit Automation System
A-smartwatch-based-on-esp8266-with-MicroPython
It's a project combined with hardware and software, the goal is to make a smart watch based on esp8266 chip. The smart watch has so many features such as time display, alarm, brightness adjustment, text scrolling, weather display, voice control and gesture recognition:. The first five features time display, alarm alert, brightness adjustment and text scrolling are based on the local programming. The basic idea for these is to check the document for esp8266, getting to know how to set up the circuit, how to get the real time, how to control the OLED screen, bus, light sensors etc. The last three features mainly depends on the cloud coding, and are all based on the Client/server architecture. For Weather Display, esp8266 severed as a client. A connection was built by Google Geolocation API between the smart watch(client) and Google Weather Service Server(server). Send the JSON string including location information to the server and get the weather information back. For Voice Control, esp8266 was configured as a server by ngrok and an Android application was used as the client. This app aims to transfer voice signal into text commands by Google Speech API. Connected the app(client) to the smart watch(server) by HTTP request, and used translated commands to control the smart watch. For gesture recognition, esp8266 was client and server was the EC2 Linux instance set up in AWS. In order to teach the smart watch to identify gestures, both the training and predicting process needs to be done. Mongodb is used to keep the large amount of data from accelerometer training. SVM model is selected to do the training, the data for the same gesture was labeled. After training, if someone is holding the smart watch and moving it in some preset gestures, the smart watch could map these data from the accelerometer into a specific gesture. The final recognition accuracy is 98%.
akashmanigandan
Config files for my GitHub profile.