top of page
IMG_20191130_150008_edited.jpg

Intro

During my Foreign winter internship at SUSTech, China, I had the opportunity to work in a Chinese government project for river water pollution early warning and traceability information monitoring system using Machine learning.

I worked on this project for six weeks at the lab.

T--SUSTech_Shenzhen--SUSTech_logo_tall_e

Early warning and traceability information monitoring system

November - December 2019

About My Project

How I did It

This is my first experience in foreign research. With this project, I not only applied my knowledge in the field of Machine learning but also learnt the field of IoT very well. I worked in this project closely for about 45 days. I loved this project so much that many times I found myself working on Sundays. 

The project can be broken down into three major parts: 

  •  Arduino and Sensors configuration

  • IoT and Backend Web development

  • Pattern recognition with OpenCV (ML)

This research was conducted by the school of environment, Southern university of science and technology, Shenzhen, China, which later got accepted as a research paper in the conference of 'AOGS'

IMG_20191224_212215.jpg
Foreign Research Internship: About My Project

Skills and Tools

  • MATLAB

  • Arduino

  • Sensors (MQ7, DHT22, BH1750 etc.)

  • Thingspeak (IoT based server)

  • PHP, Javascript, HTML, CSS

  • OpenCV

  • Web hosting

c2019__edited.jpg
Arduino and Sensor configuration
  • For phase-I, I worked on the testing of different sensors like:

MQ7, MQ135, DHT22, DHT11, ORP meter, BH1750, MS1100

  • After calibrating them with the help of Arduino Uno we used NodeMCU (ESP8266) to send the readings to the cloud. 

Given below are some of the images of the sensors I used for recording the data. 

Thingspeak.png
IoT and Backend web development
  • For phase-II, I worked on storing the data to the clouds and accessing it. For reading data from the sensors I used Arduino Uno and from there I used ESP8266 for transmission of data to the Thingspeak servers using I2C protocol. I achieved it by using the circuit shown below. 

  • Next for running a python script online, I used PHP and Javascript to run python script remotely from the system.

2.jpg
Pattern recognition with OpenCV (ML)
  • For phase-III, I worked on integrating pattern recognition algorithm from OpenCV for matching the water sample pattern with the ground image.

Result

I presented the whole module to the Government officials with the help of my Professor Jiping Jiang. Further, this research has been accepted as a paper in the AOGS conference. Here is the copy of the acceptance letter. 

bottom of page