Research and Projects

Project 1

AI based Position Estimation device for linear displacement SMA Actuator for bed leveling in 3d printer

The Embedded system for the position estimation of a linear displacement SMA actuator is developed in the form of a microcontroller circuit, which is being used in the automatic bed leveling control system algorithm based microcontroller device for 3d printer. This whole setup of system are in 2 Parts :

1st The control Circuit of 3D printer (Arduino Atmega with RAMPS Board)
2nd The Data Acquisition and NN model deployed ESP32 circuit for commands and controlling the whole setup for bed leveling and position estimating

Developed in - (CSIR-CEERI)

Project 2

Data Acquisition Embedded system for E-trike

Real time data Acquisition Embedded system is developed for capturing continuous E-tricycle running data which is developed in CSIR - CEERI.The Circuit contains an RTC module,GPS module,SD card module and a Temperature sensor module.The data like current load of cycle,speed,RPM, Voltage, controller Temperature is captured and Stored in the sd card module day and date wise format for the analysis of cycle testing.

Developed in - (CSIR-CEERI)

Project 3

Digital Health Kiosk

A full fledged IoT based Health monitoring set-up for the testing of vital parameters (all non invasive) like Heart rate ,Blood pressure,Blood glucose,Blood Oxygen with prescribed generated report of collected data and the data also get saved with person details in the home database for the analysis of person's health and past history

Developed in - (IITI Drishti TIH)

Project 4

IOT weighing Machine

Portable and Robust IOT weighing scale for digital scaling of goods and other items with functionality of storing the data and sending over the IOT cloud to save it with gps coordinates and time stamp.Usefull for daily wage and goods weighing companies and startup's.

Developed in - (IITI Drishti TIH)

Project 5

IOT Sleep Monitoring System

Portable and Robust IOT FMCW radar-based sleep monitoring system which addresses several limitations of traditional sleep tracking methods, such as wearables, cameras, and pressure mats, by providing a non-invasive, contactless solution. Using a 24GHz FMCW radar, the system achieved an impressive 79% accuracy for posture recognition and 0.76 F1-score with machine learning models like KNN, demonstrating its effectiveness in recognizing eight sleep postures, including transitional movements in a LLM generated detailed summary.

Developed in - (IISER Bhopal)

Note: I did not intend to claim any ownership of the prototypes and devices that I mentioned in this section, as they are solely under the ownership of their respective company or the institute, I just mentioned them as a part of my working experience here because I developed them while working there as a part of their research and development unit.

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