Research

The collaboration is aimed at engaging with designated students for implementation of targeted solutions for addressing specific practical problems, using Embedded and Cloud technologies that can be mutually beneficial. The project phases involve: Phase1: Conceptualization and Implementation of a basic solution for use in Agricultural domain Phase2: Expansion of the functionality of the solution in Phase 1 to have comprehensive features Phase3: Development of an Edge device for Integrated Soil Health Monitoring, Integration and Validation of solution developed Phase1 project is completed and Phase 2 and 3 is in progress.

The LoRaWAN (Long Range Wide Area Network)-based Weather Station project is a smart and efficient solution for collecting and transmitting environmental and meteorological data over long distances. It leverages the low-power, long-range communication capabilities of LoRaWAN technology, making it ideal for remote or hard-to-reach locations with minimal power and infrastructure requirements. The objective of the project is to: Monitor real-time weather parameters such as temperature, humidity, atmospheric pressure, rainfall, etc. Ensure energy-efficient and reliable data transmission over long distances. Deploy a scalable, cost-effective network for weather monitoring in rural, agricultural, and disaster-prone areas. Provide a centralized platform for data storage, visualization, and analysis. The feature includes data collection, LoRaWan Connectivity, Gateway Integration, Centralised Dashboard and can have applications in agriculture, disaster management, climate research, smart cities etc.

One of the common use cases in many larger facilities like factories, university campuses, hospitals etc. is that loads of materials are to be moved from one place to another. It is desirable to have payload carriers for automating the tasks to improve productivity and reduce the burden on humans. The automation becomes even more important for hazardous environments. The problem statement involves the development of a deployable mobile robot used as a payload carrier that can carry a load of like 25kg and a desired volume to be operated in 3 modes namely: Internet controlled mode: The robot can be remotely controlled via web application to go to different destinations. Learn and repeat mode: Repeated tasks can be made to be learnt by the robot which it can repeat by selection of a specific task. Follow me mode: The robot can be made to know whom it must follow, and it simply follows the person to go different places. This project aims to provide a reliable transportation of goods with development of enhanced methods making best use of technologies. It makes it seamless with less human intervention.

Project is funded by NAALM (Neuberg Anand Academy of Laboratory Medicine Private Ltd After a visit to Diagnostics Lab and discussions with doctors, there has been an ongoing automation for certain specific processes in lab’s testing line starting from Scan in process till archiving of samples. The entire process is divided into multiple steps but the project focusses on automation of two steps: Segregation: Segregation is the process in which the test samples (test tubes) that has come into the Diagnostic lab from different centres has been segregated to checked into different labs/departments for specific tests. Each test tube has a bar code (Code128) which indicates the automation system, the type of tests needed and assignment of the labs. Archiving: After the tests, the test samples need to be archived. The Robot system will pick up the samples from source racks and place in different destination racks based on the bar code for archival purposes.

The motivation of the project was to build a system that can monitor air quality and get the data to cloud. Such systems if installed across cities and in a country can lead to great deal applications that will include big data analysis and forecasting over global regions. Thus the trends in air pollution exposure can be projected using the measurements and model simulations so that we can take necessary actions for health safety of mankind. The first prototype hardware and software is been built between Feb to Aug 2019 to get Air quality data and upload it via free Cloud Server – Thingspeak. The initial proof of concept is prepared as part of phase 1 work. This proposal document is to build a system closer to a commercial level that can measure the air quality, store the same on SD card with the capability to upload the data to cloud via WiFi and GSM. The system will have the capability to work on power source, solar source or a battery backup that gets charged with one of power sources.

In modern healthcare, Intravenous (I-V) therapy is indispensable for delivering medications and fluids to patients. However, challenges such as the need for constant I-V level monitoring, potential oversight of bottle replacement, insufficient equipment, and risks from reverse flow compel a comprehensive strategy. To address these challenges, our project focuses on optimizing I-V therapy administration through advanced monitoring systems with dash board user interface that includes visualization of live status, automated reminders for bottle replacement, and adjustments to existing devices. The integration of modern machinery not only increases the safety of I-V treatment but also aids to practicality by minimizing the possibility of issues associated to equipment. It also enables an improved productivity for nursing fraternity. Components include Arduino Mega, LED-LDR, GSM module, OLED display, servo motor controlled pinch valve, and the Blynk IoT platform. This collaborative effort aims to establish a robust framework for safe and efficient I-V therapy, enhancing patient outcomes. The objective was to design and develop to provides real-time I-V status visualization through a dashboard interface, automated reminders for timely bottle replacement, includes mechanisms to prevent reverse flow and streamline I-V administration and enhance nursing productivity by minimizing manual monitoring.