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Organization: Amity Institute of Nuclear Science and Technology University: Amity University Department: ANIST – Amity Institute of Nuclear Science and Technology
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| Field | Details |
|---|---|
| Internship Title | Research & Development Internship |
| Project Name | RADMAP – RADiation MAPping System |
| Organization | Amity Institute of Nuclear Science and Technology |
| University | Amity University |
| Department | ANIST – Amity Institute of Nuclear Science and Technology |
| Internship Domain | AI/ML • IoT • Nuclear Technology • Embedded Systems |
| Role | Research and Development Intern |
| Internship Type | Summer Internship Program |
| Project Category | Research-Based Technical Prototype |
| Research Area | Radiation Monitoring & Environmental Safety |
| Technologies Used | Python, Raspberry Pi, GPS Module, Sensors, AI/ML |
| Development Type | Real-Time Monitoring System |
| System Type | GPS Enabled Radiation Mapping Prototype |
| Data Type | Real-Time Environmental Data |
| Working Environment | Research & Development Laboratory |
| Application Area | Nuclear Monitoring, Smart Surveillance, Environmental Mapping |
| Development Approach | AI-Assisted IoT Integrated System |
| Visualization Method | Heatmap & Geospatial Mapping |
| Project Nature | Prototype Development & Research Implementation |
| Core Concepts | Radiation Detection, GPS Mapping, Data Analytics, AI Integration |
| Future Expansion | Cloud Integration, Smart Analytics, Mobile Monitoring |
| Repository Type | Product-Based Research Repository |
| Documentation Style | Technical + Research Documentation |
| Collaboration Scope | Research, Development, Testing & Analysis |
| Expected Outcome | Intelligent Radiation Monitoring Prototype |
| Category | Information |
|---|---|
| Institution Type | Research & Higher Education Institution |
| Institute Focus | Nuclear Science & Technology Research |
| Academic Area | Scientific Research, Innovation & Technical Development |
| Research Orientation | AI Integration in Nuclear Applications |
| Technical Exposure | Research, IoT, AI/ML, Embedded Systems |
| Internship Objective | Practical Exposure to Research & Development |
| Innovation Focus | Smart Monitoring & Intelligent Systems |
RADMAP is an intelligent GPS-enabled radiation mapping system designed to monitor and visualize environmental radiation levels in real time using IoT devices, embedded systems, and AI-assisted analytics.
The system integrates radiation sensors with Raspberry Pi and GPS modules to collect location-aware radiation data. The collected information is processed and visualized using heatmaps and geospatial monitoring interfaces for intelligent environmental analysis and safety assessment.
The project aims to provide a scalable and portable solution for radiation monitoring, environmental safety, smart surveillance, and research-oriented applications.
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- Review of existing research in radiation monitoring systems and IoT-based environmental sensing.
- Study of radiation safety standards, guidelines, and regulatory frameworks.
- Evaluation of different sensor technologies and their accuracy in radiation detection.
- Methods for reliable data collection, preprocessing, and validation in real-time environments.
- Exploration of AI/ML techniques for intelligent radiation monitoring, prediction, and anomaly detection.
- Integration of embedded systems for real-time sensing and control operations.
- GPS synchronization to ensure accurate geospatial tagging of collected radiation data.
- Development of a real-time data pipeline for continuous transmission and processing of sensor outputs.
- Establishment of reliable sensor communication protocols for stable IoT connectivity.
- Hardware–software interfacing to enable seamless interaction between physical devices and processing systems.
- Classification of radiation data to identify safe, moderate, and hazardous levels.
- Pattern analysis to detect anomalies and recurring environmental behavior.
- Prediction of radiation trends using historical and real-time datasets.
- Development of intelligent monitoring systems for automated decision-making.
- Use of predictive analytics to estimate future radiation exposure risks and environmental impact.
- Preparation of structured technical reports covering system design and results.
- Maintenance of detailed research notes during development and experimentation phases.
- Recording of experimental observations for performance evaluation and validation.
- Logging of prototype testing results for debugging and iterative improvements.
- Inclusion of visualization outputs such as heatmaps, graphs, and dashboard screenshots for analysis.
| Field | Details |
|---|---|
| Internship Type | Research & Development Internship |
| Organization | Amity Institute of Nuclear Science and Technology (ANIST) |
| University | Amity University Noida |
| Project Name | RADMAP – Radiation Mapping System |
| Domain | AI/ML • IoT • Embedded Systems • Nuclear Technology |
| Technologies Used | Python, Raspberry Pi, GPS, Sensors, Data Visualization |
| Duration | Summer Internship 2026 |
| Role | Research and Development Intern |
| Project Category | Real-Time Monitoring & Mapping System |
| Application Area | Radiation Detection & Environmental Safety |
RADMAP (RADiation MAPping) is an AI-powered real-time radiation monitoring and geospatial mapping system designed to detect, analyze, and visualize environmental radiation levels using IoT-enabled devices and GPS technology.
The project integrates radiation sensors with Raspberry Pi and location-tracking modules to collect radiation measurements along with corresponding geographical coordinates. The acquired data is then processed and visualized through interactive heatmaps and intelligent monitoring dashboards.
This system aims to contribute towards environmental safety, nuclear monitoring, research applications, and smart radiation surveillance systems.
Environmental radiation monitoring systems often face significant limitations due to the absence of real-time tracking mechanisms, dependence on manual data acquisition processes, and restricted geographical visualization capabilities. Traditional monitoring approaches may struggle to instantly identify hazardous radiation zones, leading to delays in environmental assessment and safety analysis. In addition, many existing industrial-grade systems are expensive, less portable, and difficult to deploy for large-scale or field-based monitoring applications. Conventional systems also lack intelligent analytical capabilities such as automated radiation pattern recognition, AI-driven decision support, and dynamic heatmap generation for real-time visualization. These limitations highlight the need for a portable, scalable, and intelligent radiation monitoring framework capable of integrating real-time data acquisition, geospatial mapping, and advanced analytical technologies for efficient environmental surveillance and safety management.
RADMAP proposes an intelligent and portable solution that:
The proposed system is capable of collecting real-time radiation data along with live geographical coordinates for accurate environmental tracking and analysis. It generates interactive radiation heatmaps to visualize radiation intensity across different locations and supports intelligent environmental monitoring through continuous data acquisition and processing. The integration of AI-driven analytical techniques enhances data interpretation and monitoring efficiency, while the portable and scalable architecture improves the accessibility and practicality of radiation mapping systems.
The prototype combines:
The RADMAP system is developed using Raspberry Pi as the core processing unit integrated with radiation sensors for environmental data acquisition and GPS modules for real-time location tracking. Python-based analytical techniques are utilized for data processing, while mapping and visualization tools are employed to generate interactive heatmaps and geospatial radiation monitoring interfaces.
- Develop a real-time radiation monitoring system capable of collecting and processing environmental radiation data efficiently.
- Integrate GPS-enabled geospatial tracking to associate radiation measurements with precise geographical locations.
- Visualize radiation intensity and distribution using interactive heatmaps and mapping interfaces.
- Build an intelligent environmental monitoring prototype for smart radiation analysis and safety assessment.
- Enhance radiation awareness and environmental safety through intelligent monitoring and analysis systems.
- Improve portability and accessibility by developing a compact and user-friendly monitoring prototype.
- Explore the integration of AI technologies in nuclear science and radiation-based applications.
- Create a scalable system architecture capable of supporting future advancements and smart monitoring solutions.
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The radiation sensor continuously collects environmental radiation readings.
The GPS module records real-time geographical coordinates.
Raspberry Pi processes sensor readings and combines them with location data.
Python-based scripts analyze collected radiation measurements.
The processed data is visualized using heatmaps and geospatial mapping interfaces.
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| Category | Technologies |
|---|---|
| Programming Language | Python |
| Hardware | Raspberry Pi |
| Sensors | GM Tube / NaI Detector |
| GPS | GPS Module |
| Data Processing | Pandas, NumPy |
| Visualization | Matplotlib, Folium |
| AI/ML | Scikit-learn |
| Dashboard | Streamlit / Flask |
Continuous monitoring of environmental radiation levels.
Detection and mapping of hazardous zones near nuclear facilities.
Monitoring radioactive contamination during emergencies.
Integration into intelligent environmental safety systems.
Useful for nuclear science, IoT, and AI-based research applications.
Without intelligent radiation mapping systems:
- Hazardous zones may remain undetected
- Environmental risks can increase
- Radiation monitoring becomes slow and inefficient
- Manual analysis increases human error
- Real-time surveillance becomes difficult
Integration of machine learning algorithms for radiation forecasting.
Cloud-based storage and remote monitoring systems.
Real-time mobile tracking and alerts.
| Future Enhancement | Description |
|---|---|
| AI Prediction Models | Predict hazardous radiation zones using ML |
| Cloud Monitoring | Real-time cloud synchronization |
| Mobile App Integration | Portable monitoring system |
| Smart Alerts | Automatic danger notifications |
| GIS Integration | Large-scale geospatial mapping |
| Multi-Sensor Network | Environmental monitoring ecosystem |
| Live Dashboard Analytics | Real-time intelligent visualization |
| Edge AI Processing | Faster local data analysis |
| Drone Integration | Remote radiation mapping |
| Satellite Data Support | Wide-area monitoring capabilities |
Advanced data analytics and anomaly detection.
Support for additional environmental sensors:
- Temperature
- Humidity
- Air Quality
- Toxic Gas Detection
Large-scale geospatial mapping and intelligent surveillance.
- Conducted research and literature review to understand radiation monitoring systems and related technologies.
- Planned and designed the overall architecture of the RADMAP prototype system.
- Worked on sensor integration and hardware-software interfacing using Raspberry Pi and IoT modules.
- Performed data collection, processing, and analytical evaluation of environmental readings.
- Explored AI/ML techniques for intelligent monitoring and future predictive analysis.
- Maintained technical documentation, reports, and project implementation records.
- Contributed to the development of the real-time radiation mapping prototype.
- Performed system testing and validation to evaluate performance, reliability, and accuracy.
The internship provided practical exposure to Raspberry Pi integration, IoT-based system design, radiation monitoring concepts, GPS data processing, and real-time heatmap visualization. It also enhanced understanding of AI/ML applications in nuclear science and intelligent environmental monitoring systems.
During the course of the internship, significant exposure was gained in the areas of research methodology, technical problem-solving, documentation practices, collaborative development, and real-time system implementation. The project involved understanding research-oriented workflows including literature review, data collection, experimental analysis, and prototype development. Various technical challenges related to sensor integration, GPS synchronization, and real-time data processing were addressed through analytical and problem-solving approaches. The internship also emphasized the importance of maintaining proper technical documentation, including reports, system architecture, implementation details, and research observations. In addition, collaborative work environments enhanced communication, coordination, and teamwork skills while contributing towards shared research objectives. The development of a real-time radiation monitoring and mapping system further provided practical exposure to IoT-based system design, embedded technologies, and intelligent environmental monitoring applications.
The proposed RADMAP system is expected to deliver a fully functional radiation mapping prototype capable of collecting and processing real-time environmental radiation data with geographical coordinates. The system will provide interactive heatmap visualization for intelligent monitoring and spatial analysis of radiation levels across different locations. Through continuous environmental sensing and data acquisition, the project aims to establish a reliable real-time monitoring framework for radiation safety and environmental assessment. In addition, the integration of intelligent data analysis techniques will support efficient radiation pattern interpretation and future AI-based predictive analytics. The overall architecture of the system is designed to be research-oriented, scalable, and adaptable for future advancements such as cloud integration, smart surveillance, mobile monitoring applications, and advanced geospatial analytics.
RADMAP represents an innovative combination of Artificial Intelligence, IoT, Embedded Systems, and Nuclear Technology to create an intelligent radiation monitoring and mapping solution.
The project focuses on improving environmental safety, real-time monitoring, and geospatial radiation analysis through a portable and scalable system architecture.
This research-oriented prototype demonstrates the practical integration of modern technologies into scientific and environmental applications, while also providing future scalability for advanced smart monitoring systems.
Developed as part of the Research & Development Internship at ANIST – Amity Institute of Nuclear Science and Technology, Amity University Noida.
AI/ML • IoT • Embedded Systems • Nuclear Technology • Environmental Monitoring • Geospatial Analytics
Dhruv Dhayal and My Team [ WHITE ELEPHANT ]@Ankit Singh, @Mansimran Kaur, Our Mentor @Dr.Abhishek Yadav #AINST #AIIT #AMITY #AMITYNOIDA
Updated for GitHub YOLO achievement.
Update README for Pair Extraordinaire Co-authored-by: BlockNotes-4515 <137479629+BlockNotes-4515@users.noreply.github.com>