Artificial Intelligence for Detection, Identification and Localization
Advancing Chipless RFID Systems with Deep Learning IoT Integration and LoRa Connectivity
In my doctoral research, I focus on innovative technologies in the field of communication technology and localization-based systems for the Internet of Things. My work particularly emphasizes chipless RFID systems and the application of artificial intelligence and machine learning to optimize tag detection and localization. Another central component is the integration of LoRa technologies and their connection to cloud platforms for IoT applications.
Recent Research Projects
- Chipless RFID Detection using Deep Learning Models: 1 Dimensional Neural Networks in Python for robust chipless RFID tag detection in realistic environments
- IoT Integration of Chipless RFID Tags: Integration of Chipless RFID Tags in IoT-cloud Systems with real-time data analytics
- Design of Chipless RFID Tags: Optimized Chipless RFID Tag Design using CST Microwave Studio and Wireless InSite
- Integration of LoRa with Chipless RFID Systems: LoRa Connectivity for integrating Chipless RFID Tags into IoT-environments
My research is dedicated to advancing chipless RFID technology by leveraging deep learning, IoT integration and optimized tag design to enable robust, scalable, and intelligent identification systems. A central focus lies on the development of 1D convolutional neural networks in Python, which significantly enhance the detection and classification of chipless RFID tags even in challenging, real-world environments. To ensure optimal performance, I employ electromagnetic simulation and optimization tools like CST Microwave Studio and Wireless InSite, allowing for the design of chipless RFID tags.
Also, I address the critical challenge of long-range transmitting Tag IDs by integrating LoRa technology with chipless RFID systems, enabling long-range, low-power communication that is essential for large-scale IoT deployments.
This work has resulted in three peer-reviewed publications at international IEEE conferences, including studies on adaptive deep learning models for chipless RFID detection, cloud-based IoT integration and LoRa-enabled RFID networks.
