Key research themes
1. How do electromagnetic radiofrequency waves interact with biological tissues and influence cellular and molecular processes?
This research area investigates the biological effects of radiofrequency electromagnetic waves (RF-EMW) on cells and living organisms at molecular, cellular, and systemic levels. It explores mechanisms of interaction, potential health hazards, and therapeutic opportunities especially focusing on DNA fragmentation, gene expression, cellular signaling pathways, and immune responses under RF exposure. Understanding these interactions is key for assessing public health risks of radio wave exposure from common devices and for developing RF-based biomedical applications.
2. How does the atmospheric environment, including refractivity and fire plumes, affect the propagation and signal strength of radio waves in terrestrial and emergency communication contexts?
This theme examines the physical and environmental factors influencing radio wave propagation in the atmosphere, focusing on the effects of atmospheric refractivity variations and transient events such as fire plumes. Understanding these influences is crucial to improving reliability and design of terrestrial mobile communications, mission-critical networks in fire-affected rural areas, and other applications that rely on radio wave propagation under variable atmospheric conditions.
3. What advances and applications exist in radio wave technology for communication and imaging in challenging environments such as underwater and wireless body networks?
This research stream addresses methodologies and technological innovations that enable effective radio wave propagation, communication, and imaging in environments typically hostile to electromagnetic waves. It covers experimental and theoretical work on underwater radio communication facilitated by surface electromagnetic waves, mathematical modeling of wireless body area networks (WBAN) accounting for complex biological tissues, and antenna design optimization using machine learning for improved performance in various applications.