Machine Learning-Aided Dual-Function Microfluidic SIW Sensor Antenna for Frost and Wildfire Detection Applications

• Published in: Energies (Basel) 2024-10, Vol.17 (20), p.5208
• Main Authors: Altakhaineh, Amjaad T., Alrawashdeh, Rula, Zhou, Jiafeng
• Format: Article
• Language: English
• Subjects: Acoustics; Agriculture; Alternative energy sources; Antennas; Antennas (Electronics); Crack propagation; deep learning; Design; Design and construction; Emergency communications systems; Energy consumption; Energy efficiency; frost and wildfire applications; half-mode substrate integrated waveguide (HMSIW); Machine learning; microfluidic channel; Neural networks; Propagation; resonance frequency; Sensors; Signal processing; Temperature; Waveguides
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17205208

In this paper, which represents a fundamental step in ongoing research, a new smart low-energy dual-function half-mode substrate integrated waveguide cavity-interdigital capacitor (HMSIWC-DIC) antenna-based sensor is developed and investigated for remote frost and wildfire detection applications at 5.7 GHz. The proposed methodology exploits the HMSIW antenna-based sensor, a microfluidic channel (microliter water channel (50 μL)), interdigital capacitor technologies, and the resonance frequency parameters combined with machine learning algorithms. This allows for superior interaction between the water channel and the TE101 mode, resulting in high sensitivity (∆f/∆ε = 5.5 MHz/ε (F/m) and ∆f/∆°C = 1.83 MHz/°C) within the sensing range. Additionally, it exhibits high decision-making ability and immunity to interference, demonstrating a best-in-class sensory response to weather temperature across two ranges: positive (≥0 °C, including frost and wildfire) and negative (