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

Amjaad T. Altakhaineh, Rula Alrawashdeh () and Jiafeng Zhou ()
Additional contact information
Amjaad T. Altakhaineh: Electrical Engineering Department, Mutah University, Al-Karak 61710, Jordan
Rula Alrawashdeh: Electrical Engineering Department, Mutah University, Al-Karak 61710, Jordan
Jiafeng Zhou: Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3GJ, UK

Energies, 2024, vol. 17, issue 20, 1-27

Abstract: 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 (<0 °C, including ice accumulation). To address the challenges posed by the non-linear, unpredictable behavior of resonance frequency results, even when dealing with weak sensor antenna responses, an innovative sensory intelligent system was proposed. This system utilizes resonance frequency results as features to classify and predict weather temperature ranges into three environmental states: Early Frost, Normal, and Early Wildfire, achieving an accuracy of 96.4%. Several machine learning techniques are employed, including artificial neural networks (ANNs), random forests (RF), decision trees (DT), support vector machines (SVMs), and Gaussian processes (GPs). This sensor serves as an ideal solution for energy management through its utilization in RF-based weather temperature sensing applications. It boasts stable performance, minimal energy consumption, and real-time sensitivity, eliminating the necessity for manual data recording.

Keywords: frost and wildfire applications; half-mode substrate integrated waveguide (HMSIW); microfluidic channel; machine learning; deep learning; resonance frequency; low power consumption; saving mode (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/20/5208/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/20/5208/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:20:p:5208-:d:1502275

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().