Noninvasive Suspicious Liquid Detection Using Wireless Signals.

Area of Science:

• Electrical Engineering
• Materials Science
• Sensor Technology

Background:

• Conventional liquid detection methods are often expensive and impractical for widespread use.
• There is a need for accessible and scalable solutions for liquid analysis.
• Radio frequency (RF) sensing offers a potential alternative for non-invasive liquid characterization.

Purpose of the Study:

• To develop and validate a novel method for detecting and identifying suspicious liquids using 5G radio signals.
• To leverage wireless channel information (WCI) for liquid analysis based on dielectric properties.
• To assess the accuracy of the proposed method in distinguishing liquids, identifying types, and determining alcohol concentrations.

Main Methods:

• Utilizing radio signals within the 5G frequency band to probe liquids.
• Extracting amplitude information from the wireless channel information (WCI) matrix.
• Employing the K-Nearest Neighbor (KNN) algorithm for classification without prior training.
• Conducting experiments to differentiate suspicious from non-suspicious liquids, identify liquid types, and measure alcohol concentrations.

Main Results:

• The method successfully detected over 98% of suspicious liquids.
• Accurate identification of suspicious liquid types exceeded 97%.
• Distinguishing different alcohol concentrations achieved an accuracy of up to 94%.

Conclusions:

• The proposed 5G-based WCI method provides a highly accurate and potentially cost-effective solution for liquid detection and identification.
• The KNN algorithm's suitability for multimodal problems and ease of implementation without training are key advantages.
• This technology holds promise for large-scale deployment in various security and industrial applications.