ABSTRACT
An X-band, free-space microwave sensor consisting of 30 radial spokes connected in a central hub with a gap region was designed, fabricated and tested. The sensor structure results in an electric dipole at 10 GHz with a split circular disc capacitor at the center. Viruses, dust, and soot particles in the gap region change the sensor’s impedance and its reflection coefficient monitored by a horn antenna and a network analyzer. The sensor sensitivity was 85.02 MHz/microliter for deionized water, 89.5 MHz/microliter for uninfected saliva, and 94.6 MHz/microliter for SARS-COV-2 infected saliva with 103 viruses/μL. Its sensitivity to a dielectric sample (ερ~5.84) was 3.23 MHz/mm3, and for iron particles was 16.25 MHz/mm3. All these samples were smaller than λ/30 at 10 GHz and could not be detected on uniform dielectric or metallic substrates without the spoke structure. A 2x2 array of spoke sensors was also constructed and tested as a feasibility study for designing larger metamaterial (MTM) periodic arrays. IEEE
ABSTRACT
Estimating the capacity of a room or venue is essential to avoid overcrowding that could compromise people’s safety. Having enough free space to guarantee a minimal safety distance between people is also essential for health reasons, as in the current COVID-19 pandemic. Already existing systems for automatic crowd counting are mostly based on image or video data, and some of them, using deep learning architectures. In this paper, we study the viability of already existing Deep Learning Crowd Counting systems and propose new alternatives based on new network architectures containing convolutional layers, exclusively based on the use of environmental audio signals. The proposed architecture is able to infer the actual capacity with a higher accuracy in comparison to previous proposals. Consequently, conclusions from the accuracy obtained with out approach are drawn and the possible scope of deep learning based crowd counting systems is discussed. © 2022, Springer Nature Switzerland AG.