A split-ring resonator (SRR) array is experimentally demonstrated for a biosensing device at microwave frequencies. Each SRR in the array is excited by a time-varying H-field component through a microstrip transmission line in which microwaves propagate in the quasitransverse electromagnetic mode. It is found that the resonant frequency changes due to the binding of biotin and streptavidin onto the surface of the SRRs. The observed change values represent around ΔfB=120MHz and ΔfB-S=40MHz, respectively. Finally, the SRR-based biosensing device suggests a few improvements for increasing sensitivity and describes its possible application.
In this paper, the feasibility of utilization of a single element double split-ring resonator as a biosensing device has been demonstrated. The compact resonator has been excited by time-varying magnetic fields generated from the 50 Ω microstrip transmission line. In this work, DNA hybridization is recognized with shift in S21 resonant frequency. When thiol-linked single stranded-DNA is immobilized onto a gold (Au) surface and is then coupled with complementary-DNA, the frequency changes by Δfss-DNA=20 MHz and Δfhybridization=60 MHz, respectively. Thus, it is clear that the resonator can be utilized as a DNA sensing element in the microwave regime.
A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed. The approach uses a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method. Doppler processing and spectral estimation are concurrently implemented to detect vital signs from more than one subject, revealing excellent results. The parametric spectral estimation method is utilized to clearly identify multiple targets, making it possible to distinguish multiple targets located less than 40 cm apart, which is beyond the limit of the theoretical range resolution. Fourier transformation is used to extract phase information, and the result is combined with the spectral estimation result. To eliminate mutual interference, the range integration is performed when combining the range and phase information. By considering breathing and heartbeat periodicity, the proposed algorithm can accurately extract vital signs in real time by applying an auto-regressive algorithm. The capability of a contactless and unobtrusive vital sign measurement with a millimeter wave radar system has innumerable applications, such as remote patient monitoring, emergency surveillance, and personal health care.
Inspired by the human somatosensory system, pressure applied to multiple pressure sensors is received in parallel and combined into a representative signal pattern, which is subsequently processed using machine learning. The pressure signals are combined using a wireless system, where each sensor is assigned a specific resonant frequency on the reflection coefficient (S11) spectrum, and the applied pressure changes the magnitude of the S11 pole with minimal frequency shift. This allows the differentiation and identification of the pressure applied to each sensor. The pressure sensor consists of polypyrrole‐coated microstructured poly(dimethylsiloxane) placed on top of electrodes, operating as a capacitive sensor. The high dielectric constant of polypyrrole enables relatively high pressure‐sensing performance. The coils are vertically stacked to enable the reader to receive the signals from all of the sensors simultaneously at a single location, analogous to the junction between neighboring primary neurons to a secondary neuron. Here, the stacking order is important to minimize the interference between the coils. Furthermore, convolutional neural network (CNN)‐based machine learning is utilized to predict the applied pressure of each sensor from unforeseen S11 spectra. With increasing training, the prediction accuracy improves (with mean squared error of 0.12), analogous to humans' cognitive learning ability.
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