As the increasing number of diabetic around the world and the deficiencies of invasive blood glucose detection, such as prickly pain, risk of infection, and no continuous monitoring, a proper noninvasive blood glucose monitoring method is highly desired to deal better with it. In this article, an ultrawide band microwave based technique combined with a cascaded general regression neural network (C-GRNN) is proposed for blood glucose level estimation to achieve noninvasive and intelligent monitoring. Different glucose solutions are tested by using the reported monitoring system in which C-GRNN is used for intelligent detection and frequency choice. The proposed C-GRNN can reduce the prediction interval from 12.3858 to 2.1375 mg/dL compared with the conventional GRNN, and the root-mean-square error (RMSE) reaches 2.20 mg/dL. These results show good estimation performance. We also conduct a continuous-time blood glucose level monitoring on volunteer using the proposed noninvasive method. The final results show well accuracy in which the RMSE and mean absolute percentage error are 13.3208 mg/dL and 9.74%, respectively.

Finger-prick blood collection process has become unrealistic for a long-term and frequent blood glucose detection. Hence, an appropriate non-invasive detection system is highly desirable to effectively address this concern. A non-invasive and intelligent dual-sensing system is forwarded in this paper. The feasibility of the proposed system has been verified using glucose solution, animal serum, and human trials. In the in vivo experiments, the detection signal exhibited a high correlation (r = 0.96) with blood glucose levels. An improved cascade convolution neural network is suggested to accurately predict the BGL. For the estimation results of BGL, the root mean squared error of 7.3217 mg dl⁻¹ and a mean absolute relative difference of 4.7209% are achieved. The estimated results also fell by 100% in the clinically acceptable zones of the Clarke error grid analysis, indicating that the proposed system could potentially be used for clinical measurements.

Painless and non-invasive detection techniques are needed to replace finger-prick blood collection for people with diabetes. A first-of-its-kind, noninvasive, and continuous blood glucose level (BGL) detection method based on microwave imaging is introduced in this paper. This method avoids the complex task of frequency choice for the design of electromagnetic sensors. A radar-based microwave imaging technology combined with an improved very-deep super-resolution (VDSR-BL) method is presented to obtain high-resolution (HR) microwave images. After image super-resolution reconstruction by VDSR-BL, the peak signal-to-noise ratio and structural similarity index of HR images reach 35.4461 dB and 0.9761, respectively. Then, an ensemble learning strategy based on support vector regression and random forest algorithms is proposed to identify HR microwave images for BGL estimation. The developed detection system has been verified on the medium under tests with different glucose solutions. The final detection results obtain a root mean squared error of 0.1394 mg ml⁻¹ and a mean absolute relative difference of 8.02%, which show good accuracy with clinical acceptance. Meanwhile, we also conducted human trials. A high correlation coefficient (R) of 0.9254 was achieved between the results of microwave imaging and invasive BGL. Together, these results show that microwave imaging offers a promising new approach for noninvasive BGL monitoring.

This study presents a planar helical electromagnetic bandgap (EBG) structure designed for integration with an equiangular spiral antenna operating at 2.5 GHz. An equivalent-circuit model is introduced, and the effects of the unit-cell width, substrate thickness, metallic-strip width, and interstrip gap on the electromagnetic response are investigated through full-wave parametric simulations. The optimized periodic structure is subsequently employed as a backing reflector for the spiral antenna and is compared with both an unbacked configuration and a perfect electric conductor (PEC) reflector. At 2.5 GHz, the EBG-backed antenna achieves a simulated gain of approximately 6.15 dBi, compared with 2.58 dBi for the unbacked antenna, while producing an axial-ratio response that is less strongly disturbed over the investigated frequency range than that of the PEC-backed configuration. The mean axial-ratio value over the investigated band is reduced from 2.22 for the PEC-backed case to 1.67 for the EBG-backed case. The results demonstrate that the proposed helical EBG structure provides a compact approach for controlling the radiation characteristics of spiral antennas in S-band electromagnetic and wireless systems.

Microwave resonance sensors are promising for dielectric characterization and biochemical detection. However, their sensing performance is constrained by the relatively long wavelength at microwave frequencies and the limited electromagnetic interaction with small-volume samples. In this paper, an exceptional-point (EP)-enhanced chiral spoof localized surface plasmon (SLSP) resonator is proposed for high sensitivity microwave biosensing with sub-microliter sample volumes. By rotating the chiral resonator relative to the microstrip feeding line, two EPs are obtained at α = 49° and α = 210°. The simulated results show that the EP states generate larger frequency splitting than the reference state under both dielectric-constant variation and detection-limit evaluation, confirming the squareroot response to weak perturbations. Experiments further validate the sensing performance using low-loss dielectric materials and high-loss glucose solutions. For dielectric samples with relative permittivities from 2 to 6.15, the frequency splitting increases from 0.287 GHz to 0.359 GHz. For glucose solutions, the frequency splitting decreases from 1.506 GHz to 1.372 GHz as the glucose amount increases from 2.78 nmol to 27.8 nmol. The proposed sensor requires only about 0.5 μL of sample volume and shows higher sensitivity than reported microwave glucose sensors. These results demonstrate that the EP-enhanced chiral SLSP resonator provides a compact and sensitive platform for trace-volume biochemical detection and integrated microwave biosensing.