We propose a broadband tunable metamaterial absorber with near-unity absorption in the terahertz regime based on a target-patterned graphene sheet. Due to gradient diameter modulation of the graphene sheet and circular symmetry of the unit cell, broadband and polarization-independent properties are achieved in the absorber. A full-wave numerical simulation is performed, and the results show that the absorber’s bandwidth of 90% terahertz absorption reaches 1.57 THz with a central frequency of 1.83 THz under normal incidence. At oblique incidence, the broadband absorption of the absorber remains more than 75% over a wide incidence angles up to 60$°$for the transverse electric (TE) mode and 75$°$for the transverse magnetic (TM) mode. Furthermore, tunable property is implemented and the peak absorption of the absorber can be tuned from 19% to near 100% by changing the Fermi energy of the graphene sheet from 0 to 0.9 eV via electrostatic doping. The absorber is scalable to the infrared and visible frequencies, which could be used as tunable sensors, filters and photovoltaic devices.

A tunable metamaterial absorber is proposed in the terahertz regime. The amplitude and center frequency of the absorber can be tuned independently. Owing to the effective combination of graphene and strontium titanate (STO) in one metamaterial structure, the tunable properties of the amplitude and center frequency are implemented. The amplitude can be tuned by adjusting the chemical potential of graphene sheet, and center frequency can get a shift through temperature changes in the STO material. In a full-wave numerical simulation, the amplitude of the absorber can be tuned from approximately 100% to 35% with a fixed center frequency when chemical potential varies from 0.7 eV to 0.0 eV. The center frequency of the absorber can shift from 0.43 THz to 0.3 THz when temperature changes from 400 K to 200 K. The complex surface impedance of the graphene and permittivity of STO material in this research range are thoroughly examined, and the independently tunable mechanism of the absorber is explored by elucidating the electric field distribution. The influence of the oblique incidence of electromagnetic wave to the absorber is studied. The absorber can be scalable to the infrared and visible frequencies and demonstrates promising application on tunable sensors, filters, and photovoltaic devices.

In this paper, a thermally tunable metamaterial absorber comprising a periodic array of metallic circle resonator with strontium titanate (STO) has been proposed in the terahertz regime. Due to the special active material, STO is adapted as the dielectric layer, thermally tunable is implemented in the absorber. A full-wave numerical simulation is performed and the results reveal that the peak absorption of the absorber reaches 99.9% at 2.48 THz when the temperature is set as 400K, and the central frequency can shift from 2.48 to 1.71 THz when the temperature varies from 400K to 200K. Furthermore, the electric field distribution and surface current distribution are investigated to better understand the absorption mechanism. Besides, the influence of the polarization angle and oblique incident angle to the absorber is studied and the results show that the peak absorption remains above 90% up to 60${}^{\circ }$of the incidence angles for the TE mode and 55${}^{\circ }$for the TM mode. The absorber can be scalable to the infrared and visible frequencies, and can be potentially applied to imaging, detection and tunable sensing.

Objective: Passive shielding is usually applied to block electro-magnetic interference (EMI) for portable very-low field MRI scanners, but it goes against the mobility of the scanners. Here, the reference channel-based active EMI suppression (AES) system was proposed to discard them. Methods: Different from the existing studies, this work started with analyzing the interference transmission paths and discovered that the human body coupling was the main path. Then, the “ring-” shaped EMI receiving coil designed here together with the electrocardiograph (ECG) electrode patch were used to suppress the human-body-coupled interferences. For the first time, the periphery data of the k -spaces of the RF coil and the EMI detectors were utilized to calculate the transfer factors in multiple frequency bands by using the least square method. The reference EMI signals were transferred to the interferences in the MR signal by multiplying the transfer factors in each frequency band, and the denoised MR signal could be obtained by subtracting the transferred reference EMI signals. Results: The prototype of the AES system was applied to a 50 mT unshielded portable MRI scanner. The in-vivo experiments indicated that the interference suppression rate of the AES system equipped with the “ring-” shaped EMI receiving coil could reach 96.8%. Meanwhile, the SNR of the images after interference suppression by the AES system equipped with both types of detectors was 97.2% of that of the images scanned inside the shielded room. Conclusion: The proposed AES system ensures that the portable MRI scanner works normally in unshielded environment. Significance: Our study provides a solution to make portable MRI scanners truly movable.

Using photovoltaic (PV) energy to produce hydrogen through water electrolysis is an environmentally friendly approach that results in no contamination, making hydrogen a completely clean energy source. Alkaline water electrolysis (AWE) is an excellent method of hydrogen production due to its long service life, low cost, and high reliability. However, the fast fluctuations of photovoltaic power cannot integrate well with alkaline water electrolyzers. As a solution to the issues caused by the fluctuating power, a hydrogen production system comprising a photovoltaic array, a battery, and an alkaline electrolyzer, along with an electrical control strategy and energy management strategy is proposed. The energy management strategy takes into account the predicted PV power for the upcoming hour and determines the power flow accordingly. By analyzing the characteristics of PV panels and alkaline water electrolyzers and imposing the proposed strategy, this system offers an effective means of producing hydrogen while minimizing energy consumption and reducing damage to the electrolyzer. The proposed strategy has been validated under various scenarios through simulations. In addition, the system’s robustness was demonstrated by its ability to perform well despite inaccuracies in the predicted PV power.

Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.