Novel metamaterial refractive index sensor based on impedance matching concept is proposed to achieve an ultra-narrowband absorption response in terahertz (THz) frequencies. To this end, the recently proposed circuit model of the periodic arrays of graphene disks has been exploited to model the graphene layer as circuit components while a developed transmission line method has been used to provide a circuit model for the whole structure. As a result, a perfect THz absorption response with absorptivity over 99.5% is achieved in desired resonance frequency. Two sensor structures at 3 and 6 THz have been designed as numerical examples, in which the frequency sensitivity of the structures is achieved as 0.834 and 1.57 THz per refractive index while the figure-of-merit of 11.75 and 24.5 are obtained, respectively. Its particular structure is polarization insensitive, further wide-angle absorptivity for both transverse electric and transverse magnetic waves is obtained. To verify the accuracy and validity of the presented circuit model approach, full-wave numerical modeling is performed.

Rotator cuff tear (RCT) is one of the most common shoulder injuries, which can be irreparable if it develops to a severe condition. A portable imaging system for the on-site detection of RCT is necessary to identify its extent for early diagnosis. We introduce a microwave tomography system, using state-of-the-art numerical modeling and parallel computing for detection of RCT. The results show that the proposed method is capable of accurately detecting and localizing this injury in different size. In the next step, an efficient design in terms of computing time and complexity is proposed to detect the variations in the injured model with respect to the healthy model. The method is based on finite element discretization and uses parallel preconditioners from the domain decomposition method to accelerate computations. It is implemented using the open source FreeFEM software.

One of the most common shoulder injuries is the rotator cuff tear (RCT). The risk of RCTs increases with age, with a prevalence of $9.7\%$ in those under 20 years old and up to $62\%$ in individuals aged 80 years and older. In this article, we present first a microwave digital twin prototype (MDTP) for RCT detection, based on machine learning (ML) and advanced numerical modeling of the system. We generate a generalizable dataset of scattering parameters through flexible numerical modeling in order to bypass real-world data collection challenges. This involves solving the linear system as a result of finite element discretization of the forward problem with use of the domain decomposition method to accelerate the computations. We use a support vector machine (SVM) to differentiate between injured and healthy shoulder models. This approach is more efficient in terms of required memory resources and computing time compared with traditional imaging methods.

One of the most common shoulder injuries is rotator cuff tear (RCT). The risk of RCTs increases with age, with a prevalence of 9.7% in those under 20 years old to 62% in individuals aged 80 and older. In this article, we present first Microwave Digital Twin Prototype (MDTP) for RCT detection, based on machine learning (ML) and advanced numerical modeling of the system. We generate a generalizable dataset of scattering parameters through flexible numerical modeling, in order to bypass real-world data collection challenges. This involves solving the linear system in result of finite element discretization of the forward problem with use of domain decomposition method to accelerate the computations. We use support vector machine (SVM) to differentiate between injured and healthy shoulder models. This approach is more efficient in terms of required memory resources and computing time compared to the traditional imaging methods.

A portable imaging system for the on-site detection of shoulder injury is necessary to identify its extent and avoid its development to severe condition. Here, firstly a microwave tomography system is introduced using state-of-the-art numerical modeling and parallel computing for imaging different tissues in the shoulder. The results show that the proposed method is capable of accurately detecting and localizing rotator cuff tears of different size. In the next step, an efficient design in terms of computing time and complexity is proposed to detect the variations in the injured model with respect to the healthy model. The method is based on finite element discretization and uses parallel preconditioners from the domain decomposition method to accelerate computations. It is implemented using the open source FreeFEM software.