A dual-band phase shifter with simultaneous and independent phase control across two operating bands, designed for emerging low Earth orbit (LEO) satellite communications systems in the K/Ka-band, is presented. The proposed structure utilizes a dual-stub-loaded 3-dB hybrid to separate signals by their frequency, allowing for independent phase tuning in each band. To achieve a wide phase range, low insertion loss, and minimal phase range deviation, both the 3-dB hybrid and reflective loads are optimized for their respective bands. Additionally, the circuit can seamlessly switch between phase shifter and diplexer modes without redesign, enhancing its versatility. To validate the proposed concept, phase shifter and diplexer prototypes operating at$\mathrm {19.3~GHz}~\text {to}~\mathrm {20.3~GHz}$and$\mathrm {29.5~GHz}~\text {to}~\mathrm {30.5~GHz}$have been fabricated and tested. Both prototypes demonstrate a phase tuning range exceeding$\mathrm {180~\!^{\circ }}$with a maximum phase range deviation of$\mathrm {\pm 1.25~\!^{\circ }}$and an insertion loss below 2.5 dB. For the diplexer mode, port-to-port isolation exceeds 25 dB across all phase states. The proposed phase shifter design offers simplicity, robustness, and flexibility, with flat phase range and amplitude, while allowing independent tunable phase states across transmit and receive bands, making it highly suitable for LEO satellite communications systems.

Well-designed and trained deep neural networks can solve inverse electromagnetic problems much faster than conventional solvers. However, they need a physics framework to ensure producing physically correct results. Since most physics-guided deep learning inverse solvers require substantial training with numerous epochs, each involving solving a forward problem, their accuracy and efficiency are largely defined by the utilized forward solver, which becomes a bottleneck for their practical training. Thus, a fast and accurate self-supervised deep learning forward solver is presented. The solver uses a physics-based framework that divides the domain into two regions: an interior region, which includes any scatterers, and an exterior region, which represents the background medium. A hybrid loss function, incorporating Maxwell's curl equation and integral equation with the well-defined scalar background's Green's function, is employed to guide the scattered field generated from the neural network, ensuring global and local accuracy. To verify the generality of the solver, it is trained on random objects and tested on realistic models, showing high global and local metrics accuracy. For example, more than 95% of testing cases using the proposed method achieve less than 0.15 root-mean-square error in the calculated scattered field and dielectric properties of the imaged domain compared to the ground truth. In contrast, two recent deep learning methods could only realize that level of accuracy for less than 50% of the tested cases. The reported method is 97% faster than conventional solvers, enabling the development of reliable deep-learning inverse solvers.

Deep learning has the potential to address the bottleneck of conventional medical microwave tomography, which is ill-posed and has a high computation cost. However, current physics-guided deep learning methods may fail to capture the imaged object's salient regions, resulting in misdiagnosis. A deep neural network inspired by the distorted Born iterative method (DBIM) is proposed to address this challenge. This method, which avoids using Green's function, provides a theoretical explanation of why current deep learning methods guided by iterative physics algorithms fail in detecting abnormal tissues, making them unsuitable for real-life clinical applications. The proposed method consists of two main components: a set of forward neural network solvers and a series of inverse neural network blocks for updating the dielectric contrast of the imaging domain. The training of the network, designed to emulate the DBIM framework, is regularized by a hybrid loss function composed of two supervised and one self-supervised function. Each iteration in DBIM is performed using a neural network block with parameters different from those of other blocks, forming a sequential iterative approach. By calculating the perturbations in the electrical properties' profiles at each iteration, the proposed network can accurately reconstruct abnormal tissues associated with signals masked by those from healthy tissues. Assessments of the proposed method using the relative error, structure similarity index measure, Dice similarity coefficient, and Hausdorff distance show significant enhancements (19%, 18%, 40%, and 72%, respectively) compared to two recent deep learning-based microwave medical imaging algorithms.

Hepatic steatosis, a key factor in chronic liver diseases, is difficult to diagnose early. This study introduces a classifier for hepatic steatosis using microwave technology, validated through clinical trials. Our method uses microwave signals and deep learning to improve detection to reliable results. It includes a pipeline with simulation data, a new deep-learning model called HepNet, and transfer learning. The simulation data, created with 3D electromagnetic tools, is used for training and evaluating the model. HepNet uses skip connections in convolutional layers and two fully connected layers for better feature extraction and generalization. Calibration and uncertainty assessments ensure the model's robustness. Our simulation achieved an F1-score of 0.91 and a confidence level of 0.97 for classifications with entropy ≤0.1, outperforming traditional models like LeNet (0.81) and ResNet (0.87). We also use transfer learning to adapt HepNet to clinical data with limited patient samples. Using1H-MRS as the standard for two microwave liver scanners, HepNet achieved high F1-scores of 0.95 and 0.88 for 94 and 158 patient samples, respectively, showing its clinical potential.

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.

Stroke is one of the leading causes of death and disability. To address this challenge, microwave imaging has been proposed as a portable medical imaging modality. However, accurate stroke classification using microwave signals is still an open challenge. In addition, identified features of microwave signals used for stroke classification need to be linked back to the original data. This work attempts to address these issues by proposing a wavelet convolutional neural network (CNN), which combines multiresolution analysis and CNN to learn distinctive patterns in the scalogram for accurate classification. A game theoretic approach is used to explain the model and indicate distinctive features for discriminating stroke types. The proposed algorithm is tested in simulation and experiments. Different types of noise and manufacturing tolerances are modeled using data collected from healthy human trials and added to the simulation data to bridge the gap between the simulation and real-life data. The achieved classification accuracy using the proposed method ranges from 81.7% for 3D simulations to 95.7% for lab experiments using simple head phantoms. Obtained explanations using the method indicate the relevance of wavelet coefficients on frequencies 0.95-1.45 GHz and the time slot of 1.3 to 1.7 ns for distinguishing ischemic from hemorrhagic strokes.

Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic decision‐making (DM), these systems have found wide‐ranging applications across diverse fields, including education, business, healthcare industries, government, and justice sectors. While AI and DM offer significant benefits, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. This article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. The review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six different applications have been discussed, including trustworthy AI in education, environmental science, 5G‐based IoT networks, robotics for architecture, engineering and construction, financial technology, and healthcare. The review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. The insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications.

The clinical use of microwave tomography (MT) requires addressing the significant mismatch between simulated environment, which is used in the forward solver, and real-life system. To alleviate this mismatch, a calibrated tomography, which uses two homogeneous calibration phantoms and a modified distorted Born iterative method (DBIM), is presented. The two phantoms are used to derive a linear model that matches the forward solver to real-life measurements. Moreover, experimental observations indicate that signal quality at different frequencies varies between different antennas due to inevitably inconsistent manufacturing tolerance and variances in radio-frequency chains. An optimum frequency, at which the simulated and measured signals of the antenna present maximum similarity when irradiating the calibrated phantoms, is thus calculated for each antenna. A frequency-division DBIM (FD-DBIM), in which different antennas in the array transmit their corresponding optimum frequencies, is subsequently developed. A clinical brain scanner is then used to assess performance of the algorithm in lab and healthy volunteers' tests. The linear calibration model is first used to calibrate the measured data. After that FD-DBIM is used to solve the problem and map the dielectric properties of the imaged domain. The simulated and experimental results confirm validity of the presented approach and its superiority to other tomographic method.

To reduce the cost and increase reliability of the vehicle radio-frequency identification and tracking systems, an alternative placement of the interrogator is investigated. Conventional systems make use of an overhead interrogator that reads a tag in a windscreen or a license plate. The alternative approach is to embed the interrogator in the road and exclusively read license plate tags. In this paper, the link budget of such a system is fully characterized assuming the ISO/IEC 18000-63 UHF Type-C RFID standard. The obtained results indicate that a microwave sensor that has an elevated toroidal radiation pattern at around a 20°-30° elevation angle above the horizon is desired. This is a challenging task as road regulations dictate that the sensor cannot exceed a profile of 2.5 cm above the road surface. As an example of a sensor that meets those requirements, a modified discone antenna with an improved impedance matching method is presented. To reduce the antenna's profile and give the required mechanical strength to withstand the weight of different vehicles on the road, the area between the disc and the cone is filled with Acetal, which has a high dielectric constant. The proposed microwave sensor is fabricated and successfully tested in a real-road environment. The results confirm that the sensor meets the aforementioned strict requirements from the link budget analysis.

A simple method for designing of a compact planar antenna featuring ultra wideband performance and simultaneous signal rejection over a narrow sub-band is presented. This design is demonstrated when substrate is assumed in the form of low temperature cofired ceramic (LTCC). Results of simulations show that the designed antenna of 30 mmtimes 24 mm dimensions supported by DuPont951 ground plane of 1 mm thickness has a 10-dB return loss bandwidth from 2.6 GHz to more than 10 GHz excluding the 4.9-5.9 GHz rejection sub-band assigned for IEEE 802.11a and HIPERLAN/2. This sub-band rejection is obtained using a tuning slot. The antenna has near omnidirectional characteristics. Its radiation efficiency is higher than 91% over the whole pass-band