Raimi Dewan

The rising incidence of breast cancer has increased the demand for advanced healthcare solutions that enable continuous, real-time monitoring of patient health. Implantable antennas are a promising approach; however, the long-term biocompatibility of implant materials is a vital requirement for safe and reliable operation within a biological environment. Therefore, this work presents a circularly polarized coplanar waveguide-fed implantable antenna with a footprint of 39.00 × 35.00 × 2.20 mm ³ by incorporating resin as the substrate and Titanium Grade 4 as the radiating element. The implanted antenna demonstrates circular polarization with a 3 dB axial ratio of 1.35 dB at 2.40 GHz and an axial ratio bandwidth of 80 MHz (2.37–2.45 GHz), and a realized gain of −17.81 dBi in a glandular breast tissue model. The proposed antenna satisfies SAR safety requirements, exhibiting a maximum SAR value of 0.15 W kg ^-1 . Furthermore, the experimental results revealed that the antenna achieves -10 dB impedance bandwidth of 2100 MHz (1.65–3.75 GHz). Compared with reported studies, the proposed antenna demonstrates a wider measured impedance bandwidth (87.5%), while also achieving circular polarization, and eliminating the need for additional biocompatible superstrates, encapsulation layers or coatings through the use of clinically established resin substrate and titanium radiating element. Thus, the results provide a proof-of-concept and validate the feasibility of utilizing resin and titanium as biocompatible materials for implantable breast antenna applications, creating new opportunities for material selection in future implantable wireless systems.

The Internet of Things (IoT) is transforming wound care by moving beyond traditional approaches that rely on gauze dressings and visual inspection. These conventional practices often cause discomfort, require frequent hospital visits, and add financial burdens, highlighting the need for IoT-driven hydrogel dressings that enable non-invasive and efficient healing. Recently, significant efforts have been made to develop hydrogel wound dressings that integrate flexible electronics with wireless communication systems to monitor wound status in real-time and address potential infections promptly. Therefore, this systematic review presents the chronological evolution of IoT-driven hydrogel dressings incorporating biosensing for diagnosis, wireless data transmission, and on-demand therapy. In addition, the system architecture, characteristics of hydrogels that enable IoT implementation, biosensing mechanisms, the suitability of incorporating wireless communication technologies, such as Bluetooth, Wireless Fidelity (Wi-Fi), and Near Field Communication (NFC), into hydrogel dressings, data processing and analysis are discussed. Notably, recent advancements demonstrated a transition from single-parameter NFC-powered dressings to multimodal platforms that enable simultaneous physiological monitoring and controlled drug release. Despite these advancements, persistent challenges remain, including the rigidity of electronic components, dependence on batteries, and threshold-based decisions. Thus, future research should focus on developing multimodal, battery-free, therapeutic hydrogel dressings equipped with data analytics tools such as artificial intelligence (AI) for autonomous data interpretation and personalized wound management. Overall, this review establishes a foundation for advancing IoT-driven hydrogel dressings toward fully integrated, intelligent, and personalized wound care systems.

Antennas play a crucial role in implantable medical devices (IMDs), particularly in applications such as gastric endoscopy. Therefore, this paper reviews recent advancements in antenna design for IMDs, focusing on the benefits and challenges. The findings from recent research will serve as a reference for future studies, emphasizing the need to address existing challenges. Specifically, this paper investigates the miniaturization and signal performance of the antenna when simulated inside the human body and explores the potential solutions for the challenges. Additionally, this paper discusses various gastric diseases, the development of gastric body phantoms, and relevant studies from reputable journals. Finally, the future directions for further improvement of the research are being discussed.

This book chapter reviews the applications of artificial neural networks (ANNs) in microwave sensors to extract the properties of samples under test (SUTs). ANNs enable the determination of complex and nonlinear relationships between sensing data and desired output data. An overview of ANN architecture, working principle, and state-of-the-art advances of ANN-assisted microwave sensors is provided. Subsequently, a planar microwave sensor based on microstrip transmission line and complementary split ring resonators (CSRRs) is designed and simulated using Computer Simulation Technology (CST). The use of MATLAB software to implement the ANN for the prediction of permittivity of SUTs is demonstrated. Additionally, the challenges and future prospects of ANN-assisted microwave sensors are examined.

Passive Ultra-High-Frequency Radio Frequency Identification (UHF RFID) tag has been implemented as a wearable device for numerous healthcare applications. However, the tag possesses limitations owing to the absorption of electromagnetic radiation by the human body, which significantly reduces the read range of the tag. Thus, the potential strategies to mitigate the effect of the human body need to be addressed. Recently, significant efforts have been made to overcome this shortcoming by incorporating metamaterials or introducing slots to the tag antenna structure. Therefore, this article provides insight into strategies that specifically employ slots, Artificial Magnetic Conductor (AMC), and Split Ring Resonator (SRR). Prior to this, the factors affecting the read range and previously reported studies are briefly discussed. Finally, the future directions for further improvement of these strategies are discussed.

This article reviews the current advances in planar microwave sensors assisted by artificial neural networks (ANNs) for various dielectric material characterization applications. The architecture of ANNs and intelligent sensing systems are introduced. The ability of ANNs to improve the accuracy of microwave sensors in measuring samples with multiple components and under various ambient conditions is discussed. ANNs enable the determination of complex and nonlinear relationships between sensing data and desired output data, such as complex permittivity, concentrations, and defect detection. More advanced neural network algorithms facilitate the training of more complex datasets. Finally, the current challenges and future prospects of ANN-integrated microwave sensors are discussed. It is envisaged that the summarized research efforts can provide valuable insights for scientists and engineers aiming to implement ANNs in microwave sensors.

Partial discharges (PD) are small electrical discharges that occur within insulation materials and can lead to equipment failure and safety hazards. The Ultra High Frequency (UHF) sensor consists of a broadband antenna and a UHF receiver, which are designed to detect and analyse the electromagnetic emissions from partial discharges. This paper presents the design and characterization of the 4th order Hilbert fractal UHF sensor for PD detection in power transformer. The sensor is designed to operate in the frequency range of 300 MHz to 3 GHz and is optimized for high gain and directivity. The performance of the sensor is evaluated using simulation and measurement techniques. Simulation results show that the sensor has directivity of 7.9 dBi and reflection coefficient below -10 dB with VSWR ≤ 5.

Metamaterials can be integrated into planar microwave sensors for field localization and enhancement as well as sensitivity improvement in permittivity-based sensing measurements. This chapter reviews metamaterial-based planar microwave sensors for characterizing dielectric materials. It begins by introducing planar microwave sensors. The subsequent section focuses on the sensing principles of planar microwave sensors loaded with metamaterial-inspired resonators, namely frequency shift, frequency splitting, and amplitude or phase variation. Furthermore, recent advances in metamaterial-integrated sensors are discussed, focusing on the types of samples under test, including solid samples in bulk and powder form, and liquid samples in fluid tubes or microfluidic channels. Furthermore, planar antennas loaded with metamaterial elements are also explored. To enhance understanding of frequency-variation sensors, a microstrip transmission line loaded with complementary split ring resonators is simulated. Finally, the design strategies of planar metamaterial-based sensors are reviewed.