Nikolaos Karkanis

This work presents an adaptive impedance matching framework for microwave ablation (MWA) systems, leveraging tunable reactive networks based on ferroelectric materials and varactor diodes to address dynamic tissue-induced impedance variations. By exploiting the voltage-controlled dielectric properties of ferroelectric materials such as BNBT6, the proposed system achieves adaptive impedance matching, enabling a single applicator to maintain effective power transfer across a wide range of dielectric loading conditions. The adaptive matching concept is validated through electromagnetic simulations of a ferroelectric-based design and demonstrated experimentally using a varactor-diode prototype, confirming impedance restoration under varying dielectric loads. The framework is antenna-agnostic and demonstrated using a helical open waveguide structure as a representative applicator geometry. The adaptive microwave ablation system architecture is also evaluated under tissue property changes induced by thermal coagulation, supporting its suitability for future integration into adaptive MWA platforms. Characterization of a commercial PVDF ferroelectric cable confirms its insufficient tunability at microwave frequencies, consistent with prior literature, motivating experimental validation via a varactor-diode-based printed matching network, which successfully restored impedance matching at 915 MHz following a dielectric load change (S ₁₁ improvement from approximately −15 dB to −25 dB). The proposed system demonstrates robust adaptive impedance restoration and efficient power transfer across a wide range of tissue-representative loads. The primary contribution of this work is the experimental validation of adaptive impedance retuning under dynamically varying dielectric loading conditions relevant to MWA systems.

Microwave ablation (MWA) efficiency is strongly influenced by impedance mismatch caused by inter-organ dielectric variability, tumor heterogeneity, and temperature-dependent tissue evolution during treatment. This work presents a computational feasibility study of an adaptive impedance matching framework for MWA based on ferroelectric tunable elements and feedback-controlled impedance retuning. A proposed closed-loop feedback architecture is presented as a potential future means of automating the computationally demonstrated retuning principle for dielectric variations under heterogeneous tissue conditions. Full-wave electromagnetic and thermal simulations were performed at 915 MHz across 47 representative scenarios involving liver, lung, kidney, and breast tissues, including variations in tumor size, heterogeneous tumor composition, percolation-driven dielectric transitions, anisotropic tumor geometries, and temperature-dependent dielectric properties. Compensating ferroelectric tuning states were identified through manual parametric retuning based on reflected-power minimization and restoration of the input impedance toward 50 + j0 Ω. The adaptive matching framework maintained reflection coefficients below −20 dB across all simulated cases, while substantially improving impedance stability relative to conventional fixed matching configurations. Additional frequency-domain analysis demonstrated improved matching behavior across the ISM operating band. The simulations produced localized specific absorption rate and thermal distributions for the investigated tissue configurations, while the adaptive matching states restored low reflection under the corresponding dielectric conditions. The results support the feasibility of adaptive ferroelectric-based impedance matching as a promising strategy for improving MWA robustness under heterogeneous and thermally evolving tissue environments. Experimental validation and hardware implementation remain subjects of future investigation.

Microwave ablation (MWA) is a minimally invasive therapy for liver, lung, and kidney tumors. Computational modeling using finite element methods (FEMs) can simulate ablation zones but is computationally expensive and unsuitable for interactive use. This study develops and evaluates a machine learning surrogate model to rapidly approximate 50 °C isothermal contours from FEM simulations of MWA, enabling faster computational exploration. A feedforward neural network was trained on 2625 FEM simulations incorporating tumor geometry and dielectric properties. Predictions were compared with FEM-derived ablation zones, demonstrating high accuracy and strong correlation ( R > 0.95), with error distributions centered near zero. The proposed framework enables rapid approximation of electrothermal simulation outputs, significantly reducing computational cost while maintaining accuracy within the simulated parameter space. However, the model is trained and evaluated exclusively on simulation data and therefore reflects interpolation within this predefined domain. This study represents a simulation-based proof-of-concept, and the proposed model is intended to approximate the outputs of a simplified computational framework rather than directly predict clinical outcomes. Further validation using experimental and clinical data is required before practical applicability can be established. The proposed approach should be interpreted as a surrogate model of the underlying simulation framework rather than a direct predictor of clinical outcomes.

Wireless sensor networks are expanding across nearly all aspects of social and economic activity. Their deployment now spans almost all available environments, including, in some cases, off-planet applications. Applications may range from near-ground, underground, undersea, indoor–outdoor, and on- and in-body, to name a few deployment environments. Each channel model exhibits its own physical representation and statistical distributions. In this work, we categorize and re-evaluate the various propagation models published in the open literature while, at the same time, we contribute critical updates needed due to the rapid expansion of the wireless sensor network discipline. Our work includes various results, based on simulation and/or analytical formulas, that advocate for and evaluate our classification and findings. Unlike prior reviews that treat environments in isolation, this article introduces a unified analytical and simulation framework for a cross-environment comparison, with updated parameterizations aligned with modern WSN deployments. The innovation lies in the consistent cross-environment evaluation of propagation mechanisms, updated parameterizations aligned with modern WSN deployments, and the identification of feasibility and trade-off boundaries across heterogeneous media.

The integration of embedded software in multi-sensor wearable devices has revolutionized real-time monitoring across health, fitness, industrial, and environmental applications. This paper presents a comprehensive approach to designing and implementing embedded software architectures that enable efficient, low-power, and high-accuracy data acquisition and processing from heterogeneous sensor arrays. We explore key challenges such as synchronization of sensor data streams, real-time operating system (RTOS) integration, power management strategies, and wireless communication protocols. The reviewed framework supports modular scalability, allowing for seamless incorporation of additional sensors or features without significant system overhead. Future research directions of the embedded software include Hardware-in-the-Loop and real-world validation, on-device machine learning and edge intelligence, adaptive sensor fusion, energy harvesting and power autonomy, enhanced wireless communications and security, standardization and interoperability, as well as user-centered design and personalization. By adopting this focus, we can highlight the potential of the embedded software to support proactive decision-making and user feedback through edge-level intelligence, paving the way for next-generation wearable monitoring systems.

Digital telecommunications have become the backbone of modern healthcare, transforming how patients and professionals interact, share information, and deliver treatment. The integration of telecommunications with medicine, biomedical engineering and health services has enabled rapid growth in telemedicine, remote patient monitoring, wearable biomedical devices, and data-driven clinical decision-making. Emerging technologies such as artificial intelligence, big data analytics, virtual and augmented reality and robotic tele-surgery are further expanding the scope of digital health. This review provides a comprehensive overview of the role of telecommunications in medicine and biomedical engineering. We classify key applications, highlight enabling technologies and critically examine the challenges regarding interoperability, data security, latency, and cost. Finally, we discuss future directions, including 5G/6G networks, edge computing, and privacy-preserving medical AI, emphasizing the need for reliable and equitable access to telecommunications-enabled healthcare worldwide.

This work presents a comparative analysis of microwave ablation applicators, including both antenna-based and open waveguide designs, which are guided and inserted into tumors via catheters. Applicators previously proposed in the literature are evaluated through both electromagnetic and thermal simulations. The objective is to assess temperature distribution within the tumor and surrounding healthy tissues; with a focus on identifying patterns of heat diffusion. Although a variety of applicators have been designed, each typically operates at different central frequency or targets specific tumor shapes and tissue types. In this study, we standardize conditions by analyzing multiple applicators’ designs for the same tumor type. The results highlight the shape of the ablation zone and corresponding temperature distribution, offering insights into potential healthy tissue damage. This comparative analysis provides critical information for optimizing microwave ablation applicators for more precise and effective treatment.