In the context of in-vitro testing of implantable medical devices, multi-layered body phantom development poses significant challenges in terms of complexity, cost and possible formation of unwarranted air gaps between two layers or mixing chemicals of two layers while one layer is placed on other layer in hot semi-solid form. Therefore, it is recommended to use its equivalent homogeneous phantom with same dielectric properties. In this article, equivalent complex permittivity of three-layer human-body-model at 2.45 GHz is estimated by using Transmission Matrix Method (TMM) and FEM solver. The transmission matrix of the required body model is calculated based on the electrical properties of each layer. Solving nonlinear equations from TMM, multiple solutions of complex permittivity values are obtained. A two-antenna system is designed in FEM solver where one of the antennas is placed within homogeneous phantoms with obtained complex permittivity values and other is in free space to choose exact complex permittivity using scattering parameters. To validate the final phantom properties practically, the simulated setup is replicated. The practical results show good agreement with simulated responses. This approach can reduce complexity of designing multilayer-models considering only 0.53
The design and development of a novel scalp-implantable, nan-invasive, battery free, hybrid wireless sensor system for continuous intracranial pressure (ICP) monitoring are presented. Traditional methods for ICP measurement are highly invasive and pose risks such as infection and trauma. In contrast, the proposed system comprises a radio frequency (RF) powered, hybrid RF biosensor utilising a miniaturised implantable antenna and a wearable non-invasive transceiver. The dual-band operation at 2.45 GHz and 4.55 GHz enables high-data-rate transmission suitable for real-time brain telemetry and for energy harvesting. Electromagnetic simulations utilising human tissue phantoms exhibit robust impedance characteristics, featuring reflection coefficients of − 20.61 dB at 2.45 GHz and − 12.48 dB at 4.55 GHz, alongside fractional bandwidths of 20.42
Monitoring changes in the brain is fundamental for diagnosing brain diseases, selecting appropriate treatments, and long-term management of patients with neurological disorders. Radio frequency (RF) antenna biosensor technologies have increasingly gained interest as a promising, non-invasive or minimally invasive technique for neurological assessment. This approach enables the detection of a wide range of physiological and pathological conditions, including stroke events, neural activity, temperature variations, and the progression of neurodegenerative diseases. This paper presents a designed monolithic microwave integrated circuit low-noise amplifier and RF link budget and examines the recent developments in RF antenna designs specifically tailored for brain monitoring applications, with a comparative focus on operating frequencies, antenna geometries, bandwidth characteristics and safety performance evaluated through Specific Absorption Rate. In addition, an illustrative design case study of an ISM-band monolithic microwave integrated circuit (MMIC) low-noise amplifier (LNA) and associated RF link budget is presented to demonstrate how antenna-level requirements translate into front-end transceiver specifications. At 13.7 oK average receiver noise temperature, the designed low-noise amplifier yields a carrier link margin of 38.5 dB and the data link margin, 15.3 dB over the 5.725–5.875 GHz ISM band for non-invasive high-resolution microwave brain physiological monitoring applications.
The rapid advancement of ML algorithms and the increasing availability of large datasets have significantly transformed the landscape of predictive modelling in scientific research. In this context, we introduce Optima (OPTimized Interpretable Model Building & Analysis Toolkit), a user-friendly, Python-based GUI designed to simplify and accelerate the development of interpretable ML-based classification QSAR/QSPR/QSTR models. This toolkit offers an intuitive graphical user interface (GUI), enabling users with domain knowledge but limited coding experience to efficiently optimize, construct, and interpret various ML-based classification models. A most highlighting feature of this GUI is its fully customizable settings panel, allowing users to modify colour schemes, font sizes, axis labels, and plot dimensions to suit publication or presentation needs. By combining robust optimization with an explainable approach, the Optima toolkit improves classification QSAR model performance while ensuring transparency and reproducibility. This platform addresses a critical need by providing an intuitive GUI for rational dataset splitting, efficient feature selection, and the development of seven different ML-based classification QSAR models, covering the entire workflow from optimization to interpretation. The toolkit is presently available for Windows and can be downloaded from the provided link (https://github.com/Rahul-Roy-21/OPTIMA).
The Internet of Things (IoT) and software-defined networks (SDN) have opened up new opportunities for innovation. Many of the limitations of the IoT system can be rectified with the SDN concepts. Thus, the combination of SDN and IoT has tremendous potential in various application domains. As the number of IoT devices is increasing with time, the scalability issues need to be further improved. Another significant challenge in IoT environments is mobility. Maintaining seamless mobility and persistent connectivity for IoT devices operating over large-scale or geographically dispersed environments presents a significant research challenge. But scalability and mobility are complex challenges. Developing scalable, mobile, and adaptive network architectures is crucial for SDN-enabled IoT ecosystems. Using SDN-enabled IoT networks, we introduced a comprehensive approach to address these challenges. Here, a new protocol based on OpenFlow of SDN and 6LoWPAN of the IoT system, namely, 6LoWSD has been proposed. In this investigation, emphasis has been placed on techniques on how the proposed 6LoWSD can improve scalability and mobility issues. In this study, experiments with the proposed protocol were performed using physical devices and a simulated platform. The results were compared with the 6LoWPAN counterpart and were found to be satisfactory.
