Physical Multi-Layer Phantoms for Intra-Body Communications

This paper presents approaches to creating tissue mimicking materials that can be used as phantoms for evaluating the performance of Body Area Networks (BAN). The main goal of the paper is to describe a methodology to create a repeatable experimental BAN platform that can be customized depending on the BAN scenario under test. Comparisons between different material compositions and percentages are shown, along with the resulting electrical properties of each mixture over the frequency range of interest for intra-body communications; 100 KHz to 100 MHz. Test results on a composite multi-layer sample are presented confirming the efficacy of the proposed methodology. To date, this is the first paper that provides guidance on how to decide on concentration levels of ingredients, depending on the exact frequency range of operation, and the desired matched electrical characteristics (conductivity vs. permittivity), to create multi-layer phantoms for intra-body communication applications

Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning

In this paper, we propose and validate using the Intra-body communications channel as a biometric identity. Combining experimental measurements collected from five subjects and two multi-layer tissue mimicking materials’ phantoms, different machine learning algorithms were used and compared to test and validate using the channel characteristics and features as a biometric identity for subject identification. An accuracy of 98.5% was achieved, together with a precision and recall of 0.984 and 0.984, respectively, when testing the models against subject identification over results collected from the total samples. Using a simple and portable setup, this work shows the feasibility, reliability, and accuracy of the proposed biometric identity, which allows for continuous identification and verification

Modeling, Characterization, and Machine Learning Algorithm for Rectangular Choke Horn Antennas

In this work, we present the design and modeling of a new type of choke horn antenna. It incorporates a rectangular waveguide and a rectangular choke acting as a parasitic element. The four-sided geometry of the antenna is applicable to systems that utilize rectangular waveguides. Also, it can overcome the need for rectangular-to-circular transition of transmission line or mode conversion. The main objective of this paper is to develop a model that calculates the far field radiation characteristics of the proposed antenna (analytical part) and to incorporate a finite element method (FEM) solver that adds to the theoretical solution (empirical part), which finally leads to obtaining a hybrid model. The omnidirectional radiation property of the choke is demonstrated, which gives an insight into the influence of this parasitic element on the total radiated power. The same observations made on the rectangular choke can be translated to the circular choke as well. At an operating frequency of 2.45 GHz, the design is numerically and experimentally validated. Also, the demonstrated hybrid model can leverage the integration of supervised machine learning (ML) models by exporting radiation variables such as gain and half-power beamwidth (HPBW) and performing predictions based on the training data from the model. Therefore, we incorporate gradient boosting and neural network ML algorithms, which are tailored to the desired radiation pattern parameters