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  1. Article: Spectral Methods for Response Enhancement of Microwave Resonant Sensors in Continuous Non-Invasive Blood Glucose Monitoring.

    Buonanno, Giovanni / Brancaccio, Adriana / Costanzo, Sandra / Solimene, Raffaele

    Bioengineering (Basel, Switzerland)

    2022  Volume 9, Issue 4

    Abstract: In this paper, the performance of three recent algorithms for the frequency-response enhancement of microwave resonant sensors are compared. The first one, a single-step algorithm, is based on a couple of direct-inverse Fourier transforms, giving a ... ...

    Abstract In this paper, the performance of three recent algorithms for the frequency-response enhancement of microwave resonant sensors are compared. The first one, a single-step algorithm, is based on a couple of direct-inverse Fourier transforms, giving a densely sampled response as a result. The second algorithm exploits an iterative procedure to progressively restricts the frequency response. The final one is based on the super-resolution MUSIC algorithm. The comparison is carried out through a Monte Carlo analysis. In particular, synthetic signals are firstly exploited to mimic the frequency response of a resonant microwave sensor. Then, experimental data collected from water-glucose solutions are adopted as validation test for potential applications in noninvasive blood-glucose monitoring.
    Language English
    Publishing date 2022-04-04
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2746191-9
    ISSN 2306-5354
    ISSN 2306-5354
    DOI 10.3390/bioengineering9040156
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Machine Learning Approach to Quadratic Programming-Based Microwave Imaging for Breast Cancer Detection.

    Costanzo, Sandra / Flores, Alexandra / Buonanno, Giovanni

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 11

    Abstract: In this work, a novel technique is proposed that combines the Born iterative method, based on a quadratic programming approach, with convolutional neural networks to solve the ill-framed inverse problem coming from microwave imaging formulation in breast ...

    Abstract In this work, a novel technique is proposed that combines the Born iterative method, based on a quadratic programming approach, with convolutional neural networks to solve the ill-framed inverse problem coming from microwave imaging formulation in breast cancer detection. The aim is to accurately recover the permittivity of breast phantoms, these typically being strong dielectric scatterers, from the measured scattering data. Several tests were carried out, using a circular imaging configuration and breast models, to evaluate the performance of the proposed scheme, showing that the application of convolutional neural networks allows clinicians to considerably reduce the reconstruction time with an accuracy that exceeds 90% in all the performed validations.
    MeSH term(s) Algorithms ; Breast Neoplasms/diagnostic imaging ; Female ; Humans ; Machine Learning ; Microwave Imaging ; Phantoms, Imaging
    Language English
    Publishing date 2022-05-29
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22114122
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

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