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    Wyświetlanie 1-2 z 2
    Tytuł:
    Inset-FED microstrip patch antenna for glucose detection using label-free microwave sensing mechanism
    Autorzy:
    Rai, Priya
    Agarwal, Poonam
    Powiązania:
    https://bibliotekanauki.pl/articles/27311736.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    microwave sensor
    patch antenna
    glucose monitoring
    DMS
    Opis:
    In this work, a real-time label-free microwave sensing mechanism for glucose concentration monitoring using a planar biosensor configured with an inset fed microstrip patch antenna has been demonstrated. A microstrip patch antenna with the resonating frequency of 1.45 GHz has been designed and is fabricated on the Flame Retardant (FR-4) substrate. Due to the intense electromagnetic field at the edges of the patch antenna, edge length has been used as the detecting area where the sample under test (SUT) interacts with the electromagnetic field. The Poly-Dimethyl-Siloxane (PDMS) with the trench in the centre has been employed as the sample holder. Here, the SUT is the glucose dissolved in DI (de-ionized) water with the concentration range of 0.2 to 0.6 g/mL. The dielectric constant dependency on the glucose concentration has been used as the distinguishing factor which results in a shift in the S-parameter. The experimentally measured RF parameters were observed closely which showed the shift in S11 magnitude from -40 to -15 dB and resonant frequency from 1.27 to 1.3 GHz w.r.t the SUT solution of 0.2 to 0.6 g/mL with linear regression coefficient of 0.881, and 0.983 respectively.
    Źródło:
    Metrology and Measurement Systems; 2023, 30, 2; 211--222
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparative Analysis of Classifiers for the Assessment of Respiratory Disorders Using Speech Parameters
    Autorzy:
    Shrivastava, Poonam
    Tripathi, Neeta
    Singh, Bikesh Kumar
    Dewangan, Bhupesh Kumar
    Powiązania:
    https://bibliotekanauki.pl/articles/31339918.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    healthy speech
    affected speech
    machine learning
    classification techniques
    respiratory disorders
    speech analysis
    Opis:
    Non-invasive techniques for the assessment of respiratory disorders have gained increased importance in recent years due to the complexity of conventional methods. In the assessment of respiratory disorders, machine learning may play a very essential role. Respiratory disorders lead to variation in the production of speech as both go hand in hand. Thus, speech analysis can be a useful means for the pre-diagnosis of respiratory disorders. This article aims to develop a machine learning approach to differentiate healthy speech from speech corresponding to different respiratory disorders (affected). Thus, in the present work, a set of 15 relevant and efficient features were extracted from acquired data, and classification was done using different classifiers for healthy and affected speech. To assess the performance of different classifiers, accuracy, specificity (Sp), sensitivity (Se), and area under the receiver operating characteristic curve (AUC) was used by applying both multi-fold cross-validation methods (5-fold and 10-fold) and the holdout method. Out of the studied classifiers, decision tree, support vector machine (SVM), and k-nearest neighbor (KNN) were found more appropriate in providing correct assessment clinically while considering 15 features as well as three significant features (Se > 89%, Sp > 89%, AUC> 82%, and accuracy > 99%). The conclusion was that the proposed classifiers may provide an aid in the simple assessment of respiratory disorders utilising speech parameters with high efficiency. In the future, the proposed approach can be evaluated for the detection of specific respiratory disorders such as asthma, COPD, etc.
    Źródło:
    Archives of Acoustics; 2023, 48, 1; 13-24
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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