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    Wyświetlanie 1-4 z 4
    Tytuł:
    A study on the calibration of an HPM meter based on a D-dot sensor and logarithmic RF power detector
    Autorzy:
    Jakubowski, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/1849151.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    HPM measurements
    sensor calibration
    RF detector
    D-dot sensor
    Opis:
    HPM meters are required for the assessment of fields generated by sources of high-power microwaves. Finding the inverse calibration curves for such instruments is important for ensuring accuracy. The procedure is relatively simple for meters consisting of linear devices but there can also be hardware solutions implementing nonlinear ones. The objective of the present work was to develop a convenient procedure to allow finding such a curve when the meter uses a D-dot probe and a power detector. For that purpose, the results of low voltage measurements describing the properties of the detector were first analysed. Then a software code was developed to estimate the RMS value of an incident field based on measured output and frequency response. The response was estimated with very low electric field. And finally, the performance of the proposed procedure was verified by tests conducted with high electric field in a TEM cell. High conformity of the output of the meter with fields of known values was demonstrated. The maximum error related to the meter range did not exceed 4%.
    Źródło:
    Metrology and Measurement Systems; 2020, 27, 4; 673-685
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of imaging techniques to objectify the Finger Tapping test used in the diagnosis of Parkinsons disease
    Autorzy:
    Jakubowski, Jacek
    Potulska-Chromik, Anna
    Chmielińska, Jolanta
    Nojszewska, Monika
    Kostera-Pruszczyk, Anna
    Powiązania:
    https://bibliotekanauki.pl/articles/2204532.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    image processing
    medical diagnosis
    Parkinson’s disease
    finger tapping test
    przetwarzanie obrazu
    diagnoza medyczna
    choroba Parkinsona
    test stukania palcem
    Opis:
    Finger tapping is one of the standard tests for Parkinson's disease diagnosis performed to assess the motor function of patients' upper limbs. In clinical practice, the assessment of the patient's ability to perform the test is carried out visually and largely depends on the experience of clinicians. This article presents the results of research devoted to the objectification of this test. The methodology was based on the proposed measurement method consisting in frame processing of the video stream recorded during the test to determine the time series representing the distance between the index finger and the thumb. Analysis of the resulting signals was carried out in order to determine the characteristic features that were then used in the process of distinguishing patients with Parkinson's disease from healthy cases using methods of machine learning. The research was conducted with the participation of 21 patients with Parkinson's disease and 21 healthy subjects. The results indicate that it is possible to obtain the sensitivity and specificity of the proposed method at the level of approx. 80 %. However, the patients were in the so-called ON phase when symptoms are reduced due to medication, which was a much greater challenge compared to analyzing signals with clearly visible symptoms as reported in related works.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 2; art. no. e144886
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning vs feature engineering in the assessment of voice signals for diagnosis in Parkinson’s disease
    Autorzy:
    Majda-Zdancewicz, Ewelina
    Potulska-Chromik, Anna
    Jakubowski, Jacek
    Nojszewska, Monika
    Kostera-Pruszczyk, Anna
    Powiązania:
    https://bibliotekanauki.pl/articles/2173626.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    voice processing
    Parkinson’s disease
    non-linear analysis
    convolutional network
    przetwarzanie głosu
    choroba Parkinsona
    analiza nieliniowa
    sieci konwolucyjne
    Opis:
    Voice acoustic analysis can be a valuable and objective tool supporting the diagnosis of many neurodegenerative diseases, especially in times of distant medical examination during the pandemic. The article compares the application of selected signal processing methods and machine learning algorithms for the taxonomy of acquired speech signals representing the vowel a with prolonged phonation in patients with Parkinson’s disease and healthy subjects. The study was conducted using three different feature engineering techniques for the generation of speech signal features as well as the deep learning approach based on the processing of images involving spectrograms of different time and frequency resolutions. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. The discriminatory ability of feature vectors was evaluated using the SVM technique. The spectrograms were processed by the popular AlexNet convolutional neural network adopted to the binary classification task according to the strategy of transfer learning. The results of numerical experiments have shown different efficiencies of the examined approaches; however, the sensitivity of the best test based on the selected features proposed with respect to biological grounds of voice articulation reached the value of 97% with the specificity no worse than 93%. The results could be further slightly improved thanks to the combination of the selected deep learning and feature engineering algorithms in one stacked ensemble model.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e137347
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning vs feature engineering in the assessment of voice signals for diagnosis in Parkinson’s disease
    Autorzy:
    Majda-Zdancewicz, Ewelina
    Potulska-Chromik, Anna
    Jakubowski, Jacek
    Nojszewska, Monika
    Kostera-Pruszczyk, Anna
    Powiązania:
    https://bibliotekanauki.pl/articles/2090742.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    voice processing
    Parkinson’s disease
    non-linear analysis
    convolutional network
    przetwarzanie głosu
    choroba Parkinsona
    analiza nieliniowa
    sieci konwolucyjne
    Opis:
    Voice acoustic analysis can be a valuable and objective tool supporting the diagnosis of many neurodegenerative diseases, especially in times of distant medical examination during the pandemic. The article compares the application of selected signal processing methods and machine learning algorithms for the taxonomy of acquired speech signals representing the vowel a with prolonged phonation in patients with Parkinson’s disease and healthy subjects. The study was conducted using three different feature engineering techniques for the generation of speech signal features as well as the deep learning approach based on the processing of images involving spectrograms of different time and frequency resolutions. The research utilized real recordings acquired in the Department of Neurology at the Medical University of Warsaw, Poland. The discriminatory ability of feature vectors was evaluated using the SVM technique. The spectrograms were processed by the popular AlexNet convolutional neural network adopted to the binary classification task according to the strategy of transfer learning. The results of numerical experiments have shown different efficiencies of the examined approaches; however, the sensitivity of the best test based on the selected features proposed with respect to biological grounds of voice articulation reached the value of 97% with the specificity no worse than 93%. The results could be further slightly improved thanks to the combination of the selected deep learning and feature engineering algorithms in one stacked ensemble model.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e137347, 1--10
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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