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Wyszukujesz frazę "Mohammed, H.H." wg kryterium: Wszystkie pola

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    Wyświetlanie 1-2 z 2
    Tytuł:
    A Dual-Band Compact Integrated Rectenna for Implantable Medical Devices
    Autorzy:
    Hussein, Shamil H.
    Mohammed, Khalid K.
    Powiązania:
    https://bibliotekanauki.pl/articles/27311860.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    Implantable rectenna
    Folded Dipole Antenna FDA
    Phantom tissues layers
    CST suit
    simulation
    Opis:
    This work describes a dual band compact fully integrated rectenna circuit for implantable medical devices (IMDs). The implantable rectenna circuit consists of tunnel diode 10×10μm2 QW-ASPAT (Quantum Well Asymmetric Spacer Tunnel Layer diode) was used as the RF-DC rectifier due to its temperature insensitivity and nonlinearity compared with conventional SBD diode. SILVACO atlas software is used to design and simulate 100μm2 QW InGaAs ASPAT diode. A miniaturized dual band implantable folded dipole antenna with multiple L-shaped conducting sections is designed using CST microwave suits for operation in the WMTS band is 1.5GHz and ISM band of 5.8GHz. High dielectric constant material Gallium Arsenide (εr=12.94) and folded geometry helps to design compact antennas with a small footprint of 2.84mm3 (1×4.5×0.63) mm3. Four-layer human tissue model was used, where the antenna was implanted in the skin model at depth of 2mm. The 10-dB impedance bandwidth of the proposed compact antenna at 1.5GHz and 5.8GHz are 227MHz (1.4-1.63GHz) with S11 is -22.6dB and 540MHz (5.47-6.02GHz) with S11 is -23.1dB, whereas gains are -36.9dBi, and -24.3dBi, respectively. The output DC voltage and power of the rectenna using two stage voltage doubler rectifier (VDR) are twice that produced by the single stage at input RF power of 10dBm.
    Źródło:
    International Journal of Electronics and Telecommunications; 2023, 69, 2; 239--245
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Robust CNN Model for Diagnosis of COVID-19 Based on CT Scan Images and DL Techniques
    Autorzy:
    Eldeeb, Ahmed H.
    Amr, Mohammed Nagah
    Ibrahim, Amin S.
    Kamel, Hesham
    Fouad, Sara
    Powiązania:
    https://bibliotekanauki.pl/articles/2200729.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Deep learning
    COVID-19
    Artificial Intelligence
    computed tomography
    Convolutional Neural Networks
    Opis:
    The 2019 Coronavirus (COVID-19) virus has caused damage on people's respiratory systems over the world. Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patterns in medical pictures. In this paper, we proposed a simple low training proposed customized Convolutional Neural Networks (CNN) customized model based on CNN architecture that layers which are optionals may be included such as the layer of batch normalization to reduce time taken for training and a layer with a dropout to deal with overfitting. We employed a huge dataset of chest CT slices images from diverse sources COVIDx-CT, which consists of a 16,146-image dataset with 810 patients of various nationalities. The proposed customized model's classification results compared to the VGG-16, Alex Net, and ResNet50 Deep Learning models. The proposed CNN model shows robustness by achieving an overall accuracy of 93% compared to 88%, 89%, and 95% for the VGG-16, Alex Net, and ResNet50 DL models for the classification of 3 classes. When this relates to binary classification, the classification accuracy of the proposed model and the VGG-16 models were identical (almost 100% accurate), with 0.17% of misclassification in the class of Non-Covid-19, the Alex Net model achieved almost 100% classification accuracy with 0.33% misclassification in the class of Non-Covid-19. Finally, ResNet50 achieved 95% classification accuracy with 5% misclassification in the Non-Covid-19 class.
    Źródło:
    International Journal of Electronics and Telecommunications; 2022, 68, 4; 731--739
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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