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    Tytuł:
    A New Diminutive Wide-band MIMO Antenna with Frequency Agile Features for 4G and 5G Diverse Wireless Applications
    Autorzy:
    Mudda, Shivleela
    K M, Gayathri
    Mallikarjun, M
    Powiązania:
    https://bibliotekanauki.pl/articles/27311969.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    Diversity gain (DG)
    Defective ground structure (DGS)
    Fractional bandwidth (FBW)
    ITU (International Telecommunication Union)
    INSAT
    Frequency Reconfigurable antenna (FRA)
    Multiple input multiple output (MIMO)
    mutual coupling
    isolation
    5G
    Envelope correlation coefficient (ECC)
    Opis:
    This paper demonstrates a low-profile, wide-band, two-element, frequency-reconfigurable MIMO antenna that is suitable for diverse wireless applications of 4G and 5G such as WLAN/Bluetooth (2.4–2.5 GHz), WLAN (2.4–2.484 GHz, 5.15– 5.35 GHz, and 5.725–5.825 GHz), WiMAX (3.3–3.69 GHz and 5.25–5.85 GHz), Sub6GHz band proposed for 5G (3.4–3.6 GHz, 3.6-3.8GHz and 4.4–4.99 GHz), INSAT and satellite X-band(6 to 9.6 GHz). Proposed MIMO favour effortless switching between multiple bands ranging from 2.2 to 9.4 GHz without causing any interference. Both antenna elements in a MIMO array are made up of a single module comprised of a slot-loaded patch and a defective structured ground. Two PIN diodes are placed in the preset position of the ground defect to achieve frequencyreconfigurable qualities. The suggested MIMO antenna has a size of 62 ×25 ×1.5 mm3. Previous reconfigurable MIMO designs improved isolation using a meander line resonator, faulty ground structures, or self-isolation approaches. To attain the isolation requirements of modern devices, stub approach is introduced in proposed design. Without use of stub, simulated isolation is 15dB. The addition of a stub improved isolation even more. At six resonances, measured isolation is greater than 18 dB, the computed correlation coefficient is below 0.0065, and diversity gain is over 9.8 dB.
    Źródło:
    International Journal of Electronics and Telecommunications; 2023, 69, 3; 439--448
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Edge artificial intelligence-based facial pain recognition during myocardial infarction
    Autorzy:
    Mohan, H. M.
    Shivaraj Kumara, H. C.
    Mallikarjun, S. H.
    Prasad, A. Y.
    Powiązania:
    https://bibliotekanauki.pl/articles/27314262.pdf
    Data publikacji:
    2022
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    vital signs
    myocardial infarction
    facial pain expression
    computer vision
    medical assistance
    convolution neural network
    Opis:
    Medical history highlights that myocardial infarction is one of the leading factors of death in human beings. Angina pectoris is a prominent vital sign of myocardial infarction. Medical reports suggest that experiencing chest pain during heart attacks causes changes in facial muscles, resulting in variations in patterns of facial expression. This work intends to develop an automatic facial expression detection to identify the severity of chest pain as a vital sign of MI, using an algorithmic approach that is implemented with a state-of-the-art convolutional neural network (CNN). The advanced object detection lightweight CNN models are as follows: Single Shot Detector Mobile Net V2, and Single Shot Detector Inception V2, which were utilized for designing the vital signs MI model from the 500 Red Blue Green Color images private dataset. The authors developed cardiac emergency health monitoring care using an Edge Artificial Intelligence (“Edge AI”) using NVIDIA’s Jetson Nano embedded GPU platform. The proposed model is mainly focused on the factors of low cost and less power consumption for onboard real-time detection of vital signs of myocardial infarction. The evaluated metrics achieve a mean Average Precision of 85.18%, Average Recall of 88.32%, and 6.85 frames per second for the generated detections.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 3; 40--55
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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