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    Wyświetlanie 1-7 z 7
    Tytuł:
    Influence of intake system modification on the formula student engine power.
    Autorzy:
    Markiewicz, T.
    Kaczmarczyk, Ł.
    Fabiś, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/198373.pdf
    Data publikacji:
    2017
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    Formula Student
    intake
    engine power
    wlot
    moc silnika
    Opis:
    This paper is a summary of the design and workmanship of the formula student engine intake vehicle, for simulation research projects were conducted on the intake system. In the process, the most favourable model system was selected, which was capable of producing a satisfactory range of the characteristics of the engine. For chosen models, the intake system was also determined in terms of its impact on the power and torque of the test vehicle, which was driven by a four-cylinder engine with a displacement of 0.6 dm3 .
    Źródło:
    Zeszyty Naukowe. Transport / Politechnika Śląska; 2017, 94; 139-149
    0209-3324
    2450-1549
    Pojawia się w:
    Zeszyty Naukowe. Transport / Politechnika Śląska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Recognition of atherosclerotic plaques and their extended dimensioning with computerized tomography angiography imaging
    Autorzy:
    Markiewicz, T.
    Dziekiewicz, M.
    Maruszyński, M.
    Bogusławska-Walecka, R.
    Kozłowski, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/330374.pdf
    Data publikacji:
    2014
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    computed tomography
    atherosclerotic plaque
    image processing
    approximation
    tomografia komputerowa
    blaszka miażdżycowa
    przetwarzanie obrazu
    aproksymacja
    Opis:
    In this paper the authors raise the issue of automatic discrimination of atherosclerotic plaques within an artery lumen based on numerical and statistical thresholding of Computerized Tomography Angiographic (CTA) images and their advanced dimensioning as a support for preoperative vessel assessment. For the study, a set of tomograms of the aorta, as well as the ilio-femoral and femoral arteries were examined. In each case a sequence of about 130–480 images of the artery cutoff planes were analyzed prior to their segmentation based on morphological image transformation. A crucial step in the staging of atherosclerotic alteration is recognition of the plaque in the CTA image. To solve this problem, statistical and linear fitting methods, including the least-squares approximation by polynomial and spline polynomial functions, as well as the error fitting function were used. Also, new descriptors of atherosclerotic changes, such as the lumen decrease factor, the circumference occupancy factor, and the convex plaque area factor, are proposed as a means of facilitating preoperative vessel examination. Finally, ways to reduce the computational time are discussed. The proposed methods can be very useful for automatic quantification of atherosclerotic changes visualized by CTA imaging.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 33-47
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Thresholding techniques for segmentation of atherosclerotic plaque and lumen areas in vascular arteries
    Autorzy:
    Markiewicz, T.
    Dziekiewicz, M.
    Osowski, S.
    Maruszyński, M.
    Kozłowski, W.
    Bogusławska-Walecka, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/201734.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    image processing
    thresholding methods
    computed tomography
    vascular image parameterization
    przetwarzanie obrazu
    metody progowania
    tomografia komputerowa
    Opis:
    The paper develops the automatic methods of segmentation of the blood vessel area in the images of the multi-slice computed tomography, allowing to separate the lumen from the atherosclerotic plaque areas. The solution is based on the application of different implementations of thresholding, including between class variance in a bimodal mode, Gaussian mixture modeling, clustering technique, polynomial and multilayer perceptron approximations. These methods are compared with many examples of arteries of different percentage of the plaque occupancy in the iliac and femoral arteries. The numerical results of segmentation have been verified by the medical experts and prove its usefulness in medical practice. The presented system can find application in an automatic evaluation of the atherosclerosis progression/regression of patients on the basis of sequence of Computed Tomography slice images.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 1; 269-280
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning for damaged tissue detection and segmentation in Ki-67 brain tumor specimens based on the U-net model
    Autorzy:
    Swiderska-Chadaj, Z.
    Markiewicz, T.
    Gallego, J.
    Bueno, G.
    Grala, B.
    Lorent, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/202412.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    damaged tissue regions detection
    artifacts detection
    deep learning
    Ki-67 staining specimens
    wykrywanie uszkodzonych regionów tkankowych
    wykrywanie artefaktów
    artefakt
    uczenie głębokie
    próbki barwiące Ki-67
    Opis:
    The pathologists follow a systematic and partially manual process to obtain histological tissue sections from the biological tissue extracted from patients. This process is far from being perfect and can introduce some errors in the quality of the tissue sections (distortions, deformations, folds and tissue breaks). In this paper, we propose a deep learning (DL) method for the detection and segmentation of these damaged regions in whole slide images (WSIs). The proposed technique is based on convolutional neural networks (CNNs) and uses the U-net model to achieve the pixel-wise segmentation of these unwanted regions. The results obtained show that this technique yields satisfactory results and can be applied as a pre-processing step for automatic WSI analysis in order to prevent the use of the damaged areas in the evaluation processes.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 849-856
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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