Phasukkit, Pattarapong

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Phasukkit, Pattarapong
Alternative Name
Phasukkit, P.
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pattarapong.ph@kmitl.ac.th

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    Item type:Publication,
    Doppler radar for dynamic hand gesture recognition based on signal image processing
    (2019-07-01)
    Arthamanolap, Kongphum
    ;
    Gabbualoy, Somprasong
    ;
    Phasukkit, Pattarapong
    From previous researches, Doppler radar was used to detect signal for implement with many applications. Nevertheless, it is difficult to analyze for recognize object. At present, technique of deep learning in terms of signal processing and image processing are using in many researches to classify categories of data. In this paper, signal image was used by deep learning to classify hand gesture by receiving signals from 24GHz transceiver: BGT24MTR11. We transformed the signals to images for 3 categories including Spectrogram, Time domain from original signal and feature MFCC graph. After that those of converted image will be trained by Deep learning for classify the hand gesture types. From the result of this experiment has been shown that signal image can be used to recognize hand gesture and Spectrogram graph makes the highest accuracy as 94%.
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    Metal Artifact Recognition using Deep Convolutional Neural Network in Abdomen and Pelvis CT Image
    (2019-11-01)
    Sakunrat, Rattasart
    ;
    Arthamanolap, Kongphum
    ;
    Phasukkit, Pattarapong
    In medicine, radiotherapy image obtained from CT or MRI is an important part in planning to find dosage for radiation treatment for cancer patients. Those images that obtained from CT may have artifacts caused by many factors such as motion artifact, metal artifact, scatter, ring artifact, pseudo enhancement and cone beam effect which is a component that makes image analysis worse. For improvement the quality of image that have artifact, image processing technique is used to manage and reduce it before bring to apply in radiotherapy of medicine. However, for finding the artifacts of 3D image is so difficultly and take more time because 3D image has to split into 2D image before. The aim of this research is to identify the artifact noise in 2D slice by using Deep Convolutional Neural Network (DCNN) model of metal artifact for reduce time. In this experiment have divided dataset into two types of image include 100 image of artifacts and 100 image Non-artifacts for training and 20 images for testing. The result has been shown that accuracy of artifacts and Non-artifacts are 76% and 62.28% respectively. In addition, this studied has found that a small amount of data from Thailand individual results in less accuracy.
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