Nampeng, Jindapa

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Nampeng, Jindapa
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jindapa.na@kmitl.ac.th

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    Item type:Publication,
    Tele-Operation of Robotic Arm
    (2019-11-01)
    Bunkum, Manao
    ;
    Vachirasakulchai, Phakjira
    ;
    Nampeng, Jindapa
    ;
    Tommajaree, Ramita
    ;
    Visitsattapongse, Sarinporn
    This research is for facilitate the healthcare worker to approach patient that located in distant areas via internet. Also, patient need not to get to the city to see a doctor. Hence, this research will reduce time and cost of traveling. This research is to create tele-control robot arm, use in the operating room, which is controlled by joystick from anywhere. It is separated into 2 parts that is hardware part to design and print 3D robot arm and joystick and software part, which is code to control robot arm also send controlling data via internet. Furthermore, this project will send real-time video from robot arm to controller side. The data is sent between robot arm and joystick by nodeMCU or in this project use two ESP32, one will receive data from joystick and send the data to another for control robot arm. In the section of sending real-time video, this project use Raspberry pi as a device for sending.
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    Item type:Publication,
    Bacterial Colony Counting and Classification System Based on Deep Learning Model
    (2026-02-01)
    Pintavirooj, Chuchart
    ;
    Bunkum, Manao
    ;
    Vongmanee, Naphatsawan
    ;
    Nampeng, Jindapa
    ;
    Visitsattapongse, Sarinporn
    Microbiological analysis is crucial for identifying species, assessing infections, and diagnosing infectious diseases, thereby supporting both research studies and medical diagnosis. In response to these needs, accurate and efficient identification of bacterial colonies is essential. Conventionally, this process is performed through manual counting and visual inspection of colonies on agar plates. However, this approach is prone to several limitations arising from human error and external factors such as lighting conditions, surface reflections, and image resolution. To overcome these limitations, an automated bacterial colony counting and classification system was developed by integrating a custom-designed imaging device with advanced deep learning models. The imaging device incorporates controlled illumination, matte-coated surfaces, and a high-resolution camera to minimize reflections and external noise, thereby ensuring consistent and reliable image acquisition. Image-processing algorithms implemented in MATLAB were employed to detect bacterial colonies, remove background artifacts, and generate cropped colony images for subsequent classification. A dataset comprising nine bacterial species was compiled and systematically evaluated using five deep learning architectures: ResNet-18, ResNet-50, Inception V3, GoogLeNet, and the state-of-the-art EfficientNet-B0. Experimental results demonstrated high colony-counting accuracy, with a mean accuracy of 90.79% ± 5.25% compared to manual counting. The coefficient of determination (R<sup>2</sup> = 0.9083) indicated a strong correlation between automated and manual counting results. For colony classification, EfficientNet-B0 achieved the best performance, with an accuracy of 99.78% and a macro-F1 score of 0.99, demonstrating strong capability in distinguishing morphologically distinct colonies such as Serratia marcescens. Compared with previous studies, this research provides a time-efficient and scalable solution that balances high accuracy with computational efficiency. Overall, the findings highlight the potential of combining optimized imaging systems with modern lightweight deep learning models to advance microbiological diagnostics and improve routine laboratory workflows.