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    Item type:Publication,
    Waste Level Detectable Trash Can with ESP32 and Ultrasonic Sensors
    (2025-01-01)
    Soingkittiphong, Anyamanee
    ;
    Nuchairum, Nattakorn
    ;
    Somviphat, Krittaphat
    ;
    Oo, Saung Hnin Pwint
    ;
    Yoshikawa, Yuki
    This paper introduces an IoT-based waste monitoring system that integrates ultrasonic sensors with an ESP32 microcontroller to detect and display waste levels in trash bins. The system is designed to show whether a bin is full or empty, providing real-time waste level updates through an LCD panel on the bin and pop-up notifications via a web application. Our experimental setup uses five ultrasonic sensors, each emitting ultrasonic pulses at 40 kHz, to determine the distance between the trash can lid and the closest rubbish. The sensor also detects the reflected pulse when an object blocks the signal. After getting data from all sensors, the ESP32, programmed in C, processes these data and determines whether the bin is "FULL" or "EMPTY." The system integrates an IEEE1888 gateway for standardized data exchange and a Pocket-WiFi module for simplified network connectivity. This system is invented under the Zero-Piece concept, aiming to eliminate litter on streets and roads. Given the challenges of overflowing trash in Thailand's urban areas, we propose deploying these smart bins along city roads to improve waste management and maintain cleaner public spaces.
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    Item type:Publication,
    Detection of OCR Misclassifications in ALPR via Rapid Prototyping
    (2025-01-01)
    Oo, Saung Hnin Pwint
    ;
    Yoshikawa, Yuki
    ;
    Kurimoto, Ikusaburo
    This paper originates from the limitations of traditional Optical Character Recognition (OCR) systems, particularly in recognizing multilingual texts (English and Thai characters) within Automatic License Plate Recognition (ALPR). A common challenge in current OCR models is character confusion, such as misreading between “0” and “O”, and “8” and “B,” which can significantly impact character recognition accuracy. Since OCR performance directly affects the overall effectiveness of ALPR systems, such misclassifications lead to incorrect vehicle identification. Therefore, this paper introduces a rapid prototyping approach for ALPR, focusing on multilingual license plates, especially Thai license plates that include both Latin (English) and non-Latin (Thai) characters. It aims to emphasize the identification and analysis of OCR misclassifications in how characters are recognized by OCR.
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