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  <titleInfo>
    <title>Development of mobile application for seamless area surveillance box connectivity and network management</title>
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  <name type="personal">
    <namePart>Sareen, Ritik</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
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  <name type="personal">
    <namePart>Chutiporn Anutariya</namePart>
    <role>
      <roleTerm type="text">Chairperson</roleTerm>
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  <name type="personal">
    <namePart>Mongkol Ekpanyapong</namePart>
    <role>
      <roleTerm type="text">Co-chairperson</roleTerm>
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  <name type="personal">
    <namePart>Attaphongse Taparugssanagorn</namePart>
    <role>
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  <name type="personal">
    <namePart>Chantri Polprasert</namePart>
    <role>
      <roleTerm type="text">Examination Committee</roleTerm>
    </role>
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  <name type="corporate">
    <namePart>AIT Scholarship</namePart>
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  <genre authority="marc">technical report</genre>
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    <place>
      <placeTerm type="text">Pathum Thani, Thailand</placeTerm>
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    <publisher>Asian Institute of Technology</publisher>
    <dateIssued>2025</dateIssued>
    <issuance>continuing</issuance>
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    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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  <abstract>With the rapid advancement of technology, AI-powered surveillance systems are  becoming increasingly essential for security and monitoring applications. The AS Box,  an AI-driven computer vision device, is designed to perform license plate recognition,  motion detection, and other real-time surveillance tasks. However, configuring the AS  Box for network connectivity remains a challenge, requiring users to manually connect  peripherals and input Wi-Fi credentials, making the setup process complex and  inefficient. This study focuses on the development of a mobile application to streamline the  network configuration process of the AS Box. The application, developed using Flutter, facilitates hotspot-based pairing, Wi-Fi/LAN connectivity, and automated IP address  retrieval. A FastAPI backend is integrated into the AS Box to handle network requests  and ensure seamless communication between the device and the mobile application. The system also incorporates SQLite for local storage, allowing users to manage  multiple AS Boxes efficiently. The development process follows the Agile methodology to ensure rapid iterations,  software quality, and risk reduction. Extensive unit testing and quality assurance are  carried out, focusing on backend stability, API reliability, and overall system performance. The final product undergoes rigorous testing phases, from internal  validation to beta deployment, ensuring a robust and user-friendly experience before full-scale implementation.By automating network setup and device management, this mobile application enhances the usability and scalability of the AS Box, making AI-powered surveillance  solutions more accessible, efficient, and user-friendly. </abstract>
  <note>An Internship Study submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Information Management </note>
  <note>Internship Report (M. Eng.) - Asian Institute of Technology, 2025</note>
  <subject authority="lcsh">
    <topic>Mobile apps</topic>
    <topic>Development</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Artificial intelligence</topic>
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      <title>Internship Report ; no. IM-25-01</title>
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