000			03190nam a22003257a 4500
003			OSt
005			20260615152500.0
008			260615b |||||||| |||| 00| 0 eng d
040			_bEnglish _cTUPM _dTUPM _erda
050			_aBTH QA 76.9 _bC33 2025
100			_aCabangbang, Mark Joshua B. _eAuthor
245			_aDesign and Development of a Software-Integrated Thermal Night Vision for Military Field Operations/ _cMark Joshua B. Cabangbang, John Lester Luis Cruz, Britgitte Jhay Linsangan, Kriscel Ann Marie C. Noay, and Cris Lester C. Sumalpong..-
260			_aManila: _bTechnological University of the Philippines, _c2025.
300			_aix, 88 pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's Thesis
502			_aCollege of Industrial Technology..- _bBachelor of Engineering Technology Major in Computer Engineering Technology: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aThe study describes the creation of a software-integrated thermal night vision system using a FLIR Lepton 3.5 thermal camera and a Raspberry pi NoIR camera to improve the visibility of targets surfaced during military field operations in low-light and zero-light conditions. A portable tripod- or strap attachment prototype includes a 7.4 V Li-Po battery to power the device, along with solar-assisted charging, DC-DC conversion, and a Raspberry Pi 5 processing unit. The software architecture consists of Python backend processing, image analysis, and/or OpenCV-based AI object detection capabilities with a JavaScript web interface, to support remote dashboard capabilities. Cloud integration leverages Render for backend deployment, MongoDB Atlas for a secure cloud implementation, and Cloudflare for DNS routing, CDN acceleration, and edge security. This allows remote syncing, telemetry logging, scalability of data, and offline operation in the field. System performance was assessed on the basis of a lot of factors including accuracy and reliability of the image data, thermal range consistency, system responsiveness, reliability of the user interface and operation. Performance data were collected and analyzed using descriptive statistics. This study aligns with the Technological University of the Philippines’ (TUP) research agenda on Defense and Security Systems, the Department of Science and Technology’s (DOST) priority areas on emerging technologies, and the United Nations Sustainable Development Goal (SDG) on Peace, Justice, and Strong Institutions (Goal 16) by contributing to technological advancements that enhance safety, situational awareness, and field operational support. Keywords: Thermal Imaging, Night Vision, Dual-Vision Camera System, FLIR Lepton 3.5, Raspberry Pi 5
650			_aComputer Engineering Technology
650			_aThermal Imaging
650			_aNight Vision
700			_aCruz, John Lester Luis _eAuthor
700			_aLinsangan, Britgitte Jhay _eAuthor
700			_aNoay, Kriscel Ann Marie C. _eAuthor
700			_aSumalpong, Cris Lester C. _eAuthor
942			_2lcc _cBTH CIT _n0
999			_c31523 _d31522