BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260802T042945Z UID:9B213E7B-E403-4EAB-924A-3DA67BFB062E DTSTART;TZID=America/New_York:20260828T100000 DTEND;TZID=America/New_York:20260828T110000 DESCRIPTION:Abstract:\n\nThe increasing demand for sustainable agriculture and autonomous monitoring systems has accelerated the need for intelligent technologies capable of operating independently in remote environments. T his research presents the design and development of a self-powered smart a griculture platform that integrates hybrid renewable energy harvesting wit h Edge Artificial Intelligence (Edge-AI) for real-time weather monitoring\ , rainfall prediction\, and intelligent irrigation management. The propose d system combines three renewable energy sources: a Vertical Axis Wind Tur bine (VAWT)\, a rotary Triboelectric Nanogenerator (TENG)\, and solar phot ovoltaic panels. The VAWT is designed to efficiently harvest low-speed win d energy\, while the rotary TENG converts rotational mechanical motion int o electrical energy through the triboelectric effect. Solar energy complem ents the system by providing additional power during daylight hours. Toget her\, these renewable sources form a hybrid energy harvesting architecture capable of delivering continuous and reliable power for autonomous agricu ltural applications. A custom hybrid power management circuit was develope d to integrate\, regulate\, and store energy generated from the three rene wable sources. The circuit incorporates rectification\, voltage regulation \, battery charging and protection\, DC-DC conversion\, and intelligent en ergy management to ensure a stable power supply for embedded electronics w hile maximizing energy utilization. The harvested energy powers a Raspberr y Pi-based Edge-AI platform equipped with environmental sensors and a came ra module. Deep learning models process visual weather data together with sensor measurements to classify weather conditions\, predict rainfall\, an d generate intelligent irrigation recommendations. Performing inference lo cally eliminates dependence on cloud connectivity\, reduces latency\, impr oves reliability\, and enables continuous operation in rural areas with li mited internet access. This interdisciplinary research integrates renewabl e energy harvesting\, mechanical system design\, circuit development\, emb edded electronics\, and artificial intelligence into a unified autonomous platform for precision agriculture. The proposed framework demonstrates th e feasibility of powering intelligent agricultural monitoring systems enti rely through renewable energy while enabling real-time decision-making usi ng Edge-AI. The research contributes toward scalable\, energy-independent technologies that support sustainable farming\, efficient resource utiliza tion\, and climate-resilient agricultural practices.\n\nSpeaker(s): \, Pre etham\n\nAgenda: \n10.00 am - Speaker Introduction\n\n10.05 am - Presentat ion\n\n10.45 am - Q&A Session\n\n11.00 am - Conclusion\n\nRoom: Conference Room at Department of ECE\, Bldg: College of Engineering\, Lawrence Techn ological University\, 21000 West Ten Mile Road\, Southfield\, MI\, 48075-1 058\, Southfield\, Michigan\, United States\, 48075 LOCATION:Room: Conference Room at Department of ECE\, Bldg: College of Engi neering\, Lawrence Technological University\, 21000 West Ten Mile Road\, S outhfield\, MI\, 48075-1058\, Southfield\, Michigan\, United States\, 4807 5 ORGANIZER:mguduri@ltu.edu SEQUENCE:13 SUMMARY:Soft Robotic Actuation and Triboelectric Energy Harvesting: Towards Sustainable Autonomous Systems URL;VALUE=URI:https://events.vtools.ieee.org/m/570861 X-ALT-DESC:Description: <br /><p>Abstract:</p>\n<p>The increasing demand fo r sustainable agriculture and autonomous monitoring systems has accelerate d the need for intelligent technologies capable of operating independently in remote environments. This research presents the design and development of a self-powered smart agriculture platform that integrates hybrid renew able energy harvesting with Edge Artificial Intelligence (Edge-AI) for rea l-time weather monitoring\, rainfall prediction\, and intelligent irrigati on management. The proposed system combines three renewable energy sources : a Vertical Axis Wind Turbine (VAWT)\, a rotary Triboelectric Nanogenerat or (TENG)\, and solar photovoltaic panels. The VAWT is designed to efficie ntly harvest low-speed wind energy\, while the rotary TENG converts rotati onal mechanical motion into electrical energy through the triboelectric ef fect. Solar energy complements the system by providing additional power du ring daylight hours. Together\, these renewable sources form a hybrid ener gy harvesting architecture capable of delivering continuous and reliable p ower for autonomous agricultural applications. A custom hybrid power manag ement circuit was developed to integrate\, regulate\, and store energy gen erated from the three renewable sources. The circuit incorporates rectific ation\, voltage regulation\, battery charging and protection\, DC-DC conve rsion\, and intelligent energy management to ensure a stable power supply for embedded electronics while maximizing energy utilization. The harveste d energy powers a Raspberry Pi-based Edge-AI platform equipped with enviro nmental sensors and a camera module. Deep learning models process visual w eather data together with sensor measurements to classify weather conditio ns\, predict rainfall\, and generate intelligent irrigation recommendation s. Performing inference locally eliminates dependence on cloud connectivit y\, reduces latency\, improves reliability\, and enables continuous operat ion in rural areas with limited internet access. This interdisciplinary re search integrates renewable energy harvesting\, mechanical system design\, circuit development\, embedded electronics\, and artificial intelligence into a unified autonomous platform for precision agriculture. The proposed framework demonstrates the feasibility of powering intelligent agricultur al monitoring systems entirely through renewable energy while enabling rea l-time decision-making using Edge-AI. The research contributes toward scal able\, energy-independent technologies that support sustainable farming\, efficient resource utilization\, and climate-resilient agricultural practi ces.</p><br /><br />Agenda: <br /><p>10.00 am - Speaker Introduction</p>\n <p>10.05 am - Presentation</p>\n<p>10.45 am - Q&amp\;A Session</p>\n<p>11. 00 am - Conclusion</p> END:VEVENT END:VCALENDAR