Adaptive Learning for Raspberry Pi Controlled Smart Streetlights
Department
Electrical and Computer Engineering
Major
Electrical Engineering
Research Advisor
Obafemi-Ajayi, Tayo
Advisor's Department
Electrical and Computer Engineering
Funding Source
Missouri State Cooperative Engineering Department Self
Abstract
For the project, a raspberry pi, and an Arduino control house lights in response to detecting cars with ultrasonic distance sensors. The project aims to demonstrate that street lights could save energy by adapting to traffic via neural networks in the raspberry pi, which would make the lights turn off when no cars are driving by at night, and flickering could be reduced. The project also investigates whether lights consume more energy when flickering, as compared to being powered constantly. In theory, neural networks reduce the energy consumed by smart-street lights, as well as flickering; ultrasonic distance sensors detect cars and send feedback to the neural networks, and the networks adapt the lights’ behavior based off of the learned traffic patterns. The project has successfully detected a car and turned a house lamp on and off with an ultrasonic distance sensor.
Biography
Patrick Toplikar is enrolled in the cooperative engineering program in Springfield, Missouri. He is pursuing an Electrical Engineering undergraduate degree from Missouri University of Science and Technology, as well as an Applied Physics degree from Missouri State University.
Research Category
Engineering
Presentation Type
Oral Presentation
Document Type
Presentation
Location
Carver Room
Presentation Date
17 Apr 2018, 11:30 am - 12:00 pm
Adaptive Learning for Raspberry Pi Controlled Smart Streetlights
Carver Room
For the project, a raspberry pi, and an Arduino control house lights in response to detecting cars with ultrasonic distance sensors. The project aims to demonstrate that street lights could save energy by adapting to traffic via neural networks in the raspberry pi, which would make the lights turn off when no cars are driving by at night, and flickering could be reduced. The project also investigates whether lights consume more energy when flickering, as compared to being powered constantly. In theory, neural networks reduce the energy consumed by smart-street lights, as well as flickering; ultrasonic distance sensors detect cars and send feedback to the neural networks, and the networks adapt the lights’ behavior based off of the learned traffic patterns. The project has successfully detected a car and turned a house lamp on and off with an ultrasonic distance sensor.
