The 2022 InnovateFPGA contest, a sustainability-themed competition to develop projects based on Intel® SoC FPGAs, has reached the final stage with eleven finalists spread around the world. The theme behind this year’s competition is that the intelligent use of FPGAs in edge and cloud applications can help to reduce the demands on the Earth’s natural resources.
Terasic organized the contest with the help and support of the contest’s three Diamond Partners: Analog Devices, Intel, and Microsoft Azure. Other contest sponsors include Arrow, Digi-Key, Macnica, and Mouser Electronics. Regional competition finals took place in April of this year and the competition’s Grand Final takes place this month.
Here are the project names, team names, and brief descriptions of the eleven finalists:
Drone package delivery safety in turbulent atmospheric conditions, Foale Aerospace - This project seeks to develop a scout drone to detect atmospheric upsets such as turbulence generated around buildings in windy conditions or city thermals. The scout drone sends near real-time turbulence location data via the cloud to cargo drones to ensure safe delivery of packages.
Cloud-Based Management of Solar Converters, Daniel Chavez, Universidad Nacional de Ingenieria - This system collects local point-of-use energy consumption data and send it to the cloud, where it can be used to improve the efficiency of generating and delivering electricity.
Fruit Waste Reduction System, Nixon Fernando Ortiz De La Cruz, Universidad Nacional de Ingenieria and Nacional Mayor de San Marcos - This smart system monitors, reports, and manages smart fruit storage/transportation containers to help reduce the amount of produce spoilage by helping to prevent premature fruit ripening.
Intelligent Farming Optimizer, Jyotsna Bavisetti, Rajiv Gandhi University of Knowledge Technologies, Nuzvid - This comprehensive farming aid recommends suitable crops based on soil condition, climate, and water availability. It can control and optimize irrigation, detect plant disease, detect weeds, and provide guidance to farmers.
Consumer Mini-Greenhouse System, Pahan Mendis, University of Moratuwa, Sri Lanka - This smart, automated mini-greenhouse management system provides irrigation, fertilization, ventilation, and light guidance for urban food growers, who have minimal farming expertise, using AI to predict crop yields and identify abnormal growth behavior.
Mental Health Advisor, Sudhamshu B N, Dayananda Sagar College of Engineering-This project involves development of a smart glove that collects various human body and environmental parameters and uses machine-learning models to analyze and classify various targeted mental health conditions based on symptoms, while providing timely positive recommendations to the user.
Coral Reef Recovery, Jose Filho, King Abdullah University of Science and Technology - This system is designed to stop coral bleaching and aid in coral reef recovery by delivering probiotics to beneficial microorganisms in the marine environment and monitoring the long-term efficacy of these probiotics.
Indoor Air Quality Management, Ricardo Núñez Prieto, NVISION s.l. / Institute of Microelectronics of Barcelona (CSIC) / UAB - Chronic exposure to CO2 concentrations as low as 1000ppm have been linked to several health disorders. This system measures exhaled CO2 to assess air quality risks.
Smart Farm Control System, Mohamed Abdelaziz Louhab, University M'hamed Bougara Boumerdes - This smart greenhouse incorporates sensors to monitor plant health using environmental factors such as temperature, humidity, and the presence of gasses such as O2 and CO2 to control irrigation, heating, and cooling and to provide guidance to the farmer.
Automatic Garbage Sorter, Longfei Yang, Hubei University - This automatic garbage sorter helps to reduce environmental pollution, save land resources, and promote resource recycling by classifying waste to reduce the thoughtless disposal of recyclable garbage and to maximize the potential for reusing recyclable waste.
Pavement Damage Detection System, Dingwei Chen, Chongqing University - This automated detection, location, and reporting system uses 3D lidar and cameras to capture real-time road condition and precise location data. Both pieces of data can be combined and analyzed to determine if repairs are needed, which helps to prevent costly road surface deterioration.
All of these contest entries were developed using the common contest platform¾the Terasic FPGA Cloud Connectivity Kit. This contest platform is a Microsoft Azure Certified IoT plug-and-play development platform that incorporates a Cyclone® V SoC FPGA.
For more information about the contest and the finalists, click here.
You must be a registered user to add a comment. If you've already registered, sign in. Otherwise, register and sign in.