{"id":2684,"date":"2019-09-03T23:33:15","date_gmt":"2019-09-04T03:33:15","guid":{"rendered":"https:\/\/portfolios.cs.earlham.edu\/?p=2684"},"modified":"2019-09-03T23:36:55","modified_gmt":"2019-09-04T03:36:55","slug":"cs388-week2-three-ideas-2","status":"publish","type":"post","link":"https:\/\/portfolios.cs.earlham.edu\/index.php\/2019\/09\/03\/cs388-week2-three-ideas-2\/","title":{"rendered":"CS388 \u2013 Week2 \u2013 Three Ideas"},"content":{"rendered":"\n

1 ) Name of Your Project<\/strong>: Improve Fire Identification Mapping and Monitoring Algorithm (FIMMA).<\/strong><\/p>\n\n\n\n

Enhance FIMMA to reduce the number of false-positive results and apply the algorithm to detect fire in urban areas. <\/p>\n\n\n\n

2 ) Name of Your Project<\/strong>: Fire Detection Using A Combination of Different Image Analysis Techniques.<\/strong><\/p>\n\n\n\n

The traditional methods to detect forest fires such as using mechanical devices or humans are not effective on a global scale. With the advancement of technology, detecting forest fire using image analysis has proven to be promising due to its low cost and effectiveness on a global scale. However, these analyses generally focus on only one technique, either analyzing the images in one range of the electromagnetic spectrum or study the heat signature of the fire. The research aims to detect fire by analyzing different attributes (heat, temperature of the surrounding area, temperature and color of the smoke) at different ranges of the electromagnetic spectrum (UV, visible, and infrared).<\/p>\n\n\n\n

3) Name of Your Project:<\/strong> Real-time Fire Tracking System.<\/strong><\/p>\n\n\n\n

A wildfire usually spreads rapidly within hours from the start, which means responding quickly to the fire can lead to fewer damages and casualties. The research aims to create a system that can provide current data for active fire as well as calculate the direction it is moving. The system can combine the data from Google Earth and NASA’s Fire Map to provide the current condition of the fire. It will also need real-time data of a given location, particularly weather (wind, humidity) and local condition (population, buildings) in order to make predictions.
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