A team of students from an engineering college in Delhi took part in a competition conducted by the prestigious US-based Marconi Society for developing an innovative mobile application. The application developed by Delhi students estimates the quality of air prevalent in the atmosphere. An interesting thing about the mobile application is that it estimates air quality in one’s neighbourhood by analysing the images clicked through the smartphone camera.
The mobile application to check air quality levels was developed by Prerana Khanna, Kanishk Jeet and Tanmay Srivastava from Bharati Vidyapeeth’s College of Engineering. The mobile application developed by these students bagged the top spot in the contest organised by Marconi Society in India under the Celestini Programme.
The winning team bagged USD 1,500 for their solution. The mobile application developed by the team is inexpensive, real-time air quality analytics application and portable. The app has been named as Air Cognizer. Through this app, the user uploads an image snapped outdoors with half of the image covering the sky region. With the help of image processing techniques and other features, the Air Quality Index (AQI) levels for the user location will be estimated.
The mobile application – Air Cognizer is available for download on Google Play Store. The app will be useful for the citizens in the highest populated and polluted cities like Delhi, Mumbai etc.
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Radhika Dua and Divyam Madaan from UIET Chandigarh (Panjab University) bagged the second prize in the contest. These students created a website to forecast air pollution levels in Delhi over the next 24 hours. The website has been created by using advanced machine learning techniques such as LSTM (Long Short-Term Memory) to predict the causes of major pollutants in the atmosphere.
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Apart from first prize, Bharati Vidyapeeth’s Engineering College also bagged third prize in the contest. The winning team prototyped a low-latency platform that transmits vehicle-to-vehicle alerts about collisions or potential road safety hazards through computer vision techniques.
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