M. Minakshi, Pratool Bharti, Willie McClinton, Jamshidbek Mirzakhalov, R. Carney, S. Chellappan
Among all animals, mosquitoes are responsible for the most deaths worldwide. Interestingly, not all types of mosquitoes spread diseases, but rather, a select few alone are competent enough to do so. In the case of any disease outbreak, an important first step is surveillance of vectors (i.e., those mosquitoes capable of spreading diseases). To do this today, public health workers lay several mosquito traps in the area of interest. Hundreds of mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have to identify only the vectors to gauge their density. This process today is manual, requires complex expertise/ training, and is based on visual inspection of each trapped specimen under a microscope. It is long, stressful and self-limiting. This paper presents an innovative solution to this problem. Our technique assumes the presence of an embedded camera (similar to those in smart-phones) that can take pictures of trapped mosquitoes. Our techniques proposed here will then process these images to automatically classify the genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer Learning yielded an overall accuracy of 80% in classifying mosquitoes when trained on 25, 867 images of 250 trapped mosquito vector specimens captured via many smart-phone cameras. In particular, the accuracy of our model in classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are especially deadly vectors) is amongst the highest. We also present important lessons learned and practical impact of our techniques in this paper.
{"title":"Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques","authors":"M. Minakshi, Pratool Bharti, Willie McClinton, Jamshidbek Mirzakhalov, R. Carney, S. Chellappan","doi":"10.1145/3378393.3402260","DOIUrl":"https://doi.org/10.1145/3378393.3402260","url":null,"abstract":"Among all animals, mosquitoes are responsible for the most deaths worldwide. Interestingly, not all types of mosquitoes spread diseases, but rather, a select few alone are competent enough to do so. In the case of any disease outbreak, an important first step is surveillance of vectors (i.e., those mosquitoes capable of spreading diseases). To do this today, public health workers lay several mosquito traps in the area of interest. Hundreds of mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have to identify only the vectors to gauge their density. This process today is manual, requires complex expertise/ training, and is based on visual inspection of each trapped specimen under a microscope. It is long, stressful and self-limiting. This paper presents an innovative solution to this problem. Our technique assumes the presence of an embedded camera (similar to those in smart-phones) that can take pictures of trapped mosquitoes. Our techniques proposed here will then process these images to automatically classify the genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer Learning yielded an overall accuracy of 80% in classifying mosquitoes when trained on 25, 867 images of 250 trapped mosquito vector specimens captured via many smart-phone cameras. In particular, the accuracy of our model in classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are especially deadly vectors) is amongst the highest. We also present important lessons learned and practical impact of our techniques in this paper.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116141766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Menghong Feng, Noman Bashir, P. Shenoy, David E. Irwin, B. Kosanovic
Solar arrays often experience faults that go undetected for long periods of time, resulting in generation and revenue losses. In this paper, we present SunDown, a sensorless approach for detecting per-panel faults in solar arrays. SunDown's model-driven approach leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior, can handle concurrent faults in multiple panels, and performs anomaly classification to determine probable causes. Using two years of solar data from a real home and a manually generated dataset of solar faults, we show that our approach is able to detect and classify faults, including from snow, leaves and debris, and electrical failures with 99.13% accuracy, and can detect concurrent faults with 97.2% accuracy.
{"title":"SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays","authors":"Menghong Feng, Noman Bashir, P. Shenoy, David E. Irwin, B. Kosanovic","doi":"10.1145/3378393.3402257","DOIUrl":"https://doi.org/10.1145/3378393.3402257","url":null,"abstract":"Solar arrays often experience faults that go undetected for long periods of time, resulting in generation and revenue losses. In this paper, we present SunDown, a sensorless approach for detecting per-panel faults in solar arrays. SunDown's model-driven approach leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior, can handle concurrent faults in multiple panels, and performs anomaly classification to determine probable causes. Using two years of solar data from a real home and a manually generated dataset of solar faults, we show that our approach is able to detect and classify faults, including from snow, leaves and debris, and electrical failures with 99.13% accuracy, and can detect concurrent faults with 97.2% accuracy.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127384253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Santiago Correa, Noman Bashir, Andrew Tran, David E. Irwin, Jay Taneja
{"title":"Extend","authors":"Santiago Correa, Noman Bashir, Andrew Tran, David E. Irwin, Jay Taneja","doi":"10.32388/21v48h","DOIUrl":"https://doi.org/10.32388/21v48h","url":null,"abstract":"","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128789965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ambient air pollution in urban areas is a significant health hazard, with over 4.2 million deaths annually attributed to it. A crucial step in tackling these challenge is to measure air quality at a fine spatiotemporal granularity. A promising approach for several smart city projects, called drive-by sensing, is to leverage vehicles retrofitted with different sensors (pollution monitors, etc.) that can provide the desired spatiotemporal coverage at a fraction of the cost. However, deploying a drive-by sensing network at a city-scale to optimally select vehicles from a large fleet is still unexplored. In this paper, we propose Modulo -- a system to bootstrap drive-by sensing deployment by taking into consideration a variety of aspects such as spatiotemporal coverage, budget constraints. Modulo is well-suited to satisfy unique deployment constraints such as colocations with other sensors (needed for gas and PM sensor calibration), etc. We compare Modulo with two baseline algorithms on real-world taxi and bus datasets. Modulo significantly outperforms the baselines when a fleet comprises of both taxis and fixed-route vehicles such as public transport buses. Finally, we present a real-world case study that uses Modulo to select vehicles for an air pollution sensing application.
{"title":"Modulo: Drive-by Sensing at City-scale on the Cheap","authors":"Dhruv Agarwal, Srinivasan Iyengar, Manohar Swaminathan","doi":"10.1145/3378393.3402275","DOIUrl":"https://doi.org/10.1145/3378393.3402275","url":null,"abstract":"Ambient air pollution in urban areas is a significant health hazard, with over 4.2 million deaths annually attributed to it. A crucial step in tackling these challenge is to measure air quality at a fine spatiotemporal granularity. A promising approach for several smart city projects, called drive-by sensing, is to leverage vehicles retrofitted with different sensors (pollution monitors, etc.) that can provide the desired spatiotemporal coverage at a fraction of the cost. However, deploying a drive-by sensing network at a city-scale to optimally select vehicles from a large fleet is still unexplored. In this paper, we propose Modulo -- a system to bootstrap drive-by sensing deployment by taking into consideration a variety of aspects such as spatiotemporal coverage, budget constraints. Modulo is well-suited to satisfy unique deployment constraints such as colocations with other sensors (needed for gas and PM sensor calibration), etc. We compare Modulo with two baseline algorithms on real-world taxi and bus datasets. Modulo significantly outperforms the baselines when a fleet comprises of both taxis and fixed-route vehicles such as public transport buses. Finally, we present a real-world case study that uses Modulo to select vehicles for an air pollution sensing application.","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133029246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","authors":"","doi":"10.1145/3378393","DOIUrl":"https://doi.org/10.1145/3378393","url":null,"abstract":"","PeriodicalId":176951,"journal":{"name":"Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131849941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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