Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies最新文献_第6页

Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques 利用计算机视觉技术对捕获标本中的蚊虫媒介进行自动监测

Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies Pub Date : 2020-05-25 DOI: 10.1145/3378393.3402260

M. Minakshi, Pratool Bharti, Willie McClinton, Jamshidbek Mirzakhalov, R. Carney, S. Chellappan

{"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}

引用次数: 10

SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays 日落:住宅阵列模型驱动的每面板太阳异常检测

Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies Pub Date : 2020-05-25 DOI: 10.1145/3378393.3402257

Menghong Feng, Noman Bashir, P. Shenoy, David E. Irwin, B. Kosanovic

引用次数: 7

Extend 扩展

Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies Pub Date : 2020-02-07 DOI: 10.32388/21v48h

Santiago Correa, Noman Bashir, Andrew Tran, David E. Irwin, Jay Taneja

引用次数: 0

Modulo: Drive-by Sensing at City-scale on the Cheap 模组:城市规模的低成本驾驶感应

Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies Pub Date : 2019-10-21 DOI: 10.1145/3378393.3402275

Dhruv Agarwal, Srinivasan Iyengar, Manohar Swaminathan

{"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}

引用次数: 10

Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies 第三届ACM SIGCAS计算与可持续社会会议论文集

Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies Pub Date : 1900-01-01 DOI: 10.1145/3378393

引用次数: 0