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Articles 31 - 33 of 33
Full-Text Articles in Engineering
Exploring The Relationship Between Teamwork Skills And Team Members' Centrality, Francisco Cima, Pilar Pazos, Ana Maria Canto
Exploring The Relationship Between Teamwork Skills And Team Members' Centrality, Francisco Cima, Pilar Pazos, Ana Maria Canto
Engineering Management & Systems Engineering Faculty Publications
The present paper describes an exploratory study of small teams working on a four-month project as part of a graduate engineering program. The research had two primary goals. The first was to utilize the log files from shared repositories used for team collaboration to describe the network structure of the teams. The second was to determine whether the network centrality of any individual team member is associated with their teamwork skills and attitudes towards the collaboration platform. The relationship between teamwork skills, attitudes towards the collaboration technology, and the centrality index was explored using Pearson correlations. A total of 35 …
Deepmag+ : Sniffing Mobile Apps In Magnetic Field Through Deep Learning, Rui Ning, Cong Wang, Chunsheng Xin, Jiang Li, Hongyi Wu
Deepmag+ : Sniffing Mobile Apps In Magnetic Field Through Deep Learning, Rui Ning, Cong Wang, Chunsheng Xin, Jiang Li, Hongyi Wu
Electrical & Computer Engineering Faculty Publications
This paper reports a new side-channel attack to smartphones using the unrestricted magnetic sensor data. We demonstrate that attackers can effectively infer the Apps being used on a smartphone with an accuracy of over 80%, through training a deep Convolutional Neural Networks (CNN). Various signal processing strategies have been studied for feature extractions, including a tempogram based scheme. Moreover, by further exploiting the unrestricted motion sensor to cluster magnetometer data, the sniffing accuracy can increase to as high as 98%. To mitigate such attacks, we propose a noise injection scheme that can effectively reduce the App sniffing accuracy to only …
Vegetation Detection Using Deep Learning And Conventional Methods, Bulent Ayhan, Chiman Kwan, Bence Budavari, Liyun Kwan, Yan Lu, Daniel Perez, Jiang Li, Dimitrios Skarlatos, Marinos Vlachos
Vegetation Detection Using Deep Learning And Conventional Methods, Bulent Ayhan, Chiman Kwan, Bence Budavari, Liyun Kwan, Yan Lu, Daniel Perez, Jiang Li, Dimitrios Skarlatos, Marinos Vlachos
Electrical & Computer Engineering Faculty Publications
Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer …