Classes began this week in Gainesville, and while this semester looks a bit different, the Machine Learning and Sensing Lab could not be more excited! We are anticipating a great school year with plenty of new collaboration, new discoveries, new… Read More
NewsNews
MIL-CAM ACCEPTED TO ECCV 2020 WORKSHOP ON COMPUTER VISION PROBLEMS IN PLANT PHENOTYPING!
Congratulations to our labmates and collaborators: Guohao Yu, Alina Zare, Weihuang Xu, Roser Matamala, Joel Reyes-Cabrera, Felix B. Fritschi and Thomas E. Juenger! Their paper, “Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM” was recently accepted to the 16th European Conference… Read More
WEAKLY SUPERVISED MINIRHIZOTRON IMAGE SEGMENTATION WITH MIL-CAM
Abstract: We present a multiple instance learning class activation map (MIL-CAM) approach for pixel-level minirhizotron image segmentation given weak image-level labels. Minirhizotrons are used to image plant roots in situ. Minirhizotron imagery is often composed of soil containing a few… Read More
UFII LECTURE SERIES: AI ADVANCES AND APPLICATIONS
In response to the recent AI initiative launched by the University of Florida, the UF Informatics Institute (UFII) is hosting a virtual seminar series, “AI Advances and Applications”. The online series will feature innovative work being conducted in AI and… Read More
STATE ESTIMATOR ACCEPTED TO NAPS 2020!
Congratulations to our labmates and collaborators Keerthiraj Nagaraj, Nader Aljohani, Sheng Zou, Cody Ruben, Arturo Bretas, Alina Zare and Janise McNair! Their paper, “State Estimator and Machine Learning Analysis of Residual Differences to Detect and Identify FDI and Parameter Errors… Read More
STATE ESTIMATOR AND MACHINE LEARNING ANALYSIS OF RESIDUAL DIFFERENCES TO DETECT AND IDENTIFY FDI AND PARAMETER ERRORS IN SMART GRIDS
Abstract: In the modern Smart Grid (SG), cyber-security is an increasingly important topic of research. An attacker can mislead the State Estimation (SE) process through a False Data Injection (FDI) on real-time measurement values or they can attack the parameters… Read More
CROSS-LAYERED DISTRIBUTED DATA-DRIVEN FRAMEWORK FOR ENHANCED SMART GRID CYBER-PHYSICAL SECURITY
Abstract: This paper presents a cross-layer strategy for detecting a variety of potential cyber-attacks on the Smart Grid. While most literature focus on False Data Injection attacks on the measurements taken throughout the Smart Grid, there are many ways in… Read More
A NETWORK PARAMETER DATABASE FDI CORRECTION PHYSICS-BASED MODEL: A MACHINE LEARNING SYNTHETIC MEASUREMENT BASED APPROACH
Abstract: Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of… Read More
SUPER RESOLUTION FOR ROOT IMAGING PUBLISHED IN APPS!
Congratulations to our labmates Jose Ruiz-Munoz and Alina Zare as well as collaborators Jyothier Nimmagadda, Tyler Dowd and James Baciak! Their paper, titled “Super Resolution for Root Imaging”, was recently published to Applications in Plant Sciences (APPS). If you’re interested… Read More
NVIDIA PARTNERS WITH UF IN AI INITIATIVE!
Today the University of Florida announced a $70 million artificial intelligence partnership with NVIDIA! The initiative will create an AI-centric data center at UF, housing the fastest AI supercomputer in higher education. Along with this technology, UF will collaborate with… Read More