Abstract: Fresh fruit and vegetables are invaluable for human health; however, their quality often deteriorates before reaching consumers due to ongoing biochemical processes and compositional changes. We currently lack any objective indices which indicate the freshness of fruit or vegetables… Read More
PublicationPublication
A BENCHMARK DATASET FOR INDIVIDUAL TREE CROWN DELINEATION IN CO-REGISTERED AIRBORNE RGB, LIDAR AND HYPERSPECTRAL IMAGERY FROM THE NATIONAL ECOLOGICAL OBSERVATION NETWORK
Abstract: Broad scale remote sensing promises to build forest inventories at unprecedented scales. A crucial step in this process is designing individual tree segmentation algorithms to associate pixels into delineated tree crowns. While dozens of tree delineation algorithms have been… Read More
NEW APPROACH FOR MEASURING INTERCONNECTIVITY OF FISSION GAS PORES IN NUCLEAR FUELS FROM 2D MICROGRAPHS
Abstract: In this work, we developed a simple and easily reproducible method to measure the interconnectivity of fission gas pore phases in irradiated nuclear fuels. The formation, growth and interconnection of fission gas pores contribute to the release of fission… Read More
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
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
NEON TREE CROWNS DATASET
Abstract: The NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network’s Airborne Observation Platform. Each rectangular bounding box crown prediction… Read More