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
Conference PapersConference Papers
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
OUTLIER DETECTION THROUGH NULL SPACE ANALYSIS OF NEURAL NETWORKS
Abstract: Many machine learning classification systems lack competency awareness. Specifically, many systems lack the ability to identify when outliers (e.g., samples that are distinct from and not represented in the training data distribution) are being presented to the system. The… Read More
Du Accepted to The 2019 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2019)
Congratulations to Gatorsense alumna, Xiaoxiao Du! Her paper, titled “Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels”, was recently accepted for publication with The 2019 IEEE Symposium Series on Computational Intelligence (IEEE… Read More
Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels
Abstract: Classifier fusion methods integrate complementary information from multiple classifiers or detectors and can aid remote sensing applications such as target detection and hyperspectral image analysis. The Choquet integral (CI), parameterized by fuzzy measures (FMs), has been widely used in… Read More
Evaluation of image features for discriminating targets from false positives in synthetic aperture sonar imagery
Abstract: With the increasing popularity of using autonomous underwater vehicles (AUVs) to gather large quantities of Synthetic Aperture Sonar (SAS) seafloor imagery, the burden on human operators to identify targets in these seafloor images has increased significantly. Existing methods of… Read More
Deep convolutional neural network target classification for underwater synthetic aperture sonar imagery
Abstract: In underwater synthetic aperture sonar (SAS) imagery, there is a need for accurate target recognition algorithms. Automated detection of underwater objects has many applications, not the least of which being the safe extraction of dangerous explosives. In this paper,… Read More
Developing Spectral Libraries Using Multiple Target Multiple Instance Adaptive Cosine/Coherence Estimator
Abstract: Traditional methods of developing spectral libraries for unmixing hyperspectral images tend to require domain knowledge of the study area and the material’s spectra. In this paper, we propose using the Multiple Target Multiple Instance Adaptive Cosine/Coherence Estimator (Multi-Target MI-ACE)… Read More
Temporal Mapping of Hyperspectral Data
Abstract: The increasing popularity of hyperspectral sensors is dramatically increasing the temporal availability of data. To date, algorithms struggle to compare hyperspectral data collected across dates due to different environmental conditions during collection. In this work, we develop a temporal… Read More
Investigation of Initialization Strategies for the Multiple Instance Adaptive Cosine Estimator
Abstract: Sensors which use electromagnetic induction (EMI) to excite a response in conducting bodies have long been investigated for subsurface explosive hazard detection. In particular, EMI sensors have been used to discriminate between different types of objects, and to detect… Read More