LEVERAGING RESILIENCE METRICS TO SUPPORT ANALYSIS OF SECURITY SYSTEMS
Abstract not provided.
Publications
Resilience-Based Performance Measures for Next-Generation Systems Security Engineering
Abstract not provided.
Leveraging Resilience Metrics to Support Security System Analysis
Abstract not provided.
Resilience-Based Performance Measures for Next-Generation Systems Security Engineering
Abstract not provided.
Early Results from Applying a Multilayered Network Framework to Engineering Nuclear Security Systems
Abstract not provided.
Early Results from Applying a Multilayered Network Framework to Engineer Nuclear Security Systems
Abstract not provided.
Insights for Systems Security Engineering from Multilayer Network Models
Abstract not provided.
Insights for Systems Security Engineering from Multilayer Network Models
Abstract not provided.
A Complex Systems Approach to Develop a Multilayer Network Model for High Consequence Facility Security (Slides)
Abstract not provided.
Empirical Insights for a Multi-Layered Network Complex Systems Model for Engineering Nuclear Security Systems
Abstract not provided.
Empirical Insights for a Multi-Layered Network Complex Systems Model for Engineering Nuclear Security Systems-Slides
Abstract not provided.
A Complex Systems Approach to Develop a Multilayer Network Model for High Consequence Facility Security-Update
Abstract not provided.
Optically-Realized Compressive Sensing Neural Networks
Abstract not provided.
Optimizing a Compressive Imager for Machine Learning Tasks
Abstract not provided.
Optimizing a Compressive Imager for Machine Learning Tasks
Conference Record - Asilomar Conference on Signals, Systems and Computers
Images are often not the optimal data form to perform machine learning tasks such as scene classification. Compressive classification can reduce the size, weight, and power of a system by selecting the minimum information while maximizing classification accuracy.In this work we present designs and simulations of prism arrays which realize sensing matrices using a monolithic element. The sensing matrix is optimized using a neural network architecture to maximize classification accuracy of the MNIST dataset while considering the blurring caused by the size of each prism. Simulated optical hardware performance for a range of prism sizes are reported.
Quantum Algorithm Verification Using Prove-It
Abstract not provided.
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