Award Data

Through this MDA Phase I STTR program, NanoSonic shall work with the high energy laboratory at Penn State and with input from a major US integrator of hypersonic vehicles to develop improved high temperature radome materials for uncooled aerodynamic reentry and hypersonic vehicles. Through prior work, NanoSonic has developed and demonstrated high temperature materials with good thermal insulating ...

NanoSonic will work with Penn State to develop inexpensive automated manufacturing processes for the production of ceramic matrix composite thermal protective system materials with high temperature thermal performance and high thermal insulation propertiesWe will fabricate materials using automated robotic production methods, and perform testing, including high energy irradiation at Penn State’s ...

Simulations are designed to reproduce the behavior of end-to-end systems or their critical components. When designed under a stochastic framework, they enable us to identify ensembles of possible system evolutions and their associated uncertainties and sensitivities of outputs relative to the inputs. When the intent is to reproduce sensor observations they need to account for the sensor and data p ...

Cynnovative proposes Explainable Deep Reinforcement Learning with Symbolically Guided Transitions (X DRLSGT) to improve the transparency and, thus, the explainability of deep reinforcement learning (DRL) algorithms. The inability to understand the reasoning behind an Artificial Intelligence’s (AI) decision is a major limiting factor that prevents AI-enabled physical systems from being deployed a ...

We will investigate the radiation effects on microelectronics due to gamma-rays and beta-rays and compare the effects on electrical and material properties between the two radiation types to formulate a quantitative mapping of radiation types (gamma and beta) and effects. The purpose is to: 1) Develop an overall physics-based strategy; 2) Define the experimental design, guided by analytical calcul ...

Barron Associates, in partnership with the Hume Center at Virginia Tech, proposes to develop a comprehensive model generation and validation approach that leverages recent advances in generalized polynomial chaos (gPC) theory. Similar to neural-network-based artificial intelligence/machine learning (AI/ML) approaches, solutions based on gPC theory learn by adjusting the weights in a linear combina ...

The team of Signature Research, Inc. and Michigan Technological University will develop and demonstrate methods and metrics to evaluate the performance of machine learning-based computer vision algorithms with low numbers of samples of labeled EO imagery. We will use the existing xView panchromatic dataset to demonstrate a proof-of-concept set of tools. If successful, in Phase II, we will extend t ...

To present a consistent and deterministic view of time to virtual machines, ARiA will develop the Relative Real-Time Mechanism (RRTM). RRTM will work cooperatively with existing virtualization technologies such as Kernel-based Virtual Machine (KVM), extending them to support simulations of real-time and latency-sensitive systems. Relative to the virtual machine, it will be operating in a real-time ...

Department of Defense (DoD) cybersecurity test and evaluation (T&E) uses various cyber event environments throughout the development of a combat system to support fielding more secure, resilient, and cost-effective systems. Cyber ranges deploy cyber-attacks and achieve high-fidelity feedback on vulnerabilities present in the system without putting operational systems at risk; they support more rig ...

Energy and Security Group (ESG) and our partner Sandia National Laboratories (SNL) will design a modeling and simulation (M&S) framework that integrates human performance modeling with high-fidelity systems modeling to explore the impacts of human operator variability in execution of tactics, techniques, and procedures (TTP). This framework, TTPSim, can be used to develop and run simultaneous, eff ...