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Age Assessment of Modern Microelectronics, Robust Tracking using Predictive Learning, and System of System Adaptive Control System
Naval Surface Warfare Center, Crane Division is interested in receiving proposals for the following Basic Research Opportunity Areas:
a.
Age Assessment of Modern MicroelectronicsNSWC Crane Division is interested in receiving proposals directed toward the age assessment of modern microelectronics
and molecular level characterization of material degradation in modern microelectronics.
Exploiting the benefits of integrating modern commercial microelectronics into strategic and critical hardware for platform modernization does not come without inherent risks.
These risks include but are not limited to degradation of the organic materials that comprise modern electronics, such as polymeric encapsulates, resins, and epoxies, which occurs during long-term storage and dormant operating environments.
The aging mechanisms and rates of the conventional materials (e.g.
ceramics and metals) that comprise current microelectronics employed in strategic and critical hardware are well characterized.
However, little is known about how the long-term storage and dormant operating environments that strategic and critical hardware are exposed to would affect the functionality of modern microelectronics.
Improved understanding of material aging and degradation within modern microelectronics will facilitate efforts to qualify these devices for high-reliability applications enabling lower costs designs with shorter lead times.
Initial research is sought to first develop analytical chemistry methodologies, and secondly where advanced analytics are deployed upon the data sets to enable predictive analytics.
b.
Robust Tracking using Predictive Learning NSWC Crane Division seeks novel research into predictive learning techniques to build more robust target trackers.
The goal for this effort is to research novel tracking systems under constraints of limited or missing data and to develop proof of concept target trackers using predictive learning in simulated environments.
Solutions using existing object detection and recognition technologies are not sought here.
Some examples of possible topics of interest include, but are not limited to:
training and testing target tracker performance in high-fidelity simulated worlds, research and evaluate existing technical approaches to predictive learning, and implementing predictive learning in a virtual tracking system construct for training and evaluation as a robust target tracker.
Initial research is sought to demonstrate a proof of concept predictive learning algorithm for target tracking and hardware.
c.
System of System Adaptive Control System The Department of Defense and the Navy is moving toward an open system of systems architecture with plug and play capability.
This newly founded heterogeneous capability causes intentional and unintentional feedback.
This problem is further complicated when utilizing adaptive systems that orient, sense and act based on environment and observables.
Generally, secondary and tertiary priorities lack assessment and feedback to determine if it is being, as well as who it is effecting and/or if it has escalated to become the primary priority to accomplish the mission.
Research is sought to explore control theory and define an appropriate feedback loop for heterogeneous adaptive systems, which may not have originally been designed to cooperate or inter-operate.
This project intends to define a control system where a secondary adaptive system is the centrally optimized process, whereby it must balance interfering with primary processes and any adaptations that were unrealized or unanticipated from the inter-operable primary systems.
The feedback will require an assessment and efficacy estimation to determine if continuing current strategy is necessary or if a new process cycle and control are necessary.
This will in essence define a heterogenous adaptive battle manager and facilitate a better understanding of how and why the control system is performing as required.
In addition to improving system efficacy, this may also develop new tactics and techniques by driving the system into stable and instable conditions.
a.
Age Assessment of Modern MicroelectronicsNSWC Crane Division is interested in receiving proposals directed toward the age assessment of modern microelectronics
and molecular level characterization of material degradation in modern microelectronics.
Exploiting the benefits of integrating modern commercial microelectronics into strategic and critical hardware for platform modernization does not come without inherent risks.
These risks include but are not limited to degradation of the organic materials that comprise modern electronics, such as polymeric encapsulates, resins, and epoxies, which occurs during long-term storage and dormant operating environments.
The aging mechanisms and rates of the conventional materials (e.g.
ceramics and metals) that comprise current microelectronics employed in strategic and critical hardware are well characterized.
However, little is known about how the long-term storage and dormant operating environments that strategic and critical hardware are exposed to would affect the functionality of modern microelectronics.
Improved understanding of material aging and degradation within modern microelectronics will facilitate efforts to qualify these devices for high-reliability applications enabling lower costs designs with shorter lead times.
Initial research is sought to first develop analytical chemistry methodologies, and secondly where advanced analytics are deployed upon the data sets to enable predictive analytics.
b.
Robust Tracking using Predictive Learning NSWC Crane Division seeks novel research into predictive learning techniques to build more robust target trackers.
The goal for this effort is to research novel tracking systems under constraints of limited or missing data and to develop proof of concept target trackers using predictive learning in simulated environments.
Solutions using existing object detection and recognition technologies are not sought here.
Some examples of possible topics of interest include, but are not limited to:
training and testing target tracker performance in high-fidelity simulated worlds, research and evaluate existing technical approaches to predictive learning, and implementing predictive learning in a virtual tracking system construct for training and evaluation as a robust target tracker.
Initial research is sought to demonstrate a proof of concept predictive learning algorithm for target tracking and hardware.
c.
System of System Adaptive Control System The Department of Defense and the Navy is moving toward an open system of systems architecture with plug and play capability.
This newly founded heterogeneous capability causes intentional and unintentional feedback.
This problem is further complicated when utilizing adaptive systems that orient, sense and act based on environment and observables.
Generally, secondary and tertiary priorities lack assessment and feedback to determine if it is being, as well as who it is effecting and/or if it has escalated to become the primary priority to accomplish the mission.
Research is sought to explore control theory and define an appropriate feedback loop for heterogeneous adaptive systems, which may not have originally been designed to cooperate or inter-operate.
This project intends to define a control system where a secondary adaptive system is the centrally optimized process, whereby it must balance interfering with primary processes and any adaptations that were unrealized or unanticipated from the inter-operable primary systems.
The feedback will require an assessment and efficacy estimation to determine if continuing current strategy is necessary or if a new process cycle and control are necessary.
This will in essence define a heterogenous adaptive battle manager and facilitate a better understanding of how and why the control system is performing as required.
In addition to improving system efficacy, this may also develop new tactics and techniques by driving the system into stable and instable conditions.
Who's Eligible
Relevant Nonprofit Program Categories
Obtain Full Opportunity Text:
Full Funding Opportunity Announcement
Additional Information of Eligibility:
Cornell University
Full Opportunity Web Address:
https://grants.nih.gov/grants/guide/rfa-files/RFA-FD-21-024.html
Contact:
Agency Email Description:
Grantor Contact
Agency Email:
Date Posted:
2021-02-17
Application Due Date:
Archive Date:
2021-04-17
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