NASA STTR 2019-I Solicitation

Proposal Summary


PROPOSAL NUMBER:
 19-1- T13.01-4035
SUBTOPIC TITLE:
 Intelligent Sensor Systems
PROPOSAL TITLE:
 Ensuring Correct Sensor Operation and Decisions Under Harsh Environments
SMALL BUSINESS CONCERN (SBC):
RESEARCH INSTITUTION (RI):
Name:  Alphacore, Inc.
Name:  Arizona State University-Tempe
Street:  398 South Mill Avenue, Suite 304
Street:  University Drive and Mill Avenue
City:  Tempe
City:  Tempe
State/Zip:  AZ 85281-2480
State/Zip:  AZ 85281
PHONE:  (480) 494-5618
PHONE:  (480) 727-7547

Principal Investigator (Name, E-mail, Mail Address, City/State/Zip, Phone)

Name:
Dr. Doohwang Cheng
E-mail:
engineering@alphacoreinc.com
Address:
398 South Mill Avenue, Suite 304 Tempe, AZ 85281 - 2480
Phone:
(480) 494-5618

Business Official (Name, E-mail, Mail Address, City/State/Zip, Phone)

Name:
Ms. Kimberlee Olson
E-mail:
engineering@alphacoreinc.com
Address:
398 South Mill Avenue, Suite 304 Tempe, AZ 85281 - 2480
Phone:
(480) 494-5618
Estimated Technology Readiness Level (TRL) :
Begin: 1
End: 3
Technical Abstract (Limit 2000 characters, approximately 200 words)

NASA seeks to provide a highly flexible instrumentation solution to mitigate the propulsion system risks that are inherent in spaceflight. Alphacore and its Research Partner, Arizona State University, will develop a framework for self-calibrating sensors, backed by artificial intelligence with in-field calibration capabilities. Specifically, Alphacore will develop comprehensive in-field calibration tools, including a low-cost MEMS accelerometer reference chip with thermal sensor calibration that does not require application of physical stimulus. In Phase I we will prove the feasibility of our approach by modeling pressure sensor and a capacitive accelerometer, designing electrical tests to correlate with mechanical characteristics, developing an aging simulation framework for the sensors, and designing the parametrizable self-test IP. In Phase II we will fabricate test and prototype circuits that implement and validate the work done in Phase I, as well as extend the concepts developed in Phase I to other types of sensors.

This project will develop methodologies for 2-tier calibration of sensor-based machine learning systems; Front-End Calibration and Software Calibration. The goal of sensor front-end calibration is to maintain highest level of sensor performance throughout the operation. To this end, the sensor hardware is monitored and calibrated continuously in real-time based on the readings built-in self-test monitors. On the software end, there are various mechanisms to continuously monitor and calibrate the machine learning system. Software calibration will be achieved by incorporating the sensor error model into the machine learning system, adaptive boosting, and assigning a confidence level to the decisions made by the machine learning system based on residual error.

Potential NASA Applications (Limit 1500 characters, approximately 150 words)

NASA seeks to provide a highly flexible instrumentation solution to mitigate the propulsion system risks that are inherent in spaceflight. Alphacore will leverage its silicon-proven high-speed analog and RF design expertise to create a fast, stable and reliable periodic recalibration sensor with superior SWaP-C4 advantages. The sensor tool will enable self-calibration across a range of sensors and sensor types that will reduce system maintenance time and expense that offers improved NASA system performance and reliability.

Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words)

Applications for DOD and other government customers would include all their sensor-enabled systems A strong commercial market exists for this solution as well. Commercial applications based on end user industry include electronics manufacturing, communication, industrial and automotive, storage systems test and measurement systems and others (power generation and petrochemicals).

Duration: 13

Form Generated on 06/16/2019 22:59:18