2321884
Project Grant
Overview
Grant Description
Sttr Phase I: Machine Learning-Based Smart Data Compression Solutions for Structural Health Monitoring Sensors -The Broader Impact/Commercial Impact of This Small Business Technology Transfer (STTR) Phase I Project Is to Enable Efficient Monitoring of Civil Infrastructures and Rapid Decision-Making on Their Structural Safety.
The Conditions of Aging Structures Are Monitored Using Structural Health Monitoring (SHM) Sensors. These Sensors Produce Very Large Datasets. In This Project, a Data Compression Solution Will Be Developed to Reduce the Size of Such Datasets by 90%, Without Losing Important Information.
As an Example, One Sensor Can Fill Up a 128 Gigabyte Hard Disk in About 6 Hours, but With the Data Compression Solutions, It Will Take at Least 60 Hours to Fill the Hard Disk. Data Compression Is Thus a Critical Factor for Both Storage (Disk Space) and Efficient Transmission of Sensor Data.
A Microchip With a Built-In Data Compression Algorithm Will Be Developed. The Sensors With Microchips Will Need to Be Visited Less Often for Data Retrieval and Dramatically Less Bandwidth and Power Will Be Required for Data Transmission Over Existing Wireless Networks. This Will Enable Monitoring of Structures in Remote Areas.
The Data Compression Will Be Applicable to Various Market Segments, However the Initial Target Market Will Be the SHM of Aging Structures Within the Oil and Gas Industry.
This Small Business Technology Transfer (STTR) Phase I Project Aims to Develop Sensor Data Compression Schemes and Encoder/Decoder Devices Utilizing Deep Learning Methods. The Proposed System Will Consist of a Data Encoder and Decoder, Which Will Autonomously Learn the Characteristics of the Sensor Data, Extract Relevant Features, and Transmit These Using Low Bit Rates.
Even Users Without Prior Experience in Machine Learning Will Be Able to Train the Deep Neural Network With Transform Domain Layers for Different Sensor Types. The Software Version of the System Will Allow for Data Processing and Transmission Over the Internet When the Sensor Is Connected to a Computer, Making It Possible to Handle Stored Data On-Site.
The Embedded Hardware Version Will Be Designed for Edge Usage, Meaning It Will Be Implemented Next to the Sensor Itself. This Approach Will Ensure Computational Efficiency, Particularly for the Feature Extraction Part of the Network, Which Needs to Be Executed at the Edge.
The Project's Focus Will Be on Detecting Pipeline Leakage Using High-Frequency Acoustic Emission Data on the Developed Microchip System. By Reducing the Data Transmission Bitrate of SHM Devices, This System Will Enable Continuous Transmission of SHM Data to the Cloud or Data Centers.
This Award Reflects NSF's Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.
The Conditions of Aging Structures Are Monitored Using Structural Health Monitoring (SHM) Sensors. These Sensors Produce Very Large Datasets. In This Project, a Data Compression Solution Will Be Developed to Reduce the Size of Such Datasets by 90%, Without Losing Important Information.
As an Example, One Sensor Can Fill Up a 128 Gigabyte Hard Disk in About 6 Hours, but With the Data Compression Solutions, It Will Take at Least 60 Hours to Fill the Hard Disk. Data Compression Is Thus a Critical Factor for Both Storage (Disk Space) and Efficient Transmission of Sensor Data.
A Microchip With a Built-In Data Compression Algorithm Will Be Developed. The Sensors With Microchips Will Need to Be Visited Less Often for Data Retrieval and Dramatically Less Bandwidth and Power Will Be Required for Data Transmission Over Existing Wireless Networks. This Will Enable Monitoring of Structures in Remote Areas.
The Data Compression Will Be Applicable to Various Market Segments, However the Initial Target Market Will Be the SHM of Aging Structures Within the Oil and Gas Industry.
This Small Business Technology Transfer (STTR) Phase I Project Aims to Develop Sensor Data Compression Schemes and Encoder/Decoder Devices Utilizing Deep Learning Methods. The Proposed System Will Consist of a Data Encoder and Decoder, Which Will Autonomously Learn the Characteristics of the Sensor Data, Extract Relevant Features, and Transmit These Using Low Bit Rates.
Even Users Without Prior Experience in Machine Learning Will Be Able to Train the Deep Neural Network With Transform Domain Layers for Different Sensor Types. The Software Version of the System Will Allow for Data Processing and Transmission Over the Internet When the Sensor Is Connected to a Computer, Making It Possible to Handle Stored Data On-Site.
The Embedded Hardware Version Will Be Designed for Edge Usage, Meaning It Will Be Implemented Next to the Sensor Itself. This Approach Will Ensure Computational Efficiency, Particularly for the Feature Extraction Part of the Network, Which Needs to Be Executed at the Edge.
The Project's Focus Will Be on Detecting Pipeline Leakage Using High-Frequency Acoustic Emission Data on the Developed Microchip System. By Reducing the Data Transmission Bitrate of SHM Devices, This System Will Enable Continuous Transmission of SHM Data to the Cloud or Data Centers.
This Award Reflects NSF's Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.
Awardee
Funding Goals
NOT APPLICABLE
Grant Program (CFDA)
Awarding Agency
Place of Performance
Chicago,
Illinois
60640-3313
United States
Geographic Scope
Single Zip Code
Related Opportunity
NOT APPLICABLE
Analysis Notes
Amendment Since initial award the total obligations have decreased 100% from $275,000 to $0.
Zqai was awarded
Project Grant 2321884
from in September 2023 with work to be completed primarily in Chicago Illinois United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
STTR Phase I
Title
STTR Phase I: Machine Learning-Based Smart Data Compression Solutions for Structural Health Monitoring Sensors
Abstract
The broader impact/commercial impact of this Small Business Technology Transfer (STTR) Phase I project is to enable efficient monitoring of civil infrastructures and rapid decision-making on their structural safety. The conditions of aging structures are monitored using structural health monitoring (SHM) sensors. These sensors produce very large datasets.In this project, a data compression solution will be developed to reduce the size of such datasets by 90%, without losing important information.As an example, one sensor can fill up a 128 Gigabyte hard disk in about 6 hours, but with the data compression solutions, it will take at least 60 hours to fill the hard disk. Data compression is thus a critical factor for both storage (disk space) and efficient transmission of sensor data.A microchip with a built-in data compression algorithm will be developed. The sensors with microchips will need to be visited less often for data retrieval and dramatically less bandwidth and power will be required for data transmission over existing wireless networks.This will enable monitoring of structures in remote areas.The data compression will be applicable to various market segments, however the initial target market will be the SHM of aging structures within the oil and gas industry._x000D_ _x000D_ This Small Business Technology Transfer (STTR) Phase I project aims to develop sensor data compression schemes and encoder/decoder devices utilizing deep learning methods. The proposed system will consist of a data encoder and decoder, which will autonomously learn the characteristics of the sensor data, extract relevant features, and transmit these using low bit rates. Even users without prior experience in machine learning will be able to train the deep neural network with transform domain layers for different sensor types. The software version of the system will allow for data processing and transmission over the Internet when the sensor is connected to a computer, making it possible to handle stored data on-site. The embedded hardware version will be designed for "edge" usage, meaning it will be implemented next to the sensor itself. This approach will ensure computational efficiency, particularly for the feature extraction part of the network, which needs to be executed at the edge. The project's focus will be on detecting pipeline leakage using high-frequency acoustic emission data on the developed microchip system. By reducing the data transmission bitrate of SHM devices, this system will enable continuous transmission of SHM data to the cloud or data centers._x000D_ _x000D_ This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Topic Code
I
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 6/20/24
Period of Performance
9/1/23
Start Date
8/31/24
End Date
Funding Split
$0.0
Federal Obligation
$0.0
Non-Federal Obligation
$0.0
Total Obligated
Transaction History
Modifications to 2321884
Additional Detail
Award ID FAIN
2321884
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
JZ6JBLDXNCC7
Awardee CAGE
8MA38
Performance District
IL-09
Senators
Richard Durbin
Tammy Duckworth
Tammy Duckworth
Budget Funding
| Federal Account | Budget Subfunction | Object Class | Total | Percentage |
|---|---|---|---|---|
| Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $275,000 | 100% |
Modified: 6/20/24