SBIR Phase I: Rapid Solution to COVID-19 and Future Viral Pandemics
SBIR Phase I: Rapid Solution to COVID-19 and Future Viral Pandemics
批准号:
2051411
负责人:
Timothy Childs
金额:
$25.6万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-01 至 2022-02-28
中文摘要
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英文摘要
The broader impact/commercial potential of this Small Business Research Innovation (SBIR) Phase I project is a demonstration of a millimeter wave Rapid Pathogen Detector System (or “RPDS”) to autonomously detect and instantly identify pathogens at tiny concentrations as even these samples may cause health threats to individuals or to a community. The “in the field” demonstration utilizes a database of captured and learned the characteristics (i.e., electronic signature) of the pathogen using a unique machine learning/artificial intelligence model. Once the RPDS is “trained” to recognize the pathogen, the RPDS may detect and identify pathogens in human and non-human samples in the field with high accuracy. The model target pathogen of this project is the SARS-CoV-2 virus. This target is to be followed by a selection of other viruses, proteins, biomatter, defective cells (cancer), and small inanimate objects as well. The RPDS system capabilities seek to increase the testing rate by an orders of magnitude compared to the current state of the art detection and diagnostic systems. Also, the instant results can be streamed to authorized individuals and officials in seconds using the local WiFi/ ethernet systems. This small Business Innovation Research (SBIR) Phase I project is to develop and demonstrate an autonomous and remotely operated millimeter wave (MMW) device that identifies pathogens in seconds in the field. The chemical-free, mobile mini table top systems is to help expand the number of pathogen test stations throughout the USA. This effort utilizes transmitted MMW signals reflected from nanosize pathogens in a manner that captures their unique characteristic via digital signal processing assisted artificial intelligence (AI) structures and stores a reliable unique model of the target pathogen. Utilizing machine learning (ML), the detector autonomously recognizes the pathogen identity within each of the million scans (reflected response packets) of the unidentified samples, each second.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.
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依托单位:
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