SBIR Phase I: Rapid Solution to COVID-19 and Future Viral Pandemics

这个小企业研究创新（SBIR）第一阶段项目的更广泛影响/商业潜力是毫米波快速病原体检测系统（或“RPDS”）的演示，该系统可以自动检测并立即识别微小浓度的病原体，因为即使这些样本也可能对个人或社区造成健康威胁。“现场”演示使用独特的机器学习/人工智能模型，利用捕获和学习病原体特征（即电子签名）的数据库。一旦RPDS被“训练”以识别病原体，RPDS就可以在现场以高精度检测和识别人类和非人类样本中的病原体。本项目模型目标病原体为SARS-CoV-2病毒。在这个目标之后，还会选择其他病毒、蛋白质、生物物质、有缺陷的细胞（癌症）和小型无生命物体。与目前最先进的检测和诊断系统相比，RPDS系统的功能旨在将测试率提高一个数量级。此外，即时结果可以通过本地WiFi/以太网系统在几秒钟内流式传输给授权的个人和官员。这个小企业创新研究（SBIR）第一阶段项目是开发和演示一种自主和远程操作的毫米波（MMW）设备，该设备可以在现场几秒钟内识别病原体。无化学品，移动迷你桌面系统是为了帮助扩大整个美国的病原体测试站的数量。这项工作利用从纳米级病原体反射的传输毫米波信号，通过数字信号处理辅助人工智能（AI）结构捕获其独特特征，并存储目标病原体的可靠独特模型。利用机器学习（ML），检测器每秒自动识别未识别样本的百万次扫描（反射响应包）中的每一次病原体身份。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.