AI-Driven Sensor Configuration & Anomoly Detection

Develop an AI-driven configuration assistant that suggests configurations to train a clustering model (e.g., k-means) and recommends optimal parameters (gain, filter, scaling equations) based on sensor type and measurement goals. Also that validates inputs by employing an anomaly detection model (e.g., isolation forest) to flag unusual parameter combinations in real-time, alerting users to potential errors. The end goal is to seamlessly integrate both features into the MVVM architecture, leveraging Entity Framework for data access and ML.NET for lightweight, local AI processing.

Enhance the data acquisition system configuration process by providing AI-driven suggestions for sensor configurations and real-time validation of user inputs to reduce configuration time, minimize errors, and improve measurement reliability.

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

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