← Case studies Hardware–Software Integration
From detector physics to a product someone can operate
Sensor, readout, FPGA/DAQ, pipelines, and application UX as one vertical—not a pile of vendor PDFs.
Challenge
Most “AI for medical devices” programs start with the model and inherit someone else’s signal. When the sensor path is opaque, the product fails in the field even if the demo looked clean.
Approach
- Own the signal chain: detector / RF front-end → readout / timing → FPGA or edge DSP → data contracts.
- Define verification against physics and use environment—not only against a curated CSV.
- Co-design firmware, MLOps, and operator workflows so the model sees the same signals production sees.
- Package for OEM or clinical ops: calibration, diagnostics, update path, and serviceability.
Outcomes
- A stack that can be argued for in diligence: what you measure, how you prove it, how you ship it.
- Shorter handoffs between hardware, firmware, and software owners.
- Products that survive the gap between bench demo and deployed instrument.
What this demonstrates
Sensor architecture · ASIC/FPGA/DAQ · Edge ML · Product engineering