The paper claims that injecting hardware noise into a random forest ECG classifier raises accuracy from 85% to 92% while a ring oscillator PUF provides 98% uniqueness, all within a simulated 50 µW budget.
Silicon physical random functions,
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Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs
The paper claims that injecting hardware noise into a random forest ECG classifier raises accuracy from 85% to 92% while a ring oscillator PUF provides 98% uniqueness, all within a simulated 50 µW budget.