From 2f4ab7d05fae73b5a3a0cd0c9bcb641d3a879fd1 Mon Sep 17 00:00:00 2001 From: Marius Drechsler Date: Tue, 20 Aug 2024 17:19:09 +0200 Subject: [PATCH] Finished first section --- content/BACH.typ | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/BACH.typ b/content/BACH.typ index 2af008a..a4acb33 100644 --- a/content/BACH.typ +++ b/content/BACH.typ @@ -172,6 +172,8 @@ During every iteration of @alg:bach_1, we will store all weights $bold(h)$ used The output of @alg:bach_1 is the vector of optimal weights $bold(h)_"opt"$. $bold(h)_"opt"$ can now be used to complete the enrollment phase and quantize the values $bold(z)_"opt"$. +To perform reconstruction, we can construct the same linear combination used during enrollment with the found helper-data and the new PUF readout measurements. + === Maximum quantizing bound distance approximation Instead of defining the optimal positions for $z$ with fixed values, we can also provide a more loose definition of $bold(cal(o))$.