Tootfinder

WakeMod: A 6.9uW Wake-Up Radio Module with -72.6dBm Sensitivity for On-Demand IoT
Lukas Schulthess, Silvano Cortesi, Michele Magno
https://arxiv.org/abs/2505.21529

WakeMod: A 6.9uW Wake-Up Radio Module with -72.6dBm Sensitivity for On-Demand IoT
Large-scale Internet of Things (IoT) applications, such as asset tracking and remote sensing, demand multi-year battery lifetimes to minimize maintenance and operational costs. Traditional wireless protocols often employ duty cycling, introducing a tradeoff between latency and idle consumption - both unsuitable for event-driven and ultra-low power systems. A promising approach to address these issues is the integration of always-on wake-up radios (WuRs). They provide asynchronous, ultra-low pow…

This https://arxiv.org/abs/2306.16904 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

Endogenous Barriers to Learning
Building on the idea that lack of experience is a source of errors but that experience should reduce them, we model agents' behavior using a stochastic choice model (logit quantal response), leaving endogenous the accuracy of their choices. In some games, higher accuracy leads to unstable logit-response dynamics. Starting from the lowest possible accuracy, we define the barrier to learning as the maximum accuracy which keeps the logit-response dynamic stable (for all lower accuracies). This def…