Personalized deep learning models on multimodal physiological signals from an Empatica E4 sensor achieve 92.68% accuracy for driver state classification in real-world automated driving, compared to 54% for generalized models across four drivers.
An evaluation of open source serverless computing frameworks support at the edge
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Go outperforms Python and Node.js in throughput and CPU efficiency on OpenFaaS; K3s and Kubeadm deliver higher throughput and lower latency than MicroK8s or K0s under concurrent load.
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Human Centered Non Intrusive Driver State Modeling Using Personalized Physiological Signals in Real World Automated Driving
Personalized deep learning models on multimodal physiological signals from an Empatica E4 sensor achieve 92.68% accuracy for driver state classification in real-world automated driving, compared to 54% for generalized models across four drivers.
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Optimizing OpenFaaS on Kubernetes: Comparative Analysis of Language Runtimes and Cluster Distributions
Go outperforms Python and Node.js in throughput and CPU efficiency on OpenFaaS; K3s and Kubeadm deliver higher throughput and lower latency than MicroK8s or K0s under concurrent load.