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- W2912460102 abstract "Safety is a critical objective for automotive developments. Functional Safety of automotive analog and mixed-signal circuits faces several challenges; on the other hand, analog behavior provides an opportunity for early anomaly alert, thus improving functional safety. In this paper we propose a machine learning based methodology using data-driven anomaly detection for analog automotive circuits. The contribution of this work is to provide a framework of mining the dynamic in-field time series data in the context of system operation to detect anomalous events from analog functional safety perspective, with minimal hardware overhead. We present a realistic example to illustrate and analyze the proposed method. It presents an approach for improving functional safety of analog circuits in automotive applications." @default.
- W2912460102 created "2019-02-21" @default.
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- W2912460102 date "2018-10-01" @default.
- W2912460102 modified "2023-09-25" @default.
- W2912460102 title "Improving Analog Functional Safety Using Data-Driven Anomaly Detection" @default.
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- W2912460102 doi "https://doi.org/10.1109/test.2018.8624716" @default.
- W2912460102 hasPublicationYear "2018" @default.
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