Pattern recognition, Industrial signal processing

AutoSense: Adaptives energieautarkes Sensornetzwerk zur Überwachung von sicherheitskritischen Selbstbedienungssystemen

01.06.2013 bis 31.05.2016

This system is intended to reliably identify manipulation attempts in a resource-efficient, robust and adaptive way. The necessity to apply such a system will be demonstrated in an exemplary way with self-service systems for cash dispensing. This scenario is especially suited due to the large number and variety of criminal attacks on automated teller machines (ATMs). These attacks can be classified into two basic scenarios. On the one hand the manipulation of the operating area for skimming customer data or stealing directly from customers and on the other hand the manipulation or theft of the safe installed in the ATM.

The objective of the „Autosense“ project is the research for a process of holistic monitoring of safety-critical systems. This is supposed to be realised by innovative piezoelectric sensor networks characterised by the following features:

  • partly actuatory operation by using the indirect piezo effect of the sensors

  • autonomous operation and energy autarkic activation

  • context-based generation of information based on the individual sensor signals

  • anticipatory information fusion to evaluate the events

This project is promoted by:
Bundesministerium für Bildung und Forschung (BMBF)
Funding Code: 16ES0064
Funding Lines: KMU-innovativ
Stakeholders / Contacts: Alexander Dicks, M. Sc., Prof. Dr.-Ing. Volker Lohweg
Employees: Alexander Dicks, M. Sc.
Alexander Dicks, M. Sc., Prof. Dr.-Ing. Volker Lohweg, Henrik Wittke, Stefan Linke
Structural Health Monitoring of Plastic Components with Piezoelectric Sensors
In: 20th IEEE International Conference on Emerging Technologies and Factory Automation, Sep 2015
Dr. rer. nat. Sahar Deppe, Alexander Dicks, M. Sc., Prof. Dr.-Ing. Volker Lohweg
Anomaly Detection on ATMs via Time Series Motif Discovery
In: 21th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2016), Sep 2016
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