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M. Sc. Erik Engelhardt

Room D-019
Kaiserstraße 2, 24143 Kiel, Germany
Phone: +49 431 880-6129
Telefax: +49 431 880-6128
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


Research: Real-time Signal Processing for Magnetoelectric Sensors

To measure signals emitted by bio-medical sources like the heart or a skeleton muscle with a high temporal resolution, the generated electrical potentials on the body surface or the generated magnetic flux are usually measured. One big difference between the two measurement types (and the main advantage of the measurement of the magnetic flux) is that the magnetically measured signals are less distorted by surrounding materials of the source compared to their electric counterparts. Unfortunately, the operation of suitable state-of-the-art sensors, which are mainly based on super-conducting quantum interference devices (SQUIDs), is in general very expensive.

Sensors based on the magneto-electric (ME) effect have the potential to be a suitable alternative. During the last years the new ME sensors, developed in collaborative research groups at Kiel University, reached a sensitivity level which makes it possible to detect the human heart beat. Since no cooling is required for this sensor type, the operational cost are relative low. This could lead to an increased number of magnetic measurements for medical diagnostics in the future. In addition the sensors can be placed closer to the measured object and arrays of higher sensor probability can be built up, because the sensors are smaller in size.

The challenging part arises, because these sensors also record mechanical vibrations, whereby the desired signals are superimposed by unwanted signal components. To reduce this coupling effect and increase the usability of such sensors in measurement environments signal processing techniques are applied. Approaches are based on adaptive noise cancellers using non-magnetic noise reference sensors or intelligent sensor read-out schemes.

Related topics:

  • Noise cancellation and suppression
  • Signal combination
  • Beamforming
  • Signal analysis
  • Biomagnetic measurements


Further interests:

  • Software Engineering
  • High Frequency Technology
  • Supervised Machine Learning
  • Reinforcement Learning


Short CV

Time span Details
2020 - current Research assistant at the Christian-Albrechts-Universität zu Kiel, Kiel, Germany
2020 - 2020 Working Student at Basler AG, Ahrensburg, Germany
2019 - 2020 M.Sc. in Microelectronic Systems at the Hamburg University for Applied Sciences, Hamburg, Germany
2019 - 2020 Embedded Engineer at Siemens AG, Hamburg, Germany
2017 - 2019 Working Student at Siemens AG, Hamburg, Germany
2014 - 2019 B.Sc. in Electrical Engineering at the Hamburg University for Applied Sciences, Hamburg, Germany
2014 - 2017 Vocational Training: Electronics Technician for Automation Technology at Siemens Professional Education, Hamburg, Germany