On the practicability of Images Processing Methods for xMR Arrays

Meier, Phil; Rohrmann, Kris; Sandner, Marvin; Prochaska, Marcus

doi:10.1109/mwscas.2018.8624074

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…In contrast, the array proposed in this work is made of AMR angular sensors that are unable to quantify the magnetic field strength. The potential advantages of angular sensing arrays are examined in several other works applying different methods [14], [15] or [16]. Although those examples have proven the capability to improve the robustness of angular sensing systems against stray fields they utilize complex algorithms that need an increased computational power and require more time for the calculations.…”

Section: Related Workmentioning

confidence: 99%

“…The FFN topology was chosen as a subject of this work because such networks are capable of approximating a given function [48]. The possible application of classical image processing methods is shown in [14] if the array is seen as a gray scale image. Because of this similarity a CNN topology is also considered since it is the primary approach for several modern image processing applications [49] and well researched.…”

Section: Artificial Neuronal Networkmentioning

confidence: 99%

“…Several simulations with the system modeling used in [14], [15] were conducted. The modeling consists of an analytic model for a cubic permanent magnet that was verified against CST Studio.…”

Section: Network Optimizationmentioning

confidence: 99%

“…Therefore, alternative methodologies are required. In order to enable alternative data processing methodologies, the concept of an array built of angular position sensors is introduced prior works [14], [15], or [16]. Since the array structure resembles a CCD sensor, the application of image processing methods [14] possesses great potential for the desired task.…”

Section: Introductionmentioning

confidence: 99%

“…In order to enable alternative data processing methodologies, the concept of an array built of angular position sensors is introduced prior works [14], [15], or [16]. Since the array structure resembles a CCD sensor, the application of image processing methods [14] possesses great potential for the desired task. On this account, the presented work is intended to improve angular sensing systems with respect to their stray field immunity using artificial neuronal networks.…”

Section: Introductionmentioning

confidence: 99%

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Compensation of Measurement Errors for a Magnetoresistive Angular Sensor Array Using Artificial Neuronal Networks

et al. 2020

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Sensing setups based on the magnetic field possess many benefits that make such sensors an important class in many application fields. Especially in industrial or automotive applications, such sensing concepts are very important parts in many essential system components. Magnetic field sensors are typically used to measure angular positions and rotational velocity as well as linear positions. Due to their high robustness and accuracy, MR-based sensors have a dominant role in the angular sensing domain. Nevertheless, novel vehicle concepts such as electrical driven or hybrid cars as well as increasing quality and reliability requirements demand improvements. However, those sensors face challenges due to new technologies, which are accompanied by new regulations. From this follows that manufactures can either adapt existing technologies or develop new sensing concepts. The goal of the following work is the evaluation of the capability of neuronal networks to enhance the measurement results of magnetic field sensors if these are disturbed by magnetic stray fields. Therefore, the system setup and possible error sources are analyzed before different neural networks are designed, tried and tested, whereby special attention is given to the network configuration in order to enable a deployment close to the individual sensors. Finally, the results are verified with an adapted measurement setup.

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Section: Related Workmentioning

confidence: 99%

Section: Artificial Neuronal Networkmentioning

confidence: 99%