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Luleå University of Technology, Department of Engineering Sciences and Mathematics. 2017 (English)Independent thesis Basic level (professional degree), 20 credits / 30 HE creditsStudent thesis

Abstract [en] : Online monitoring applications requiring advanced pattern recognition capabilities implemented in resource-constrained wireless sensor systems are challenging to construct using standard digital computers. An interesting alternative solution is to use a low-power neuromorphic processor like the ROLLS, with subthreshold mixed analog/digital circuits and online learning capabilities that approximate the behavior of real neurons and synapses. This requires that the monitoring algorithm is implemented with spiking neural networks, which in principle are efficient computational models for tasks such as pattern recognition. In this work, I investigate how spiking neural networks can be used as a pre-processing and feature learning system in a condition monitoring application where the vibration of a machine with healthy and faulty rolling-element bearings is considered. Pattern recognition with spiking neural networks is investigated using simulations with Brian -- a Python-based open source toolbox -- and an implementation is developed for the ROLLS neuromorphic processor. I analyze the learned feature-response properties of individual neurons. When pre-processing the input signals with a neuromorphic cochlea known as the AER-EAR system, the ROLLS chip learns to classify the resulting spike patterns with a training error of less than 1 %, at a combined power consumption of approximately 30 mW. Thus, the neuromorphic hardware system can potentially be realized in a resource-constrained wireless sensor for online monitoring applications.However, further work is needed for testing and cross validation of the feature learning and pattern recognition networks.i

Place, publisher, year, edition, pages: 2017. , 50 p.

Keyword [en] : Pattern recognition, Spiking neural network, Neuromorphic hardware, Spike-based learning, Brain-inspired computing, Signal processing

National Category : Electrical Engineering, Electronic Engineering, Information Engineering

Identifiers: URN: urn:nbn:se:ltu:diva-63033OAI: oai:DiVA.org:ltu-63033DiVA: diva2:1088709

External cooperation : Institute of Neuroinformatics, University of Zürich and ETH Zürich

Educational program: Engineering Physics and Electrical Engineering, master's level

Supervisors : Sandin, Fredrik

Examiners : Sandin, FredrikLuleå University of Technology, Department of Engineering Sciences and Mathematics. Available from: 2017-05-30 Created: 2017-04-13 Last updated: 2017-05-30Bibliographically approved



Autor: Ternstedt, Andreas

Fuente: http://ltu.diva-portal.org/







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