7 research outputs found
Event-Driven Tactile Learning with Location Spiking Neurons
The sense of touch is essential for a variety of daily tasks. New advances in
event-based tactile sensors and Spiking Neural Networks (SNNs) spur the
research in event-driven tactile learning. However, SNN-enabled event-driven
tactile learning is still in its infancy due to the limited representative
abilities of existing spiking neurons and high spatio-temporal complexity in
the data. In this paper, to improve the representative capabilities of existing
spiking neurons, we propose a novel neuron model called "location spiking
neuron", which enables us to extract features of event-based data in a novel
way. Moreover, based on the classical Time Spike Response Model (TSRM), we
develop a specific location spiking neuron model - Location Spike Response
Model (LSRM) that serves as a new building block of SNNs. Furthermore, we
propose a hybrid model which combines an SNN with TSRM neurons and an SNN with
LSRM neurons to capture the complex spatio-temporal dependencies in the data.
Extensive experiments demonstrate the significant improvements of our models
over other works on event-driven tactile learning and show the superior energy
efficiency of our models and location spiking neurons, which may unlock their
potential on neuromorphic hardware.Comment: accepted by IJCNN 2022 (oral), the source code is available at
https://github.com/pkang2017/TactileLocNeuron
On Adaptive Time-Constrained Macro X-Ray Fluorescence Scanning and Analysis of Works of Art
XRFast a new software package for processing of MA-XRF datasets using machine learning
X-ray fluorescence (XRF) spectroscopy is a common technique in the field of heritage science. However, data processing and data interpretation remain a challenge as they are time consuming and often require a priori knowledge of the composition of the materials present in the analyzed objects. For this reason, we developed an open-source, unsupervised dictionary learning algorithm reducing the complexity of large datasets containing 10s of thousands of spectra and identifying patterns. The algorithm runs in Julia, a programming language that allows for faster data processing compared to Python and R. This approach quickly reduces the number of variables and creates correlated elemental maps, characteristic for pigments containing various elements or for pigment mixtures. This alternative approach creates an overcomplete dictionary which is learned from the input data itself, therefore reducing the a priori user knowledge. The feasibility of this method was first confirmed by applying it to a mock-up board containing various known pigment mixtures. The algorithm was then applied to a macro XRF (MA-XRF) data set obtained on an 18th century Mexican painting, and positively identified smalt (pigment characterized by the co-occurrence of cobalt, arsenic, bismuth, nickel, and potassium), mixtures of vermilion and lead white, and two complex conservation materials/interventions. Moreover, the algorithm identified correlated elements that were not identified using the traditional elemental maps approach without image processing. This approach proved very useful as it yielded the same conclusions as the traditional elemental maps approach followed by elemental maps comparison but with a much faster data processing time. Furthermore, no image processing or user manipulation was required to understand elemental correlation. This open-source, open-access, and thus freely available code running in a platform allowing faster processing and larger data sets represents a useful resource to understand better the pigments and mixtures used in historical paintings and their possible various conservation campaigns.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Matthias Alfel