123,456 research outputs found
A real-time noise cancelling EEG electrode employing Deep Learning
Two major problems of head worn electroencephalogram (EEG) are muscle and
eye-blink artefacts, in particular in non-clinical environments while
performing everyday tasks. Current artefact removal techniques such as
principle component analysis (PCA) or independent component analysis (ICA) take
signals from a high number of electrodes and separate the noise from the signal
by processing them offline in a computationally expensive and slow way. In
contrast, we present a smart compound electrode which is able to learn in
real-time to remove artefacts. The smart 3D printed electrode consists of a
central electrode and a ring electrode where poly-lactate acid (PLA) was used
for the the base and Ag/AgCl for the conductive parts allowing standard
manufacturing processes. A new deep learning algorithm then learns continuously
to remove both eye-blink and muscle artefacts which combines the real-time
capabilities of adaptive filters with the power of deep neural networks. The
electrode setup together with the deep learning algorithm increases the signal
to noise ratio of the EEG in average by 20 dB. Our approach offers a simple 3D
printed design in combination with a real-time algorithm which can be
integrated into the electrode itself. This electrode has the potential to
provide high quality EEG in non-clinical and consumer applications, such as
sleep monitoring and brain-computer interface (BCI).Comment: 12 pages, 4 figures, code available under
http://doi.org/10.5281/zenodo.413110
2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA
We present a new approach for face recognition system. The method is based on
2D face image features using subset of non-correlated and Orthogonal Gabor
Filters instead of using the whole Gabor Filter Bank, then compressing the
output feature vector using Linear Discriminant Analysis (LDA). The face image
has been enhanced using multi stage image processing technique to normalize it
and compensate for illumination variation. Experimental results show that the
proposed system is effective for both dimension reduction and good recognition
performance when compared to the complete Gabor filter bank. The system has
been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and
achieved average recognition rate of 98.9 %
Free Energy Methods for Bayesian Inference: Efficient Exploration of Univariate Gaussian Mixture Posteriors
Because of their multimodality, mixture posterior distributions are difficult
to sample with standard Markov chain Monte Carlo (MCMC) methods. We propose a
strategy to enhance the sampling of MCMC in this context, using a biasing
procedure which originates from computational Statistical Physics. The
principle is first to choose a "reaction coordinate", that is, a "direction" in
which the target distribution is multimodal. In a second step, the marginal
log-density of the reaction coordinate with respect to the posterior
distribution is estimated; minus this quantity is called "free energy" in the
computational Statistical Physics literature. To this end, we use adaptive
biasing Markov chain algorithms which adapt their targeted invariant
distribution on the fly, in order to overcome sampling barriers along the
chosen reaction coordinate. Finally, we perform an importance sampling step in
order to remove the bias and recover the true posterior. The efficiency factor
of the importance sampling step can easily be estimated \emph{a priori} once
the bias is known, and appears to be rather large for the test cases we
considered. A crucial point is the choice of the reaction coordinate. One
standard choice (used for example in the classical Wang-Landau algorithm) is
minus the log-posterior density. We discuss other choices. We show in
particular that the hyper-parameter that determines the order of magnitude of
the variance of each component is both a convenient and an efficient reaction
coordinate. We also show how to adapt the method to compute the evidence
(marginal likelihood) of a mixture model. We illustrate our approach by
analyzing two real data sets
Data-driven Soft Sensors in the Process Industry
In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work
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