100,426 research outputs found
Visual Servoing from Deep Neural Networks
We present a deep neural network-based method to perform high-precision,
robust and real-time 6 DOF visual servoing. The paper describes how to create a
dataset simulating various perturbations (occlusions and lighting conditions)
from a single real-world image of the scene. A convolutional neural network is
fine-tuned using this dataset to estimate the relative pose between two images
of the same scene. The output of the network is then employed in a visual
servoing control scheme. The method converges robustly even in difficult
real-world settings with strong lighting variations and occlusions.A
positioning error of less than one millimeter is obtained in experiments with a
6 DOF robot.Comment: fixed authors lis
Automated Classification of Stellar Spectra. II: Two-Dimensional Classification with Neural Networks and Principal Components Analysis
We investigate the application of neural networks to the automation of MK
spectral classification. The data set for this project consists of a set of
over 5000 optical (3800-5200 AA) spectra obtained from objective prism plates
from the Michigan Spectral Survey. These spectra, along with their
two-dimensional MK classifications listed in the Michigan Henry Draper
Catalogue, were used to develop supervised neural network classifiers. We show
that neural networks can give accurate spectral type classifications (sig_68 =
0.82 subtypes, sig_rms = 1.09 subtypes) across the full range of spectral types
present in the data set (B2-M7). We show also that the networks yield correct
luminosity classes for over 95% of both dwarfs and giants with a high degree of
confidence.
Stellar spectra generally contain a large amount of redundant information. We
investigate the application of Principal Components Analysis (PCA) to the
optimal compression of spectra. We show that PCA can compress the spectra by a
factor of over 30 while retaining essentially all of the useful information in
the data set. Furthermore, it is shown that this compression optimally removes
noise and can be used to identify unusual spectra.Comment: To appear in MNRAS. 15 pages, 17 figures, 7 tables. 2 large figures
(nos. 4 and 15) are supplied as separate GIF files. The complete paper can be
obtained as a single gziped PS file from
http://wol.ra.phy.cam.ac.uk/calj/p1.htm
Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals
Energy consumption is an important issue in continuous wireless
telemonitoring of physiological signals. Compressed sensing (CS) is a promising
framework to address it, due to its energy-efficient data compression
procedure. However, most CS algorithms have difficulty in data recovery due to
non-sparsity characteristic of many physiological signals. Block sparse
Bayesian learning (BSBL) is an effective approach to recover such signals with
satisfactory recovery quality. However, it is time-consuming in recovering
multichannel signals, since its computational load almost linearly increases
with the number of channels.
This work proposes a spatiotemporal sparse Bayesian learning algorithm to
recover multichannel signals simultaneously. It not only exploits temporal
correlation within each channel signal, but also exploits inter-channel
correlation among different channel signals. Furthermore, its computational
load is not significantly affected by the number of channels. The proposed
algorithm was applied to brain computer interface (BCI) and EEG-based driver's
drowsiness estimation. Results showed that the algorithm had both better
recovery performance and much higher speed than BSBL. Particularly, the
proposed algorithm ensured that the BCI classification and the drowsiness
estimation had little degradation even when data were compressed by 80%, making
it very suitable for continuous wireless telemonitoring of multichannel
signals.Comment: Codes are available at:
https://sites.google.com/site/researchbyzhang/stsb
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