13,716 research outputs found
Output Regulation for Systems on Matrix Lie-group
This paper deals with the problem of output regulation for systems defined on
matrix Lie-Groups. Reference trajectories to be tracked are supposed to be
generated by an exosystem, defined on the same Lie-Group of the controlled
system, and only partial relative error measurements are supposed to be
available. These measurements are assumed to be invariant and associated to a
group action on a homogeneous space of the state space. In the spirit of the
internal model principle the proposed control structure embeds a copy of the
exosystem kinematic. This control problem is motivated by many real
applications fields in aerospace, robotics, projective geometry, to name a few,
in which systems are defined on matrix Lie-groups and references in the
associated homogenous spaces
Dynamic spot analysis in the 2D electrophoresis gels images
Práce shrnuje faktory a parametry, které ovlivňují výsledky 2D elektroforézy, se zaměřením na zpracování obrazu jako jeden ze způsobů snížení nesprávné interpretace jejích výstupů. Proces zpracování obrazu využívá jako zdroj dat především obrazů z opakovaných provedení téhož pokusu, neboli víceplik. Pomocí analýzy obrazů víceplik je možno pozorovat nebo korigovat změny jednoho pokusu a také porovnávat je s výstupy jiných pokusů. Cílem práce je poskytnout podporu specialistovi, který má na starosti popsat vlastnosti struktur nacházejících se v elektroforetických obrazech.The text briefly describes factors and parameters which influence the results of 2D electrophoresis focusing on image processing as one manner to reduce incorrect interpretation of its outputs. As dataset, image processing performance uses images from repeated execution of one experiment also known as multiplicates. Using multiplicates analysis it is possible to observe or lower the changes of one experiment and to compare them with outputs of other experiments. The aim of this work is to provide support for specialist who takes care about describing the character patterns located in electrophoretic images.
Visualizing dimensionality reduction of systems biology data
One of the challenges in analyzing high-dimensional expression data is the
detection of important biological signals. A common approach is to apply a
dimension reduction method, such as principal component analysis. Typically,
after application of such a method the data is projected and visualized in the
new coordinate system, using scatter plots or profile plots. These methods
provide good results if the data have certain properties which become visible
in the new coordinate system and which were hard to detect in the original
coordinate system. Often however, the application of only one method does not
suffice to capture all important signals. Therefore several methods addressing
different aspects of the data need to be applied. We have developed a framework
for linear and non-linear dimension reduction methods within our visual
analytics pipeline SpRay. This includes measures that assist the interpretation
of the factorization result. Different visualizations of these measures can be
combined with functional annotations that support the interpretation of the
results. We show an application to high-resolution time series microarray data
in the antibiotic-producing organism Streptomyces coelicolor as well as to
microarray data measuring expression of cells with normal karyotype and cells
with trisomies of human chromosomes 13 and 21
Local observers on linear Lie groups with linear estimation error dynamics
This paper proposes local exponential observers for systems on linear Lie
groups. We study two different classes of systems. In the first class, the full
state of the system evolves on a linear Lie group and is available for
measurement. In the second class, only part of the system's state evolves on a
linear Lie group and this portion of the state is available for measurement. In
each case, we propose two different observer designs. We show that, depending
on the observer chosen, local exponential stability of one of the two
observation error dynamics, left- or right-invariant error dynamics, is
obtained. For the first class of systems these results are developed by showing
that the estimation error dynamics are differentially equivalent to a stable
linear differential equation on a vector space. For the second class of system,
the estimation error dynamics are almost linear. We illustrate these observer
designs on an attitude estimation problem
Blindfold learning of an accurate neural metric
The brain has no direct access to physical stimuli, but only to the spiking
activity evoked in sensory organs. It is unclear how the brain can structure
its representation of the world based on differences between those noisy,
correlated responses alone. Here we show how to build a distance map of
responses from the structure of the population activity of retinal ganglion
cells, allowing for the accurate discrimination of distinct visual stimuli from
the retinal response. We introduce the Temporal Restricted Boltzmann Machine to
learn the spatiotemporal structure of the population activity, and use this
model to define a distance between spike trains. We show that this metric
outperforms existing neural distances at discriminating pairs of stimuli that
are barely distinguishable. The proposed method provides a generic and
biologically plausible way to learn to associate similar stimuli based on their
spiking responses, without any other knowledge of these stimuli
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