38 research outputs found
Digital Filters
The new technology advances provide that a great number of system signals can be easily measured with a low cost. The main problem is that usually only a fraction of the signal is useful for different purposes, for example maintenance, DVD-recorders, computers, electric/electronic circuits, econometric, optimization, etc. Digital filters are the most versatile, practical and effective methods for extracting the information necessary from the signal. They can be dynamic, so they can be automatically or manually adjusted to the external and internal conditions. Presented in this book are the most advanced digital filters including different case studies and the most relevant literature
Covariance and Gramian matrices in control and systems theory.
Covariance and Gramian matrices in control and systems
theory and pattern recognition are studied in the context of
reduction of dimensionality and hence complexity of large-scale
systems. This is achieved by the removal of redundant or
'almost' redundant information contained in the covariance
and Grarrdan matrices. The Karhunen-Loeve expansion (principal
component analysis) and its extensions and the singular value
decomposition of matrices provide the framework for the work
presented in the thesis. The results given for linear dynamical
systems are based on controllability and observability Gramians
and some new developments in singular perturbational analysis
are also presented
AFIT School of Engineering Contributions to Air Force Research and Technology. Calendar Year 1972
This report contains abstracts of Master of Science Theses, Doctoral Dissertations, and faculty publications completed during the 1972 calendar year at the School of Engineering, Air Force Institute of Technology, at Wright-Patterson Air Force Base, Ohio
Cosmic cartography
The cosmic origin and evolution is encoded in the large-scale matter distribution
observed in astronomical surveys. Galaxy redshift surveys have become in the
recent years one of the best probes for cosmic large-scale structures. They are
complementary to other information sources like the cosmic microwave background, since they
trace a different epoch of the
Universe, the time after reionization at which the Universe
became transparent, covering about the last twelve billion years.
Regarding that the Universe is about
thirteen billion years old, galaxy
surveys cover a huge range of time, even if the sensitivity limitations of the
detectors do not permit to reach the furthermost sources in the transparent
Universe. This makes galaxy surveys extremely interesting for cosmological evolution studies.
The observables, galaxy position in the sky, galaxy ma
gnitude and redshift, however, give an incomplete representation of the real
structures in the Universe, not only due to the limitations and
uncertainties in the measurements, but also due to their biased
nature. They trace the underlying continuous dark matter field only partially
being a discrete sample of the luminous baryonic distribution.
In addition, galaxy catalogues are plagued by many complications. Some have a
physical foundation, as mentioned before, others are due to the
observation process. The problem of reconstructing the underlying density
field, which permits to make cosmological studies, thus requires a
statistical approach.
This thesis describes a cosmic cartography project.
The necessary concepts, mathematical frame-work, and numerical algorithms are
thoroughly analyzed.
On that basis a Bayesian software tool is implemented. The resulting Argo-code allows to
investigate the characteristics of the large-scale cosmological structure with unprecedented
accuracy and flexibility. This is achieved by jointly estimating the large-scale density along
with a variety of other parameters ---such as the cosmic flow, the small-scale peculiar velocity
field, and the power-spectrum--- from the information provided by galaxy redshift
surveys. Furthermore, Argo is capable of dealing with many observational issues like
mask-effects, galaxy selection criteria, blurring and noise in a very efficient
implementation of an operator based formalism which was carefully derived for this purpose.
Thanks to the achieved high efficiency of Argo the application of iterative sampling algorithms
based on Markov Chain Monte Carlo is now possible. This will ultimately lead to a full
description of the matter distribution with all its relevant parameters like velocities,
power spectra, galaxy bias, etc., including the associated uncertainties. Some applications
are shown, in which such techniques are used.
A rejection sampling scheme is successfully applied to correct for the observational
redshift-distortions effect which is especially severe in regimes of non-linear structure
formation, causing the so-called finger-of-god effect.
Also a Gibbs-sampling algorithm for power-spectrum determination is presented
and some preliminary results are shown in which the correct level and shape of
the power-spectrum is recovered solely from the data.
We present in an additional appendix the gravitational collapse and subsequent neutrino-driven
explosion of the low-mass end of stars that undergo core-collapse Supernovae.
We obtain results which are for the first time compatible with the Crab Nebula