90,595 research outputs found
Classification and Redshift Estimation in Multi-Color Surveys
We present a photometric method for identifying stars, galaxies and quasars
in multi-color surveys and estimating multi-color redshifts for the
extragalactic objects. We use a library of >65000 color templates for
comparison with observed objects. The method was originally developed for the
Calar Alto Deep Imaging Survey (CADIS), but is now used in a variety of survey
projects. We checked its performance by spectroscopy of CADIS objects, where it
provides high reliability (6 mistakes among 151 objects with R<24), especially
for the quasar selection, and redshifts accurate within sigma_z = 0.03 for
galaxies and sigma_z = 0.1 for quasars. For an optimization of future surveys,
a few model surveys are compared, which use the same amount of telescope time
but different filter sets. In summary, medium-band surveys perform superior to
broad-band surveys although they collect less photons. A full account of this
work is already in print.Comment: 7 pages, 2 figures, proceedings of MPA/ESO/MPE Joint Astronomy
Conference Mining THE SKY held in Garching, Germany, July 31 - Aug 4, 200
Perceptually Motivated Wavelet Packet Transform for Bioacoustic Signal Enhancement
A significant and often unavoidable problem in bioacoustic signal processing is the presence of background noise due to an adverse recording environment. This paper proposes a new bioacoustic signal enhancement technique which can be used on a wide range of species. The technique is based on a perceptually scaled wavelet packet decomposition using a species-specific Greenwood scale function. Spectral estimation techniques, similar to those used for human speech enhancement, are used for estimation of clean signal wavelet coefficients under an additive noise model. The new approach is compared to several other techniques, including basic bandpass filtering as well as classical speech enhancement methods such as spectral subtraction, Wiener filtering, and EphraimâMalah filtering. Vocalizations recorded from several species are used for evaluation, including the ortolan bunting (Emberiza hortulana), rhesus monkey (Macaca mulatta), and humpback whale (Megaptera novaeanglia), with both additive white Gaussian noise and environment recording noise added across a range of signal-to-noise ratios (SNRs). Results, measured by both SNR and segmental SNR of the enhanced wave forms, indicate that the proposed method outperforms other approaches for a wide range of noise conditions
Object Classification in Astronomical Multi-Color Surveys
We present a photometric method for identifying stars, galaxies and quasars
in multi-color surveys, which uses a library of >65000 color templates. The
method aims for extracting the information content of object colors in a
statistically correct way and performs a classification as well as a redshift
estimation for galaxies and quasars in a unified approach. For the redshift
estimation, we use an advanced version of the MEV estimator which determines
the redshift error from the redshift dependent probability density function.
The method was originally developed for the CADIS survey, where we checked
its performance by spectroscopy. The method provides high reliability (6 errors
among 151 objects with R<24), especially for quasar selection, and redshifts
accurate within sigma ~ 0.03 for galaxies and sigma ~ 0.1 for quasars.
We compare a few model surveys using the same telescope time but different
sets of broad-band and medium-band filters. Their performance is investigated
by Monte-Carlo simulations as well as by analytic evaluation in terms of
classification and redshift estimation. In practice, medium-band surveys show
superior performance. Finally, we discuss the relevance of color calibration
and derive important conclusions for the issues of library design and choice of
filters. The calibration accuracy poses strong constraints on an accurate
classification, and is most critical for surveys with few, broad and deeply
exposed filters, but less severe for many, narrow and less deep filters.Comment: 21 pages including 10 figures. Accepted for publication in Astronomy
& Astrophysic
Spectral proper orthogonal decomposition
The identification of coherent structures from experimental or numerical data
is an essential task when conducting research in fluid dynamics. This typically
involves the construction of an empirical mode base that appropriately captures
the dominant flow structures. The most prominent candidates are the
energy-ranked proper orthogonal decomposition (POD) and the frequency ranked
Fourier decomposition and dynamic mode decomposition (DMD). However, these
methods fail when the relevant coherent structures occur at low energies or at
multiple frequencies, which is often the case. To overcome the deficit of these
"rigid" approaches, we propose a new method termed Spectral Proper Orthogonal
Decomposition (SPOD). It is based on classical POD and it can be applied to
spatially and temporally resolved data. The new method involves an additional
temporal constraint that enables a clear separation of phenomena that occur at
multiple frequencies and energies. SPOD allows for a continuous shifting from
the energetically optimal POD to the spectrally pure Fourier decomposition by
changing a single parameter. In this article, SPOD is motivated from
phenomenological considerations of the POD autocorrelation matrix and justified
from dynamical system theory. The new method is further applied to three sets
of PIV measurements of flows from very different engineering problems. We
consider the flow of a swirl-stabilized combustor, the wake of an airfoil with
a Gurney flap, and the flow field of the sweeping jet behind a fluidic
oscillator. For these examples, the commonly used methods fail to assign the
relevant coherent structures to single modes. The SPOD, however, achieves a
proper separation of spatially and temporally coherent structures, which are
either hidden in stochastic turbulent fluctuations or spread over a wide
frequency range
Design and real time implementation of nonlinear minimum variance filter
In this paper, the design and real time implementation of a Nonlinear Minimum Variance (NMV) estimator is presented using a laboratory based ball and beam system. The real time implementation employs a LabVIEW based tool. The novelty of this work lies in the design steps and the practical implementation of the NMV estimation technique which up till now only investigated using simulation studies. The paper also discusses the advantages and limitations of the NMV estimator based on the real time application results. These are compared with results obtained using an extended Kalman filter
Noise Corruption of Empirical Mode Decomposition and Its Effect on Instantaneous Frequency
Huang's Empirical Mode Decomposition (EMD) is an algorithm for analyzing
nonstationary data that provides a localized time-frequency representation by
decomposing the data into adaptively defined modes. EMD can be used to estimate
a signal's instantaneous frequency (IF) but suffers from poor performance in
the presence of noise. To produce a meaningful IF, each mode of the
decomposition must be nearly monochromatic, a condition that is not guaranteed
by the algorithm and fails to be met when the signal is corrupted by noise. In
this work, the extraction of modes containing both signal and noise is
identified as the cause of poor IF estimation. The specific mechanism by which
such "transition" modes are extracted is detailed and builds on the observation
of Flandrin and Goncalves that EMD acts in a filter bank manner when analyzing
pure noise. The mechanism is shown to be dependent on spectral leak between
modes and the phase of the underlying signal. These ideas are developed through
the use of simple signals and are tested on a synthetic seismic waveform.Comment: 28 pages, 19 figures. High quality color figures available on Daniel
Kaslovsky's website: http://amath.colorado.edu/student/kaslovsk
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