14,160 research outputs found
Echo Cancellation - A Likelihood Ratio Test for Double-talk Versus Channel Change
Echo cancellers are in wide use in both electrical (four wire to two wire mismatch) and acoustic (speaker-microphone coupling) applications. One of the main design problems is the control logic for adaptation. Basically, the algorithm weights should be frozen in the presence of double-talk and adapt quickly in the absence of double-talk. The control logic can be quite complicated since it is often not easy to discriminate between the echo signal and the near-end speaker. This paper derives a log likelihood ratio test (LRT) for deciding between double-talk (freeze weights) and a channel change (adapt quickly) using a stationary Gaussian
stochastic input signal model. The probability density function of a sufficient statistic under each hypothesis is obtained and the performance of the test is evaluated as a function of the system parameters. The receiver operating characteristics (ROCs) indicate that it is difficult to correctly decide between double-talk and a channel change based upon a single look. However, post-detection integration of approximately one hundred sufficient statistic samples yields a detection probability close to unity (0.99) with a small false alarm probability (0.01)
Detection of curved lines with B-COSFIRE filters: A case study on crack delineation
The detection of curvilinear structures is an important step for various
computer vision applications, ranging from medical image analysis for
segmentation of blood vessels, to remote sensing for the identification of
roads and rivers, and to biometrics and robotics, among others. %The visual
system of the brain has remarkable abilities to detect curvilinear structures
in noisy images. This is a nontrivial task especially for the detection of thin
or incomplete curvilinear structures surrounded with noise. We propose a
general purpose curvilinear structure detector that uses the brain-inspired
trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear
filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis
thresholding and morphological closing. We demonstrate its effectiveness on a
data set of noisy images with cracked pavements, where we achieve
state-of-the-art results (F-measure=0.865). The proposed method can be employed
in any computer vision methodology that requires the delineation of curvilinear
and elongated structures.Comment: Accepted at Computer Analysis of Images and Patterns (CAIP) 201
A learning approach to the detection of gravitational wave transients
We investigate the class of quadratic detectors (i.e., the statistic is a
bilinear function of the data) for the detection of poorly modeled
gravitational transients of short duration. We point out that all such
detection methods are equivalent to passing the signal through a filter bank
and linearly combine the output energy. Existing methods for the choice of the
filter bank and of the weight parameters rely essentially on the two following
ideas: (i) the use of the likelihood function based on a (possibly
non-informative) statistical model of the signal and the noise, (ii) the use of
Monte-Carlo simulations for the tuning of parametric filters to get the best
detection probability keeping fixed the false alarm rate. We propose a third
approach according to which the filter bank is "learned" from a set of training
data. By-products of this viewpoint are that, contrarily to previous methods,
(i) there is no requirement of an explicit description of the probability
density function of the data when the signal is present and (ii) the filters we
use are non-parametric. The learning procedure may be described as a two step
process: first, estimate the mean and covariance of the signal with the
training data; second, find the filters which maximize a contrast criterion
referred to as deflection between the "noise only" and "signal+noise"
hypothesis. The deflection is homogeneous to the signal-to-noise ratio and it
uses the quantities estimated at the first step. We apply this original method
to the problem of the detection of supernovae core collapses. We use the
catalog of waveforms provided recently by Dimmelmeier et al. to train our
algorithm. We expect such detector to have better performances on this
particular problem provided that the reference signals are reliable.Comment: 22 pages, 4 figure
Ensemble Transport Adaptive Importance Sampling
Markov chain Monte Carlo methods are a powerful and commonly used family of
numerical methods for sampling from complex probability distributions. As
applications of these methods increase in size and complexity, the need for
efficient methods increases. In this paper, we present a particle ensemble
algorithm. At each iteration, an importance sampling proposal distribution is
formed using an ensemble of particles. A stratified sample is taken from this
distribution and weighted under the posterior, a state-of-the-art ensemble
transport resampling method is then used to create an evenly weighted sample
ready for the next iteration. We demonstrate that this ensemble transport
adaptive importance sampling (ETAIS) method outperforms MCMC methods with
equivalent proposal distributions for low dimensional problems, and in fact
shows better than linear improvements in convergence rates with respect to the
number of ensemble members. We also introduce a new resampling strategy,
multinomial transformation (MT), which while not as accurate as the ensemble
transport resampler, is substantially less costly for large ensemble sizes, and
can then be used in conjunction with ETAIS for complex problems. We also focus
on how algorithmic parameters regarding the mixture proposal can be quickly
tuned to optimise performance. In particular, we demonstrate this methodology's
superior sampling for multimodal problems, such as those arising from inference
for mixture models, and for problems with expensive likelihoods requiring the
solution of a differential equation, for which speed-ups of orders of magnitude
are demonstrated. Likelihood evaluations of the ensemble could be computed in a
distributed manner, suggesting that this methodology is a good candidate for
parallel Bayesian computations
Diverse Structural Evolution at z > 1 in Cosmologically Simulated Galaxies
From mock Hubble Space Telescope images, we quantify non-parametric
statistics of galaxy morphology, thereby predicting the emergence of
relationships among stellar mass, star formation, and observed rest-frame
optical structure at 1 < z < 3. We measure automated diagnostics of galaxy
morphology in cosmological simulations of the formation of 22 central galaxies
with 9.3 < log10 M_*/M_sun < 10.7. These high-spatial-resolution zoom-in
calculations enable accurate modeling of the rest-frame UV and optical
morphology. Even with small numbers of galaxies, we find that structural
evolution is neither universal nor monotonic: galaxy interactions can trigger
either bulge or disc formation, and optically bulge-dominated galaxies at this
mass may not remain so forever. Simulated galaxies with M_* > 10^10 M_sun
contain relatively more disc-dominated light profiles than those with lower
mass, reflecting significant disc brightening in some haloes at 1 < z < 2. By
this epoch, simulated galaxies with specific star formation rates below 10^-9.7
yr^-1 are more likely than normal star-formers to have a broader mix of
structural types, especially at M_* > 10^10 M_sun. We analyze a cosmological
major merger at z ~ 1.5 and find that the newly proposed MID morphology
diagnostics trace later merger stages while G-M20 trace earlier ones. MID is
sensitive also to clumpy star-forming discs. The observability time of typical
MID-enhanced events in our simulation sample is less than 100 Myr. A larger
sample of cosmological assembly histories may be required to calibrate such
diagnostics in the face of their sensitivity to viewing angle, segmentation
algorithm, and various phenomena such as clumpy star formation and minor
mergers.Comment: 23 pages, 16 figures, MNRAS accepted versio
Systematic approach to nonlinear filtering associated with aggregation operators. Part 2. Frechet MIMO-filters
Median filtering has been widely used in scalar-valued image processing as an edge preserving operation. The basic idea is that the pixel value is replaced by the median of the pixels contained in a window around it. In this work, this idea is extended onto vector-valued images. It is based on the fact that the median is also the value that minimizes the sum of distances between all grey-level pixels in the window. The Frechet median of a discrete set of vector-valued pixels in a metric space with a metric is the point minimizing the sum of metric distances to the all sample pixels. In this paper, we extend the notion of the Frechet median to the general Frechet median, which minimizes the Frechet cost function (FCF) in the form of aggregation function of metric distances, instead of the ordinary sum. Moreover, we propose use an aggregation distance instead of classical metric distance. We use generalized Frechet median for constructing new nonlinear Frechet MIMO-filters for multispectral image processing. (C) 2017 The Authors. Published by Elsevier Ltd.This work was supported by grants the RFBR No 17-07-00886, No 17-29-03369 and by Ural State Forest University Engineering's Center of Excellence in "Quantum and Classical Information Technologies for Remote Sensing Systems"
Kalman-Takens filtering in the presence of dynamical noise
The use of data assimilation for the merging of observed data with dynamical
models is becoming standard in modern physics. If a parametric model is known,
methods such as Kalman filtering have been developed for this purpose. If no
model is known, a hybrid Kalman-Takens method has been recently introduced, in
order to exploit the advantages of optimal filtering in a nonparametric
setting. This procedure replaces the parametric model with dynamics
reconstructed from delay coordinates, while using the Kalman update formulation
to assimilate new observations. We find that this hybrid approach results in
comparable efficiency to parametric methods in identifying underlying dynamics,
even in the presence of dynamical noise. By combining the Kalman-Takens method
with an adaptive filtering procedure we are able to estimate the statistics of
the observational and dynamical noise. This solves a long standing problem of
separating dynamical and observational noise in time series data, which is
especially challenging when no dynamical model is specified
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