6,552 research outputs found
Nonparametric Edge Detection in Speckled Imagery
We address the issue of edge detection in Synthetic Aperture Radar imagery.
In particular, we propose nonparametric methods for edge detection, and
numerically compare them to an alternative method that has been recently
proposed in the literature. Our results show that some of the proposed methods
display superior results and are computationally simpler than the existing
method. An application to real (not simulated) data is presented and discussed.Comment: Accepted for publication in Mathematics and Computers in Simulatio
Detection of fast radio transients with multiple stations: a case study using the Very Long Baseline Array
Recent investigations reveal an important new class of transient radio
phenomena that occur on sub-millisecond timescales. Often transient surveys'
data volumes are too large to archive exhaustively. Instead, an on-line
automatic system must excise impulsive interference and detect candidate events
in real-time. This work presents a case study using data from multiple
geographically distributed stations to perform simultaneous interference
excision and transient detection. We present several algorithms that
incorporate dedispersed data from multiple sites, and report experiments with a
commensal real-time transient detection system on the Very Long Baseline Array
(VLBA). We test the system using observations of pulsar B0329+54. The
multiple-station algorithms enhanced sensitivity for detection of individual
pulses. These strategies could improve detection performance for a future
generation of geographically distributed arrays such as the Australian Square
Kilometre Array Pathfinder and the Square Kilometre Array.Comment: 12 pages, 14 figures. Accepted for Ap
Detection of an anomalous cluster in a network
We consider the problem of detecting whether or not, in a given sensor
network, there is a cluster of sensors which exhibit an "unusual behavior."
Formally, suppose we are given a set of nodes and attach a random variable to
each node. We observe a realization of this process and want to decide between
the following two hypotheses: under the null, the variables are i.i.d. standard
normal; under the alternative, there is a cluster of variables that are i.i.d.
normal with positive mean and unit variance, while the rest are i.i.d. standard
normal. We also address surveillance settings where each sensor in the network
collects information over time. The resulting model is similar, now with a time
series attached to each node. We again observe the process over time and want
to decide between the null, where all the variables are i.i.d. standard normal,
and the alternative, where there is an emerging cluster of i.i.d. normal
variables with positive mean and unit variance. The growth models used to
represent the emerging cluster are quite general and, in particular, include
cellular automata used in modeling epidemics. In both settings, we consider
classes of clusters that are quite general, for which we obtain a lower bound
on their respective minimax detection rate and show that some form of scan
statistic, by far the most popular method in practice, achieves that same rate
to within a logarithmic factor. Our results are not limited to the normal
location model, but generalize to any one-parameter exponential family when the
anomalous clusters are large enough.Comment: Published in at http://dx.doi.org/10.1214/10-AOS839 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments
Optical microscopy provides rich spatio-temporal information characterizing
in vivo molecular motion. However, effective forces and other parameters used
to summarize molecular motion change over time in live cells due to latent
state changes, e.g., changes induced by dynamic micro-environments,
photobleaching, and other heterogeneity inherent in biological processes. This
study focuses on techniques for analyzing Single Particle Tracking (SPT) data
experiencing abrupt state changes. We demonstrate the approach on GFP tagged
chromatids experiencing metaphase in yeast cells and probe the effective forces
resulting from dynamic interactions that reflect the sum of a number of
physical phenomena. State changes are induced by factors such as microtubule
dynamics exerting force through the centromere, thermal polymer fluctuations,
etc. Simulations are used to demonstrate the relevance of the approach in more
general SPT data analyses. Refined force estimates are obtained by adopting and
modifying a nonparametric Bayesian modeling technique, the Hierarchical
Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT
applications. The HDP-SLDS method shows promise in systematically identifying
dynamical regime changes induced by unobserved state changes when the number of
underlying states is unknown in advance (a common problem in SPT applications).
We expand on the relevance of the HDP-SLDS approach, review the relevant
background of Hierarchical Dirichlet Processes, show how to map discrete time
HDP-SLDS models to classic SPT models, and discuss limitations of the approach.
In addition, we demonstrate new computational techniques for tuning
hyperparameters and for checking the statistical consistency of model
assumptions directly against individual experimental trajectories; the
techniques circumvent the need for "ground-truth" and subjective information.Comment: 25 pages, 6 figures. Differs only typographically from PLoS One
publication available freely as an open-access article at
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.013763
A new method to detect event-related potentials based on Pearson\u2019s correlation
Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience.
Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise. The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP\u2019s waveform, these waveforms being time- and phase-locked. In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson\u2019s correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase\u2014in consonance with the stimuli\u2014in EEG signal correlation over all channels. This new method is also useful for selectively identifying
and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs. These hidden components seem to be caused by variations (between each successive stimulus) of the ERP\u2019s inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology. The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language
Dynamic Experiment Design Regularization Approach to Adaptive Imaging with Array Radar/SAR Sensor Systems
We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the “model-free” variational analysis (VA)-based image enhancement approach and the “model-based” descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations
Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI
Background: Prostate cancer is one of the most common forms of cancer found
in males making early diagnosis important. Magnetic resonance imaging (MRI) has
been useful in visualizing and localizing tumor candidates and with the use of
endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The
coils introduce intensity inhomogeneities and the surface coil intensity
correction built into MRI scanners is used to reduce these inhomogeneities.
However, the correction typically performed at the MRI scanner level leads to
noise amplification and noise level variations. Methods: In this study, we
introduce a new Monte Carlo-based noise compensation approach for coil
intensity corrected endorectal MRI which allows for effective noise
compensation and preservation of details within the prostate. The approach
accounts for the ERC SNR profile via a spatially-adaptive noise model for
correcting non-stationary noise variations. Such a method is useful
particularly for improving the image quality of coil intensity corrected
endorectal MRI data performed at the MRI scanner level and when the original
raw data is not available. Results: SNR and contrast-to-noise ratio (CNR)
analysis in patient experiments demonstrate an average improvement of 11.7 dB
and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong
performance when compared to existing approaches. Conclusions: A new noise
compensation method was developed for the purpose of improving the quality of
coil intensity corrected endorectal MRI data performed at the MRI scanner
level. We illustrate that promising noise compensation performance can be
achieved for the proposed approach, which is particularly important for
processing coil intensity corrected endorectal MRI data performed at the MRI
scanner level and when the original raw data is not available.Comment: 23 page
A review of RFI mitigation techniques in microwave radiometry
Radio frequency interference (RFI) is a well-known problem in microwave radiometry (MWR). Any undesired signal overlapping the MWR protected frequency bands introduces a bias in the measurements, which can corrupt the retrieved geophysical parameters. This paper presents a literature review of RFI detection and mitigation techniques for microwave radiometry from space. The reviewed techniques are divided between real aperture and aperture synthesis. A discussion and assessment of the application of RFI mitigation techniques is presented for each type of radiometer.Peer ReviewedPostprint (published version
Canonical correlation analysis of high-dimensional data with very small sample support
This paper is concerned with the analysis of correlation between two
high-dimensional data sets when there are only few correlated signal components
but the number of samples is very small, possibly much smaller than the
dimensions of the data. In such a scenario, a principal component analysis
(PCA) rank-reduction preprocessing step is commonly performed before applying
canonical correlation analysis (CCA). We present simple, yet very effective
approaches to the joint model-order selection of the number of dimensions that
should be retained through the PCA step and the number of correlated signals.
These approaches are based on reduced-rank versions of the Bartlett-Lawley
hypothesis test and the minimum description length information-theoretic
criterion. Simulation results show that the techniques perform well for very
small sample sizes even in colored noise
- …