37,233 research outputs found
Stimulus-invariant processing and spectrotemporal reverse correlation in primary auditory cortex
The spectrotemporal receptive field (STRF) provides a versatile and
integrated, spectral and temporal, functional characterization of single cells
in primary auditory cortex (AI). In this paper, we explore the origin of, and
relationship between, different ways of measuring and analyzing an STRF. We
demonstrate that STRFs measured using a spectrotemporally diverse array of
broadband stimuli -- such as dynamic ripples, spectrotemporally white noise,
and temporally orthogonal ripple combinations (TORCs) -- are very similar,
confirming earlier findings that the STRF is a robust linear descriptor of the
cell. We also present a new deterministic analysis framework that employs the
Fourier series to describe the spectrotemporal modulations contained in the
stimuli and responses. Additional insights into the STRF measurements,
including the nature and interpretation of measurement errors, is presented
using the Fourier transform, coupled to singular-value decomposition (SVD), and
variability analyses including bootstrap. The results promote the utility of
the STRF as a core functional descriptor of neurons in AI.Comment: 42 pages, 8 Figures; to appear in Journal of Computational
Neuroscienc
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter
The likelihood calculation of a vast number of particles is the computational
bottleneck for the particle filter in applications where the observation
information is rich. For fast computing the likelihood of particles, a
numerical fitting approach is proposed to construct the Likelihood Probability
Density Function (Li-PDF) by using a comparably small number of so-called
fulcrums. The likelihood of particles is thereby analytically inferred,
explicitly or implicitly, based on the Li-PDF instead of directly computed by
utilizing the observation, which can significantly reduce the computation and
enables real time filtering. The proposed approach guarantees the estimation
quality when an appropriate fitting function and properly distributed fulcrums
are used. The details for construction of the fitting function and fulcrums are
addressed respectively in detail. In particular, to deal with multivariate
fitting, the nonparametric kernel density estimator is presented which is
flexible and convenient for implicit Li-PDF implementation. Simulation
comparison with a variety of existing approaches on a benchmark 1-dimensional
model and multi-dimensional robot localization and visual tracking demonstrate
the validity of our approach.Comment: 42 pages, 17 figures, 4 tables and 1 appendix. This paper is a
draft/preprint of one paper submitted to the IEEE Transaction
DOLPHIn - Dictionary Learning for Phase Retrieval
We propose a new algorithm to learn a dictionary for reconstructing and
sparsely encoding signals from measurements without phase. Specifically, we
consider the task of estimating a two-dimensional image from squared-magnitude
measurements of a complex-valued linear transformation of the original image.
Several recent phase retrieval algorithms exploit underlying sparsity of the
unknown signal in order to improve recovery performance. In this work, we
consider such a sparse signal prior in the context of phase retrieval, when the
sparsifying dictionary is not known in advance. Our algorithm jointly
reconstructs the unknown signal - possibly corrupted by noise - and learns a
dictionary such that each patch of the estimated image can be sparsely
represented. Numerical experiments demonstrate that our approach can obtain
significantly better reconstructions for phase retrieval problems with noise
than methods that cannot exploit such "hidden" sparsity. Moreover, on the
theoretical side, we provide a convergence result for our method
Identifying nonlinear wave interactions in plasmas using two-point measurements: a case study of Short Large Amplitude Magnetic Structures (SLAMS)
A framework is described for estimating Linear growth rates and spectral
energy transfers in turbulent wave-fields using two-point measurements. This
approach, which is based on Volterra series, is applied to dual satellite data
gathered in the vicinity of the Earth's bow shock, where Short Large Amplitude
Magnetic Structures (SLAMS) supposedly play a leading role. The analysis
attests the dynamic evolution of the SLAMS and reveals an energy cascade toward
high-frequency waves.Comment: 26 pages, 13 figure
Improvement of speech recognition by nonlinear noise reduction
The success of nonlinear noise reduction applied to a single channel
recording of human voice is measured in terms of the recognition rate of a
commercial speech recognition program in comparison to the optimal linear
filter. The overall performance of the nonlinear method is shown to be
superior. We hence demonstrate that an algorithm which has its roots in the
theory of nonlinear deterministic dynamics possesses a large potential in a
realistic application.Comment: see urbanowicz.org.p
Renormalization group and perfect operators for stochastic differential equations
We develop renormalization group methods for solving partial and stochastic
differential equations on coarse meshes. Renormalization group transformations
are used to calculate the precise effect of small scale dynamics on the
dynamics at the mesh size. The fixed point of these transformations yields a
perfect operator: an exact representation of physical observables on the mesh
scale with minimal lattice artifacts. We apply the formalism to simple
nonlinear models of critical dynamics, and show how the method leads to an
improvement in the computational performance of Monte Carlo methods.Comment: 35 pages, 16 figure
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