128 research outputs found
Image restoration using HOS and the Radon transform
The authors propose the use of higher-order statistics (HOS) to study the problem of image restoration. They consider images degraded by linear or zero phase blurring point spread functions (PSF) and additive Gaussian noise. The complexity associated with the combination of two-dimensional signal processing and higher-order statistics is reduced by means of the Radon transform. The projection at each angle is an one-dimensional signal that can be processed by any existing 1-D higher-order statistics-based method. They apply two methods that have proven to attain good one-dimensional signal reconstruction, especially in the presence of noise. After the ideal projections have been estimated, the inverse Radon transform gives the restored image. Simulation results are provided.Peer ReviewedPostprint (published version
A new positive time-frequency distribution
This article studies the formulation of new members of the Cohen-Posch (1985) class of positive time-frequency energy distributions. Members of this class are always positive functionals and satisfy the marginal constraints. Therefore, they can be properly interpreted as distributions. We considered the minimization of cross-entropy measures with respect to different priors orPeer ReviewedPostprint (published version
Blind Inversion of Wiener Systems
A system in which a linear dynamic part is followed by a non
linear memoryless distortion a Wiener system is blindly inverted
This
kind of systems can be modelised as a postnonlinear mixture and using
some results about these mixtures an e cient algorithm is proposed
Results in a hard situation are presented and illustrate the e ciency of
this algorith
Cramer-Rao bounds for target angle and Doppler estimation for airborne radar in Cauchy interference
Can Classical Noise Enhance Quantum Transmission?
A modified quantum teleportation protocol broadens the scope of the classical
forbidden-interval theorems for stochastic resonance. The fidelity measures
performance of quantum communication. The sender encodes the two classical bits
for quantum teleportation as weak bipolar subthreshold signals and sends them
over a noisy classical channel. Two forbidden-interval theorems provide a
necessary and sufficient condition for the occurrence of the nonmonotone
stochastic resonance effect in the fidelity of quantum teleportation. The
condition is that the noise mean must fall outside a forbidden interval related
to the detection threshold and signal value. An optimal amount of classical
noise benefits quantum communication when the sender transmits weak signals,
the receiver detects with a high threshold, and the noise mean lies outside the
forbidden interval. Theorems and simulations demonstrate that both
finite-variance and infinite-variance noise benefit the fidelity of quantum
teleportation.Comment: 11 pages, 3 figures, replaced with published version that includes
new section on imperfect entanglement and references to J. J. Ting's earlier
wor
New Evidence of Discrete Scale Invariance in the Energy Dissipation of Three-Dimensional Turbulence: Correlation Approach and Direct Spectral Detection
We extend the analysis of [Zhou and Sornette, Physica D 165, 94-125, 2002]
showing statistically significant log-periodic corrections to scaling in the
moments of the energy dissipation rate in experiments at high Reynolds number
() of three-dimensional fully developed turbulence. First, we
develop a simple variant of the canonical averaging method using a rephasing
scheme between different samples based on pairwise correlations that confirms
Zhou and Sornette's previous results. The second analysis uses a simpler local
spectral approach and then performs averages over many local spectra. This
yields stronger evidence of the existence of underlying log-periodic
undulations, with the detection of more than 20 harmonics of a fundamental
logarithmic frequency corresponding to the preferred
scaling ratio .Comment: 9 RevTex4 papes including 8 eps figure
Quantum Forbidden-Interval Theorems for Stochastic Resonance
We extend the classical forbidden-interval theorems for a
stochastic-resonance noise benefit in a nonlinear system to a quantum-optical
communication model and a continuous-variable quantum key distribution model.
Each quantum forbidden-interval theorem gives a necessary and sufficient
condition that determines whether stochastic resonance occurs in quantum
communication of classical messages. The quantum theorems apply to any quantum
noise source that has finite variance or that comes from the family of
infinite-variance alpha-stable probability densities. Simulations show the
noise benefits for the basic quantum communication model and the
continuous-variable quantum key distribution model.Comment: 13 pages, 2 figure
PARENTS' SATISFACTION AND DISSATISFACTION WITH THEIR CHILDREN'S DENTIST *
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66344/1/j.1752-7325.1973.tb03989.x.pd
The smarty4covid dataset and knowledge base: a framework enabling interpretable analysis of audio signals
Harnessing the power of Artificial Intelligence (AI) and m-health towards
detecting new bio-markers indicative of the onset and progress of respiratory
abnormalities/conditions has greatly attracted the scientific and research
interest especially during COVID-19 pandemic. The smarty4covid dataset contains
audio signals of cough (4,676), regular breathing (4,665), deep breathing
(4,695) and voice (4,291) as recorded by means of mobile devices following a
crowd-sourcing approach. Other self reported information is also included (e.g.
COVID-19 virus tests), thus providing a comprehensive dataset for the
development of COVID-19 risk detection models. The smarty4covid dataset is
released in the form of a web-ontology language (OWL) knowledge base enabling
data consolidation from other relevant datasets, complex queries and reasoning.
It has been utilized towards the development of models able to: (i) extract
clinically informative respiratory indicators from regular breathing records,
and (ii) identify cough, breath and voice segments in crowd-sourced audio
recordings. A new framework utilizing the smarty4covid OWL knowledge base
towards generating counterfactual explanations in opaque AI-based COVID-19 risk
detection models is proposed and validated.Comment: Submitted for publication in Nature Scientific Dat
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