21,368 research outputs found
Noise enhanced detection
Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 64-67.Performance of some suboptimal detectors can be improved by adding independent
noise to their measurements. Improving the performance of a detector by
adding a stochastic signal to the measurement can be considered in the framework
of stochastic resonance (SR), which can be regarded as the observation of
“noise benefits” related to signal transmission in nonlinear systems. Such noise
benefits can be in various forms, such as a decrease in probability of error, or
an increase in probability of detection under a false-alarm rate constraint. The
main focus of this thesis is to investigate noise benefits in the Bayesian, minimax
and Neyman-Pearson frameworks, and characterize optimal additional noise
components, and quantify their effects.
In the first part of the thesis, a Bayesian framework is considered, and
the previous results on optimal additional noise components for simple binary
hypothesis-testing problems are extended to M-ary composite hypothesis-testing
problems. In addition, a practical detection problem is considered in the Bayesian
framework. Namely, binary hypothesis-testing via a sign detector is studied for
antipodal signals under symmetric Gaussian mixture noise, and the effects of
shifting the measurements (observations) used by the sign detector are investigated.
First, a sufficient condition is obtained to specify when the sign detectorbased on the modified measurements (called the “modified” sign detector) can
have smaller probability of error than the original sign detector. Also, two suf-
ficient conditions under which the original sign detector cannot be improved by
measurement modification are derived in terms of desired signal and Gaussian
mixture noise parameters. Then, for equal variances of the Gaussian components
in the mixture noise, it is shown that the probability of error for the modified
detector is a monotone increasing function of the variance parameter, which is
not always true for the original detector. In addition, the maximum improvement,
specified as the ratio between the probabilities of error for the original
and the modified detectors, is specified as 2 for infinitesimally small variances of
the Gaussian components in the mixture noise. Finally, numerical examples are
presented to support the theoretical results, and some extensions to the case of
asymmetric Gaussian mixture noise are explained.
In the second part of the thesis, the effects of adding independent noise to
measurements are studied for M-ary hypothesis-testing problems according to
the minimax criterion. It is shown that the optimal additional noise can be
represented by a randomization of at most M signal values. In addition, a convex
relaxation approach is proposed to obtain an accurate approximation to the noise
probability distribution in polynomial time. Furthermore, sufficient conditions
are presented to determine when additional noise can or cannot improve the
performance of a given detector. Finally, a numerical example is presented.
Finally, the effects of additional independent noise are investigated in the
Neyman-Pearson framework, and various sufficient conditions on the improvability
and the non-improvability of a suboptimal detector are derived. First, a
sufficient condition under which the performance of a suboptimal detector cannot
be enhanced by additional independent noise is obtained according to the
Neyman-Pearson criterion. Then, sufficient conditions are obtained to specifywhen the detector performance can be improved. In addition to a generic condition,
various explicit sufficient conditions are proposed for easy evaluation of
improvability. Finally, a numerical example is presented and the practicality of
the proposed conditions is discussed.Bayram, SuatM.S
Fisher-information condition for enhanced signal detection via stochastic resonance
Various situations where a signal is enhanced by noise through stochastic resonance are now known. This paper contributes to determining general conditions under which improvement by noise can be a priori decided as feasible or not. We focus on the detection of a known signal in additive white noise. Under the assumptions of a weak signal and a sufficiently large sample size, it is proved, with an inequality based on the Fisher information, that improvement by adding noise is never possible, generically, in these conditions. However, under less restrictive conditions, an example of signal detection is shown with favorable action of adding noise.Fabing Duan, François Chapeau-Blondeau, Derek Abbot
Evaluation of bistable systems versus matched filters in detecting bipolar pulse signals
This paper presents a thorough evaluation of a bistable system versus a
matched filter in detecting bipolar pulse signals. The detectability of the
bistable system can be optimized by adding noise, i.e. the stochastic resonance
(SR) phenomenon. This SR effect is also demonstrated by approximate statistical
detection theory of the bistable system and corresponding numerical
simulations. Furthermore, the performance comparison results between the
bistable system and the matched filter show that (a) the bistable system is
more robust than the matched filter in detecting signals with disturbed pulse
rates, and (b) the bistable system approaches the performance of the matched
filter in detecting unknown arrival times of received signals, with an
especially better computational efficiency. These significant results verify
the potential applicability of the bistable system in signal detection field.Comment: 15 pages, 9 figures, MikTex v2.
On the distillation and purification of phase-diffused squeezed states
Recently it was discovered that non-Gaussian decoherence processes, such as
phase-diffusion, can be counteracted by purification and distillation protocols
that are solely built on Gaussian operations. Here, we make use of this
experimentally highly accessible regime, and provide a detailed experimental
and theoretical analysis of several strategies for purification/distillation
protocols on phase-diffused squeezed states. Our results provide valuable
information for the optimization of such protocols with respect to the choice
of the trigger quadrature, the trigger threshold value and the probability of
generating a distilled state
Nonlinear fiber gyroscope for quantum metrology
We examine the performance of a nonlinear fiber gyroscope for improved signal
detection beating the quantum limits of its linear counterparts. The
performance is examined when the nonlinear gyroscope is illuminated by
practical field states, such as coherent and quadrature squeezed states. This
is compared with the case of more ideal probes such as photon-number states.Comment: 8 pages, 1 figur
Quantum metrology and its application in biology
Quantum metrology provides a route to overcome practical limits in sensing
devices. It holds particular relevance to biology, where sensitivity and
resolution constraints restrict applications both in fundamental biophysics and
in medicine. Here, we review quantum metrology from this biological context,
focusing on optical techniques due to their particular relevance for biological
imaging, sensing, and stimulation. Our understanding of quantum mechanics has
already enabled important applications in biology, including positron emission
tomography (PET) with entangled photons, magnetic resonance imaging (MRI) using
nuclear magnetic resonance, and bio-magnetic imaging with superconducting
quantum interference devices (SQUIDs). In quantum metrology an even greater
range of applications arise from the ability to not just understand, but to
engineer, coherence and correlations at the quantum level. In the past few
years, quite dramatic progress has been seen in applying these ideas into
biological systems. Capabilities that have been demonstrated include enhanced
sensitivity and resolution, immunity to imaging artifacts and technical noise,
and characterization of the biological response to light at the single-photon
level. New quantum measurement techniques offer even greater promise, raising
the prospect for improved multi-photon microscopy and magnetic imaging, among
many other possible applications. Realization of this potential will require
cross-disciplinary input from researchers in both biology and quantum physics.
In this review we seek to communicate the developments of quantum metrology in
a way that is accessible to biologists and biophysicists, while providing
sufficient detail to allow the interested reader to obtain a solid
understanding of the field. We further seek to introduce quantum physicists to
some of the central challenges of optical measurements in biological science.Comment: Submitted review article, comments and suggestions welcom
Machine-learning nonstationary noise out of gravitational-wave detectors
Signal extraction out of background noise is a common challenge in high-precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal-to-noise ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is nonstationary, linear techniques often fail or are suboptimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove nonstationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational-wave observatory, where we could obtain an improvement of the detector gravitational-wave reach without introducing any bias on the source parameter estimation
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