Detection of noise-corrupted sinusoidal signals with Josephson junctions

We investigate the possibility of exploiting the speed and low noise features of Josephson junctions for detecting sinusoidal signals masked by Gaussian noise. We show that the escape time from the static locked state of a Josephson junction is very sensitive to a small periodic signal embedded in the noise, and therefore the analysis of the escape times can be employed to reveal the presence of the sinusoidal component. We propose and characterize two detection strategies: in the first the initial phase is supposedly unknown (incoherent strategy), while in the second the signal phase remains unknown but is fixed (coherent strategy). Our proposals are both suboptimal, with the linear filter being the optimal detection strategy, but they present some remarkable features, such as resonant activation, that make detection through Josephson junctions appealing in some special cases.Comment: 22 pages, 13 figure

Self-tuning to the Hopf bifurcation in fluctuating systems

The problem of self-tuning a system to the Hopf bifurcation in the presence of noise and periodic external forcing is discussed. We find that the response of the system has a non-monotonic dependence on the noise-strength, and displays an amplified response which is more pronounced for weaker signals. The observed effect is to be distinguished from stochastic resonance. For the feedback we have studied, the unforced self-tuned Hopf oscillator in the presence of fluctuations exhibits sharp peaks in its spectrum. The implications of our general results are briefly discussed in the context of sound detection by the inner ear.Comment: 37 pages, 7 figures (8 figure files

Synchronization of OFDM at low SNR over an AWGN channel

This paper is based on Extended Symbol OFDM (ES-OFDM) where symbols are extended in time. This way ES-OFDM can operate at low SNR. Each doubling of the symbol length improves the SNR performance by 3 dB in case of a coherent receiver. One of the basic questions is how to synchronize to signals far below the noise floor. An algorithm is presented which is based on the transmission of pilot symbols. At the receiver, the received signal is cross correlated with the known pilot symbol and the maximum magnitude is determined. The position of the maximum value within the cross correlation function indicates the time difference between transmitter and receiver. The performance of the algorithm in case of an Additive White Gaussian Noise (AWGN) channel, is assessed based on a theoretical approximation of the probability of correct detection of the time difference. The theoretical approximation matches with simulation results and shows that synchronization can be achieved for low (negative) SNRs

Study of optimum methods of optical communication

Abstracts are reported relating to the techniques used in the research concerning optical transmission of information. Communication through the turbulent atmosphere, quantum mechanics, and quantum communication theory are discussed along with the results

Dansgaard-Oeschger events: tipping points in the climate system

Dansgaard-Oeschger events are a prominent mode of variability in the records of the last glacial cycle. Various prototype models have been proposed to explain these rapid climate fluctuations, and no agreement has emerged on which may be the more correct for describing the paleoclimatic signal. In this work, we assess the bimodality of the system reconstructing the topology of the multi--dimensional attractor over which the climate system evolves. We use high-resolution ice core isotope data to investigate the statistical properties of the climate fluctuations in the period before the onset of the abrupt change. We show that Dansgaard-Oeschger events have weak early warning signals if the ensemble of events is considered. We find that the statistics are consistent with the switches between two different climate equilibrium states in response to a changing external forcing (e.g. solar, ice sheets...), either forcing directly the transition or pacing it through stochastic resonance. These findings are most consistent with a model that associates Dansgaard-Oeschger with changing boundary conditions, and with the presence of a bifurcation point.Comment: Final typeset version freely available at: Clim. Past, 9, 323-333, 2013 www.clim-past.net/9/323/2013/ doi:10.5194/cp-9-323-201

Robust statistics for deterministic and stochastic gravitational waves in non-Gaussian noise I: Frequentist analyses

Gravitational wave detectors will need optimal signal-processing algorithms to extract weak signals from the detector noise. Most algorithms designed to date are based on the unrealistic assumption that the detector noise may be modeled as a stationary Gaussian process. However most experiments exhibit a non-Gaussian ``tail'' in the probability distribution. This ``excess'' of large signals can be a troublesome source of false alarms. This article derives an optimal (in the Neyman-Pearson sense, for weak signals) signal processing strategy when the detector noise is non-Gaussian and exhibits tail terms. This strategy is robust, meaning that it is close to optimal for Gaussian noise but far less sensitive than conventional methods to the excess large events that form the tail of the distribution. The method is analyzed for two different signal analysis problems: (i) a known waveform (e.g., a binary inspiral chirp) and (ii) a stochastic background, which requires a multi-detector signal processing algorithm. The methods should be easy to implement: they amount to truncation or clipping of sample values which lie in the outlier part of the probability distribution.Comment: RevTeX 4, 17 pages, 8 figures, typos corrected from first version

Dynamic Decomposition of Spatiotemporal Neural Signals

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals

Chaotic Dynamics Enhance the Sensitivity of Inner Ear Hair Cells

Hair cells of the auditory and vestibular systems are capable of detecting sounds that induce sub-nanometer vibrations of the hair bundle, below the stochastic noise levels of the surrounding fluid. Hair bundles of certain species are also known to oscillate without external stimulation, indicating the presence of an underlying active mechanism. We propose that chaotic dynamics enhance the sensitivity and temporal resolution of the hair bundle response, and provide experimental and theoretical evidence for this effect. By varying the viscosity and ionic composition of the surrounding fluid, we are able to modulate the degree of chaos observed in the hair bundle dynamics in vitro. We consistently find that the hair bundle is most sensitive to a stimulus of small amplitude when it is poised in the weakly chaotic regime. Further, we show that the response time to a force step decreases with increasing levels of chaos. These results agree well with our numerical simulations of a chaotic Hopf oscillator and suggest that chaos may be responsible for the sensitivity and temporal resolution of hair cells