A general formulation for fault detection in stochastic continuous-time dynamical systems

In this work, a general formulation for fault detection in stochastic continuoustime dynamical systems is presented. This formulation is based on the definition of a pre-Hilbert space so that orthogonal projection techniques, based on the statistics of the involved stochastic processes can be applied. The general setting gathers different existing schemes within a unifying framework

The astrophysical gravitational wave stochastic background

A gravitational wave stochastic background of astrophysical origin may have resulted from the superposition of a large number of unresolved sources since the beginning of stellar activity. Its detection would put very strong constrains on the physical properties of compact objects, the initial mass function or the star formation history. On the other hand, it could be a 'noise' that would mask the stochastic background of cosmological origin. We review the main astrophysical processes able to produce a stochastic background and discuss how it may differ from the primordial contribution by its statistical properties. Current detection methods are also presented.Comment: appeared in Research in Astronomy & Astrophysics (RAA), vol 11 (2011) as Invited paper ; 20 pages and 7 figures; version corrected after we found an error in equation (5

Noise-enhanced computation in a model of a cortical column

Varied sensory systems use noise in order to enhance detection of weak signals. It has been conjectured in the literature that this effect, known as stochastic resonance, may take place in central cognitive processes such as the memory retrieval of arithmetical multiplication. We show in a simplified model of cortical tissue, that complex arithmetical calculations can be carried out and are enhanced in the presence of a stochastic background. The performance is shown to be positively correlated to the susceptibility of the network, defined as its sensitivity to a variation of the mean of its inputs. For nontrivial arithmetic tasks such as multiplication, stochastic resonance is an emergent property of the microcircuitry of the model network

Discriminating between a Stochastic Gravitational Wave Background and Instrument Noise

The detection of a stochastic background of gravitational waves could significantly impact our understanding of the physical processes that shaped the early Universe. The challenge lies in separating the cosmological signal from other stochastic processes such as instrument noise and astrophysical foregrounds. One approach is to build two or more detectors and cross correlate their output, thereby enhancing the common gravitational wave signal relative to the uncorrelated instrument noise. When only one detector is available, as will likely be the case with the Laser Interferometer Space Antenna (LISA), alternative analysis techniques must be developed. Here we show that models of the noise and signal transfer functions can be used to tease apart the gravitational and instrument noise contributions. We discuss the role of gravitational wave insensitive "null channels" formed from particular combinations of the time delay interferometry, and derive a new combination that maintains this insensitivity for unequal arm length detectors. We show that, in the absence of astrophysical foregrounds, LISA could detect signals with energy densities as low as $\Omega_{\rm gw} = 6 \times 10^{-13}$ with just one month of data. We describe an end-to-end Bayesian analysis pipeline that is able to search for, characterize and assign confidence levels for the detection of a stochastic gravitational wave background, and demonstrate the effectiveness of this approach using simulated data from the third round of Mock LISA Data Challenges.Comment: 10 Pages, 10 Figure

PROGNOSIS OF MONTHLY UNEMPLOYMENT RATE IN THE EUROPEAN UNION THROUGH METHODS BASED ON ECONOMETRIC MODELS

In this paper we propose the prognosis of the unemployment rate in the European Union through the Box-Jenkins method and the TRAMO/SEATS method as well as the detection of the method which proves to provide the best results. The monthly unemployment rate in the European Union is affected by seasonal variations of deterministic and stochastic nature. The prognosis through the Box-Jenkins nature supposes the separate consideration of seasonal variations, according to their specific nature. The stochastic seasonal variations are modelled and prognosticated simultaneously with the other components of the time series, based on the generating stochastic process. The prognosis of the monthly unemployment rate in the European Union through the TRAMO/SEATS methods is done by aggregating the individual prognoses of the components of the time series, obtained according to the stochastic processes models that generate them.seasonal variations, stochastic process, moving average, prognosis, performance indicators of the prognosis

Scattering of polarized laser light by an atomic gas in free space: a QSDE approach

We propose a model, based on a quantum stochastic differential equation (QSDE), to describe the scattering of polarized laser light by an atomic gas. The gauge terms in the QSDE account for the direct scattering of the laser light into different field channels. Once the model has been set, we can rigorously derive quantum filtering equations for balanced polarimetry and homodyne detection experiments, study the statistics of output processes and investigate a strong driving, weak coupling limit.Comment: 9 pages, 2 figure

Detection of Patches of Outliers in Stochastic Volatility Processes.

Because the volatility of nancial asset returns tends to arrive in clusters, it is quite likely that outliers appear in patches. In this case, most of the statistical tests developed to detect outliers have low power. We propose to use the posterior distribution of the size of the outlier and of the probability of the presence of an outlier at each observation to detect and estimate the outlier. This sampling algorithm is an adapted version of the algorithm proposed by Justel et al. (2001) for autoregressive time-series models. Our proposed sampling procedure is applied to a simulated sample according to the stochastic volatility, a sample of the New York Stock Exchange daily returns, and a sample of the Brazilian S~ao Paulo Stock Exchange daily returns.Because the volatility of nancial asset returns tends to arrive in clusters, it is quite likely that outliers appear in patches. In this case, most of the statistical tests developed to detect outliers have low power. We propose to use the posterior distribution of the size of the outlier and of the probability of the presence of an outlier at each observation to detect and estimate the outlier. This sampling algorithm is an adapted version of the algorithm proposed by Justel et al. (2001) for autoregressive time-series models. Our proposed sampling procedure is applied to a simulated sample according to the stochastic volatility, a sample of the New York Stock Exchange daily returns, and a sample of the Brazilian S~ao Paulo Stock Exchange daily returns