Noise and ultraviolet divergences in the dynamics of the chiral condensate in QCD

The time evolution of the matter produced in high energy heavy-ion collisions seems to be well described by relativistic viscous hydrodynamics. In addition to the hydrodynamic degrees of freedom related to energy-momentum conservation, degrees of freedom associated with order parameters of broken continuous symmetries must be considered because they are all coupled to each other. of particular interest is the coupling of degrees of freedom associated with the chiral symmetry of QCD. Quantum and thermal fluctuations of the chiral fields act as noise sources in the classical equations of motion, turning them into stochastic differential equations in the form of Ginzburg-Landau-Langevin (GLL) equations. Analytic solutions of GLL equations are attainable only in very special circumstances and extensive numerical simulations are necessary, usually by discretizing the equations on a spatial lattice. However, a not much appreciated issue in the numerical simulations of GLL equations is that ultraviolet divergences in the form of lattice-spacing dependence plague the solutions. The divergences are related to the well-known Rayleigh-Jeans catastrophe in classical field theory. In the present communication we present a systematic lattice renormalization method to control the catastrophe. We discuss the implementation of the method for a GLL equation derived in the context of a model for the QCD chiral phase transition and consider the nonequilibrium evolution of the chiral condensate during the hydrodynamic flow of the quark-gluon plasma.

Langevin dynamics of the deconfinement transition for pure gauge theory

We investigate the effects of dissipation in the deconfinement transition for pure SU(2) and SU(3) gauge theories. Using an effective theory for the order parameter, we study its Langevin evolution numerically. Noise effects are included for the case of SU(2). We find that both dissipation and noise have dramatic effects on the spinodal decomposition of the order parameter and delay considerably its thermalization. For SU(3) the effects of dissipation are even larger than for SU(2).

ALTERNATIVE SAMPLING STRATEGY FOR A RANDOM OPTIMIZATION ALGORITHM

Alternative sampling procedures are compared to the pure random search method. It is shown that the efficiency of the algorithm can be improved with respect to the expected number of steps to reach an epsilon-neighborhood of the optimal point.

The use of current transformers for noise detection due to sparking in insulators strings in high voltage transmission lines

In a general way, in an electric power utility the current transformers (CT) are used to measurement and protection of transmission lines (TL) 1 The Power Line Carriers systems (PLC) are used for communication between electrical substations and transmission line protection. However, with the increasing use of optical fiber to communication (due mainly to its high data transmission rate and low signal-noise relation) this application loses potentiality. Therefore, other functions must be defined to equipments that are still in using, one of them is detecting faults (short-circuits) and transmission lines insulator strings damages 2. The purpose of this paper is to verify the possibility of using the path to the ground offered by the CTs instead of capacitive couplings / capacitive potential transformers to detect damaged insulators, since the current transformers are always present in all transmission lines (TL's) bays. To this a comparison between this new proposal and the PLC previous proposed system 2 is shown, evaluating the economical and technical points of view. ©2010 IEEE.

Aplicação de deconvolução homomórfica a dados sísmicos

Um registro sísmico é frequentemente representado como a convolução de um pulso-fonte com a resposta do meio ao impulso, relacionada ao caminho da propagação. O processo de separação destes dois componentes da convolução é denominado deconvolução. Existe uma variedade de aproximações para o desenvolvimento de uma deconvolução. Uma das mais comuns é o uso da filtragem linear inversa, ou seja, o processamento do sinal composto, através de um filtro linear, cuja resposta de frequência é a recíproca da transformada de Fourier de um dos componentes do sinal. Obviamente, a fim de usarmos a filtragem inversa, tais componentes devem ser conhecidas ou estimadas. Neste trabalho, tratamos da aplicação a sinais sísmicos, de uma técnica de deconvolução não linear, proposta por Oppenheim (1965), a qual utiliza a teoria de uma classe de sistemas não lineares, que satisfazem um princípio generalizado de superposição, denominados de sistemas homomórficos. Tais sistemas são particularmente úteis na separação de sinais que estão combinados através da operação de convolução. O algoritmo da deconvolução homomórfica transforma o processo de convolução em uma superposição aditiva de seus componentes, com o resultado de que partes simples podem ser separadas mais facilmente. Esta classe de técnicas de filtragem representa uma generalização dos problemas de filtragem linear. O presente método oferece a considerável vantagem de que não é necessário fazer qualquer suposição prévia sobre a natureza do pulso sísmico fonte, ou da resposta do meio ao impulso, não requerendo assim, as considerações usuais de que o pulso seja de fase-mínima e que a distribuição dos impulsos seja aleatória, embora a qualidade dos resultados obtidos pela análise homomórfica seja muito sensível à razão sinal/ruído, como demonstrado.

Migração em profundidade usando a solução numérica da equação da eiconal

Nos últimos anos tem-se verificado um interesse crescente no desenvolvimento de algoritmos de imageamento sísmico com a finalidade de obter uma imagem da subsuperfície da terra. A migração pelo método de Kirchhoff, por exemplo, é um método de imageamento muito eficiente empregado na busca da localização de refletores na subsuperficie, quando dispomos do cálculo dos tempos de trânsito necessários para a etapa de empilhamento, sendo estes obtidos neste trabalho através da solução da equação eiconal. Primeiramente, é apresentada a teoria da migração de Kirchhoff em profundidade baseada na teoria do raio, sendo em seguida introduzida a equação eiconal, através da qual são obtidos os tempos de trânsitos empregados no empilhamento das curvas de difrações. Em seguida é desenvolvido um algoritmo de migração em profundidade fazendo uso dos tempos de trânsito obtidos através da equação eiconal. Finalmente, aplicamos este algoritmo a dados sintéticos contendo ruído aditivo e múltiplas e obtemos como resultado uma seção sísmica na profundidade. Através dos experimentos feitos neste trabalho observou-se que o algoritmo de migração desenvolvido mostrou-se bastante eficiente e eficaz na reconstrução da imagem dos refletores.

Partially Observed Markov Random Fields Are Variable Neighborhood Random Fields

The present paper has two goals. First to present a natural example of a new class of random fields which are the variable neighborhood random fields. The example we consider is a partially observed nearest neighbor binary Markov random field. The second goal is to establish sufficient conditions ensuring that the variable neighborhoods are almost surely finite. We discuss the relationship between the almost sure finiteness of the interaction neighborhoods and the presence/absence of phase transition of the underlying Markov random field. In the case where the underlying random field has no phase transition we show that the finiteness of neighborhoods depends on a specific relation between the noise level and the minimum values of the one-point specification of the Markov random field. The case in which there is phase transition is addressed in the frame of the ferromagnetic Ising model. We prove that the existence of infinite interaction neighborhoods depends on the phase.

Anderson-like Transition for a Class of Random Sparse Models in d >= 2 Dimensions

We show that the Kronecker sum of d >= 2 copies of a random one-dimensional sparse model displays a spectral transition of the type predicted by Anderson, from absolutely continuous around the center of the band to pure point around the boundaries. Possible applications to physics and open problems are discussed briefly.

Imperceptible electrical noise attenuates isometric plantar flexion force fluctuations with correlated reductions in postural sway

Optimal levels of noise stimulation have been shown to enhance the detection and transmission of neural signals thereby improving the performance of sensory and motor systems. The first series of experiments in the present study aimed to investigate whether subsensory electrical noise stimulation applied over the triceps surae (TS) in seated subjects decreases torque variability during a force-matching task of isometric plantar flexion and whether the same electrical noise stimulation decreases postural sway during quiet stance. Correlation tests were applied to investigate whether the noise-induced postural sway decrease is linearly predicted by the noise-induced torque variability decrease. A second series of experiments was conducted to investigate whether there are differences in torque variability between conditions in which the subsensory electrical noise is applied only to the TS, only to the tibialis anterior (TA) and to both TS and TA, during the force-matching task with seated subjects. Noise stimulation applied over the TS muscles caused a significant reduction in force variability during the maintained isometric force paradigm and also decreased postural oscillations during quiet stance. Moreover, there was a significant correlation between the reduction in force fluctuation and the decrease in postural sway with the electrical noise stimulation. This last result indicates that changes in plantar flexion force variability in response to a given subsensory random stimulation of the TS may provide an estimate of the variations in postural sway caused by the same subsensory stimulation of the TS. We suggest that the decreases in force variability and postural sway found here are due to stochastic resonance that causes an improved transmission of proprioceptive information. In the second series of experiments, the reduction in force variability found when noise was applied to the TA muscle alone did not reach statistical significance, suggesting that TS proprioception gives a better feedback to reduce force fluctuation in isometric plantar flexion conditions.

Spin accumulation encoded in electronic noise for mesoscopic billiards with finite tunneling rates

We study the effects of spin accumulation (inside reservoirs) on electronic transport with tunneling and reflections at the gates of a quantum dot. Within the stub model, the calculations focus on the current-current correlation function for the flux of electrons injected into the quantum dot. The linear response theory used allows us to obtain the noise power in the regime of thermal crossover as a function of parameters that reveal the spin polarization at the reservoirs. The calculation is performed employing diagrammatic integration within the universal groups (ensembles of Dyson) for a nonideal, nonequilibrium chaotic quantum dot. We show that changes in the spin distribution determine significant alterations in noise behavior at values of the tunneling rates close to zero, in the regime of strong reflection at the gates.

Spectral fluctuation and 1/f(alpha) noise in the energy level statistics of interacting trapped bosons

It has been recently shown numerically that the transition from integrability to chaos in quantum systems and the corresponding spectral fluctuations are characterized by 1/f(alpha) noise with 1 <= alpha <= 2. The system of interacting trapped bosons is inhomogeneous and complex. The presence of an external harmonic trap makes it more interesting as, in the atomic trap, the bosons occupy partly degenerate single-particle states. Earlier theoretical and experimental results show that at zero temperature the low-lying levels are of a collective nature and high-lying excitations are of a single-particle nature. We observe that for few bosons, the P(s) distribution shows the Shnirelman peak, which exhibits a large number of quasidegenerate states. For a large number of bosons the low-lying levels are strongly affected by the interatomic interaction, and the corresponding level fluctuation shows a transition to a Wigner distribution with an increase in particle number. It does not follow Gaussian orthogonal ensemble random matrix predictions. For high-lying levels we observe the uncorrelated Poisson distribution. Thus it may be a very realistic system to prove that 1/f(alpha) noise is ubiquitous in nature.

Dependence of Generation-Recombination Noise With Gate Voltage in FD SOI MOSFETs

A model for computing the generation-recombination noise due to traps within the semiconductor film of fully depleted silicon-on-insulator MOSFET transistors is presented. Dependence of the corner frequency of the Lorentzian spectra on the gate voltage is addressed in this paper, which is different to the constant behavior expected for bulk transistors. The shift in the corner frequency makes the characterization process easier. It helps to identify the energy position, capture cross sections, and densities of the traps. This characterization task is carried out considering noise measurements of two different candidate structures for single-transistor dynamic random access memory devices.

Spin-glass behaviour on random lattices

The ground-state phase diagram of an Ising spin-glass model on a random graph with an arbitrary fraction w of ferromagnetic interactions is analysed in the presence of an external field. Using the replica method, and performing an analysis of stability of the replica-symmetric solution, it is shown that w = 1/2, corresponding to an unbiased spin glass, is a singular point in the phase diagram, separating a region with a spin-glass phase (w < 1/2) from a region with spin-glass, ferromagnetic, mixed and paramagnetic phases (w > 1/2).

Non-Markovian stochastic processes and their applications: from anomalous diffusion to time series analysis

This work provides a forward step in the study and comprehension of the relationships between stochastic processes and a certain class of integral-partial differential equation, which can be used in order to model anomalous diffusion and transport in statistical physics. In the first part, we brought the reader through the fundamental notions of probability and stochastic processes, stochastic integration and stochastic differential equations as well. In particular, within the study of H-sssi processes, we focused on fractional Brownian motion (fBm) and its discrete-time increment process, the fractional Gaussian noise (fGn), which provide examples of non-Markovian Gaussian processes. The fGn, together with stationary FARIMA processes, is widely used in the modeling and estimation of long-memory, or long-range dependence (LRD). Time series manifesting long-range dependence, are often observed in nature especially in physics, meteorology, climatology, but also in hydrology, geophysics, economy and many others. We deepely studied LRD, giving many real data examples, providing statistical analysis and introducing parametric methods of estimation. Then, we introduced the theory of fractional integrals and derivatives, which indeed turns out to be very appropriate for studying and modeling systems with long-memory properties. After having introduced the basics concepts, we provided many examples and applications. For instance, we investigated the relaxation equation with distributed order time-fractional derivatives, which describes models characterized by a strong memory component and can be used to model relaxation in complex systems, which deviates from the classical exponential Debye pattern. Then, we focused in the study of generalizations of the standard diffusion equation, by passing through the preliminary study of the fractional forward drift equation. Such generalizations have been obtained by using fractional integrals and derivatives of distributed orders. In order to find a connection between the anomalous diffusion described by these equations and the long-range dependence, we introduced and studied the generalized grey Brownian motion (ggBm), which is actually a parametric class of H-sssi processes, which have indeed marginal probability density function evolving in time according to a partial integro-differential equation of fractional type. The ggBm is of course Non-Markovian. All around the work, we have remarked many times that, starting from a master equation of a probability density function f(x,t), it is always possible to define an equivalence class of stochastic processes with the same marginal density function f(x,t). All these processes provide suitable stochastic models for the starting equation. Studying the ggBm, we just focused on a subclass made up of processes with stationary increments. The ggBm has been defined canonically in the so called grey noise space. However, we have been able to provide a characterization notwithstanding the underline probability space. We also pointed out that that the generalized grey Brownian motion is a direct generalization of a Gaussian process and in particular it generalizes Brownain motion and fractional Brownain motion as well. Finally, we introduced and analyzed a more general class of diffusion type equations related to certain non-Markovian stochastic processes. We started from the forward drift equation, which have been made non-local in time by the introduction of a suitable chosen memory kernel K(t). The resulting non-Markovian equation has been interpreted in a natural way as the evolution equation of the marginal density function of a random time process l(t). We then consider the subordinated process Y(t)=X(l(t)) where X(t) is a Markovian diffusion. The corresponding time-evolution of the marginal density function of Y(t) is governed by a non-Markovian Fokker-Planck equation which involves the same memory kernel K(t). We developed several applications and derived the exact solutions. Moreover, we considered different stochastic models for the given equations, providing path simulations.

Characterization and modeling of low-frequency noise in MOSFETs

For many years, RF and analog integrated circuits have been mainly developed using bipolar and compound semiconductor technologies due to their better performance. In the last years, the advance made in CMOS technology allowed analog and RF circuits to be built with such a technology, but the use of CMOS technology in RF application instead of bipolar technology has brought more issues in terms of noise. The noise cannot be completely eliminated and will therefore ultimately limit the accuracy of measurements and set a lower limit on how small signals can be detected and processed in an electronic circuit. One kind of noise which affects MOS transistors much more than bipolar ones is the low-frequency noise. In MOSFETs, low-frequency noise is mainly of two kinds: flicker or 1/f noise and random telegraph signal noise (RTS). The objective of this thesis is to characterize and to model the low-frequency noise by studying RTS and flicker noise under both constant and switched bias conditions. The effect of different biasing schemes on both RTS and flicker noise in time and frequency domain has been investigated.