Relative Intensity Noise in Optical Single Side Band Systems with Multiple In-Line Amplifiers: Analysis and Validation

This article analyzes and validates through simulation the influence of fiber nonlinearity, dispersion, and loss on the Relative Noise Intensity (RIN) spectrum at fiber output in externally modulated optical single side band (OSSB) systems. The analysis includes the RIN generated by the laser source, the effect of the OSSB modulator, and noise generated by the in-line optical amplifiers. The analysis takes into account conversion between phase noise and intensity noise introduced by fiber transmission. It is shown that, depending on the operating conditions, the system performance may be predominantly affected either by the laser RIN or by the noise generated by the in-line optical amplifiers. The impact of the OSSB modulator can be relevant when the RIN of the system is dominated by the laser noise and should therefore be considered.     

Stochastic bifurcation in FitzHugh-Nagumo ensembles subjected to additive and/or multiplicative noises

We have studied the dynamical properties of finite N-unit FitzHugh-Nagumo (FN) ensembles subjected to additive and/or multiplicative noises, reformulating the augmented moment method (AMM) with the Fokker-Planck equation (FPE) method [H. Hasegawa, J. Phys. Soc. Japan 75 (2006) 033001]. In the AMM, original 2N-dimensional stochastic equations are transformed to eight-dimensional deterministic ones, and the dynamics is described in terms of averages and fluctuations of local and global variables. The stochastic bifurcation is discussed by a linear stability analysis of the deterministic AMM equations. The bifurcation transition diagram of multiplicative noise is rather different from that of additive noise: the former has the wider oscillating region than the latter. The synchronization in globally-coupled FN ensembles is also investigated. Results of the AMM are in good agreement with those of direct simulations (DSs).     

Sequential estimation of velocity fields using episodic proper orthogonal decomposition

In this paper, the problem of approximating velocity fields at future and past times based on information available at the current time is addressed. A novel method called “episodic POD” is described and developed that enables us to achieve our objective. Application of episodic POD to an ensemble of flow data results in a set of spatio-temporal eigenfunctions and a set of coefficients associated with the eigenfunctions. From these eigenfunctions, we develop two models called the “forward model” and “inverse model” that enable us to approximate the velocity fields at future and past times, based on information provided at the current time. A second set of models, the forward and inverse sequential models are also developed that enable the dynamic update of approximated velocity fields when new information is made available, making these models more adept at on-line estimation. The various properties associated with these models are described in detail, and four different examples are used to validate the models and show the different properties associated with episodic POD. It is shown through numerical validation, that the episodic POD model has a form that is dynamically consistent with the original system. It is also shown that episodic POD outperforms linear Kalman filters in the presence of noise.     

Two Stochastic Resonances Induced by Two Different Multiplicative Telegraphic Noises for an Electric System

In this paper, an electric system with two dichotomous resistors is investigated. It is shown that this system can display two stochastic resonances, which are the amplitude of the periodic response as the functions of the two dichotomous resistors strengthes respectively. In the limits of Gaussian white noise and shot white noise （i.e., the two noises are both Gaussian white noise or shot white noise） no phenomena of resonance appear. By further study, we find that when the system is with three or more multiplicative telegraphic noises, there are three or more stochastic resonances.     

Enhanced Synchronization of Intercellular Calcium Oscillations by Noise Contaminated Signals

We consider the dynamics of locally coupled calcium oscillation systems, each cell is subjected to extracellular contaminated signal, which contains common sub-threshold signal and independent Gaussian noise. It is found that intermediate noise can enhance synchronized oscillations of calcium ions, where the frequency of noise-induced oscillations is matched with the one of sub-threshold external signal. We show that synchronization is enhanced as a result of the entrainment of external signal. Furthermore, the effect of coupling strength is considered. We find above-mentioned phenomenon exists only when coupling strength is very small. Our findings may exhibit that noise can enhance the detection of feeble external signal through the mechanism of synchronization of intercellular calcium ions.     

Development of a highway traffic noise prediction model that considers various road surface types

A highway traffic noise prediction model has been developed for environmental assessment in South Korea. The model is based on an outdoor sound propagation method and is fully compliant with ISO 9613 and the sound power level (PWL) estimation for a road segment, as suggested in the ASJ Model-1998 that is based on PWLs. Due to that model’s selection of two pavement types, such as asphalt or concrete pavement, an unacceptable traffic noise prediction is made in cases where the road surface is different from that on which the model is based. In order to address this problem, several road surface types are categorized, and the PWL of each surface type is determined and modeled by measuring the noise levels obtained from newly developed methods. An evaluation of the traffic noise prediction model using field measurements finds good agreement between predicted and measured noise levels.     

A standardised test environment to compare aircraft noise calculation programs

A new standardised test environment for aircraft noise calculation programs is presented. By means of this test environment different programs for aircraft noise calculation can be compared systematically. For this purpose a complete data set was developed, containing all relevant information needed to perform unambiguous aircraft noise calculations in the vicinity of an artificial airport. The input data is provided in a generic, non-program-specific form in order to be applicable to programs based on different calculation methods and data structures. In addition to the input data, guidelines on the file format of the results of calculations performed with different programs are specified to facilitate direct comparisons. The test environment and its data structure are outlined and examples of calculations according to this scheme are presented.     

Distinguishing between the interior pressures induced from two independent sources within a room using the probabilistic approach

In this paper, a probabilistic approach is introduced and used to distinguish between the interior pressures that are induced from two independent sound sources within a rectangular room. One source is a vibrating wall of the room and the other is an interior point source. The model is set up using the modal analysis method. In the probabilistic method, the probability densities of the uncertain acoustic model parameters are computed. The values of highest probability density are identified and assigned to the uncertain parameters in the acoustic model. The contributions of the sound sources to the total pressure are then obtained from the acoustic model input with the identified parameters. The effects of the modal truncations, the modeling errors, and the measurement locations on the accuracies of the identification process are studied.     

Adaptive fuzzy neural network control on the acoustic field in a duct

Base on the principle of the superposition of waves, active noise control is achieved by adaptively tuning a secondary source which produces an anti-noise of equal amplitude and opposite phase with primary source. This paper presents the study on the acoustic attenuation in a duct by using the combination of fuzzy neural network with error back propagation algorithm to control secondary source. The most important advantage of fuzzy inference system is that the structured knowledge is represented in the form of fuzzy IF-THEN rules. But it lacks the ability to accommodate the change of external environments. Combining neural network with fuzzy system can help in this tuning process by adapting fuzzy sets and creating fuzzy rules. The performance of attenuation and control error can be measured by the microphone placed in the downstream of duct. The results of this study, show that the acoustic attenuation by 40 dB for pure-tone noise and nearly 30 dB for dual-tones noise are obtained.