Presence Of White Noise Research Articles

Highly dispersive optical solitons with differential group delay and multiplicative white noise for Fokas–Lenells equation

AbstractThe current article addresses highly dispersive optical solitons with polarization mode dispersion, modeled by the Fokas–Lenells equation in presence of white noise. The corresponding soliton solutions are retrieved with the usage of two integration algorithms. They are the enhanced Kudryashov’s method and the enhanced direct algebraic method. The intermediary solutions are in terms of Jacobi’s elliptic functions and Weierstrass’ elliptic functions. The existence criteria of soliton solutions, by virtue of parameter constraints, are also presented.

Highly dispersive optical solitons with differential group delay for Sasa-Satsuma equation having multiplicative white noise

AbstractThis paper is about the retrieval of highly dispersive optical solitons for Sasa-Satsuma equation with differential group delay in presence of white noise. There are four integration schemes that make this retrieval possible. A full spectrum of optical solitons have been revealed from these schemes. The parametric restrictions for the existence of such solitons are also presented. The displayed surface plots support the analytical findings.

Optical Solitons with Dispersive Concatenation Model Having Multiplicative White Noise by the Enhanced Direct Algebraic Method

This paper investigates the significance of the dispersive concatenation model, incorporating the Kerr law of self-phase modulation in the presence of white noise. Our methodology relies on the enhanced direct algebraic method for integration. We reveal that intermediate solutions are expressed in terms of Jacobi's elliptic functions, leading to soliton solutions as the modulus of ellipticity approaches unity. This discovery culminates in the emergence of a diverse range of optical solitons. Our findings contribute novelty to the existing literature by offering insights into the behavior of optical solitons within the dispersive concatenation model, presenting a significant advancement in understanding this complex phenomenon.

Optical solitons for the dispersive concatenation model with power law of self-phase modulation and multiplicative white noise

AbstractThis paper recovers optical solitons to the newly proposed dispersive concatenation model that comes with power law of self-phase modulation. The presence of white noise in the Itô sence makes the model stochastic. Two integration approaches retrieve bright and singular optical solitons. The intermediary Weierstrass’ elliptic functions are implemented for this retrieval. It has been established that the effect of white noise stays confined to the phase component of the solitons.

Net transport and stochastic resonance in an underdamped system due to the application of a traveling-wave force-field

We investigate the effect of a zero-mean longitudinal traveling-wave force-field acting on an underdamped system with no static fixed potential. It is particularly interesting when the damping is periodic in space. A simple physical example of the present theoretical problem could be a plane longitudinal wave propagating in a moderately compressible medium wherein particulate matter is interspersed and ask the question if, in the process, the particulate matter can get transported, on the average, from one end of the medium to the other. We also investigate how the net transport can get affected in the presence of white noise.

The mouse suprachiasmatic nucleus encodes irradiance via a diverse population of neurons monotonically tuned to different ranges of intensity.

Many neurons of the mammalian master circadian oscillator in the suprachiasmatic nuclei (SCN) respond to light pulses with irradiance-dependent changes in firing. Here, we set out to better understand this irradiance coding ability by considering how the SCN tracks more continuous changes in irradiance at both population and single unit level. To this end, we recorded extracellular activity in the SCN of anaesthetised mice presented with up+down irradiance staircase stimuli covering moonlight to daylight conditions and incorporating epochs with steady light or superimposed higher frequency modulations (temporal white noise (WN) and frequency/contrast chirps). Single unit activity was extracted by spike sorting. The population response of SCN units to this stimulus was a progressive increase in firing rate at higher irradiances. This relationship was symmetrical for up vs. down phases of the ramp in the presence of white noise or chirps but exhibited hysteresis for steady light, with firing systematically higher during increasing irradiance. Single units also showed a monotonic relationship between firing and irradiance but exhibited diversity not only in response polarity (increases vs. decreases in firing), but also in the sensitivity (EC50 ) and slope of fitted functions. These data show that individual SCN neurons exhibit monotonic relationships between irradiance and firing rate but differ in the irradiance range over which they respond. This property may help the SCN to encode the large differences in irradiance found in nature using neurons with a constrained range of firing rates. KEY POINTS: Daily changes in environmental light (irradiance) entrain the suprachiasmatic nucleus (SCN) circadian clock. The mouse SCN shows graded increases in neurophysiological activity with light pulses of increasing irradiance. We show that this monotonic relationship between firing rate and irradiance is retained at population and single unit level when probed with more naturalistic staircase increases and decreases in irradiance. The irradiance response is more reliable in the presence of ongoing higher temporal frequency modulations in light intensity than under steady light. Single units varied in sensitivity allowing the population to cover a wide range of irradiances. Irradiance coding in the SCN has characteristics of a sparse code with individual neurons tracking different portions of the natural irradiance range. This property may address the challenge of encoding a 109 -fold day:night difference in irradiance within the constrained range of firing rates available to individual neurons.

Controlling mean exit time of stochastic dynamical systems based on quasipotential and machine learning

The mean exit time escaping basin of attraction in the presence of white noise is of practical importance in various scientific fields. In this work, we propose a strategy to control mean exit time of general stochastic dynamical systems to achieve a desired value based on the quasipotential concept and machine learning. Specifically, we develop a neural network architecture to compute the global quasipotential function. Then we design a systematic iterated numerical algorithm to calculate the controller for a given mean exit time. Moreover, we identify the most probable path between metastable attractors with the help of the effective Hamilton–Jacobi scheme and the trained neural network. Numerical experiments with various dimensions and structures demonstrate that our control strategy is effective and sufficiently accurate.

Nonlocal correlations in an asymmetric quantum network

The nonlocality revealed in a multiparty multisource network Bell experiment is conceptually different than the standard multiparty Bell nonlocality involving a single common source. Here, by introducing variants of asymmetric bilocal as well as trilocal network scenarios, we go beyond the typical bilocal network scenario where both the edge parties have an equal number of measurement settings. We first introduce an asymmetric bilocal network where one of the edge parties (say, Alice) receives ${2}^{n\ensuremath{-}1}$ inputs and the other edge party (say, Charlie) receives $n$ inputs. We derive two variants of asymmetric bilocality inequalities and demonstrate their optimal quantum violations. Further, we explore two types of asymmetric trilocal scenarios: (i) when two edge parties receive ${2}^{n\ensuremath{-}1}$ inputs each and the other edge party receives $n$ inputs, and (ii) when one edge party receives ${2}^{n\ensuremath{-}1}$ inputs and the other two edge parties have $n$ inputs each. We use an elegant sum-of-squares technique that enables us to evaluate the quantum optimal values of the proposed network inequalities without assuming the dimension of the systems for both the asymmetric bilocal as well as the trilocal scenarios. Further, we demonstrate the robustness of the quantum violations of the proposed inequalities in the presence of white noise.

The Impact of Varied Music Applications on Pain Perception and Situational Pain Catastrophizing

We evaluated how pain processing and situational pain catastrophizing differed between 2 music interventions (Unwind and favorite music) and a control condition (white noise). Healthy adults (n = 70) completed quantitative sensory testing (QST) measuring pressure pain threshold (PPTh) and tolerance (PPTol), heat pain threshold (HPTh), offset analgesia (OA), temporal summation of pain (TSP), and conditioned pain modulation (CPM). Participants completed 3 QST rounds with the presence of white noise (control condition), a relaxing music app (Unwind), and their favorite music, which were presented in a randomized order. The Situational Pain Catastrophizing Scale was completed after each round. Friedman tests and post hoc Wilcoxon signed-rank tests were used to compare pain processing and catastrophizing across the 3 conditions. Participants’ PPTh, PPTol, and HPTh were significantly higher during the favorite music condition compared to the other 2 conditions, indicating lower pain sensitivity when listening to favorite music. In contrast, OA was lower in the favorite music condition. Although TSP and CPM were induced by the QST paradigm, these did not differ across the 3 conditions. Situational pain catastrophizing was also significantly lower during the favorite music condition. Several measures of pain sensitivity and situational pain catastrophizing were lower when listening to favorite music compared to relaxing music or white noise. More research is necessary to determine the mechanism(s) by which music modulates pain processing. PerspectiveThis article presents evidence that participant-chosen favorite music can alter several aspects of nociceptive processing, including catastrophic thinking about pain, compared to white noise or relaxing music. Employing an individual's favorite music during episodic or procedural pain might represent a cost effective adjunctive analgesic strategy.

Parameter Estimation of Fractional-Order Chaotic Power System Based on Lens Imaging Learning Strategy State Transition Algorithm

Parameter identification of fractional-order chaotic power systems is a multidimensional optimization problem that plays a decisive role in the synchronization and control of fractional-order chaotic power systems. In this paper, a state transition algorithm based on the lens imaging learning strategy is proposed for parameter identification of fractional-order chaotic power systems. Taking a fractional-order six-dimensional chaotic power system mathematical model as an example, the mathematical model and chaotic state are analyzed. First, the Tent chaotic mapping is used to initialize the population, thus increasing population diversity. The randomness and ergodicity of the Tent chaotic sequence are used to enhance the global searching ability of the algorithm. Second, a maturity index is employed to determine population maturity. The lens imaging learning strategy is used to suppress the premature convergence of the state transition algorithm effectively and help the population jump out of local optima. Finally, the improved state transition algorithm is used to identify the parameters of the fractional-order six-dimensional chaotic power system model. The proposed improved state transition algorithm shows high estimation accuracy and convergence speed, and is superior to the traditional state transition and particle swarm optimization algorithms. The simulation results show that the parameters of the fractional-order chaotic power system are identified accurately even in the presence of white noise, demonstrating the strong robustness and versatility of the proposed algorithm.

Algorithms for synthesis of control systems for dynamic objects based on the modal method

Algorithms for the synthesis of control systems for dynamic objects based on the modal method are given. Algorithms have been developed for determining the placement of poles in multidimensional systems with proportional-differential feedback on the output, providing the desired distributions on the complex plane of the eigenvalues of the matrix of a closed system. Algorithms for selecting feedback in control systems of dynamic objects are proposed, which allow for stable identification of the coefficients of the equations of the object and the observed values of the object used in the control device. Adaptive algorithms have been developed to place poles of non-minimal-phase stochastic systems in the presence of white noise at the output, which makes it possible to exclude the operation of matrix inversion, which requires significant computational costs. Adaptive control algorithms based on the modal method have been developed for dynamic objects with a delay under conditions of a priori uncertainty and unsteadiness of technological process parameters, providing exponential dissipativity of the control process. Based on the developed regular algorithms, an adaptive control system for the synthesis of ammonia is proposed.

Noise-induced masking of hearing in a labyrinth fish: effects on sound detection in croaking gouramis.

An increasing level of anthropogenic underwater noise (shipping, drilling, sonar use, etc.) impairs acoustic orientation and communication in fish by hindering signal transmission or detection. Different noise regimes can reduce the ability to detect sounds of conspecifics due to an upward shift of the hearing threshold, a phenomenon termed masking. We therefore investigated the masking effect of white noise on the auditory thresholds in female croaking gouramis (Trichopsis vittata, Osphronemidae). We hypothesized that noise would influence the detection of conspecific vocalizations and thus acoustic communication. The auditory evoked potentials (AEP) thresholds were measured at six different frequencies between 0.1 and 4 kHz using the AEP recording technique. Sound pressure level audiograms were determined under quiet laboratory conditions (no noise) and continuous white noise of 110 dB RMS. Thresholds increased in the presence of white noise at all tested frequencies by 12-18 dB, in particular at 1.5 kHz. Moreover, hearing curves were compared to spectra of conspecific sounds to assess sound detection in the presence of noise in various contexts. We showed that masking hinders the detection of conspecific sounds, which have main energies between 1.0 and 1.5 kHz. We predict that this will particularly affect hearing of female's low-intensity purring sounds during mating. Accordingly, noise will negatively affect acoustic communication and most likely reproductive success.

Gain and noise spectral density in a parametric amplifier with added white noise: Theory and experiment

In this paper, we discuss the behavior of a linear classical parametric amplifier in the presence of white noise and give theoretical estimates of the noise spectral density based on approximate Green’s functions obtained by using averaging techniques. To validate our theory, we compare the analytical results with experimental data from an analog circuit and with numerical simulations of the model’s stochastic differential equations. The experimental data were accurately described by our model. Moreover, we noticed spectral components in the output signal of the amplifier, which are due to noisy precursors of instability. The position, width, and magnitude of these components are in agreement with the noise spectral density obtained by the theory proposed here.

Rapid Harmonic Detection Scheme Based on Expanded Input Observer

The existence of harmonics will cause the quality of power supply in a power system to decline and will affect the normal use of the power system. Therefore, it is important to suppress harmonics in the power system, and the first step of harmonic suppression is harmonic detection. To address this phenomenon, a fast harmonic detection method is proposed in this paper. It is based on the input observer theory to construct a state space model based on the original signal and harmonic components and estimate the state variables so as to achieve harmonic extraction. The characteristic roots are used to prove the convergence of the observer. In addition, the Second-Order Generalized Integration (SOGI) frequency estimation method is chosen to cascade with it so that harmonic detection can be accomplished with unknown frequencies. The simulation results prove that the proposed method can quickly converge and accurately extract each harmonic in the case of fluctuations in the fundamental amplitude, fundamental frequency and phase of the input signal, and the whole process can be completed in 0.02 s. The possible effects of white noise on harmonic extraction are also simulated, and the results prove that the accuracy of harmonic extraction can still be guaranteed in the presence of white noise. By using the Speedgoat real-time target machine built Rapid Control Prototype (RCP) as a testbed, experiments with similar simulation conditions were performed. The results show that the method has fast and accurate harmonic detection performance.

Coherency analysis of polarity reversed diffracted wavefields using local semblance

ABSTRACTDiffractions carry out important information about subsurface features. These features include small‐scale objects, fracture zones and faults. There have been several robust pre‐ and post‐stack diffraction imaging workflows in the literature to attribute diffraction locations and properties. Most of the traditional workflows are not fully capable of dealing with polarity reversals in the case of polarity reversed diffracted wavefields. This challenge causes null measures at the location of such diffractions. To overcome this issue, which is an ongoing subject of research, we propose to implement local semblance analysis along moveout curves. To do so, the global scanning window is subdivided into smaller windows followed by semblance analysis over each window. The final coherency measure in each image point is computed by averaging the semblance measures from all the subdivided windows. We demonstrated the proposed workflow on synthetic as well as field recorded datasets in the post‐stack domain. The results prove the capability of the proposed method in circumventing polarity reversals without any need to conduct polarity correction prior to imaging. At the end, we studied the seismic imaging resolution in the presence of white noise through the proposed approach.

Stationary auditory white noise improves postural control in healthy adults: A novel study on head-shaking.

Auditory cues might play a role in postural control. The primary aim of this study was to investigate the association between white noise and head-shake (HS) related changes in postural sway. Fifty healthy adults underwent Synapsys Posturography System (SPS) evaluation. The posturography (PG) evaluation consisted of two protocols: sensory organization test (SOT) and SOT with head-shake (HS) (HS-SOT). The standard SOT protocol of SPS involves a battery of six postural conditions. In the current study, participants underwent only four SOT conditions. The participants were asked to stand barefoot on the SPS platform (static platform and foam). The SOT standing conditions were as follow: (1) firm surface (force platform only) with eyes open (SOT1); (2) firm surface with eyes closed (SOT2); (3) foam surface (which was positioned on the force platform) with eyes open (SOT4); and (4) foam surface with eyes closed (SOT5). For the HS-SOT protocol, we asked the participants to move their heads left and right (i.e., yaw head rotation) in the mentioned SOT conditions. Each postural condition was 10 seconds long. Both SOT and HS-SOT postural conditions were conducted across the two hearing modes of silence and noise. To achieve our aims, comparison of sway parameters between SOT and HS-SOT, and between hearing modes were considered. White noise was associated with a reduction in postural sway. The reduction observed in sway area, sway amplitude, and sway frequency. Moreover, HS significantly increased postural sway in all HS-SOT conditions compared to their SOT equivalents (i.e., HS-SOT1 compared to SOT1, etc.). The presence of white noise was associated with a decrease in the HS-related increase in postural sway. Considering the results, this study adds to the body of literature suggesting that white noise contributes to postural control, and the implications of this for rehabilitation need to be further investigated.

MMC-HVDC line fault identification scheme based on single-ended transient voltage information entropy

High resistance faults are the main cause of protection refusal action in high voltage direct current transmission line protection based on modular multilevel converters (MMC-HVDC). Furthermore, noise can affect the protection method. It is imperative to mitigate the High resistance faults and the influence of noise on the protection method. This paper analyzes the reasons for the difference in the initial voltage slopes of the internal and external faults under the traveling wave level. Subsequently, it analyzes the reasons for the difference in the complexity of the internal and external fault waveforms. Based on these analyses, a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) information entropy method for line fault identification in the presence of white noise is proposed in this paper. The fault identification criteria are subsequently used to identify the internal and external faults. The transmission model of 77 level bipolar MMC-HVDC system grounded via a grounding electrode lead is simulated in the RTDS. The results show that the proposed method can enhance the anti-interference ability of the signal, identify the high resistance fault effectively and reliably, and avoid the error problem caused by a synchronous clock.

An Ultra-High-Speed Algorithm for Fault Classification in Double Circuit Transmission Lines Using Only the First Group of Received Current Traveling Waves

This paper presents a new and very fast fault detection (FD), fault classification (FC), and fault phase selection (FPS) method, which is just based on the first group of current traveling waves (cTW) in double circuit transmission lines (DCTLs). All internal, inter-circuit faults and lightning strikes on transmission line (TL) phases and shield wires are reviewed. By the proposed algorithm, FC and FPS are performed in the shortest possible time. In this study, wavelet transform (WT) is used to extract the traveling wave (TW) caused by the fault from the current signal of one DCTL terminal, and only the first group of these waves is used for FC and PFS to increase velocity. To classify various fault types, the zero components of TWs of the two lines, which can be calculated using the conventional modal transforms and another modal transform of 6*6 dimensions defined on all six phases of the DCTL, is used. All simulations are performed in PSCAD on a 230kV grid and a DCTL of 200km length. Also, the performance of the proposed algorithm is tested under various fault and lightning conditions in the presence of white noise with different incidence angles and various fault resistances.

Computer Simulation of Extended Neyman–Pearson Detector in Monostatic Pulse Radar

An extended Neyman–Pearson detector based on the combination of a traditional matched and wavelet filter in pulsed monostatic radar was proposed in this paper. Its properties were compared with properties of the cell average constant false alarm radar detector in the presence of white noise. The properties of detectors were examined in the receiver operating characteristic curves, parameter space. It consisted of false detection rate and the rate of true positive detected targets. In order to investigate the influence of temperature, the new parameter space was constructed: the temperature versus the rate of true positive detected targets. The traditional Neyman–Pearson detector based on a matched filter was fully operational and superior in the temperature range between 290[Formula: see text]K and 350[Formula: see text]K. Below this temperature range, its performance is completely degraded. It has been shown that by adding a wavelet filter, it can extend this temperature range between 260[Formula: see text]K and 350[Formula: see text]K. In the temperature range, where all detectors are fully operational, extended and classic Neyman–Pearson detectors had better performance than constant false alarm radar detector in the receiver operating characteristic curves’ parameter space.

Bottom Attenuation Coefficient Inversion Based on the Modal Phase Difference Between Pressure and Vertical Velocity from a Single Vector Sensor

An inversion scheme based on time-warping is presented for estimating the attenuation coefficient of a sediment bottom using a single vector sensor, restricted to shallow water and using low-frequency impulsive sources. The attenuation information is extracted from the modal phase difference between pressure and vertical velocity. The method is derived from Pekeris waveguide theoretical equations and the eigen values are obtained using the normal mode model Kraken. Some changes are made to the time-warping process to mitigate the inherent interference between adjacent modes, which improves the phase extraction capabilities. Results are presented for a two-layer, homogeneous environment using the RAM propagation model for depth-dependent sound speed profile simulations. This version of RAM was updated to provide radial and vertical velocities. For additional generality, the technique is evaluated in the presence of white noise.