Influences of Randomly Uncertain Factors on Dynamic Coefficients of an Interlocking Labyrinth Seal-Rotor System

Many randomly uncertain factors inevitably arise when gas flows through a labyrinth seal, and the orbit of the rotor center will not rotate along a steady trajectory, as previously studied. Here, random uncertainty is considered in an interlocking labyrinth seal-rotor system to investigate the fluctuations of dynamic coefficients. The bounded noise excitation is introduced into the momentum equation of the gas flow, and as a result, the orbit of the rotor center is expressed as the combination of an elliptic trajectory with the bounded noise perturbation. Simulation results of the coefficients under randomly uncertain perturbations with various strengths are comparatively investigated with the traditional predictions under ideal conditions, from which the influences of random uncertain factors on dynamic coefficients are analyzed in terms of the rotor speed, pressure difference, and inlet whirl velocity. It is shown that the deviation levels of the dynamic coefficients are directly related to the random perturbations and routinely increase with such perturbation strengths, and the coefficients themselves may exhibit distinct variation patterns against the rotor speed, pressure difference, and inlet whirl velocity.

Structure supports function: informing directed and dynamic functional connectivity with anatomical priors

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections: the lack of a direct structural link between two brain regions prevents direct functional interactions. Despite the intrinsic relationship between structural (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited, especially for electrophysiological data. In the present work, we propose a new linear adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. Our results show that SC priors increase the resilience of FC estimates to noise perturbation while promoting sparser networks under biologically plausible constraints. The proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new method for multimodal imaging and dynamic FC analysis.

Solitons in a box-shaped wavefield with noise: perturbation theory and statistics

<p>The propagation of nonlinear waves is well-described by a number of integrable models leading to the concept of the scattering data also known as the nonlinear Fourier spectrum. Here we investigate the fundamental problem of the nonlinear wavefield scattering data corrections in response to a perturbation of initial condition using inverse scattering transform theory. We present a complete theoretical linear perturbation framework to evaluate first-order corrections of the full set of the scattering data within the integrable one-dimensional focusing nonlinear Schrodinger (NLSE) equation, see our recent preprint [1]. The general scattering data portrait reveals nonlinear coherent structures - solitons - playing the key role in the wavefield evolution. Applying the developed theory to a classic box-shaped wavefield we solve the derived equations analytically for a single Fourier mode acting as a perturbation to the initial condition, thus, leading to the sensitivity closed-form expressions for basic soliton characteristics, i.e. the amplitude, velocity, phase and its position. With the appropriate statistical averaging we model the soliton noise-induced effects resulting in compact relations for standard deviations of soliton parameters. Relying on a concept of a virtual soliton eigenvalue we derive the probability of a soliton emergence or the opposite due to noise and illustrate these theoretical predictions with direct numerical simulations of the NLSE evolution. Note that the evolution of the box field within the NLSE model represents a classical so-called dam-break problem. A wide box-shaped field is unstable to long wave perturbations constituting the phenomena of modulation instability. In conclussion we discuss possible applications of the developed theory to these fundamental problems of physics of nonlinear waves.</p><p><br>The work was supported by Russian Science Foundation grant No. 20-71-00022.</p><p><br>[1] R. Mullyadzhanov and A. Gelash. Solitons in a box-shaped wavefield with noise: perturbation theory and statistics. arXiv preprint arXiv:2008.08874, 2020.</p>

Antiforensics of Speech Resampling Using Dual-Path Strategy

Resampling is an operation to convert a digital speech from a given sampling rate to a different one. It can be used to interface two systems with different sampling rates. Unfortunately, resampling may also be intentionally utilized as a postoperation to remove the manipulated artifacts left by pitch shifting, splicing, etc. To detect the resampling, some forensic detectors have been proposed. Little consideration, however, has been given to the security of these detectors themselves. To expose weaknesses of these resampling detectors and hide the resampling artifacts, a dual-path resampling antiforensic framework is proposed in this paper. In the proposed framework, 1D median filtering is utilized to destroy the linear correlation between the adjacent speech samples introduced by resampling on low-frequency component. And for high-frequency component, Gaussian white noise perturbation (GWNP) is adopted to destroy the periodic resampling traces. The experimental results show that the proposed method successfully deceives the existing resampling forensic algorithms while keeping good perceptual quality of the resampled speech.

Compensatory mechanisms affect sensorimotor integration during ongoing vocal-motor acts in marmoset monkeys

In vertebrates, any transmission of vocal signals faces the challenge of acoustic interferences such as heavy rain, wind, animal, or urban sounds. Consequently, several mechanisms and strategies have evolved to optimize the signal-to-noise ratio. Examples to increase detectability are the Lombard effect, an involuntary rise in call amplitude in response to masking ambient noise, which is often associated with several other vocal changes such as call frequency and duration, as well as the animals’ capability of limiting calling to periods where noise perturbation is absent. Previous studies revealed rapid vocal flexibility and various audio-vocal integration mechanisms in marmoset monkeys. Using acoustic perturbation triggered by vocal behavior, we investigated whether marmoset monkeys are capable of exhibiting changes in call structure when perturbing noise starts after call onset or whether such effects only occur if noise perturbation starts prior to call onset. We show that marmoset monkeys are capable of rapidly modulating call amplitude and frequency in response to such perturbing noise bursts. Vocalizations swiftly increased call frequency after noise onset indicating a rapid effect of perturbing noise on vocal motor pattern production. Call amplitudes were also affected. Interestingly, however, the marmosets did not exhibit the Lombard effect as previously reported but decreased their call intensity in response to perturbing noise. Our findings indicate that marmosets possess a general avoidance strategy to call in the presences of ambient noise and suggest that these animals are capable of counteracting a previously thought involuntary audio-vocal mechanism, the Lombard effect, presumably via cognitive control processes.