Multi-channel feedforward active noise control system for reducing snore noise with snore noise-term detection

In this paper, a multi-channel feedforward active noise control system for reducing snore noise with noise-term detection is proposed. The snore noise consists of a noise-term and a silent-term, and it is difficult to reduce the snore noise by the active noise control system. Since the conventional multi-channel feedforward active noise control system updates the noise control filters even in the silent-term, the conventional active noise control system updates the noise control filters unnecessarily. Therefore, the proposed multi-channel feedforward active noise control system introduces threshold processing to update the noise control filters only in the noise-term. Owing to this process, it is possible to reduce the update count of the noise control filters. Simulation results show that the proposed active noise control system can reduce the snore noise as same as the conventional active noise control system and can reduce the update count of the noise control filters compared to the conventional active noise control system.

Approximations of the solution of a stochastic Ginzburg-Landau equation

"This paper presents a method to approximate the solution of a stochas- tic Ginzburg-Landau equation with multiplicative noise term. Error estimates for the approximation of the solution are given."

Galerkin methods for fractional-stochastic systems

Applicability of undetermined coefficients methods to several fractional-stochastic models is investigated. These models are mostly generated by fractional-order derivative operators and include a fractional white noise term. Application of a polynomial chaos algorithm to stochastic Lotka-Volterra and Benney systems are also investigated. Fractional-stochastic equations considered in this paper are totally original systems which may serve as models for many scientific and engineering phenomena. It is pointed out that Galerkin type methods employed in this paper may be efficiently applied to fractional-order systems having uncertainty or a noise term.

Noise-induced scaling in skull suture interdigitation

Sutures, the thin, soft tissue between skull bones, serve as the major craniofacial growth centers during postnatal development. In a newborn skull, the sutures are straight; however, as the skull develops, the sutures wind dynamically to form an interdigitation pattern. Moreover, the final winding pattern had been shown to have fractal characteristics. Although various molecules involved in suture development have been identified, the mechanism underlying the pattern formation remains unknown. In a previous study, we reproduced the formation of the interdigitation pattern in a mathematical model combining an interface equation and a convolution kernel. However, the generated pattern had a specific characteristic length, and the model was unable to produce a fractal structure with the model.In the present study, we focused on the anterior part of the sagittal suture and formulated a new mathematical model with time–space-dependent noise that was able to generate the fractal structure. We reduced our previous model to represent the linear dynamics of the centerline of the suture tissue and included a time–space-dependent noise term. We showed theoretically that the final pattern from the model follows a scaling law due to the scaling of the dispersion relation in the full model, which we confirmed numerically. Furthermore, we observed experimentally that stochastic fluctuation of the osteogenic signal exists in the developing skull, and found that actual suture patterns followed a scaling law similar to that of the theoretical prediction.Author summarySkull sutures (thin, undifferentiated tissue between bones) act as the growth centers for the skull. Sutures are straight at birth but later develop an interdigitated pattern that ultimately becomes a fractal structure. While our previous mathematical model of sutures generated a periodic pattern, the mechanism underlying the fractal structure formation remained to be elucidated. Here, we focused only on the anterior part of the sagittal suture and formulated a reduced model representing the initial linear phase of pattern formation with the addition of a time–space-dependent noise term. We showed analytically that the model generates patterns with a scaling law. This result was confirmed numerically and experimentally.

Crossing the quasi-threshold manifold of a noise-driven excitable system

We consider the noise-induced escapes in an excitable system possessing a quasi-threshold manifold, along which there exists a certain point of minimal quasi-potential. In the weak noise limit, the optimal escaping path turns out to approach this particular point asymptotically, making it analogous to an ordinary saddle. Numerical simulations are performed and an elaboration on the effect of small but finite noise is given, which shows that the ridges where the prehistory probability distribution peaks are located mainly within the region where the quasi-potential increases gently. The cases allowing anisotropic noise are discussed and we found that varying the noise term in the slow variable would dramatically raise the whole level of quasi-potentials, leading to significant changes in both patterns of optimal paths and exit locations.

Detection of varied defects in diverse fabric images via modified RPCA with noise term and defect prior

Purpose – The purpose of this paper is to present a novel method for fabric defect detection. Design/methodology/approach – The method based on joint low-rank and spare matrix recovery, since patterned fabric is manufactured by a set of predefined symmetry rules, and it can be seen as the superposition of sparse defective regions and low-rank defect-free regions. A robust principal component analysis model with a noise term is designed to handle fabric images with diverse patterns robustly. The authors also estimate a defect prior and use it to guide the matrix recovery process for accurate extraction of various fabric defects. Findings – Experiments on plain and twill, dot-, box- and star-patterned fabric images with various defects demonstrate that the method is more efficient and robust than previous methods. Originality/value – The authors present a RPCA-based model for fabric defects detection, and show how to incorporate defect prior to improve the detection results. The authors also show that more robust detection and less running time can be obtained by introducing a noise term into the model.