We introduce a model of squeezing and noise reduction in the Kerr effect that accounts for noise in all quadratures of the driving field. Consequently we show that Kerr squeezing is much more sensitive to driving noise than squeezing produced by second harmonic generation (SHG). We experimentally demonstrate this sensitivity using a nonlinear system that tunes between strong classical SHG and K...

Abstract. In standard nonrelativistic quantum mechanics the expectation of the energy is a conserved quantity. It is possible to extend the dynamical law associated with the evolution of a quantum state consistently to include a nonlinear stochastic component, while respecting the conservation law. According to the dynamics thus obtained, referred to as the energy-based stochastic Schrödinger e...

The dependency on the quality of the training data has led to significant work in noise reduction for instance-based learning algorithms. This paper presents an empirical evaluation of current noise reduction techniques, not just from the perspective of their comparative performance, but from the perspective of investigating the types of instances that they focus on for removal. A novel instanc...

Gap filling and noise reduction of unevenly sampled data by means of the Lomb-Scargle periodogram K. Hocke and N. Kämpfer Institute of Applied Physics, University of Bern, Switzerland Received: 25 January 2008 – Accepted: 4 February 2008 – Published: 4 March 2008 Correspondence to: K. Hocke ([email protected]) Published by Copernicus Publications on behalf of the European Geoscience...

Fluctuations of the white-light supercontinuum produced by ultrashort laser pulses in selfguided filaments (spatio-temporal solitons) in air are investigated. We demonstrate that correlations exist within the white-light supercontinuum, and that they can be used to significantly reduce the laser intensity noise by filtering the spectrum. More precisely, the fundamental wavelength is anticorrela...

This paper introduces a new signal denoising based on the Empirical mode decomposition (EMD) framework. The method is a fully data driven approach. Noisy signal is decomposed adaptively into oscillatory components called Intrinsic mode functions (IMFs) by means of a process called sifting. The EMD denoising involves filtering or thresholding each IMF and reconstructs the estimated signal using ...

We introduce an algorithm for nonlinear noise reduction which is based on locally linear ts to the nonlinear dynamics. We claim that it is both robust and exible enough to apply it to a variety of experimental time series. In the second part of the paper we show its performance on data set A. The correlation dimension, Liapunov exponent and the Poincar e map are computed before and after the no...

In this paper we explore speech dereverberation techniques whose principle is the recovery of the envelope modulations of the original (anechoic) speech [3],[4]. Based on our previous experience with such kind of processing for additive noise reduction applications, we apply a data designed lterbank technique [2] to the reverberant speech. Comparing our results with other works we discuss the e...

The results of application of multichannel Kalman filtering to reduction of uncorrelated noise in magnetotelluric recordings are discussed in this article. Magnetotelluric method of Earth structure recognition is shortly presented together with the its most popular measurement method called the remote reference method. The basic theory of nonstationar, discrete Kalman filter and its implementat...