Adaptive stochastic resonance in bistable system driven by noisy NLFM signal: phenomenon and application

The stochastic resonance (SR) in a bistable system driven by nonlinear frequency modulation (NLFM) signal and strong noise is studied. Combined with empirical mode decomposition (EMD) and piecewise idea, an adaptive piecewise re-scaled SR method based on the optimal intrinsic mode function (IMF), is proposed to enhance the weak NLFM signal. At first, considering the advantages of EMD for dealing with non-stationary signals, the segmented NLFM signal is processed by EMD. Meanwhile, the cross-correlation coefficient is used as the measure to select the optimal IMF that contains the NLFM signal feature. Then, the spectral amplification gain indicator is proposed to realize the adaptive SR of the optimal IMF of each sub-segment signal and reconstruct the enhanced NLFM signal. Finally, the effectiveness of the proposed method is highlighted with the analysis of the short-time Fourier transform spectrum of the simulation results. As an application example, the proposed method is verified adaptability in bearing fault diagnosis under the speed-varying condition that represents a typical and complicated NLFM signal in mechanical engineering. The research provides a new way for the enhancement of weak non-stationary signals. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 1)’.

The Optogalvanic Spectrum of Neutral Lanthanum between 5610 and 6110 Å

We report on a complete optogalvanic spectrum of a discharge burning in a La-Ar gas mixture, in the spectral range 5610–6110 Å (17,851 to 16,364 cm−1). About 1900 overlapping laser scans, each between 1 and 1.5 cm−1 wide, were necessary to cover this range. The resolution of the spectra is limited by the Doppler width of the spectral features to about 0.03 cm−1 (or ca. 0.01 Å) and is comparable with a Fourier-transform spectrum, but the sensitivity is much higher. Indeed, we could find more than 1800 lines, from which about 800 could be classified as transitions between known energy levels. The main focus of the investigations was to discover previously unknown energy levels by means of excitation of unclassified spectral features.

Dysfluency Recognition by using Spectral Entropy Features

In recent decades, speech recognition technology has improved effectively and significantly, but it is restricted only for a stream of words. These recognition systems assist human’s effectively for structured speech but for unstructured speech this assistance is not so effective for humans to communicate with machines, because unstructured stream of word lacks in providing useful information about pronunciation and punctuation. Recovering of such structural information by detecting the position of each phones in a sentence by locating the sentence boundaries, repeated words and missing phones in each phrase. The proposed work investigates the spectral entropy features, for the automatic detection of voiced and non-voiced regions, in the process of dysfluent speech recognition. The entropy features are estimated by normalizing the Fourier transform spectrum as Probability mass function (PMF). For clear formants of speech, the value of entropy is low and the value of entropy is high for flat distribution of silence part or if there is any noise in speech sample. A comparison of entropy features with Word Error Rate is presented in the proposed work

Windowing Design and Performance Assessment for Mitigation of Spectrum Leakage

This paper investigates the windowing design and performance assessment for mitigation of spectral leakage. A pretreatment method to reduce the spectral leakage is developed. In addition to selecting appropriate window functions, the Welch method is introduced. Windowing is implemented by multiplying the input signal with a windowing function. The periodogram technique based on Welch method is capable of providing good resolution if data length samples are selected optimally. Windowing amplitude modulates the input signal so that the spectral leakage is evened out. Thus, windowing reduces the amplitude of the samples at the beginning and end of the window, altering leakage. The influence of various window functions on the Fourier transform spectrum of the signals was discussed, and the characteristics and functions of various window functions were explained. In addition, we compared the differences in the influence of different data lengths on spectral resolution and noise levels caused by the traditional power spectrum estimation and various window-function-based Welch power spectrum estimations.