Search results for "Continuous wave"
showing 10 items of 103 documents
Fractional wavelet transform
1997
The wavelet transform, which has had a growing importance in signal and image processing, has been generalized by association with both the wavelet transform and the fractional Fourier transform. Possible implementations of the new transformation are in image compression, image transmission, transient signal processing, etc. Computer simulations demonstrate the abilities of the novel transform. Optical implementation of this transform is briefly discussed.
Pattern recognition using sequential matched filtering of wavelet coefficients
1997
Abstract A bank of wavelets is used for pattern recognition by means of sequential filtering. Each element of the bank is matched to a different wavelet coefficient of the target. A sequential process leads to a set of correlation outputs. Post-processing by means of a fast blending method provides the final output correlation. Both computer simulations and optical experiments are presented, showing the discrimination capability for this implementation.
Application of wavelet analysis to acoustic emission pulses generated by partial discharges
2004
The subject matter of this paper refers to the improvement of the acoustic emission (AE) method when used for detection, measurement and location of partial discharges (PDs) in oil insulation systems of power appliances. The detailed subject matter refers to the issues connected with the application of modern methods of digital processing of signals obtained during technical high-power measurements. The paper presents the results of measurements and analyses of the AE pulses generated in setups making the modeling of basic PD forms that can occur in oil insulations possible. The research concentrated mainly on the following types of PDs: point-plane, multipoint-plane, multipoint-plane with …
Two-dimensional wavelet transform by wavelength multiplexing
1996
The wavelet transform is a useful tool for data compression, analysis of short transient pulses, optical correlators, etc. This transform was obtained optically by the use of the spatial or temporal multiplexing approaches. A two-dimensional wavelet transform is obtained with only one spatial channel. The information of the different scalings is carried in different wavelengths and summed incoherently at the output plane. Laboratory experimental results are demonstrated.
Effect of parametric variation of center frequency and bandwidth of morlet wavelet transform on time-frequency analysis of event-related potentials
2017
Time-frequency (TF) analysis of event-related potentials (ERPs) using Complex Morlet Wavelet Transform has been widely applied in cognitive neuroscience research. It has been widely suggested that the center frequency (fc) and bandwidth (σ) should be considered in defining the mother wavelet. However, the issue how parametric variation of fc and σ of Morlet wavelet transform exerts influence on ERPs time-frequency results has not been extensively discussed in previous research. The current study, through adopting the method of Complex Morlet Continuous Wavelet Transform (CMCWT), aims to investigate whether time-frequency results vary with different parametric settings of fc and σ. Besides, …
EMG artifacts removal during electrical stimulation, a CWT based technique
2014
International audience; A technique of artifacts removal based on the continuous wavelet transform is presented. It uses common mother wavelets to find the temporal localization of stimulation artifacts on electromyogram (EMG) signal during an electrically evoked contraction of a muscle. This method can be used with standard stimulation pulse waveforms like monophasics or biphasics ones. It uses a histogram representation to find the best threshold to apply on the CWT domain. The algotithm is presented with Haar wavelet and then it is used with common wavelet famillies such as Daubechies or Symlets.
The Wavelet Scalogram in the Study of Time Series
2014
Wavelet theory has been proved to be a useful tool in the study of time series. Specifically, the scalogram allows the detection of the most representative scales (or frequencies) of a signal. In this work, we present the scalogram as a tool for studying some aspects of a given signal. Firstly, we introduce a parameter called scale index, interpreted as a measure of the degree of the signal’s non-periodicity. In this way, it can complement the maximal Lyapunov exponent method for determining chaos transitions of a given dynamical system. Secondly, we introduce a method for comparing different scalograms. This can be applied for determining if two time series follow similar patterns.
Non-Contact Measurement of River Surface Velocity and Discharge Estimation with a Low-Cost Doppler Radar Sensor
2020
River discharge is an important variable to measure in order to predict droughts and flood occurrences. Once the cross-sectional geometry of the river is known, discharge can be inferred from water level and surface flow velocity measurements. Since river discharges are of particular interest during extreme weather events, when river sites cannot be safely accessed, noncontact sensing technologies are particularly appealing. To this purpose, this work proposes a prototype of a low-cost continuous wave (CW) Doppler radar sensor, which is able to monitor the surface flow velocity of rivers. The prototype is tested at two gauged sites in central Italy, along the Tiber River. The surface flow v…
Diagnosis of Incipient Bearing Faults using Convolutional Neural Networks
2019
The majority of faults occurring in rotating electrical machinery is attributed to bearings. To reduce downtime, it is desired to apply various diagnostic methods so that bearing degradation can be detected in good time prior to a complete failure. The work presented in this paper utilizes a data-driven machine learning approach based on convolutional neural networks (CNNs) in order to diagnose different types of bearing faults. A one-dimensional CNN is trained on vibration signals and compared to a two-dimensional CNN trained in time-frequency domain using continuous wavelet transform (CWT). The proposed method is demonstrated on data collected from run-to-failure tests.The results show th…
A wavelet-based tool for studying non-periodicity
2010
This paper presents a new numerical approach to the study of non-periodicity in signals, which can complement the maximal Lyapunov exponent method for determining chaos transitions of a given dynamical system. The proposed technique is based on the continuous wavelet transform and the wavelet multiresolution analysis. A new parameter, the \textit{scale index}, is introduced and interpreted as a measure of the degree of the signal's non-periodicity. This methodology is successfully applied to three classical dynamical systems: the Bonhoeffer-van der Pol oscillator, the logistic map, and the Henon map.