Wavelet example sentences

"Wavelet" Example Sentences

The scientists used wavelet analysis to extract information from the data.
Wavelets proved very useful in extracting information from the measurements.
They decomposed the signal using a wavelet transform.
Wavelet compression is useful for images with sharp changes and edges.
I applied a wavelet transform to the time series to identify the periodic patterns.
The sound began as a low intensity wavelet and grew rapidly into a massive tsunami.
The wavelet analysis revealed crucial features that the Fourier transform had missed.
He developed new wavelet bases to improve the sparsity and efficiency of the representation.
The wavelet transform allows for a time-frequency analysis of non-stationary signals.
The compression ratio using wavelets was far better than with Fourier methods.
The 2D wavelet transform is useful for decomposing and analyzing images.
The wavelet transform maps a signal onto a wavelet basis.
She pioneered the use of wavelets in signal and image processing.
The Fourier transform assumes stationarity while wavelets look for localized changes.
They used a wavelet packet transform to divide the frequency range more finely.
The wavelet coefficients provide a compact representation of the data.
Wavelet denoising works by thresholding the wavelet coefficients.
The wavelet basis functions are localized both in space and frequency.
He used the wavelet transform to isolate the electromagnetic signals from noise.
The analysis with wavelets provided more insight than Fourier methods alone.
Wavelets orthonormalize the signal while maintaining time localization.
She developed new wavelets tailored for the specific application.
The standard wavelet transform performs a multifrequency analysis.
The multiresolution analysis provided by wavelets is very useful for texture analysis.
They applied denoising via wavelet shrinkage to reduce the clutter in the data.
The wavelets enabled feature extraction at multiple scales and resolutions.
The waveforms seemed to consist of oscillations at multiple scales and frequencies.
We employed the fast wavelet transform to decompose the data efficiently.
The wavelet scaling functions provide a low resolution approximation of the signal.
The wavelets characterized the transient features of the signal well.
Wavelet thresholding methods provide a simple yet effective way for noise removal.
The wavelet network model used wavelets as activation functions.
Filter banks are used to implement the discrete wavelet transform.
The wavelet coefficients characterize the frequency content locally.
The coefficients are computed by correlating the wavelet basis functions with the signal.
Wavelet noise filtering is typically more effective than linear filtering.
The advantages of wavelets come from their flexibility and adaptability.
The orthogonal wavelet basis ensures that all information is retained.
The wavelet basis provides a sparse representation of the data.
The Daubechies wavelets are commonly used as basis functions.
The wavelet transform provides a more useful representation of the data than the Fourier transform.
The wavelet transform exhibits the important property of multiresolution analysis.
The Haar wavelet is the simplest wavelet and is used primarily for pedagogical purposes.
Wavelets provide a means of hierarchically decomposing functions.
Wavelet packets give a richer representation by decomposing both details and approximations.
Periodic boundaries cause discontinuities that wavelet techniques handle well.
The wavelet coefficients at one scale are used to predict the coefficients at the next scale.
The wavelet transform can be performed very efficiently with the pyramid algorithm.
Stationary wavelet transforms preserve the mean of the data at each scale.
The wavelets provide self-similarity across different scales.
The wavelets enabled multi-resolution representation of the data.
The wavelet basis functions themselves are localized wavelets.
Wavelets allow for inferences to be made at multiple resolutions.
The Mallat algorithm provides a fast implementation of the wavelet transform.
Wavelet packets provide a richer basis set than wavelets alone.
Bayes shrinkage provides a simple approach for wavelet noise filtering.
The wavelets localized both temporal variations and frequency components within a waveform.
The lifting scheme provides an efficient method for the implementation and design of wavelets.
Wavelet coherence analysis allows identification of correlation at different frequencies.
The continuous wavelet transform provides more precise frequency information than discrete wavelets.