Wavelet Packet
Wavelet Packet - Related Videos
07:45
Quasi-light Storage for Optical Data Packets
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Cited by 1 •
2014
The article describes a procedure to store optical data packets with an arbitrary modulation, wavelength, and data rate. These packets are the basis of modern telecommunications.
08:08
Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities
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Cited by 14 •
2017
This manuscript describes how to use the wavelet entropy index to analyze high-density electroencephalography (EEG) and electrocardiography (ECG) data. We show that the irregularity of cerebral and cardiac activities became more coordinated during mindfulness-based stress reduction practice.
08:42
Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
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Cited by 4 •
2021
This protocol describes partial wavelet transform coherence (pWTC) for calculating the time-lagged pattern of interpersonal neural synchronization (INS) to infer the direction and temporal pattern of information flow during social interaction. The effectiveness of pWTC in removing the confounds of signal autocorrelation on INS was proved by two experiments.
02:26
Photoelectric Effect
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2020
When light of a particular wavelength strikes a metal surface, electrons are emitted. This is called the photoelectric effect. The minimum frequency of light that can cause such emission of electrons is called the threshold frequency, which is specific to the metal. Light with a frequency lower than the threshold frequency, even if it is of high intensity, cannot initiate the emission of electrons. However, when the frequency is higher than the threshold value, the number of electrons ejected...
09:52
Non-invasive Optical Imaging of the Lymphatic Vasculature of a Mouse
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Cited by 25 •
2013
Recently developed imaging techniques using near-infrared fluorescence (NIRF) may help elucidate the role the lymphatic system plays in cancer metastasis, immune response, wound repair, and other lymphatic-associated diseases.
FAQs
The standard discrete wavelet transform separates an approximation branch and detail branches, whereas Wavelet Packet analysis recursively decomposes both types of coefficients. This produces a fuller tree of frequency subbands rather than concentrating decomposition on approximation components alone. The additional branches give engineers more choices for examining signals whose important information is distributed across several frequency regions.
Basis selection determines which paths through the decomposition tree best represent the signal. Criteria such as signal energy or entropy can identify an efficient collection of subbands, reducing unnecessary representation while preserving informative structure. In engineering analysis, this adaptive choice can make subsequent interpretation, compression, feature extraction, or fault diagnosis more focused than using every available subband.
Different signal features may be localized in time, frequency, or both, especially when transient events occur within otherwise changing data. Wavelet Packet analysis provides multiple time-frequency scales so engineers can examine these localized patterns without relying on one fixed resolution. This flexibility can expose transient or frequency information that conventional Fourier analysis may not reveal clearly.
A typical workflow begins with the engineering signal, recursively decomposes approximation and detail coefficients into a subband tree, and evaluates the resulting components using a selection criterion such as energy or entropy. The chosen basis then supports the intended analysis, whether the goal is to interpret signal structure, reduce noise, compress data, extract features, or diagnose faults.
The method supports several practical tasks, including noise reduction, compression, feature extraction, and fault diagnosis. These applications use information distributed across selected frequency bands and time scales rather than treating the signal as a single undifferentiated waveform. As a result, engineers can prepare sensor data for interpretation or derive patterns relevant to monitoring and diagnosis.
Wavelet Packet methods are relevant to nonstationary signals in vibration, acoustic, biomedical, and industrial sensor measurements. Their multiscale representation is particularly useful when a signal contains changing frequency patterns or localized transients. In engineering contexts, the resulting subband information can improve interpretation and support analysis tasks such as feature extraction and fault diagnosis.