Integrated Wavelet Packet modulation and signal analysis using Analytic Wavelet Packets

2009 ◽  
Author(s):  
Michael Bauer ◽  
Rene Anselment ◽  
Klaus Dostert
Keyword(s):  
Signal Analysis ◽  
Wavelet Packet ◽  
Wavelet Packets ◽  
Analytic Wavelet

CONSTRUCTING MULTIPLE WAVELET PACKETS WITH FIFS

Fractals ◽  
2001 ◽  
Vol 09 (02) ◽  
pp. 165-169
Author(s):  
GANG CHEN ◽  
ZHIGANG FENG
Keyword(s):  
Multiresolution Analysis ◽  
Wavelet Packet ◽  
Wavelet Packets ◽  
Scaling Functions ◽  
Wavelet Packet Decomposition ◽  
Fractal Interpolation ◽  
Fractal Interpolation Functions ◽  
Interpolation Functions

By using fractal interpolation functions (FIF), a family of multiple wavelet packets is constructed in this paper. The first part of the paper deals with the equidistant fractal interpolation on interval [0, 1]; next, the proof that scaling functions ϕ1, ϕ2,…,ϕr constructed with FIF can generate a multiresolution analysis of L2(R) is shown; finally, the direct wavelet and wavelet packet decomposition in L2(R) are given.

Seismic signal analysis based on the dual-tree complex wavelet packet transform

2004 ◽  
Vol 17 (S1) ◽  
pp. 117-122 ◽  
Author(s):  
Zhou-min Xie ◽  
En-fu Wang ◽  
Guo-hong Zhang ◽  
Guo-cun Zhao ◽  
Xu-geng Chen
Keyword(s):  
Signal Analysis ◽  
Wavelet Packet ◽  
Seismic Signal ◽  
Wavelet Packet Transform ◽  
Complex Wavelet

scholarly journals Minimization of Harmonics Noise Using Wavelet Transformation Technology

2011 ◽  
pp. 216-223
Author(s):  
Mr. Debasis Dash ◽  
Mr. Shatyaprakasha Satapathy ◽  
Dr. Chittaranjan Panda
Keyword(s):  
Wavelet Analysis ◽  
Wavelet Packet ◽  
Statistical Significance ◽  
Audio Signal ◽  
Design Procedure ◽  
Wavelet Packets ◽  
Threshold Function ◽  
Analysis Tool ◽  
Real Time Processing ◽  
Wavelet Spectra

The field programmable gate array technology can design high performance system at low cost for wavelet analysis. Wavelet transform has gained the reputation of being a very effective signal analysis tool for much practical application. Implementation of transform needs the meeting of real-time processing for most application. The objectives of this paper are to compare the Haar and Daubeches technology and to calculate the bit error rate (BER) between the input audio signal and reconstructed output signal. It is seen that the BER using Daubechies wavelet technology is less than Haar wavelet. The design procedure is explained using the stat of art electronic design. Automation tools for system design on FPGA, simulation, synthesis and implementation on the FPGA technology has been carried out. The power hovmoller, cross wavelet spectra and coherence are described. A Practical step-up-step guide to wavelet analysis is given with examples taken from time series. The guide includes a comparison to the windowed Fourier transform. New statistical significance test for wavelet power spectra are developed by deriving theoretical wavelet spectra for white and red noise. Empirical formula is given for the effect of smoothing on significance levels and filtering. The notion of orthogonal no separable trivet wavelet packets, which is the generation of orthogonal university wavelet packets is introduced. A de-noising method based on wavelet packet shrinkage is developed. The principle of wavelet packet shrinkage for de-noising and the section of thresholds and threshold function are analyzed.

scholarly journals Analytic wavelet packets for robust ultrasonic flow measurement

2020 ◽  
Vol 87 (1) ◽  
pp. 45-54
Author(s):  
Matthias Bächle ◽  
Daniel Alexander Schwär ◽  
Fernando Puente León
Keyword(s):  
Transit Time ◽  
Flow Measurement ◽  
Principal Component ◽  
Time Difference ◽  
Frequency Resolution ◽  
Wavelet Packets ◽  
Accurate Estimation ◽  
Time Frequency ◽  
Analytic Wavelet ◽  
Conventional Methods

AbstractA key element in robust transit-time ultrasonic flow measurement is the accurate estimation of the transit-time difference. Conventional methods, such as cross-correlation or the estimation in the phase domain, are limited in their robustness against signal distortions, interfering signals or noise. In this work, we present a novel method to estimate the transit-time difference through the fusion of selected analytic wavelet packet coefficients. The combination of the complex coefficients, which represent a projection of the signal on analytic wavelets, with a configurable time-frequency resolution allows a sub-sample estimation at the frequency of interest. After giving an introduction into the fundamentals of analytic wavelet packets based on multi-scale filtering, we introduce two features that correlate strongly with the transit-time difference. The selection and fusion of these features is done by using correlation coefficients with a calibration set and principal component analysis. Finally, using a clamp-on flow measurement system, the robustness against temperature variation and measurement noise is shown and compared with conventional methods.

scholarly journals Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms

2020 ◽  
Vol 14 (5-6) ◽  
pp. 693-705
Author(s):  
Tiziana Segreto ◽  
Doriana D’Addona ◽  
Roberto Teti
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Inconel 718 ◽  
Signal Analysis ◽  
Elevated Temperatures ◽  
Wavelet Packet ◽  
Research Work ◽  
High Strength ◽  
Signal Features ◽  
Tool Wear Estimation

AbstractIn the last years, hard-to-machine nickel-based alloys have been widely employed in the aerospace industry for their properties of high strength, excellent resistance to corrosion and oxidation, and long creep life at elevated temperatures. As the machinability of these materials is quite low due to high cutting forces, high temperature development and strong work hardening, during machining the cutting tool conditions tend to rapidly deteriorate. Thus, tool health monitoring systems are highly desired to improve tool life and increase productivity. This research work focuses on tool wear estimation during turning of Inconel 718 using wavelet packet transform (WPT) signal analysis and machine learning paradigms. A multiple sensor monitoring system, based on the detection of cutting force, acoustic emission and vibration acceleration signals, was employed during experimental turning trials. The detected sensor signals were subjected to WPT decomposition to extract diverse signal features. The most relevant features were then selected, using correlation measurements, in order to be utilized in artificial neural network based machine learning paradigms for tool wear estimation.

scholarly journals On Generalized Carleson Operators of Periodic Wavelet Packet Expansions

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Shyam Lal ◽  
Manoj Kumar
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packets ◽  
Arithmetic Means ◽  
Periodic Wavelet

Three new theorems based on the generalized Carleson operators for the periodic Walsh-type wavelet packets have been established. An application of these theorems as convergence a.e. for the periodic Walsh-type wavelet packet expansion of block function with the help of summation by arithmetic means has been studied.

Research of Automobile Engine Fault Diagnosis Based on Wavelet Packet

2012 ◽  
Vol 472-475 ◽  
pp. 795-798
Author(s):  
Min Yong Tong
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Wavelet Packet ◽  
Automobile Engine ◽  
Band Energy ◽  
Diagnosis Method ◽  
S Model ◽  
Frequency Band Energy

A diagnosis method using wavelet packet, frequency band energy analysis and neural network was presented for the automobile engine fault diagnosis. Fault signal of automobile engine was decomposed at different frequency band by wavelet packet. According to the change of frequency band energy, fault frequency band of the automobile engine was found. Fault diagnosis knowledge is described by means of applying T-S model. Results from the experimental signal analysis show that the proposed method is effective in diagnosing the automobile engine faults.

The Energy Distribution of Tire-Road Noise and Noisy Speech Based on Wavelet Packet

2011 ◽  
Vol 464 ◽  
pp. 721-724 ◽  
Author(s):  
Zhi Yong He ◽  
Li Heng Luo
Keyword(s):  
Energy Distribution ◽  
Speech Enhancement ◽  
Mobile Communications ◽  
Wavelet Packet ◽  
Noise Signal ◽  
Wavelet Packets ◽  
Road Noise ◽  
Noisy Speech ◽  
Time Frequency ◽  
The Difference

Speech enhancement is very important for mobile communications or some other applications in car. The energy distribution of signal is the basis of algorithms which denoise noisy speech in time-frequency domain. In this work, the noise regarded is the tire-road noise when driving in expressway. Wavelet packets transform is used in the analysis. After decomposing noise signal and noisy speech signal by wavelet packet transform, the analysis for the difference of the energy distribution between noisy speech and noise is finished.

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