DATA HANDLING METHODOLOGY FOR DISCRETE WAVELETS AND ITS APPLICATIONS TO THE DYNAMIC VECTOR FIELDS

2006 ◽  
Vol 04 (02) ◽  
pp. 263-271 ◽  
Author(s):  
SAWA MATSUYAMA ◽  
SHIHO MATSUYAMA ◽  
YOSHIFURU SAITO
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Vector Fields ◽  
Three Dimensional ◽  
Image Data ◽  
Original Data ◽  
Discrete Wavelet ◽  
Novel Approach ◽  
Target Data

A discrete wavelet transform is one of the effective methodologies for compressing the image data and extracting the major characteristics from various data, but it always requires a number of target data composed of a power of 2. To overcome this difficulty without losing any original data information, we propose here a novel approach based on the Fourier transform. The key idea is simple but effective because it keeps all of the frequency components comprising the target data exactly. The raw data is firstly transformed to the Fourier coefficients by Fourier transform. Then, the inverse Fourier transform makes it possible to the number of data comprising a power of 2. We have applied this interpolation for the wind vector image data, and we have tried to compress the data by the multiresolution analysis by using the three-dimensional discrete wavelet transform. Several examples demonstrate the usefulness of our new method to work out the graphical communication tools.

scholarly journals An Approach on MCSA-Based Fault Detection Using Discrete Wavelet Transform and Fault Classification Based on Deep Neural Networks

2021 ◽  
Vol 10 (3) ◽  
pp. 2256-2259
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Fast Fourier Transform ◽  
Discrete Wavelet Transform ◽  
Three Dimensional ◽  
Fault Classification ◽  
Signal Frequency ◽  
Discrete Wavelet ◽  
Novel Approach ◽  
Motor Current Signature Analysis

This paper presents a novel approach on motor current signature analysis (MCSA) forbroken Rotor Bar fault and High Contact Resistance fault using stator current signals as an input from the three phases of Induction motors. Discrete Wavelet Transform is preferred over the Fast Fourier Transform (FFT). Fast Fourier Transform (FFT) converts signals from time domain to frequency domain on the other hand Discrete Wavelet Transform (DWT) gives complete three-dimensional information of the signal, frequency, amplitude, and the time where the frequency components exist. In wavelet analysis, thesignal is converted into scaled and translated version of mother wavelet, which is very irregular so cannot be predicted. Hence, mother wavelets are more appropriate for predicting the local behavior of the signal including irregularities and spikes. In this research features are extracted using DWT and then features are trained in Deep NN sequential model for the purpose of classification of the faults. In this research, MATLAB software has been used for building the motor model in Simulink environment and PyCharm software is used to implement Deep NN for getting accuracy and classification results. This research helps in early detection of the faults that assists in prevention from unscheduled downtimes in industry, economy loss and production loss as well.

Mobile GPU Computing Based Filter Bank Convolution for Three-Dimensional Wavelet Transform

Biometrics ◽  
2017 ◽  
pp. 761-777
Author(s):  
Di Zhao
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Real Time ◽  
Gpu Computing ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Computational Results ◽  
Real Time Processing ◽  
Time Processing ◽  
Mobile Gpu

Mobile GPU computing, or System on Chip with embedded GPU (SoC GPU), becomes in great demand recently. Since these SoCs are designed for mobile devices with real-time applications such as image processing and video processing, high-efficient implementations of wavelet transform are essential for these chips. In this paper, the author develops two SoC GPU based DWT: signal based parallelization for discrete wavelet transform (sDWT) and coefficient based parallelization for discrete wavelet transform (cDWT), and the author evaluates the performance of three-dimensional wavelet transform on SoC GPU Tegra K1. Computational results show that, SoC GPU based DWT is significantly faster than SoC CPU based DWT. Computational results also show that, sDWT can generally satisfy the requirement of real-time processing (30 frames per second) with the image sizes of 352×288, 480×320, 720×480 and 1280×720, while cDWT can only obtain read-time processing with small image sizes of 352×288 and 480×320.

Multi-Spectral Holographic Three-Dimensional Image Fusion Using Discrete Wavelet Transform

2006 ◽  
Vol 2 (4) ◽  
pp. 411-417 ◽  
Author(s):  
Bahram Javidi ◽  
Cuong Manh Do ◽  
Seung-Hyun Hong ◽  
Takanori Nomura
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Dimensional Image
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