scholarly journals Rapid Recognition System of Circuit Breaker Status Based on Wavelet Packet Decomposition and SVM

2016 ◽  
Author(s):  
Jin Tan ◽  
Yao Wei ◽  
Mengyuan He
Keyword(s):  
Wavelet Packet ◽  
Circuit Breaker ◽  
Recognition System ◽  
Wavelet Packet Decomposition

scholarly journals Multilinear principal component analysis for iris biometric system

2021 ◽  
Vol 23 (3) ◽  
pp. 1458
Author(s):  
Chetana Kamlaskar ◽  
Aditya Abhyankar
Keyword(s):  
Principal Component Analysis ◽  
Iris Recognition ◽  
Feature Fusion ◽  
Wavelet Packet ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Good Time ◽  
Wavelet Packet Decomposition ◽  
Multilinear Principal Component Analysis

<p>Iris biometric modality possesses inherent characteristics which make the iris recognition system highly reliable and noninvasive. Nowadays, research in this area is challenging compact template size and fast verification algorithms. Special efforts have been employed to minimize the size of the extracted features without degrading the performance of the iris recognition system. In response, we propose an improved feature fusion approach based on multilinear subspace learning to analyze Iris recognition. This approach consists of four stages. In the first stage, the eye image is segmented to extract the iris region. In the second step, wavelet packet decomposition is conducted to extract features of the iris image, since good time and frequency resolutions can be provided simultaneously by the wavelet packet decomposition. In the next step, all decomposed nodes or packets are arranged as a 3<sup>rd</sup> order tensor rather than a long vector, in which feature fusion is directly implemented with multilinear principal component analysis (MPCA). This approach provides a more compact or useful low-dimensional representation directly from the original tensorial representation. Finally, a discriminative tensor feature selection mechanism and classification strategy are applied to iris recognition problem. The obtained results indicate the usefulness of MPCA to select discriminative features and fuse them effectively. The experimental results reveal that the proposed tensor-based MPCA approach achieved a competitive matching performance on the SDUMLA-HMT Iris database with an adequate acceptable rate.</p>

scholarly journals Examining the Applicability of Wavelet Packet Decomposition on Different Forecasting Models in Annual Rainfall Prediction

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 1997
Author(s):  
Hua Wang ◽  
Wenchuan Wang ◽  
Yujin Du ◽  
Dongmei Xu
Keyword(s):  
Wavelet Packet ◽  
Moving Average ◽  
Back Propagation ◽  
Processing Technique ◽  
Annual Rainfall ◽  
Wavelet Packet Decomposition ◽  
Rainfall Forecasting ◽  
Promising Alternative ◽  
Forecasting Models ◽  
Precipitation Prediction

Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action. This paper examines the applicability of several forecasting models based on wavelet packet decomposition (WPD) in annual rainfall forecasting, and a novel hybrid precipitation prediction framework (WPD-ELM) is proposed coupling extreme learning machine (ELM) and WPD. The works of this paper can be described as follows: (a) WPD is used to decompose the original precipitation data into several sub-layers; (b) ELM model, autoregressive integrated moving average model (ARIMA), and back-propagation neural network (BPNN) are employed to realize the forecasting computation for the decomposed series; (c) the results are integrated to attain the final prediction. Four evaluation indexes (RMSE, MAE, R, and NSEC) are adopted to assess the performance of the models. The results indicate that the WPD-ELM model outperforms other models used in this paper and WPD can significantly enhance the performance of forecasting models. In conclusion, WPD-ELM can be a promising alternative for annual precipitation forecasting and WPD is an effective data pre-processing technique in producing convincing forecasting models.

scholarly journals Adaptive frequency scaled wavelet packet decomposition for frog call classification

2016 ◽  
Vol 32 ◽  
pp. 134-144 ◽  
Author(s):  
Jie Xie ◽  
Michael Towsey ◽  
Jinglan Zhang ◽  
Paul Roe
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Call Classification

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.

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