scholarly journals Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

2013 ◽  
Vol 43 (10) ◽  
pp. 1407-1414 ◽  
Author(s):  
Fatemeh Safara ◽  
Shyamala Doraisamy ◽  
Azreen Azman ◽  
Azrul Jantan ◽  
Asri Ranga Abdullah Ramaiah
Keyword(s):  
Heart Sound ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Decomposition Tree ◽  
Sound Classification ◽  
Multi Level ◽  
Selection Of

The Simulation of the Psychological Impact of Computer Vision De-Noising Technology

2014 ◽  
Vol 556-562 ◽  
pp. 5013-5016 ◽  
Author(s):  
Fa Zhao Gao
Keyword(s):  
Computer Vision ◽  
Wavelet Packet ◽  
Psychological Impact ◽  
Memory Capacity ◽  
Wavelet Packet Decomposition ◽  
Decomposition Tree ◽  
Analysis Method ◽  
Filtering Algorithm ◽  
Psychological Influence ◽  
Psychological Acceptance

The paper mainly discusses the analysis method for the psychological impact of computer vision noising technology. Actually, people's psychological acceptance and corresponding memory capacity of computer vision images with lots of noise are relatively poor. The de-noising process to computer vision images can improve the clarity, thus generating passive psychological impact. Therefore, the paper proposes a spatial domain filtering algorithm-based de-noising method for computer vision. It establishes wavelet packet decomposition tree for computer vision images and de-noises accordance with the decomposing results. The experiment results show that the proposed de-noising method has passive psychological influence and improves the memory capacity of computer vision images.

The most discriminant subbands for face recognition: A novel information-theoretic framework

2018 ◽  
Vol 16 (05) ◽  
pp. 1850040
Author(s):  
Affan Alim ◽  
Imran Naseem ◽  
Roberto Togneri ◽  
Mohammed Bennamoun
Keyword(s):  
Face Recognition ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Information Theoretic ◽  
Face Images ◽  
Automatic Selection ◽  
The Face ◽  
Selection Of

In this paper, we propose a consolidated framework for the automatic selection of the most discriminant subbands for the problem of face recognition. Essentially, the face images are transformed into textures using the linear binary pattern (LBP) approach, these texturized-faces undergo the wavelet packet decomposition resulting in several subband images. We propose to use the energy features to effectively represent these subband images. The underlying statistical patterns of the data are harnessed in form of information-theoretic metrics to select the most discriminant subbands. The proposed algorithms are extensively evaluated on several standard databases and are shown to always pick the most significant subbands resulting in better performance. The proposed algorithms are entirely generic and do not depend on the selection of features or/and classifiers.

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 A Multiple and Multi-Level Substructure Method for the Dynamics of Complex Structures

2021 ◽  
Vol 11 (12) ◽  
pp. 5570
Author(s):  
Binbin Wang ◽  
Jingze Liu ◽  
Zhifu Cao ◽  
Dahai Zhang ◽  
Dong Jiang
Keyword(s):  
Structural Characteristics ◽  
Complex Structure ◽  
Complex Structures ◽  
System Matrix ◽  
Substructure Method ◽  
Residual Structure ◽  
The Matrix ◽  
Multi Level ◽  
Fixed Interface ◽  
Selection Of

Based on the fixed interface component mode synthesis, a multiple and multi-level substructure method for the modeling of complex structures is proposed in this paper. Firstly, the residual structure is selected according to the structural characteristics of the assembled complex structure. Secondly, according to the assembly relationship, the parts assembled with the residual structure are divided into a group of substructures, which are named the first-level substructure, the parts assembled with the first-level substructure are divided into a second-level substructure, and consequently the multi-level substructure model is established. Next, the substructures are dynamically condensed and assembled on the boundary of the residual structure. Finally, the substructure system matrix, which is replicated from the matrix of repeated physical geometry, is obtained by preserving the main modes and the constrained modes and the system matrix of the last level of the substructure is assembled to the upper level of the substructure, one level up, until it is assembled in the residual structure. In this paper, an assembly structure with three panels and a gear box is adopted to verify the method by simulation and a rotor is used to experimentally verify the method. The results show that the proposed multiple and multi-level substructure modeling method is not unique to the selection of residual structures, and different classification methods do not affect the calculation accuracy. The selection of 50% external nodes can further improve the analysis efficiency while ensuring the calculation accuracy.

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