scholarly journals Adaptive Wavelet Packet Modulation

2018 ◽  
Vol 66 (7) ◽  
pp. 2947-2957 ◽  
Author(s):  
Marwa Chafii ◽  
Jacques Palicot ◽  
Remi Gribonval ◽  
Faouzi Bader
Keyword(s):  
Wavelet Packet ◽  
Adaptive Wavelet

Efficient and Adaptive Rotation Invariant Wavelet Transform

2003 ◽  
Vol 01 (03) ◽  
pp. 353-372
Author(s):  
Chi-Man Pun
Keyword(s):  
Wavelet Transform ◽  
Texture Classification ◽  
Wavelet Packet ◽  
Sampling Rate ◽  
Discrete Wavelet ◽  
Wavelet Coefficients ◽  
Information Cost ◽  
Image Size ◽  
Rotation Invariant ◽  
Adaptive Wavelet

It is well known that the sensitivity to translations and orientations is a major drawback in 2D discrete wavelet transform (DWT). In this paper, we have proposed an effective scheme for rotation invariant adaptive wavelet packet transform. During decomposition, the wavelet coefficients are obtained by applying a polar transform (PT) followed by a row-shift invariant wavelet packet decomposition (RSIWPD). In the first stage, the polar transform generates a row-shifted image and is adaptive to the image size to achieve complete and minimum sampling rate. In the second stage, the RSIWPD is applied to the row-shifted image to generate rotation invariant but over completed subbands of wavelet coefficients. In order to reduce the redundancy and computational complexity, we adaptively select some subbands to decompose and form a best basis representation with minimal information cost with respect to an appropriate information cost function. With this best basis representation, the original image can be reconstructed easily by applying a row-shift invariant wavelet packet reconstruction (RSIWPR) followed by an inverse polar transform (IPT). In the experiments, we study the application of this representation for texture classification and achieve 96.5% classification accuracy.

Adaptive Wavelet Packet-Based De-speckling of Ultrasound Images with Bilateral Filter

2013 ◽  
Vol 39 (12) ◽  
pp. 2463-2476 ◽  
Author(s):  
Sankaralingam Esakkirajan ◽  
Chinna Thambi Vimalraj ◽  
Rashad Muhammed ◽  
Ganapathi Subramanian
Keyword(s):  
Wavelet Packet ◽  
Bilateral Filter ◽  
Ultrasound Images ◽  
Adaptive Wavelet

Gear fault detection based on adaptive wavelet packet feature extraction and relevance vector machine

2011 ◽  
Vol 225 (11) ◽  
pp. 2727-2738 ◽  
Author(s):  
N Li ◽  
C Liu ◽  
C He ◽  
Y Li ◽  
X F Zha
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Time Domain ◽  
Wavelet Packet ◽  
Relevance Vector Machine ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Fisher Criterion ◽  
Discrimination Power ◽  
Adaptive Wavelet

In this article, a novel fault detection method based on adaptive wavelet packet feature extraction and relevance vector machine (RVM) is proposed for incipient fault detection of gear. First, ten statistical characteristics in time domain and all node energies of full wavelet packet tree are extracted as candidate features. Then, Fisher criterion is applied to evaluate the discrimination power of each feature. Finally, two optimal features from time domain and wavelet domain, respectively, are selected to be used as inputs to the RVM. Furthermore, moving average is applied to each feature to improve accuracy for online continuous fault detection. By combining wavelet packet transform with Fisher criterion, it is able to adaptively find the optimal decomposition level and select the global optimal features. The RVM, a Bayesian learning framework of statistical pattern recognition, is adopted to train the fault detection model. The RVM was compared with the popular support vector machine (SVM) with the increase of training samples. Experimental results validate the effectiveness of the proposed method, and indicate that RVM is more suitable than SVM for online fault detection.

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