Transfer Colors from CVHD to MRI Based on Wavelets Transform

2007 ◽  
pp. 381-388
Author(s):  
Xiaolin Tian ◽  
Xueke Li ◽  
Yankui Sun ◽  
Zesheng Tang
Keyword(s):  
Wavelets Transform

Comparative Analysis and Automatic Segmentation of PCG Signal Using Wavelets Transform

2015 ◽  
Vol 3 (3) ◽  
pp. 24-29
Author(s):  
Lekram Premlal Bahekar ◽  
◽  
Deepali Shende ◽  
Simran Kaur Digwa ◽  
◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Automatic Segmentation ◽  
Wavelets Transform

Grinding Acoustic Emission Classification in Terms of Mechanical Behaviours

2007 ◽  
Vol 329 ◽  
pp. 15-20 ◽  
Author(s):  
Xun Chen ◽  
James Griffin
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Material Removal ◽  
Grinding Process ◽  
Short Time Fourier Transform ◽  
Fundamental Investigation ◽  
Mechanical Behaviours ◽  
Wavelets Transform ◽  
Short Time ◽  
Ae Signals

The material removal in grinding involves rubbing, ploughing and cutting. For grinding process monitoring, it is important to identify the effects of these different phenomena experienced during grinding. A fundamental investigation has been made with single grit cutting tests. Acoustic Emission (AE) signals would give the information relating to the groove profile in terms of material removal and deformation. A combination of filters, Short-Time Fourier Transform (STFT), Wavelets Transform (WT), statistical windowing of the WT with the kurtosis, variance, skew, mean and time constant measurements provided the principle components for classifying the different grinding phenomena. Identification of different grinding phenomena was achieved from the principle components being trained and tested against a Neural Network (NN) representation.

Discrete wavelets transform for improving greenness image segmentation in agricultural images

2015 ◽  
Vol 118 ◽  
pp. 396-407 ◽  
Author(s):  
M. Guijarro ◽  
I. Riomoros ◽  
G. Pajares ◽  
P. Zitinski
Keyword(s):  
Image Segmentation ◽  
Wavelets Transform ◽  
Discrete Wavelets

Adaptive Speech Compression Based on Discrete Wave Atoms Transform

2016 ◽  
Vol 6 (5) ◽  
pp. 2150
Author(s):  
Bousselmi Souha ◽  
Aloui Nouredine ◽  
Cherif Adnane
Keyword(s):  
Compression Ratio ◽  
Speech Quality ◽  
Huffman Coding ◽  
Current Work ◽  
Speech Compression ◽  
Snr Estimation ◽  
Run Length ◽  
Compression System ◽  
Wave Atoms ◽  
Wavelets Transform

<p>This paper proposes a new adaptive speech compression system based on discrete wave atoms transform. First, the signal is decomposed on wave atoms, then wave atom coefficients are truncated using a new adaptive thresholding which depends on the SNR estimation. The thresholded coefficients are quantized using Max Lloyd scalar quantizer. Besides, they are encoded using zero run length encoding followed by Huffman coding. Numerous simulations are performed to prove the robustness of our approach. The results of current work are compared with wavelet based compression by using objective criteria, namely CR, SNR, PSNR and NRMSE. This study shows that the wave atoms transform is more appropriate than wavelets transform since it offers a higher compression ratio and a better speech quality.</p>

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