enhancement technique
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2022 ◽  
Vol 70 (2) ◽  
pp. 3549-3564
Author(s):  
Wan Azani Mustafa ◽  
Haniza Yazid ◽  
Ahmed Alkhayyat ◽  
Mohd Aminudin Jamlos ◽  
Hasliza A. Rahim

2021 ◽  
Vol 9 (12) ◽  
pp. 676-685
Author(s):  
Waleed Abdulhadi Ethbayah ◽  

The enhancement of laminar forced convection inside helical pipes is studied numerically and compared with plain pipes. The study is achieved numerically using the (Fluent-CFD 6.3.26) software program for solving the governing equations. The heat transfer factor and friction factor are calculated using the enhancement technique and compared with the plain tube. In this research the factors that affect the enhancement technique using helical pipes are studied, these factors are the ratio of (pitch /pipe length) (SL), Reynolds number and the heat flux applied to the external surface of the pipe. The results showed that there is an increasing in the heat transfer factor is related to the decreasing of (SL), increasing of Reynolds number and heat flux. The performance of the helical pipes is evaluated depending on the calculation of (Enhancement ratio), and its found that the enhancement ratio increases as Reynolds number increases and (SL) decreases. It is found that the best enhancement ratio was (200%) at (SR=0.05), (Re=2000),(Heat flux=3000W/m2).The results are compared with the literature and there is a good agreement.


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel

Speech enhancement has gained considerable attention in the employment of speech transmission via the communication channel, speaker identification, speech-based biometric systems, video conference, hearing aids, mobile phones, voice conversion, microphones, and so on. The background noise processing is needed for designing a successful speech enhancement system. In this work, a new speech enhancement technique based on Stationary Bionic Wavelet Transform (SBWT) and Minimum Mean Square Error (MMSE) Estimate of Spectral Amplitude is proposed. This technique consists at the first step in applying the SBWT to the noisy speech signal, in order to obtain eight noisy wavelet coefficients. The denoising of each of those coefficients is performed through the application of the denoising method based on MMSE Estimate of Spectral Amplitude. The SBWT inverse, S B W T − 1 , is applied to the obtained denoised stationary wavelet coefficients for finally obtaining the enhanced speech signal. The proposed technique’s performance is proved by the calculation of the Signal to Noise Ratio (SNR), the Segmental SNR (SSNR), and the Perceptual Evaluation of Speech Quality (PESQ).


2021 ◽  
Author(s):  
Bojun Hu ◽  
Sanfeng Zhang ◽  
Xiong Zhou ◽  
Zehao Li ◽  
Xiangxin Pan ◽  
...  

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