Research on the speech de-noising method based on improved wavelet threshold function

2014 ◽  
Author(s):  
Haiyan Yang ◽  
Xinxing Jing ◽  
Ping Zhou
Keyword(s):  
Threshold Function

Performance Analysis of RNC Clustering Protocol in Wireless Sensor Network

2019 ◽  
Vol 10 ◽  
Author(s):  
Piyush Rawat ◽  
Siddhartha Chauhan
Keyword(s):  
Energy Consumption ◽  
Network Performance ◽  
Energy Levels ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Threshold Function ◽  
Efficient Manner ◽  
Clustering Protocol

Background and Objective: The functionalities of wireless sensor networks (WSN) are growing in various areas, so to handle the energy consumption of network in an efficient manner is a challenging task. The sensor nodes in the WSN are equipped with limited battery power, so there is a need to utilize the sensor power in an efficient way. The clustering of nodes in the network is one of the ways to handle the limited energy of nodes to enhance the lifetime of the network for its longer working without failure. Methods: The proposed approach is based on forming a cluster of various sensor nodes and then selecting a sensor as cluster head (CH). The heterogeneous sensor nodes are used in the proposed approach in which sensors are provided with different energy levels. The selection of an efficient node as CH can help in enhancing the network lifetime. The threshold function and random function are used for selecting the cluster head among various sensors for selecting the efficient node as CH. Various performance parameters such as network lifespan, packets transferred to the base station (BS) and energy consumption are used to perform the comparison between the proposed technique and previous approaches. Results and Discussion: To validate the working of the proposed technique the simulation is performed in MATLAB simulator. The proposed approach has enhanced the lifetime of the network as compared to the existing approaches. The proposed algorithm is compared with various existing techniques to measure its performance and effectiveness. The sensor nodes are randomly deployed in a 100m*100m area. Conclusion: The simulation results showed that the proposed technique has enhanced the lifespan of the network by utilizing the node’s energy in an efficient manner and reduced the consumption of energy for better network performance.

scholarly journals Stage Division of Landslide Deformation and Prediction of Critical Sliding Based on Inverse Logistic Function

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1091
Author(s):  
Liulei Bao ◽  
Guangcheng Zhang ◽  
Xinli Hu ◽  
Shuangshuang Wu ◽  
Xiangdong Liu
Keyword(s):  
Direct Method ◽  
Logistic Function ◽  
New Method ◽  
Threshold Function ◽  
Critical Threshold ◽  
Time Curve ◽  
Acceleration Rate ◽  
Deformation Stages ◽  
Distance Weighting ◽  
Landslide Deformation

The cumulative displacement-time curve is the most common and direct method used to predict the deformation trends of landslides and divide the deformation stages. A new method based on the inverse logistic function considering inverse distance weighting (IDW) is proposed to predict the displacement of landslides, and the quantitative standards of dividing the deformation stages and determining the critical sliding time are put forward. The proposed method is applied in some landslide cases according to the displacement monitoring data and shows that the new method is effective. Moreover, long-term displacement predictions are applied in two landslides. Finally, summarized with the application in other landslide cases, the value of displacement acceleration, 0.9 mm/day2, is suggested as the first early warning standard of sliding, and the fitting function of the acceleration rate with the volume or length of landslide can be considered the secondary critical threshold function of landslide failure.

Segmentation of Welding Defect Image Based on Exponential Cross Entropy and Improved PCNN

2013 ◽  
Vol 760-762 ◽  
pp. 1467-1471 ◽  
Author(s):  
Zhi Long Ye ◽  
Yi Quan Wu ◽  
Hong Wan ◽  
Zhao Qing Cao
Keyword(s):  
Cross Entropy ◽  
Projection Algorithm ◽  
Threshold Function ◽  
Dynamic Threshold ◽  
Segmentation Method ◽  
Low Contrast ◽  
Segmented Image ◽  
Weighted Matrix ◽  
Defect Image ◽  
Welding Defect

Aiming at welding defect image with complex background and low contrast, a segmentation method of welding defect image based on exponential cross entropy and improved pulse coupled neural network (PCNN) is proposed. Firstly, the area of weld is extracted by gray projection algorithm. Then, link weighted matrix and dynamic threshold function of PCNN are improved. Finally, the exponential cross entropy is calculated as criterion to determine the number of iteration for improved PCNN and get the optimal segmented image. The experimental results are given. Compared with the threshold segmentation method based on exponential cross entropy, the segmentation method based on PCNN and Shannon entropy, the proposed method can achieve better segmented results.

Denoising ECG by a New Wavelet Threshold Function

2021 ◽  
Author(s):  
Bingze Dai ◽  
Dequan Yang ◽  
Dongbo Feng
Keyword(s):  
Threshold Function

An Improved Wavelet Threshold De-Noising Data Processing Method Research in Deformation Monitoring

2011 ◽  
Vol 90-93 ◽  
pp. 2858-2863
Author(s):  
Wei Li ◽  
Xu Wang
Keyword(s):  
Data Processing ◽  
Deformation Monitoring ◽  
Processing Method ◽  
Threshold Function ◽  
Wavelet Coefficients ◽  
New Approach ◽  
Data Processing Method ◽  
Soft Threshold ◽  
Fixed Bias ◽  
Better Than

Due to the soft and hard threshold function exist shortcomings. This will reduce the performance in wavelet de-noising. in order to solve this problem，This article proposes Modulus square approach. the new approach avoids the discontinuity of the hard threshold function and also decreases the fixed bias between the estimated wavelet coefficients and the wavelet coefficients of the soft-threshold method．Simulation results show that SNR and MSE are better than simply using soft and hard threshold，having good de-noising effect in Deformation Monitoring.

A critique of an incremental threshold function

1977 ◽  
Vol 17 (7) ◽  
pp. 881-882 ◽  
Author(s):  
Theodore P. Williams ◽  
John G. Gale
Keyword(s):  
Threshold Function

Improved Wavelet Threshold Denoising Method

2014 ◽  
Vol 602-605 ◽  
pp. 3177-3180
Author(s):  
Wei Ping Cui ◽  
Li Juan Du
Keyword(s):  
Traditional Method ◽  
Wavelet Denoising ◽  
Threshold Function ◽  
Signal Denoising ◽  
Improved Method ◽  
Denoising Method ◽  
Comparison And Analysis ◽  
Selection Of

In this paper, through comparison and analysis of various wavelet denoising methods, a new threshold function is constructed, and the selection of threshold is improved. Signal denoising simulation is made by the software MATLAB, the results show that the improved method is superior to the traditional method, and obtain a better denoising effect.

scholarly journals Morphometric characteristics of the collagen of periimplant zones by using suture material of different structure and chemical composition with the aim of correction of age skin changes

2019 ◽  
Vol 23 (1) ◽  
pp. 104-109
Author(s):  
M. Turkevуch
Keyword(s):  
Chemical Composition ◽  
Suture Material ◽  
Threshold Function ◽  
Quantitative Composition ◽  
Skin Changes ◽  
Collagen Formation ◽  
Black And White ◽  
Straight Line ◽  
Collagen Density ◽  
The Absolute

Morphometric analysis of collagenogenesis of perimplant zones was carried out at application of suture material of different structure and chemical composition for the purpose of establishing the quantitative composition of collagen. The thickness of the sleeve wall was measured using ImageJ ver.1.48u (1,2) using the “straight line” tool. The collagen density of the clutch wall and the collagen density in the surrounding tissues were measured by converting the images into black and white format, followed by obtaining a binary image using the “threshold” function of the ImageJ program. It is established that according to the indicators of thickness of a collagen sleeve and its density leaders are materials LLS. In terms of the density of collagen in tissues close to the clutch, the absolute leader is LLS material. In general, according to the values of all three parameters, the absolute leader in the intensity of collagen formation as the walls of the muffle and surrounding tissues is the LLS material, in the second place, on the aggregate of the three indicators, the materials NS and EV are located.

scholarly journals Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhuxiang Shen ◽  
Wei Li ◽  
Hui Han
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Similarity Index ◽  
Speckle Noise ◽  
Disease Diagnosis ◽  
Function Optimization ◽  
Threshold Function ◽  
Ultrasound Images ◽  
Better Than

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

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