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Relevant bibliographies by topics / Signal de-noising

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Dissertations / Theses
Book chapters
Conference papers

Academic literature on the topic 'Signal de-noising'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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Journal articles on the topic "Signal de-noising"

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Li, Xin, Xue Jun Li, and Guang Bin Wang. "De-Noising Method of Acoustic Emission Signal for Rolling Bearing Based on Adaptive Wavelet Correlation Analysis." Applied Mechanics and Materials 273 (January 2013): 188–92. http://dx.doi.org/10.4028/www.scientific.net/amm.273.188.

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In acoustic emission (AE) detection technique, to avoid the serious noise disturbance in the fault diagnosis of rotary machine, a de-noising method based on adaptive wavelet correlation analysis to be applied to the AE signal is proposed. First, AE signals are decomposed by dyadic wavelet transform and at the same time the AE signal is divided into available coefficients and noise coefficients. Secondly, the available coefficients are reconstructed to restore the original real signal after de-noising process. Finally, the de-noising threshold is set by adaptive threshold method based on wavele

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Song, Bo, Ye Cao, and Hong Biao Gao. "Wavelet De-Noising Method of Blasting Vibration Signal Considering Different Level Noise." Applied Mechanics and Materials 204-208 (October 2012): 4556–61. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4556.

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The original signals always mix with certain noise through blasting engineering test of underground structure, such as fan noise, environmental noise and mechanical noise. That makes the useful information of signal characteristics hidden in the noise signal to cause major error for the site test. The superposed signals of the original signal got by using numerical simulation and artificial white noise signal were processed through different de-noising methods based on wavelet transform. After the de-noising effects were evaluated by the evaluation standard signal de-noising performance, the b

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Bi, Zhou Yang, Jian Hui Chen, Wen Jie Ju, Ming Wang, and Ji Chen Li. "Method of Ultrasonic Signal De-Noising Based on Lifting Wavelet Improved Threshold." Applied Mechanics and Materials 513-517 (February 2014): 3818–21. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3818.

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The article established the mathematical model of ultrasonic flaw echo signals. First, the basic theory of wavelet transform is introduced, the principle of the wavelet threshold de-noising method is analyzed; Then on the basis of soft and hard threshold function, the paper proposes a method based on lifting wavelet de-noising. And from two aspects of signal-to-noise ratio (SNR) and mean square error (MSE) the de-noising performance is analysed. The results show that the method improved the shortcomings of soft and hard threshold de-noising method, and got a better de-noising performance and h

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Ahmed, Asia Sh, Khalida Sh Rijab, and Salwa A. Alagha. "A Study of Chosen an Optimum Type of Wavelet Filter for De-Noising an ECG signal." International Journal of Current Engineering and Technology 10, no. 05 (2020): 749–56. http://dx.doi.org/10.14741/ijcet/v.10.5.9.

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Among various biological signals for the diagnosing of cardiac arrhythmia, Electrocardiographic (ECG) signal is the most significant one. The interesting challenge is an accurate analysis of the noisy ECG signal. Prior to accurate analysis, these signals need for de-noising to remove these unwanted noises in the signal to get an accurate diagnosis. In order to get the best de-noising results, it should have an accurate decision about the filters that we deal with for de-noising the signals. So, in this paper we present a study for choosing the optimum wavelet filter for de-noising the electroc

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Yuqing Liang, Yuqing Liang, Wenhui Fan Wenhui Fan, and Bing Xue Bing Xue. "Terahertz TDS signal de-noising using wavelet shrinkage." Chinese Optics Letters 9, s1 (2011): s10504–310505. http://dx.doi.org/10.3788/col201109.s10504.

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Fan, Wei, Z. K. Zhu, Wei Guo Huang, and Gai Gai Cai. "Sparse Representation De-Noising Based on Morlet Wavelet Basis and its Application for Transient Feature Extraction." Applied Mechanics and Materials 526 (February 2014): 200–204. http://dx.doi.org/10.4028/www.scientific.net/amm.526.200.

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Signals with multiple transients are often encountered with much noise in engineering. The transient feature extraction has always been the key issue for signal analysis. A new signal de-noising method combining sparse representation and Morlet wavelet basis is proposed for signal de-noising and feature extraction. Simulation study concerning multiple transients signal shows the effectiveness of this method in transient feature extraction. The efficiency of this de-noising method is also verified by its application to extract fault signature for gearbox.

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Z. Abdullah, A., M. Isa, S. N. M. Arshad, et al. "Wavelet based de-noising for on-site partial discharge measurement signal." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (2019): 259. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp259-266.

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<span>This paper presents, wavelet based de-noising technique for on-site partial discharge (PD) measurement signal. The signal is measured from medium voltage power cable at 11 kV distribution substation. The best mother wavelet, decomposition level and the type of threshold for the de-noising technique are selected based on the signal to noise ratio (SNR) aggregation. The SNR aggregation is determined based on the minimum, maximum, mean and standard deviation parameters. The same standard de-noising procedure is applied for two different PD signals and the selection parameters are done

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Zheng, Si Li, Yu Feng Gui, and Xian Qiao Chen. "The Study of Smoothness and Similarity for Denoising Signal Based on Wavelet Transform." Advanced Materials Research 655-657 (January 2013): 984–88. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.984.

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Focus on the problem of de-noising signals smoothness and similarity.The three signals were processed by four signal de-noising methods,which are inhibition detail coefficients,Fourier transform,global threshold and layered threshold method.And the energy ratio(PERF) and standard deviation(ERR) were obtained.Experiment results show that the global threshold de-noising method is the best for its high similarity;the layered threshold de-noising method is the best for its high smoothness.

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S. Ashwin, J., and N. Manoharan. "Audio Denoising Based on Short Time Fourier Transform." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 1 (2018): 89. http://dx.doi.org/10.11591/ijeecs.v9.i1.pp89-92.

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<p>This paper presents a novel audio de-noising scheme in a given speech signal. The recovery of original from the communication channel without any noise is a difficult task. Many de-noising techniques have been proposed for the removal of noises from a digital signal. In this paper, an audio de-noising technique based on Short Time Fourier Transform (STFT) is implemented. The proposed architecture uses a novel approach to estimate environmental noise from speech adaptively. Here original speech signals are given as input signal. Using AWGN, noises are added to the signal. Then noised s

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Nie, Hai Zhao, Hui Liu, and Lei Shi. "Application of Wavelet De-Noising in Non-Stationary Signal Analysis Based on the Parameter Optimization of Improved Threshold Function." Applied Mechanics and Materials 448-453 (October 2013): 2068–76. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.2068.

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Using wavelet analysis for non-stationary signal de-noising of electro-mechanical system is considered to be the best approach, and wavelet threshold de-noising method is the most simple method that needs the minimum amount of calculation. But this method in selecting threshold functions needs to be improved. Based on different domestic and foreign methods of improving threshold function, propose an improved bivariate threshold function. According to the simulation of non-stationary signal de-noising, the results show that the optimal de-noising results of different signals exist by taking dif

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Dissertations / Theses on the topic "Signal de-noising"

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Khorbotly, Sami. "DESIGN AND IMPLEMENTATION OF LOW COST DE-NOISING SYSTEMS FOR REAL-TIME CONTROL APPLICATIONS." University of Akron / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=akron1180976720.

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Frigo, Guglielmo. "Compressive Sensing Applications in Measurement: Theoretical issues, algorithm characterization and implementation." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3424133.

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At its core, signal acquisition is concerned with efficient algorithms and protocols capable to capture and encode the signal information content. For over five decades, the indisputable theoretical benchmark has been represented by the wellknown Shannon’s sampling theorem, and the corresponding notion of information has been indissolubly related to signal spectral bandwidth. The contemporary society is founded on almost instantaneous exchange of information, which is mainly conveyed in a digital format. Accordingly, modern communication devices are expected to cope with huge amounts of data,

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Carter, Drew Davis. "Characterisation of cardiac signals using level crossing representations." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/130760/1/Drew_Carter_Thesis.pdf.

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This study examines a type of event-based sampling known as Level Crossing - its behaviour when applied to noisy signals, and an application to cardiac arrhythmia detection. Using a probabilistic approach, it presents a mathematical description of events sampled from noisy signals, and uses the model to estimate characteristics of the underlying clean signal. It evaluates the use of segments of polynomials, calculated from the Level Crossing samples of real cardiac signals, as features for machine learning algorithms to identify various types of arrhythmia.

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Tsao, Chien-Kung, and 曹鍵滎. "Improcments of Wavelet-Shrinkage for De-noising of Nonstationary Signal." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/00099785220522681752.

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碩士<br>國立海洋大學<br>電機工程學系<br>88<br>Denosing processing of speech random signals is one of the important and challenging topics in modern signal processing. The wavelet-shrinkage method is an important scheme for denosing processing. In the wavelet shrinkage method, the wavelet coefficients of the noisy signal are obtained by wavelet transform. These coefficients are used to estimate a suitable threshold for shrinkage of the original wavelet coefficients. After shrinkage, the reconstructed signal can be generated from the shrank wavelet coefficients using inverse wavelet transform. Though the wave

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Teng, You-Yang, and 滕有揚. "The Research of Digital Signal Processing Chip Set Applied on Acoustic Signal De-noising." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/q7fn2b.

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碩士<br>中原大學<br>資訊工程研究所<br>92<br>In the process of transmitting various acoustic signals , because of being affected by the environment and all kinds of noises that permeate through the propagation channel, it is necessary to have an appropriate signal processing procedure to identify the signals which energy is already decreased by long distance transmission and environment interruption. This research is based in a wavelet-based method by choosing threshold value for de-noising. The procedure is divided into three stages: (1) Wavelet transform of the acoustic signals (2) Thresholding of wavelet

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Weng, Mu-Shen, and 翁睦盛. "A Study and Comparison on De-noising of Power Quality Transient Signal with Wavelet Transform." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/95221913866722380682.

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碩士<br>中原大學<br>電機工程研究所<br>90<br>With the rapid developments of the high-tech industries as well as much more usages of the precise production equipments and test instruments, the far higher power quality (PQ) is demanded nowadays. However, the primary work of improving the power quality has to widely collect the power signals through the PQ monitoring instruments. Based the analysis of the PQ data collected, the causes and the problems of the PQ events can be inferred for the references of the PQ improvement. In the process of monitoring power signals, the PQ related signals are recorded vi

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Huang, Min-yu, and 黃敏煜. "A Discrete Wavelet Transform (DWT) based De-noising Circuit Design with its Applications to Medical Signal Processing." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/57371780328343232025.

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碩士<br>長庚大學<br>電子工程研究所<br>93<br>Wavelet Transform is a multiresolution analysis that decomposes an original signal to multi-octave based functions, and we can analysis the original signal using these functions. It provides a novel and effective tool for many applications in signal processing area. Also, it has the advantage over the traditional Fourier Transform with respect to time-frequency analysis because of its characteristic of multiresolution. Therefore, it has been widely applied into many aspects of signal/image processing-related researches. In this thesis, we proposed and realized a

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Pandey, Santosh Kumar. "Signal Processing Tools To Enhance Interpretation Of Impulse Tests On Power Transformers." Thesis, 1997. http://etd.iisc.ernet.in/handle/2005/1821.

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Yu, Chen Kuan, and 陳冠宇. "An Improved Wavelet Thresholding Method for De-Noising Electrocardiogram Signals." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/28576494141875361473.

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碩士<br>輔仁大學<br>資訊工程學系<br>97<br>Abstract The electrocardiogram (ECG) signal records the electrical activity of the heart and the signal is widely used for diagnosis of heart diseases. However, the ECG signals are easy to be interfered with by the different noises. A de-noising method is often used to filter noise and the produced ECG is then used to help physicians to diagnose cardiovascular disease. In recent years, several de-noising methods based on discrete wavelet transform (DWT) are proposed to deal with the problem of extracting the weak ECG signal in a strong noisy environment. Alth

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Salgado, Patarroyo Ivan Camilo. "Spatially Regularized Spherical Reconstruction: A Cross-Domain Filtering Approach for HARDI Signals." Thesis, 2013. http://hdl.handle.net/10012/7847.

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Despite the immense advances of science and medicine in recent years, several aspects regarding the physiology and the anatomy of the human brain are yet to be discovered and understood. A particularly challenging area in the study of human brain anatomy is that of brain connectivity, which describes the intricate means by which different regions of the brain interact with each other. The study of brain connectivity is deeply dependent on understanding the organization of white matter. The latter is predominantly comprised of bundles of myelinated axons, which serve as connecting pathways betw

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Book chapters on the topic "Signal de-noising"

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Germán-Salló, Zoltán, Márta Germán-Salló, and Horaţiu-Ştefan Grif. "Empirical Mode Decomposition in ECG Signal De-noising." In 6th International Conference on Advancements of Medicine and Health Care through Technology; 17–20 October 2018, Cluj-Napoca, Romania. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6207-1_24.

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Rivet, Bertrand, Vincent Vigneron, Anisoara Paraschiv-Ionescu, and Christian Jutten. "Wavelet De-noising for Blind Source Separation in Noisy Mixtures." In Independent Component Analysis and Blind Signal Separation. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30110-3_34.

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Sun, Jinchao. "Electrocardiogram Signal De-noising and Reconstruction Based on Compressed Sensing." In Lecture Notes in Electrical Engineering. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3229-5_66.

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Fan, Yuanyuan, Yingjun Sang, Qingxia Kong, Fei Huang, Qi Chen, and Bin Liu. "Voltage Transient Signal De-noising Based on Wavelet Decomposition Level." In Advances in Mechanical and Electronic Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31528-2_8.

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Zhang, Yufeng, Le Wang, Yali Gao, Jianhua Chen, and Xinling Shi. "Automatic De-noising of Doppler Ultrasound Signals Using Matching Pursuit Method." In Independent Component Analysis and Blind Signal Separation. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11679363_65.

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Anbazhagan, S., and K. Vaidehi. "Short-Term Load Forecasting Using Wavelet De-noising Signal Processing Techniques." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1097-7_58.

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China Venkateswarlu, S., G. Soma Sekhar, N. Uday Kumar, and Vallabhuni Vijay. "A Wavelet-Based De-Noising Speech Signal Performance with Objective Measures." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1669-4_25.

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Sang, Yingjun, Yuanyuan Fan, Qingxia Kong, Fei Huang, Qi Chen, and Bin Liu. "A New Threshold Algorithm Applied in the Voltage Sag Signal De-noising." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29387-0_79.

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Huali, Chen, and Cheng Gengguo. "Study on Adaptive Signal De-noising Method Based on Hilbert-Huang Transform." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-26001-8_10.

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Li, Junyao, Yongbin Li, Xiaoqiang Wang, and Peijie Zhang. "De-noising Method Research on RF Signal by Combining Wavelet Transform and SVD." In Proceedings of the 28th Conference of Spacecraft TT&C Technology in China. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4837-1_38.

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Conference papers on the topic "Signal de-noising"

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Sawant, Chitrangi, and Harishchandra T. Patii. "Wavelet based ECG signal de-noising." In 2014 International Conference on Networks & Soft Computing (ICNSC). IEEE, 2014. http://dx.doi.org/10.1109/cnsc.2014.6906684.

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Oktem, H., K. O. Egiazarian, and J. Nousiainen. "Local adaptive de-noising techniques in transform domain for EMCG de-noising." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.758262.

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Shan Liu, Keming Yue, Hua Yang, and Ting Guo. "The research on ICG signal de-noising." In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2016. http://dx.doi.org/10.1109/imcec.2016.7867513.

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Sivakumar, Venkataraman. "De-noising LiDAR signal using wavelet technique." In Optical Engineering + Applications, edited by Upendra N. Singh. SPIE, 2007. http://dx.doi.org/10.1117/12.739450.

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Aiboud, Youssef, Jamal El Mhamdi, Abdelilah Jilbab, and Hamza Sbaa. "Review of ECG signal de-noising techniques." In 2015 Third World Conference on Complex Systems (WCCS). IEEE, 2015. http://dx.doi.org/10.1109/icocs.2015.7483313.

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Guo, Rui, Yiqin Wang, Jianjun Yan, Fufeng Li, and Haixia Yan. "Wavelet based De-noising of pulse signal." In 2008 IEEE International Symposium on IT in Medicine and Education (ITME). IEEE, 2008. http://dx.doi.org/10.1109/itme.2008.4743939.

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DING, YUGANG, KEDONG ZHOU, LEI HE, and HAOMIN YANG. "APPLICATION OF WAVELET THRESHOLD ALGORITHM OPTIMIZED BY CHAOTIC ADAPTIVE FIREWORKS ALGORITHM IN THE DE-NOISING OF MUZZLE RESPONSE SIGNALS." In 32ND INTERNATIONAL SYMPOSIUM ON BALLISTICS. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/ballistics22/36084.

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To extract the muzzle response signals effectively and reduce the noise in the signals, a novel de-noising method, i.e., wavelet threshold algorithm optimized by the chaotic adaptive fireworks algorithm, is proposed for the de-noising of the muzzle response signals. This method obtains the wavelet threshold by calculating the Stein’s unbiased risk estimate (SURE), and uses the chaotic adaptive fireworks algorithm to search for the global optimal threshold, which can avoid the threshold falling into local optimal value effectively. Meanwhile, a novel threshold function is adopted to keep the ba

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Li Su and Guoliang Zhao. "De-Noising of ECG Signal Using Translation- Invariant Wavelet De-Noising Method with Improved Thresholding." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1615845.

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Gautam, Alka, Young-Dong Lee, and Wan-Young Chung. "ECG Signal De-noising with Signal Averaging and Filtering Algorithm." In 2008 Third International Conference on Convergence and Hybrid Information Technology (ICCIT). IEEE, 2008. http://dx.doi.org/10.1109/iccit.2008.393.

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Shi, Xiao-xia, and Jun-zhi Li. "Research on the De-Noising Algorithm." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5304431.

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