Noise filtering techniques for photon-counting ladar data

2012 ◽  
Author(s):  
Lori A. Magruder ◽  
Michael E. Wharton ◽  
Kevin D. Stout ◽  
Amy L. Neuenschwander
Keyword(s):  
Photon Counting ◽  
Noise Filtering ◽  
Filtering Techniques

Noise filtering techniques for Lamb waves in structural health monitoring

2018 ◽  
Vol 14 (4) ◽  
pp. 676-694
Author(s):  
Ambuj Sharma ◽  
Sandeep Kumar ◽  
Amit Tyagi
Keyword(s):  
Crack Detection ◽  
Lamb Waves ◽  
Random Noise ◽  
Matched Filtering ◽  
Noise Filtering ◽  
Content Type ◽  
Time Frequency ◽  
Structural Health ◽  
Filtering Technique ◽  
Filtering Techniques

Purpose The real challenges in online crack detection testing based on guided waves are random noise as well as narrow-band coherent noise; and to achieve efficient structural health assessment methodology, magnificent extraction of noise and analysis of the signals are essential. The purpose of this paper is to provide optimal noise filtering technique for Lamb waves in the diagnosis of structural singularities. Design/methodology/approach Filtration of time-frequency information of guided elastic waves through the noisy signal is investigated in the present analysis using matched filtering technique which “sniffs” the signal buried in noise and most favorable mother wavelet based denoising methods. The optimal wavelet function is selected using Shannon’s entropy criterion and verified by the analysis of root mean square error of the filtered signal. Findings Wavelet matched filter method, a newly developed filtering technique in this work and which is a combination of the wavelet transform and matched filtering method, significantly improves the accuracy of the filtered signal and identifies relatively small damage, especially in enormously noisy data. A comparative study is also performed using the statistical tool to know acceptability and practicability of filtered signals for guided wave application. Practical implications The proposed filtering techniques can be utilized in online monitoring of civil and mechanical structures. The algorithm of the method is easy to implement and found to be successful in accurately detecting damage. Originality/value Although many techniques have been developed over the past several years to suppress random noise in Lamb wave signal but filtration of interferences of wave modes and boundary reflection is not in a much matured stage and thus needs further investigation. The present study contains detailed information about various noise filtering methods, newly developed filtration technique and their efficacy in handling the above mentioned issues.

Impulse Noise Filtering

2016 ◽  
pp. 102-115
Author(s):  
Abhijit Chandra ◽  
Srideep Maity
Keyword(s):  
Noise Reduction ◽  
Impulse Noise ◽  
Digital Images ◽  
State Of The Art ◽  
Noise Filtering ◽  
Two Dimensional ◽  
Design And Development ◽  
Image Details ◽  
Filtering Techniques

Digital images are often corrupted by various types of noises amongst which impulse noise is most prevalent. Impulse noise appears during transmission and/or acquisition of images. Intrusion of impulse noise degrades the quality of the image and causes the loss of fine image details. Reducing the effect of impulse noise from corrupted images is therefore considered as an essential task to be performed before letting the image for further processing. However, the process of noise reduction from an image should also take proper care towards the preservation of edges and fine details of an image. A number of efficient noise reduction algorithms have already been proposed in the literature over the last few decades which have nurtured this issue with utmost importance. Design and development of new two dimensional (2D) filters has grown sufficient interest amongst the researchers. This chapter attempts to throw enough light on the advancement in this field by illustratively describing existing state-of-the-art filtering techniques along with their capability of denoising impulse noises.

scholarly journals Label noise filtering techniques to improve monotonic classification

2019 ◽  
Vol 353 ◽  
pp. 83-95 ◽  
Author(s):  
José-Ramón Cano ◽  
Julián Luengo ◽  
Salvador García
Keyword(s):  
Noise Filtering ◽  
Label Noise ◽  
Monotonic Classification ◽  
Filtering Techniques
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