On the Effect of Impulse Noise on the Accuracy of Discrimination of Time-Frequency Coded Signals

1998 ◽  
Vol 30 (2-3) ◽  
pp. 28-32
Author(s):  
D. I. Zelinskiy
Keyword(s):  
Impulse Noise ◽  
Time Frequency

Impulse Noise Mitigation for OFDM by Time-Frequency Spreading

2013 ◽  
pp. 8-20 ◽  
Author(s):  
Jiri Blumenstein ◽  
Roman Marsalek ◽  
Ales Prokes ◽  
Christoph Mecklenbräuker
Keyword(s):  
Impulse Noise ◽  
Noise Mitigation ◽  
Time Frequency

The Defect Detection in Ceramic Materials Based on Time-Frequency Analysis by Using the Method of Impulse Noise

2011 ◽  
Vol 36 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Tahir Akinci
Keyword(s):  
Frequency Analysis ◽  
Impulse Noise ◽  
Good Condition ◽  
Ceramic Materials ◽  
Surface Cracks ◽  
Test Technique ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Impact Noise ◽  
The Impact

AbstractIn this study, it was achieved by using the method of impulse noise to detect internal or surface cracks that can occur in the production of ceramic plates. Ceramic materials are often used in the industry, especially as kitchenware and in areas such as the construction sector. Many different methods are used in the quality assurance processes of ceramic materials. In this study, the impact noise method was examined. This method is a test technique that was not used in applications. The method is presented as an examination technique based on whether there is a deformation on the material according to the sound coming from it as a result of a plastic bit hammer impact on the ceramic material. The application of the study was performed on plates made of ceramic materials. Here, it was made with the same type of model plates manufactured from the same material. The noise that would occur as a result of the impact applied on a point determined on the materials to be tested has been examined by the method of time-frequency analysis. The method applied gives pretty good results for distinguishing ceramic plates in good condition from those which are cracked.

Applications and Comparison of Continuous Wavelet Transforms on Analysis of A-wave Impulse Noise

2015 ◽  
Vol 40 (4) ◽  
pp. 503-512 ◽  
Author(s):  
Jun Qin ◽  
Pengfei Sun
Keyword(s):  
Hearing Loss ◽  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Impulse Noise ◽  
Continuous Wavelet ◽  
Continuous Wavelet Transforms ◽  
Time Frequency ◽  
Mexican Hat ◽  
Meyer Wavelets ◽  
Hazard Level

Abstract Noise induced hearing loss (NIHL) is a serious occupational related health problem worldwide. The A-wave impulse noise could cause severe hearing loss, and characteristics of such kind of impulse noise in the joint time-frequency (T-F) domain are critical for evaluation of auditory hazard level. This study focuses on the analysis of A-wave impulse noise in the T-F domain using continual wavelet transforms. Three different wavelets, referring to Morlet, Mexican hat, and Meyer wavelets, were investigated and compared based on theoretical analysis and applications to experimental generated A-wave impulse noise signals. The underlying theory of continuous wavelet transform was given and the temporal and spectral resolutions were theoretically analyzed. The main results showed that the Mexican hat wavelet demonstrated significant advantages over the Morlet and Meyer wavelets for the characterization and analysis of the A-wave impulse noise. The results of this study provide useful information for applying wavelet transform on signal processing of the A-wave impulse noise.

An Effective FPGA Implementation of Denoising of Impulse Noise in Images

2016 ◽  
Vol 1 (3) ◽  
pp. 19-25
Author(s):  
Jeniffer A ◽  
Haripasath S ◽  
Chinthamani S ◽  
Chitra G ◽  
Karthiga V
Keyword(s):  
Impulse Noise ◽  
Fpga Implementation

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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