scholarly journals File Entropy Signal Analysis Combined With Wavelet Decomposition for Malware Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 158961-158971
Author(s):  
Hui Guo ◽  
Shuguang Huang ◽  
Cheng Huang ◽  
Zulie Pan ◽  
Min Zhang ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Malware Classification

IIR implementation of wavelet decomposition for digital signal analysis

1992 ◽  
Vol 28 (5) ◽  
pp. 513 ◽  
Author(s):  
F. Argenti ◽  
G. Benelli ◽  
A. Sciorpes
Keyword(s):  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Digital Signal

scholarly journals A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Chaolong Jia ◽  
Lili Wei ◽  
Hanning Wang ◽  
Jiulin Yang
Keyword(s):  
Time Series ◽  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Forecast Model ◽  
Practical Significance ◽  
Frequency Signal ◽  
Time Frequency ◽  
Simple Basis ◽  
Best Fitting ◽  
Track Irregularity

Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.

The power load’s signal analysis and short-term prediction based on wavelet decomposition

2017 ◽  
Vol 22 (S5) ◽  
pp. 11129-11141 ◽  
Author(s):  
Huan Wang ◽  
Min Ouyang ◽  
Zhibing Wang ◽  
Ruishi Liang ◽  
Xin Zhou
Keyword(s):  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Wavelet-based audio features of DC motor sound

2021 ◽  
Vol 34 (1) ◽  
pp. 71-88
Author(s):  
Djordje Damnjanovic ◽  
Dejan Ciric ◽  
Zoran Peric
Keyword(s):  
Fourier Transform ◽  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Dc Motor ◽  
Feature Representation ◽  
Production Environment ◽  
Audio Features ◽  
The Fourier Transform ◽  
Approximation Coefficients ◽  
Motor Production

The usage of wavelets is widespread in many fields nowadays, especially in signal processing. Their nature provides some advantages in comparison to the Fourier transform, and therefore many applications rely on wavelets rather than on other methods. The decomposition of wavelets into detail and approximation coefficients is one of the methods to extract representative audio features. They can be used in signal analysis and further classification. This paper investigates the usage of various wavelet families in the wavelet decomposition to extract audio features of direct current (DC) motor sounds recorded in the production environment. The purpose of feature representation and analysis is the detection of DC motor failures in motor production. The effects of applying different wavelet families and parameters in the decomposition process are studied using sounds of more than 60 motors. Time and frequency analysis is also done for the tested DC motor sounds.

Analyzing Variations in Roundness Profile Parameters During the Wavelet Decomposition Process Using the Matlab Environment

2011 ◽  
Vol 18 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Stanisław Adamczak ◽  
Włodzimierz Makieła
Keyword(s):  
Surface Texture ◽  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Decomposition Process ◽  
Profile Measurement ◽  
Texture Measurement ◽  
Spearman’S Rank Correlation ◽  
The Fourier Transform

Analyzing Variations in Roundness Profile Parameters During the Wavelet Decomposition Process Using the Matlab Environment Signal analysis performed during surface texture measurement frequently involves applying the Fourier transform. The method is particularly useful for assessing roundness and cylindrical profiles. Since the wavelet transform is becoming a common tool for signal analysis in many metrological applications, it is vital to evaluate its suitability for surface texture profiles. The research presented in this paper focused on signal decomposition and reconstruction during roundness profile measurement and the effect of these processes on the changes in selected roundness profile parameters. The calculations were carried out on a sample of 100 roundness profiles for 12 different forms of mother wavelets using MATLAB. The use of Spearman's rank correlation coefficients allowed us to evaluate the relationship between the two chosen criteria for selecting the optimal mother wavelet.

EEG Feature Extraction Based on Wavelet Decomposition

2014 ◽  
Vol 496-500 ◽  
pp. 2023-2026
Author(s):  
Jian Feng Hu ◽  
Zhen Dong Mu ◽  
Jing Hai Yin
Keyword(s):  
Feature Extraction ◽  
Signal Analysis ◽  
Wavelet Decomposition ◽  
Eeg Signals ◽  
Calculation Results

Wavelet decomposition is a commonly used tool, signal analysis using the wavelet decomposition, can put the source according to their demand is decomposed into different frequency signals, so as to provide convenience for the feature extraction and identification in this paper, the EEG signals, using the theory of wavelet decomposition is calculated, look from the calculation results, the wavelet decomposition can be used for feature extraction of EEG signals as well.

Improved ECG Signal Analysis Using Wavelet and Feature Extraction

2007 ◽  
Vol 46 (02) ◽  
pp. 227-230 ◽  
Author(s):  
M. Jonkman ◽  
F. de Boer ◽  
A. Matsuyama
Keyword(s):  
Feature Extraction ◽  
Signal Analysis ◽  
Significant Variation ◽  
Wavelet Decomposition ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Feature Vectors ◽  
Different Types ◽  
Before And After

Summary Objectives : Automatic detection of arrhythmias is important for diagnosis of heart problems. However, in ECG signals, there is significant variation of waveforms in both normal and abnormal beats. It is this phenomenon, which makes it difficult to analyse ECG signals. The aim of developing methodology is to distinguish between normal beats and abnormal beats in an ECG signal. Methods : ECG signals were first decomposed using wavelet transform. The feature vectors were then extracted from these decomposed signals as normalised energy and entropy. To improve the classification of the feature vectors of normal and abnormal beats, the normal beats which occur before and after the abnormal beats were eliminated from the group of normal beats. Results : With our proposed methods, the normal beats and abnormal beats formed different clusters of vector points. By eliminating normal beats which occur before and after the abnormal beats, the clusters of different types of beats showed more apparent separation. Conclusions : The combination of wavelet decomposition and the classification using feature vectors of the beats in ECG signals separate abnormal beats from normal beats. The elimination of the normal beats which occur before and after the abnormal beats succeeded in minimising the size of normal beats cluster.

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.

Diagnostic System of Gravitational Solid Flow Based on Weight and Accelerometer Signal Analysis Using Wireless Data Transmission Technology

2012 ◽  
Vol 17 (4) ◽  
pp. 319-326 ◽  
Author(s):  
Zbigniew Chaniecki ◽  
Krzysztof Grudzień ◽  
Tomasz Jaworski ◽  
Grzegorz Rybak ◽  
Andrzej Romanowski ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Scale Up ◽  
Three Dimensional ◽  
Diagnostic System ◽  
Wireless Data ◽  
Stick Slip ◽  
Wireless Data Transmission ◽  
Accelerometer Signal ◽  
Flow Investigation ◽  
Material Information

Abstract The paper presents results of the scale-up silo flow investigation in based on accelerometer signal analysis and Wi-Fi transmission, performed in distributed laboratory environment. Prepared, by the authors, a set of 8 accelerometers allows to measure a three-dimensional acceleration vector. The accelerometers were located outside silo, on its perimeter. The accelerometers signal changes allowed to analyze dynamic behavior of solid (vibrations/pulsations) at silo wall during discharging process. These dynamic effects are caused by stick-slip friction between the wall and the granular material. Information about the material pulsations and vibrations is crucial for monitoring the interaction between silo construction and particle during flow. Additionally such spatial position of accelerometers sensor allowed to collect information about nonsymmetrical flow inside silo.

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