scholarly journals Radio Station Background Noise Detection Based on Time-Frequency Domain Electromagnetic Spectrum

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fuzhai Wang ◽  
Zhenjia Chen ◽  
Xuanfeng Chen ◽  
Ting Chen
Keyword(s):  
Frequency Domain ◽  
Frequency Spectrum ◽  
Frequency Band ◽  
Background Noise ◽  
Radio Station ◽  
Electromagnetic Spectrum ◽  
Target Frequency ◽  
Time Frequency ◽  
Effective Signal ◽  
Spectrum Data

The electromagnetic spectrum resource is one of the important national resources. It is a physical channel for wireless communication between ships and between ships and radio stations. Good communication quality must be guaranteed, so it is urgent to monitor and analyze the environmental background noise of the electromagnetic spectrum. The estimation of the radio frequency signal coverage in the target area during the monitoring process is of great significance to the study of electromagnetic spectrum resource management and control. This paper estimates the upper envelope and lower envelope of the background noise of the target frequency band based on the electromagnetic spectrum data in the time-frequency domain and combines the forward difference algorithm to estimate the background noise envelope curve. We set up fixed detection nodes and mobile detection nodes for specific construction areas and collect time-frequency spectrum data of electromagnetic spectrum in multiple locations. The instantaneous frequency spectrum and the collected data of a specific frequency point are compared, and it is difficult to judge whether there is a valid signal. This paper is based on the time-frequency domain electromagnetic spectrum data in the construction area of the project and estimates the background noise of the coast station frequency band in the current environment. It is based on the energy gradient estimation of the time-frequency domain spectrum, and the effective signal of the target frequency band is obtained and combines the noise envelope and the effective signal location to improve the estimation result of the background noise envelope. The experimental results show that the background noise estimation algorithm can reflect the changes in the noise floor of different target frequency bands.

Design of Adaptive Filter for Health Monitoring on a Gearbox

2006 ◽  
Vol 321-323 ◽  
pp. 1237-1240
Author(s):  
Sang Kwon Lee ◽  
Jung Soo Lee
Keyword(s):  
Signal Processing ◽  
Frequency Domain ◽  
Adaptive Filter ◽  
Health Monitoring ◽  
Background Noise ◽  
Adaptive Signal Processing ◽  
Vibration Signals ◽  
Time Frequency ◽  
Frequency Representation ◽  
Adaptive Signal

Impulsive vibration signals in gearbox are often associated with faults, which lead to due to irregular impacting. Thus these impulsive vibration signals can be used as indicators of machinery faults. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the measurement of impulsive signal embedded in background noise, we enhance the impulsive signals using adaptive signal processing and then analyze them in time and frequency domain by using time-frequency representation. This technique is applied to the diagnosis of faults within laboratory gearbox.

scholarly journals An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis

2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Sinus Rhythm ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Time Frequency ◽  
Electrocardiogram Signals ◽  
Cart Algorithm

Abstract Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable frequency feature - the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.

scholarly journals An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis

2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Sinus Rhythm ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Time Frequency ◽  
Electrocardiogram Signals ◽  
Cart Algorithm

Abstract Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable frequency feature - the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.

scholarly journals An effective frequency-domain feature of atrial fibrillation based on time–frequency analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Sinus Rhythm ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Time Frequency ◽  
Electrocardiogram Signals ◽  
Cart Algorithm

Abstract Background Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods This paper proposed a high distinguishable frequency feature—the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R–R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features—the maximum amplitude in the frequency spectrum and R–R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.

scholarly journals An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis

2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Sinus Rhythm ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Time Frequency ◽  
Electrocardiogram Signals ◽  
Cart Algorithm

Abstract Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable frequency feature - the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.

scholarly journals Denoising Method Based on Spectral Subtraction in Time-Frequency Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lei Hao ◽  
Shuai Cao ◽  
Pengfei Zhou ◽  
Lei Chen ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Frequency Spectrum ◽  
Seismic Data ◽  
Random Noise ◽  
Seismic Exploration ◽  
Denoising Method ◽  
Time Frequency ◽  
Interpretation Errors ◽  
Effective Signal ◽  
Voice Data ◽  
Spectrum Selection

In view of the key problem that a large amount of noise in seismic data can easily induce false anomalies and interpretation errors in seismic exploration, the time-frequency spectrum subtraction (TF-SS) method is adopted into data processing to reduce random noise in seismic data. On this basis, the main frequency information of seismic data is calculated and used to optimize the filtering coefficients. According to the characteristics of effective signal duration between seismic data and voice data, the time-frequency spectrum selection method and filtering coefficient are modified. In addition, simulation tests were conducted by using different S/R, which indicates the effectiveness of the TF-SS in removing the random noise.

scholarly journals Characterization of Frequency Domains of Bandlimited Frame Multiresolution Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1050
Author(s):  
Zhihua Zhang
Keyword(s):  
Frequency Domain ◽  
Multiresolution Analysis ◽  
Background Noise ◽  
Sparse Reconstruction ◽  
Time Frequency ◽  
Processing Data ◽  
Narrowband Signal ◽  
Frequency Domains ◽  
Frame Multiresolution Analysis

Framelets have been widely used in narrowband signal processing, data analysis, and sampling theory, due to their resilience to background noise, stability of sparse reconstruction, and ability to capture local time-frequency information. The well-known approach to construct framelets with useful properties is through frame multiresolution analysis (FMRA). In this article, we characterize the frequency domain of bandlimited FMRAs: there exists a bandlimited FMRA with the support of frequency domain G if and only if G satisfies G⊂2G, ⋃m2mG≅Rd, and G\G2⋂G2+2πν≅∅(ν∈Zd).

scholarly journals An effective frequency domain feature of atrial fibrillation based on time-frequency analysis

2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Sampling Rate ◽  
Maximum Amplitude ◽  
Decision Tree Algorithm ◽  
Transform Method ◽  
Tree Algorithm ◽  
Time Frequency

Abstract Background: Atrial fibrillation(AF) is a kind of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram (ECG) has great importance on further treatment. The practical ECG signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the AF signal features extracted by algorithm. But some of the discovered AF features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable atrial fibrillation feature - the frequency corresponding to the maximum amplitude in the frequency spectrum (MAiFS). We used the R-R interval detection method optimized with mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the MAiFS and R-R interval irregular, we can recognize AF in ECG signal by decision tree classification algorithm. Results: The data used in the experiment comes from the MIT-BIH database [16] , which is publicly accessible via the web and with ethics approval and consent. The dataset contains 23 annotated ECG records, each of which is approximately 10 hours with a sampling rate of 250Hz and a 12-bit resolution with a range of 10mv. Based on the input of time-domain and frequency-domain features, a supervised classifier is constructed by using decision tree algorithm, and the data obtained from the above experiments are brought in to carry out a 5-fold cross validation test, the accuracy of classification reaches 98.9%. Conclusions: The frequency corresponding to the maximum amplitude in frequency spectrum in the normal signal is concentrated and the fluctuation is weak. But the frequency corresponding to the maximum amplitude in frequency spectrum in the atrial fibrillation signal is divergent and irregular. The decision tree algorithm can detect the normal signal and AF signal with 98.9% accuracy.

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