A hybrid evolutionary approach to segmentation of non-stationary signals
Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary si...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Azami, Hamed [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013transfer abstract |
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| Schlagwörter: |
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| Umfang: |
12 |
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| Übergeordnetes Werk: |
Enthalten in: Modelling SARS-CoV-2 transmission in a UK university setting - Hill, Edward M. ELSEVIER, 2021, a review journal, Orlando, Fla |
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| Übergeordnetes Werk: |
volume:23 ; year:2013 ; number:4 ; pages:1103-1114 ; extent:12 |
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| DOI / URN: |
10.1016/j.dsp.2013.02.019 |
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| 520 | |a Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. | ||
| 520 | |a Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. | ||
| 650 | 7 | |a Adaptive segmentation |2 Elsevier | |
| 650 | 7 | |a Fractal dimension |2 Elsevier | |
| 650 | 7 | |a Genetic algorithm |2 Elsevier | |
| 650 | 7 | |a Kalman filter |2 Elsevier | |
| 650 | 7 | |a Evolutionary algorithm |2 Elsevier | |
| 650 | 7 | |a Non-stationary signal |2 Elsevier | |
| 650 | 7 | |a Imperialist competitive algorithm |2 Elsevier | |
| 700 | 1 | |a Sanei, Saeid |4 oth | |
| 700 | 1 | |a Mohammadi, Karim |4 oth | |
| 700 | 1 | |a Hassanpour, Hamid |4 oth | |
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10.1016/j.dsp.2013.02.019 doi GBVA2013017000007.pica (DE-627)ELV017034779 (ELSEVIER)S1051-2004(13)00064-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.75 bkl Azami, Hamed verfasserin aut A hybrid evolutionary approach to segmentation of non-stationary signals 2013transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. Adaptive segmentation Elsevier Fractal dimension Elsevier Genetic algorithm Elsevier Kalman filter Elsevier Evolutionary algorithm Elsevier Non-stationary signal Elsevier Imperialist competitive algorithm Elsevier Sanei, Saeid oth Mohammadi, Karim oth Hassanpour, Hamid oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:23 year:2013 number:4 pages:1103-1114 extent:12 https://doi.org/10.1016/j.dsp.2013.02.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 23 2013 4 1103-1114 12 045F 620 |
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10.1016/j.dsp.2013.02.019 doi GBVA2013017000007.pica (DE-627)ELV017034779 (ELSEVIER)S1051-2004(13)00064-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.75 bkl Azami, Hamed verfasserin aut A hybrid evolutionary approach to segmentation of non-stationary signals 2013transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. Adaptive segmentation Elsevier Fractal dimension Elsevier Genetic algorithm Elsevier Kalman filter Elsevier Evolutionary algorithm Elsevier Non-stationary signal Elsevier Imperialist competitive algorithm Elsevier Sanei, Saeid oth Mohammadi, Karim oth Hassanpour, Hamid oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:23 year:2013 number:4 pages:1103-1114 extent:12 https://doi.org/10.1016/j.dsp.2013.02.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 23 2013 4 1103-1114 12 045F 620 |
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10.1016/j.dsp.2013.02.019 doi GBVA2013017000007.pica (DE-627)ELV017034779 (ELSEVIER)S1051-2004(13)00064-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.75 bkl Azami, Hamed verfasserin aut A hybrid evolutionary approach to segmentation of non-stationary signals 2013transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. Adaptive segmentation Elsevier Fractal dimension Elsevier Genetic algorithm Elsevier Kalman filter Elsevier Evolutionary algorithm Elsevier Non-stationary signal Elsevier Imperialist competitive algorithm Elsevier Sanei, Saeid oth Mohammadi, Karim oth Hassanpour, Hamid oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:23 year:2013 number:4 pages:1103-1114 extent:12 https://doi.org/10.1016/j.dsp.2013.02.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 23 2013 4 1103-1114 12 045F 620 |
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10.1016/j.dsp.2013.02.019 doi GBVA2013017000007.pica (DE-627)ELV017034779 (ELSEVIER)S1051-2004(13)00064-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.75 bkl Azami, Hamed verfasserin aut A hybrid evolutionary approach to segmentation of non-stationary signals 2013transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. Adaptive segmentation Elsevier Fractal dimension Elsevier Genetic algorithm Elsevier Kalman filter Elsevier Evolutionary algorithm Elsevier Non-stationary signal Elsevier Imperialist competitive algorithm Elsevier Sanei, Saeid oth Mohammadi, Karim oth Hassanpour, Hamid oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:23 year:2013 number:4 pages:1103-1114 extent:12 https://doi.org/10.1016/j.dsp.2013.02.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 23 2013 4 1103-1114 12 045F 620 |
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10.1016/j.dsp.2013.02.019 doi GBVA2013017000007.pica (DE-627)ELV017034779 (ELSEVIER)S1051-2004(13)00064-X DE-627 ger DE-627 rakwb eng 620 620 DE-600 610 VZ 44.75 bkl Azami, Hamed verfasserin aut A hybrid evolutionary approach to segmentation of non-stationary signals 2013transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. Adaptive segmentation Elsevier Fractal dimension Elsevier Genetic algorithm Elsevier Kalman filter Elsevier Evolutionary algorithm Elsevier Non-stationary signal Elsevier Imperialist competitive algorithm Elsevier Sanei, Saeid oth Mohammadi, Karim oth Hassanpour, Hamid oth Enthalten in Academic Press Hill, Edward M. ELSEVIER Modelling SARS-CoV-2 transmission in a UK university setting 2021 a review journal Orlando, Fla (DE-627)ELV006540295 volume:23 year:2013 number:4 pages:1103-1114 extent:12 https://doi.org/10.1016/j.dsp.2013.02.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.75 Infektionskrankheiten parasitäre Krankheiten Medizin VZ AR 23 2013 4 1103-1114 12 045F 620 |
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Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. |
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Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. |
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Automatic segmentation of non-stationary signals such as electroencephalogram (EEG), electrocardiogram (ECG) and brightness of galactic objects has many applications. In this paper an improved segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals is proposed. After using Kalman filter (KF) to reduce existing noises, FD which can detect the changes in both the amplitude and frequency of the signal is applied to reveal segments of the signal. In order to select two acceptable parameters of FD, in this paper two authoritative EAs, namely, genetic algorithm (GA) and imperialist competitive algorithm (ICA) are used. The proposed approach is applied to synthetic multi-component signals, real EEG data, and brightness changes of galactic objects. The proposed methods are compared with some well-known existing algorithms such as improved nonlinear energy operator (INLEO), Varriʼs and wavelet generalized likelihood ratio (WGLR) methods. The simulation results demonstrate that segmentation by using KF, FD, and EAs have greater accuracy which proves the significance of this algorithm. |
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