Hilbert-Huang transform and its applications:
Saved in:
| Other Authors: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New Jersey
World Scientific
[2014]
|
| Edition: | 2nd edition |
| Series: | Interdisciplinary mathematical sciences
16 |
| Subjects: | |
| Online Access: | FAW01 FAW02 Volltext |
| Item Description: | Description based on print version record |
| Physical Description: | 1 online resource (xii, 386 pages) illustrations |
| ISBN: | 9789814508230 9789814508247 9814508233 9814508241 |
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| 245 | 1 | 0 | |a Hilbert-Huang transform and its applications |c editors, Norden E. Huang, National Central University, Taiwan, Samuel S.P. Shen, San Diego State University, USA. |
| 250 | |a 2nd edition | ||
| 264 | 1 | |a New Jersey |b World Scientific |c [2014] | |
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| 490 | 0 | |a Interdisciplinary mathematical sciences |v 16 | |
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| 505 | 8 | |a This book is written for scientists and engineers who use HHT (Hilbert-Huang Transform) to analyze data from nonlinear and non-stationary processes. It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and various application examples, ranging from the correction of satellite orbit drifting to detection of failure of highway bridges. The thirteen chapters of the first edition are based on the presentations made at a mini-symposium at the Society for Industrial and Applied Mathematics in 2003. Some outstanding mathematical research problems regarding HHT development are discussed in the first three chapters. The three new chapters of the second edition reflect the latest HHT development, including ensemble empirical mode decomposition (EEMD) and modified EMD. The book also provides a platform for researchers to develop the HHT method further and to identify more applications | |
| 505 | 8 | |a Ch. 1. Introduction to the Hilbert-Huang transform and its related mathematical problems. 1.1. Introduction. 1.2. The Hilbert-Huang transform. 1.3. Recent developments. 1.4. Mathematical problems related to the HHT. 1.5. Conclusion -- ch. 2. Ensemble empirical mode decomposition and its multi-dimensional extensions. 2.1. Introduction. 2.2. The empirical mode decomposition. 2.3. The ensemble empirical mode decomposition. 2.4. The multi-dimensional ensemble empirical mode decomposition. 2.5. Summary and discussions -- ch. 3. Multivariate extensions of empirical mode decomposition. 3.1. Introduction. 3.2. Multivariate extensions of EMD. 3.3. Mode-alignment property of MEMD. 3.4. Filter bank property of MEMD and noise-assisted MEMD. 3.5. Applications. 3.6. Discussion and conclusions -- | |
| 505 | 8 | |a ch. 4. B-spine based empirical mode decomposition. 4.1. Introduction. 4.2. A B-spline algorithm for empirical mode decomposition. 4.3. Some related mathematical results. 4.4. Performance analysis of BS-EMD. 4.5. Application examples. 4.6. Conclusion and future research topics -- ch. 5. EMD equivalent filter banks, from interpretation to applications. 5.1. Introduction. 5.2. A stochastic perspective in the frequency domain. 5.3. A deterministic perspective in the time domain. 5.4. Selected applications. 5.5. Concluding remarks -- ch. 6. HHT sifting and filtering. 6.1. Introduction. 6.2. Objectives of HHT sifting. 6.3. Huang's sifting algorithm. 6.4. Incremental, real-time HHT sifting. 6.5. Filtering in standard time. 6.6. Case studies. 6.7. Summary and conclusions -- | |
| 505 | 8 | |a ch. 7. Statistical significance test of intrinsic mode functions. 7.1. Introduction. 7.2. Characteristics of Gaussian white noise in EMD. 7.3. Spread functions ofmean energy density. 7.4. Examples of a statistical significance test of noisy data. 7.5. Summary and discussion -- ch. 8. The time-dependent intrinsic correlation. 8.1. Introduction. 8.2. Limitations of correlation coefficient analysis. 8.3. TDIC based on EMD. 8.4. Applications of TDIC for geophysical data. 8.5. Summary and conclusions -- ch. 9. The application of Hilbert-Huang transforms to meteorological datasets. 9.1. Introduction. 9.2. Procedure. 9.3. Applications. 9.4. Conclusion -- ch. 10. Empirical mode decomposition and climate variability. 10.1. Introduction. 10.2. Data. 10.3. Methodology. 10.4. Statistical tests of confidence. 10.5. Results and physical interpretations. 10.6. Conclusions -- | |
| 505 | 8 | |a ch. 11. EMD correction of orbital drift artifacts in satellite data stream. 11.1. Introduction. 11.2. Processing of NDVI imagery. 11.3. Empiricalmode decomposition. 11.4. Impact of orbital drift on NDVI and EMD-SZA filtering. 11.5. Results and discussion. 11.6. Extension to 8-km data. 11.7. Integration of NOAA-16 data. 11.8. Conclusions -- ch. 12. HHT analysis of the nonlinear and non-stationary annual cycle of daily surface air temperature data. 12.1. Introduction. 12.2. Analysis method and computational algorithms. 12.3. Data. 12.4. Time analysis. 12.5. Frequency analysis. 12.6. Conclusions and discussion -- ch. 13. Hilbert spectra of nonlinear ocean waves. 13.1. Introduction. 13.2. The Hilbert-Huang spectral analysis. 13.3. Spectrum of wind-generated waves. 13.4. Statistical properties and group structure. 13.5. Summary -- | |
| 505 | 8 | |a ch. 14. EMD and instantaneous phase detection of structural damage. 14.1. Introduction to structural health monitoring. 14.2. Instantaneous phase and EMD. 14.3. Damage detection application. 14.4. Frame structure with multiple damage. 14.5. Summary and conclusions -- ch. 15. HHT-based bridge structural health-monitoring method. 15.1. Introduction. 15.2. A review of the present state-of-the-art methods. 15.3. The Hilbert-Huang transform. 15.4. Damage-detection criteria. 15.5. Case study of damage detection. 15.6. Conclusions -- ch. 16. Applications of HHT in image analysis. 16.1. Introduction. 16.2. Overview. 16.3. The analysis of digital slope images. 16.4. Summary | |
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| 650 | 7 | |a MATHEMATICS / Mathematical Analysis |2 bisacsh | |
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| 650 | 4 | |a Hilbert-Huang transform | |
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| contents | This book is written for scientists and engineers who use HHT (Hilbert-Huang Transform) to analyze data from nonlinear and non-stationary processes. It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and various application examples, ranging from the correction of satellite orbit drifting to detection of failure of highway bridges. The thirteen chapters of the first edition are based on the presentations made at a mini-symposium at the Society for Industrial and Applied Mathematics in 2003. Some outstanding mathematical research problems regarding HHT development are discussed in the first three chapters. The three new chapters of the second edition reflect the latest HHT development, including ensemble empirical mode decomposition (EEMD) and modified EMD. The book also provides a platform for researchers to develop the HHT method further and to identify more applications Ch. 1. Introduction to the Hilbert-Huang transform and its related mathematical problems. 1.1. Introduction. 1.2. The Hilbert-Huang transform. 1.3. Recent developments. 1.4. Mathematical problems related to the HHT. 1.5. Conclusion -- ch. 2. Ensemble empirical mode decomposition and its multi-dimensional extensions. 2.1. Introduction. 2.2. The empirical mode decomposition. 2.3. The ensemble empirical mode decomposition. 2.4. The multi-dimensional ensemble empirical mode decomposition. 2.5. Summary and discussions -- ch. 3. Multivariate extensions of empirical mode decomposition. 3.1. Introduction. 3.2. Multivariate extensions of EMD. 3.3. Mode-alignment property of MEMD. 3.4. Filter bank property of MEMD and noise-assisted MEMD. 3.5. Applications. 3.6. Discussion and conclusions -- ch. 4. B-spine based empirical mode decomposition. 4.1. Introduction. 4.2. A B-spline algorithm for empirical mode decomposition. 4.3. Some related mathematical results. 4.4. Performance analysis of BS-EMD. 4.5. Application examples. 4.6. Conclusion and future research topics -- ch. 5. EMD equivalent filter banks, from interpretation to applications. 5.1. Introduction. 5.2. A stochastic perspective in the frequency domain. 5.3. A deterministic perspective in the time domain. 5.4. Selected applications. 5.5. Concluding remarks -- ch. 6. HHT sifting and filtering. 6.1. Introduction. 6.2. Objectives of HHT sifting. 6.3. Huang's sifting algorithm. 6.4. Incremental, real-time HHT sifting. 6.5. Filtering in standard time. 6.6. Case studies. 6.7. Summary and conclusions -- ch. 7. Statistical significance test of intrinsic mode functions. 7.1. Introduction. 7.2. Characteristics of Gaussian white noise in EMD. 7.3. Spread functions ofmean energy density. 7.4. Examples of a statistical significance test of noisy data. 7.5. Summary and discussion -- ch. 8. The time-dependent intrinsic correlation. 8.1. Introduction. 8.2. Limitations of correlation coefficient analysis. 8.3. TDIC based on EMD. 8.4. Applications of TDIC for geophysical data. 8.5. Summary and conclusions -- ch. 9. The application of Hilbert-Huang transforms to meteorological datasets. 9.1. Introduction. 9.2. Procedure. 9.3. Applications. 9.4. Conclusion -- ch. 10. Empirical mode decomposition and climate variability. 10.1. Introduction. 10.2. Data. 10.3. Methodology. 10.4. Statistical tests of confidence. 10.5. Results and physical interpretations. 10.6. Conclusions -- ch. 11. EMD correction of orbital drift artifacts in satellite data stream. 11.1. Introduction. 11.2. Processing of NDVI imagery. 11.3. Empiricalmode decomposition. 11.4. Impact of orbital drift on NDVI and EMD-SZA filtering. 11.5. Results and discussion. 11.6. Extension to 8-km data. 11.7. Integration of NOAA-16 data. 11.8. Conclusions -- ch. 12. HHT analysis of the nonlinear and non-stationary annual cycle of daily surface air temperature data. 12.1. Introduction. 12.2. Analysis method and computational algorithms. 12.3. Data. 12.4. Time analysis. 12.5. Frequency analysis. 12.6. Conclusions and discussion -- ch. 13. Hilbert spectra of nonlinear ocean waves. 13.1. Introduction. 13.2. The Hilbert-Huang spectral analysis. 13.3. Spectrum of wind-generated waves. 13.4. Statistical properties and group structure. 13.5. Summary -- ch. 14. EMD and instantaneous phase detection of structural damage. 14.1. Introduction to structural health monitoring. 14.2. Instantaneous phase and EMD. 14.3. Damage detection application. 14.4. Frame structure with multiple damage. 14.5. Summary and conclusions -- ch. 15. HHT-based bridge structural health-monitoring method. 15.1. Introduction. 15.2. A review of the present state-of-the-art methods. 15.3. The Hilbert-Huang transform. 15.4. Damage-detection criteria. 15.5. Case study of damage detection. 15.6. Conclusions -- ch. 16. Applications of HHT in image analysis. 16.1. Introduction. 16.2. Overview. 16.3. The analysis of digital slope images. 16.4. Summary |
| ctrlnum | (OCoLC)881416737 (DE-599)BVBBV043026879 |
| dewey-full | 515.723 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 515 - Analysis |
| dewey-raw | 515.723 |
| dewey-search | 515.723 |
| dewey-sort | 3515.723 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| edition | 2nd edition |
| format | Electronic eBook |
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It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and various application examples, ranging from the correction of satellite orbit drifting to detection of failure of highway bridges. The thirteen chapters of the first edition are based on the presentations made at a mini-symposium at the Society for Industrial and Applied Mathematics in 2003. Some outstanding mathematical research problems regarding HHT development are discussed in the first three chapters. The three new chapters of the second edition reflect the latest HHT development, including ensemble empirical mode decomposition (EEMD) and modified EMD. The book also provides a platform for researchers to develop the HHT method further and to identify more applications</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Ch. 1. Introduction to the Hilbert-Huang transform and its related mathematical problems. 1.1. Introduction. 1.2. The Hilbert-Huang transform. 1.3. Recent developments. 1.4. Mathematical problems related to the HHT. 1.5. Conclusion -- ch. 2. Ensemble empirical mode decomposition and its multi-dimensional extensions. 2.1. Introduction. 2.2. The empirical mode decomposition. 2.3. The ensemble empirical mode decomposition. 2.4. The multi-dimensional ensemble empirical mode decomposition. 2.5. Summary and discussions -- ch. 3. Multivariate extensions of empirical mode decomposition. 3.1. Introduction. 3.2. Multivariate extensions of EMD. 3.3. Mode-alignment property of MEMD. 3.4. Filter bank property of MEMD and noise-assisted MEMD. 3.5. Applications. 3.6. Discussion and conclusions -- </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">ch. 4. B-spine based empirical mode decomposition. 4.1. Introduction. 4.2. A B-spline algorithm for empirical mode decomposition. 4.3. Some related mathematical results. 4.4. Performance analysis of BS-EMD. 4.5. Application examples. 4.6. Conclusion and future research topics -- ch. 5. EMD equivalent filter banks, from interpretation to applications. 5.1. Introduction. 5.2. A stochastic perspective in the frequency domain. 5.3. A deterministic perspective in the time domain. 5.4. Selected applications. 5.5. Concluding remarks -- ch. 6. HHT sifting and filtering. 6.1. Introduction. 6.2. Objectives of HHT sifting. 6.3. Huang's sifting algorithm. 6.4. Incremental, real-time HHT sifting. 6.5. Filtering in standard time. 6.6. Case studies. 6.7. Summary and conclusions -- </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">ch. 7. Statistical significance test of intrinsic mode functions. 7.1. Introduction. 7.2. Characteristics of Gaussian white noise in EMD. 7.3. Spread functions ofmean energy density. 7.4. Examples of a statistical significance test of noisy data. 7.5. Summary and discussion -- ch. 8. The time-dependent intrinsic correlation. 8.1. Introduction. 8.2. Limitations of correlation coefficient analysis. 8.3. TDIC based on EMD. 8.4. Applications of TDIC for geophysical data. 8.5. Summary and conclusions -- ch. 9. The application of Hilbert-Huang transforms to meteorological datasets. 9.1. Introduction. 9.2. Procedure. 9.3. Applications. 9.4. Conclusion -- ch. 10. Empirical mode decomposition and climate variability. 10.1. Introduction. 10.2. Data. 10.3. Methodology. 10.4. Statistical tests of confidence. 10.5. Results and physical interpretations. 10.6. Conclusions -- </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">ch. 11. EMD correction of orbital drift artifacts in satellite data stream. 11.1. Introduction. 11.2. Processing of NDVI imagery. 11.3. Empiricalmode decomposition. 11.4. Impact of orbital drift on NDVI and EMD-SZA filtering. 11.5. Results and discussion. 11.6. Extension to 8-km data. 11.7. Integration of NOAA-16 data. 11.8. Conclusions -- ch. 12. HHT analysis of the nonlinear and non-stationary annual cycle of daily surface air temperature data. 12.1. Introduction. 12.2. Analysis method and computational algorithms. 12.3. Data. 12.4. Time analysis. 12.5. Frequency analysis. 12.6. Conclusions and discussion -- ch. 13. Hilbert spectra of nonlinear ocean waves. 13.1. Introduction. 13.2. The Hilbert-Huang spectral analysis. 13.3. Spectrum of wind-generated waves. 13.4. Statistical properties and group structure. 13.5. Summary -- </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">ch. 14. EMD and instantaneous phase detection of structural damage. 14.1. Introduction to structural health monitoring. 14.2. Instantaneous phase and EMD. 14.3. Damage detection application. 14.4. Frame structure with multiple damage. 14.5. Summary and conclusions -- ch. 15. 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| id | DE-604.BV043026879 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T07:15:22Z |
| institution | BVB |
| isbn | 9789814508230 9789814508247 9814508233 9814508241 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028451533 |
| oclc_num | 881416737 |
| open_access_boolean | |
| owner | DE-1046 DE-1047 |
| owner_facet | DE-1046 DE-1047 |
| physical | 1 online resource (xii, 386 pages) illustrations |
| psigel | ZDB-4-EBA ZDB-4-EBA FAW_PDA_EBA |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | World Scientific |
| record_format | marc |
| series2 | Interdisciplinary mathematical sciences |
| spelling | Hilbert-Huang transform and its applications editors, Norden E. Huang, National Central University, Taiwan, Samuel S.P. Shen, San Diego State University, USA. 2nd edition New Jersey World Scientific [2014] © 2014 1 online resource (xii, 386 pages) illustrations txt rdacontent c rdamedia cr rdacarrier Interdisciplinary mathematical sciences 16 Description based on print version record This book is written for scientists and engineers who use HHT (Hilbert-Huang Transform) to analyze data from nonlinear and non-stationary processes. It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and various application examples, ranging from the correction of satellite orbit drifting to detection of failure of highway bridges. The thirteen chapters of the first edition are based on the presentations made at a mini-symposium at the Society for Industrial and Applied Mathematics in 2003. Some outstanding mathematical research problems regarding HHT development are discussed in the first three chapters. The three new chapters of the second edition reflect the latest HHT development, including ensemble empirical mode decomposition (EEMD) and modified EMD. The book also provides a platform for researchers to develop the HHT method further and to identify more applications Ch. 1. Introduction to the Hilbert-Huang transform and its related mathematical problems. 1.1. Introduction. 1.2. The Hilbert-Huang transform. 1.3. Recent developments. 1.4. Mathematical problems related to the HHT. 1.5. Conclusion -- ch. 2. Ensemble empirical mode decomposition and its multi-dimensional extensions. 2.1. Introduction. 2.2. The empirical mode decomposition. 2.3. The ensemble empirical mode decomposition. 2.4. The multi-dimensional ensemble empirical mode decomposition. 2.5. Summary and discussions -- ch. 3. Multivariate extensions of empirical mode decomposition. 3.1. Introduction. 3.2. Multivariate extensions of EMD. 3.3. Mode-alignment property of MEMD. 3.4. Filter bank property of MEMD and noise-assisted MEMD. 3.5. Applications. 3.6. Discussion and conclusions -- ch. 4. B-spine based empirical mode decomposition. 4.1. Introduction. 4.2. A B-spline algorithm for empirical mode decomposition. 4.3. Some related mathematical results. 4.4. Performance analysis of BS-EMD. 4.5. Application examples. 4.6. Conclusion and future research topics -- ch. 5. EMD equivalent filter banks, from interpretation to applications. 5.1. Introduction. 5.2. A stochastic perspective in the frequency domain. 5.3. A deterministic perspective in the time domain. 5.4. Selected applications. 5.5. Concluding remarks -- ch. 6. HHT sifting and filtering. 6.1. Introduction. 6.2. Objectives of HHT sifting. 6.3. Huang's sifting algorithm. 6.4. Incremental, real-time HHT sifting. 6.5. Filtering in standard time. 6.6. Case studies. 6.7. Summary and conclusions -- ch. 7. Statistical significance test of intrinsic mode functions. 7.1. Introduction. 7.2. Characteristics of Gaussian white noise in EMD. 7.3. Spread functions ofmean energy density. 7.4. Examples of a statistical significance test of noisy data. 7.5. Summary and discussion -- ch. 8. The time-dependent intrinsic correlation. 8.1. Introduction. 8.2. Limitations of correlation coefficient analysis. 8.3. TDIC based on EMD. 8.4. Applications of TDIC for geophysical data. 8.5. Summary and conclusions -- ch. 9. The application of Hilbert-Huang transforms to meteorological datasets. 9.1. Introduction. 9.2. Procedure. 9.3. Applications. 9.4. Conclusion -- ch. 10. Empirical mode decomposition and climate variability. 10.1. Introduction. 10.2. Data. 10.3. Methodology. 10.4. Statistical tests of confidence. 10.5. Results and physical interpretations. 10.6. Conclusions -- ch. 11. EMD correction of orbital drift artifacts in satellite data stream. 11.1. Introduction. 11.2. Processing of NDVI imagery. 11.3. Empiricalmode decomposition. 11.4. Impact of orbital drift on NDVI and EMD-SZA filtering. 11.5. Results and discussion. 11.6. Extension to 8-km data. 11.7. Integration of NOAA-16 data. 11.8. Conclusions -- ch. 12. HHT analysis of the nonlinear and non-stationary annual cycle of daily surface air temperature data. 12.1. Introduction. 12.2. Analysis method and computational algorithms. 12.3. Data. 12.4. Time analysis. 12.5. Frequency analysis. 12.6. Conclusions and discussion -- ch. 13. Hilbert spectra of nonlinear ocean waves. 13.1. Introduction. 13.2. The Hilbert-Huang spectral analysis. 13.3. Spectrum of wind-generated waves. 13.4. Statistical properties and group structure. 13.5. Summary -- ch. 14. EMD and instantaneous phase detection of structural damage. 14.1. Introduction to structural health monitoring. 14.2. Instantaneous phase and EMD. 14.3. Damage detection application. 14.4. Frame structure with multiple damage. 14.5. Summary and conclusions -- ch. 15. HHT-based bridge structural health-monitoring method. 15.1. Introduction. 15.2. A review of the present state-of-the-art methods. 15.3. The Hilbert-Huang transform. 15.4. Damage-detection criteria. 15.5. Case study of damage detection. 15.6. Conclusions -- ch. 16. Applications of HHT in image analysis. 16.1. Introduction. 16.2. Overview. 16.3. The analysis of digital slope images. 16.4. Summary MATHEMATICS / Calculus bisacsh MATHEMATICS / Mathematical Analysis bisacsh Decomposition (Mathematics) fast Hilbert-Huang transform fast Hilbert-Huang transform Decomposition (Mathematics) Prozess (DE-588)4047577-3 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Hilbert-Transformation (DE-588)4375311-5 gnd rswk-swf Hilbert-Transformation (DE-588)4375311-5 s Prozess (DE-588)4047577-3 s Datenanalyse (DE-588)4123037-1 s 1\p DE-604 Huang, Norden E. Sonstige oth Shen, Samuel S. edt Erscheint auch als Druck-Ausgabe Hilbert-Huang transform and its applications http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=787894 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hilbert-Huang transform and its applications This book is written for scientists and engineers who use HHT (Hilbert-Huang Transform) to analyze data from nonlinear and non-stationary processes. It can be treated as a HHT user manual and a source of reference for HHT applications. The book contains the basic principle and method of HHT and various application examples, ranging from the correction of satellite orbit drifting to detection of failure of highway bridges. The thirteen chapters of the first edition are based on the presentations made at a mini-symposium at the Society for Industrial and Applied Mathematics in 2003. Some outstanding mathematical research problems regarding HHT development are discussed in the first three chapters. The three new chapters of the second edition reflect the latest HHT development, including ensemble empirical mode decomposition (EEMD) and modified EMD. The book also provides a platform for researchers to develop the HHT method further and to identify more applications Ch. 1. Introduction to the Hilbert-Huang transform and its related mathematical problems. 1.1. Introduction. 1.2. The Hilbert-Huang transform. 1.3. Recent developments. 1.4. Mathematical problems related to the HHT. 1.5. Conclusion -- ch. 2. Ensemble empirical mode decomposition and its multi-dimensional extensions. 2.1. Introduction. 2.2. The empirical mode decomposition. 2.3. The ensemble empirical mode decomposition. 2.4. The multi-dimensional ensemble empirical mode decomposition. 2.5. Summary and discussions -- ch. 3. Multivariate extensions of empirical mode decomposition. 3.1. Introduction. 3.2. Multivariate extensions of EMD. 3.3. Mode-alignment property of MEMD. 3.4. Filter bank property of MEMD and noise-assisted MEMD. 3.5. Applications. 3.6. Discussion and conclusions -- ch. 4. B-spine based empirical mode decomposition. 4.1. Introduction. 4.2. A B-spline algorithm for empirical mode decomposition. 4.3. Some related mathematical results. 4.4. Performance analysis of BS-EMD. 4.5. Application examples. 4.6. Conclusion and future research topics -- ch. 5. EMD equivalent filter banks, from interpretation to applications. 5.1. Introduction. 5.2. A stochastic perspective in the frequency domain. 5.3. A deterministic perspective in the time domain. 5.4. Selected applications. 5.5. Concluding remarks -- ch. 6. HHT sifting and filtering. 6.1. Introduction. 6.2. Objectives of HHT sifting. 6.3. Huang's sifting algorithm. 6.4. Incremental, real-time HHT sifting. 6.5. Filtering in standard time. 6.6. Case studies. 6.7. Summary and conclusions -- ch. 7. Statistical significance test of intrinsic mode functions. 7.1. Introduction. 7.2. Characteristics of Gaussian white noise in EMD. 7.3. Spread functions ofmean energy density. 7.4. Examples of a statistical significance test of noisy data. 7.5. Summary and discussion -- ch. 8. The time-dependent intrinsic correlation. 8.1. Introduction. 8.2. Limitations of correlation coefficient analysis. 8.3. TDIC based on EMD. 8.4. Applications of TDIC for geophysical data. 8.5. Summary and conclusions -- ch. 9. The application of Hilbert-Huang transforms to meteorological datasets. 9.1. Introduction. 9.2. Procedure. 9.3. Applications. 9.4. Conclusion -- ch. 10. Empirical mode decomposition and climate variability. 10.1. Introduction. 10.2. Data. 10.3. Methodology. 10.4. Statistical tests of confidence. 10.5. Results and physical interpretations. 10.6. Conclusions -- ch. 11. EMD correction of orbital drift artifacts in satellite data stream. 11.1. Introduction. 11.2. Processing of NDVI imagery. 11.3. Empiricalmode decomposition. 11.4. Impact of orbital drift on NDVI and EMD-SZA filtering. 11.5. Results and discussion. 11.6. Extension to 8-km data. 11.7. Integration of NOAA-16 data. 11.8. Conclusions -- ch. 12. HHT analysis of the nonlinear and non-stationary annual cycle of daily surface air temperature data. 12.1. Introduction. 12.2. Analysis method and computational algorithms. 12.3. Data. 12.4. Time analysis. 12.5. Frequency analysis. 12.6. Conclusions and discussion -- ch. 13. Hilbert spectra of nonlinear ocean waves. 13.1. Introduction. 13.2. The Hilbert-Huang spectral analysis. 13.3. Spectrum of wind-generated waves. 13.4. Statistical properties and group structure. 13.5. Summary -- ch. 14. EMD and instantaneous phase detection of structural damage. 14.1. Introduction to structural health monitoring. 14.2. Instantaneous phase and EMD. 14.3. Damage detection application. 14.4. Frame structure with multiple damage. 14.5. Summary and conclusions -- ch. 15. HHT-based bridge structural health-monitoring method. 15.1. Introduction. 15.2. A review of the present state-of-the-art methods. 15.3. The Hilbert-Huang transform. 15.4. Damage-detection criteria. 15.5. Case study of damage detection. 15.6. Conclusions -- ch. 16. Applications of HHT in image analysis. 16.1. Introduction. 16.2. Overview. 16.3. The analysis of digital slope images. 16.4. Summary MATHEMATICS / Calculus bisacsh MATHEMATICS / Mathematical Analysis bisacsh Decomposition (Mathematics) fast Hilbert-Huang transform fast Hilbert-Huang transform Decomposition (Mathematics) Prozess (DE-588)4047577-3 gnd Datenanalyse (DE-588)4123037-1 gnd Hilbert-Transformation (DE-588)4375311-5 gnd |
| subject_GND | (DE-588)4047577-3 (DE-588)4123037-1 (DE-588)4375311-5 |
| title | Hilbert-Huang transform and its applications |
| title_auth | Hilbert-Huang transform and its applications |
| title_exact_search | Hilbert-Huang transform and its applications |
| title_full | Hilbert-Huang transform and its applications editors, Norden E. Huang, National Central University, Taiwan, Samuel S.P. Shen, San Diego State University, USA. |
| title_fullStr | Hilbert-Huang transform and its applications editors, Norden E. Huang, National Central University, Taiwan, Samuel S.P. Shen, San Diego State University, USA. |
| title_full_unstemmed | Hilbert-Huang transform and its applications editors, Norden E. Huang, National Central University, Taiwan, Samuel S.P. Shen, San Diego State University, USA. |
| title_short | Hilbert-Huang transform and its applications |
| title_sort | hilbert huang transform and its applications |
| topic | MATHEMATICS / Calculus bisacsh MATHEMATICS / Mathematical Analysis bisacsh Decomposition (Mathematics) fast Hilbert-Huang transform fast Hilbert-Huang transform Decomposition (Mathematics) Prozess (DE-588)4047577-3 gnd Datenanalyse (DE-588)4123037-1 gnd Hilbert-Transformation (DE-588)4375311-5 gnd |
| topic_facet | MATHEMATICS / Calculus MATHEMATICS / Mathematical Analysis Decomposition (Mathematics) Hilbert-Huang transform Prozess Datenanalyse Hilbert-Transformation |
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