Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain
Abstract A sequential data fusion approach via higher dimensional vector spaces is introduced. This is achieved by making use of the representation of directional signals within the field of complex numbers $$C$$. The concept of data fusion is next introduced and the place of the proposed approach w...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mandic, Danilo P. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2006 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2006 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of VLSI signal processing systems for signal, image and video technology - Springer US, 1989, 48(2006), 1-2 vom: 28. Dez., Seite 99-108 |
|---|---|
| Übergeordnetes Werk: |
volume:48 ; year:2006 ; number:1-2 ; day:28 ; month:12 ; pages:99-108 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11265-006-0025-6 |
|---|
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OLC2062088817 |
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| 520 | |a Abstract A sequential data fusion approach via higher dimensional vector spaces is introduced. This is achieved by making use of the representation of directional signals within the field of complex numbers $$C$$. The concept of data fusion is next introduced and the place of the proposed approach within that framework is identified. The benefits of such an approach are illustrated and a range of possible applications is shown. The concept introduced is supported by a real world case study which focuses on simultaneous forecasting of wind speed and direction. The architectures and learning algorithms which support this concept are introduced and their distributed sequential fusion nature is highlighted. | ||
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10.1007/s11265-006-0025-6 doi (DE-627)OLC2062088817 (DE-He213)s11265-006-0025-6-p DE-627 ger DE-627 rakwb eng 620 VZ Mandic, Danilo P. verfasserin aut Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2006 Abstract A sequential data fusion approach via higher dimensional vector spaces is introduced. This is achieved by making use of the representation of directional signals within the field of complex numbers $$C$$. The concept of data fusion is next introduced and the place of the proposed approach within that framework is identified. The benefits of such an approach are illustrated and a range of possible applications is shown. The concept introduced is supported by a real world case study which focuses on simultaneous forecasting of wind speed and direction. The architectures and learning algorithms which support this concept are introduced and their distributed sequential fusion nature is highlighted. data fusion spatio-temporal complex-valued vector spaces complex nonlinearity Goh, Su Lee aut Aihara, Kazuyuki aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer US, 1989 48(2006), 1-2 vom: 28. Dez., Seite 99-108 (DE-627)130761508 (DE-600)1000618-7 (DE-576)02508416X 0922-5773 nnns volume:48 year:2006 number:1-2 day:28 month:12 pages:99-108 https://doi.org/10.1007/s11265-006-0025-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_23 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_4307 GBV_ILN_4319 AR 48 2006 1-2 28 12 99-108 |
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10.1007/s11265-006-0025-6 doi (DE-627)OLC2062088817 (DE-He213)s11265-006-0025-6-p DE-627 ger DE-627 rakwb eng 620 VZ Mandic, Danilo P. verfasserin aut Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2006 Abstract A sequential data fusion approach via higher dimensional vector spaces is introduced. This is achieved by making use of the representation of directional signals within the field of complex numbers $$C$$. The concept of data fusion is next introduced and the place of the proposed approach within that framework is identified. The benefits of such an approach are illustrated and a range of possible applications is shown. The concept introduced is supported by a real world case study which focuses on simultaneous forecasting of wind speed and direction. The architectures and learning algorithms which support this concept are introduced and their distributed sequential fusion nature is highlighted. data fusion spatio-temporal complex-valued vector spaces complex nonlinearity Goh, Su Lee aut Aihara, Kazuyuki aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer US, 1989 48(2006), 1-2 vom: 28. Dez., Seite 99-108 (DE-627)130761508 (DE-600)1000618-7 (DE-576)02508416X 0922-5773 nnns volume:48 year:2006 number:1-2 day:28 month:12 pages:99-108 https://doi.org/10.1007/s11265-006-0025-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_23 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_4307 GBV_ILN_4319 AR 48 2006 1-2 28 12 99-108 |
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10.1007/s11265-006-0025-6 doi (DE-627)OLC2062088817 (DE-He213)s11265-006-0025-6-p DE-627 ger DE-627 rakwb eng 620 VZ Mandic, Danilo P. verfasserin aut Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2006 Abstract A sequential data fusion approach via higher dimensional vector spaces is introduced. This is achieved by making use of the representation of directional signals within the field of complex numbers $$C$$. The concept of data fusion is next introduced and the place of the proposed approach within that framework is identified. The benefits of such an approach are illustrated and a range of possible applications is shown. The concept introduced is supported by a real world case study which focuses on simultaneous forecasting of wind speed and direction. The architectures and learning algorithms which support this concept are introduced and their distributed sequential fusion nature is highlighted. data fusion spatio-temporal complex-valued vector spaces complex nonlinearity Goh, Su Lee aut Aihara, Kazuyuki aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer US, 1989 48(2006), 1-2 vom: 28. Dez., Seite 99-108 (DE-627)130761508 (DE-600)1000618-7 (DE-576)02508416X 0922-5773 nnns volume:48 year:2006 number:1-2 day:28 month:12 pages:99-108 https://doi.org/10.1007/s11265-006-0025-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_23 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_4307 GBV_ILN_4319 AR 48 2006 1-2 28 12 99-108 |
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10.1007/s11265-006-0025-6 doi (DE-627)OLC2062088817 (DE-He213)s11265-006-0025-6-p DE-627 ger DE-627 rakwb eng 620 VZ Mandic, Danilo P. verfasserin aut Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2006 Abstract A sequential data fusion approach via higher dimensional vector spaces is introduced. This is achieved by making use of the representation of directional signals within the field of complex numbers $$C$$. The concept of data fusion is next introduced and the place of the proposed approach within that framework is identified. The benefits of such an approach are illustrated and a range of possible applications is shown. The concept introduced is supported by a real world case study which focuses on simultaneous forecasting of wind speed and direction. The architectures and learning algorithms which support this concept are introduced and their distributed sequential fusion nature is highlighted. data fusion spatio-temporal complex-valued vector spaces complex nonlinearity Goh, Su Lee aut Aihara, Kazuyuki aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer US, 1989 48(2006), 1-2 vom: 28. Dez., Seite 99-108 (DE-627)130761508 (DE-600)1000618-7 (DE-576)02508416X 0922-5773 nnns volume:48 year:2006 number:1-2 day:28 month:12 pages:99-108 https://doi.org/10.1007/s11265-006-0025-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_23 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_4307 GBV_ILN_4319 AR 48 2006 1-2 28 12 99-108 |
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10.1007/s11265-006-0025-6 doi (DE-627)OLC2062088817 (DE-He213)s11265-006-0025-6-p DE-627 ger DE-627 rakwb eng 620 VZ Mandic, Danilo P. verfasserin aut Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain 2006 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2006 Abstract A sequential data fusion approach via higher dimensional vector spaces is introduced. This is achieved by making use of the representation of directional signals within the field of complex numbers $$C$$. The concept of data fusion is next introduced and the place of the proposed approach within that framework is identified. The benefits of such an approach are illustrated and a range of possible applications is shown. The concept introduced is supported by a real world case study which focuses on simultaneous forecasting of wind speed and direction. The architectures and learning algorithms which support this concept are introduced and their distributed sequential fusion nature is highlighted. data fusion spatio-temporal complex-valued vector spaces complex nonlinearity Goh, Su Lee aut Aihara, Kazuyuki aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer US, 1989 48(2006), 1-2 vom: 28. Dez., Seite 99-108 (DE-627)130761508 (DE-600)1000618-7 (DE-576)02508416X 0922-5773 nnns volume:48 year:2006 number:1-2 day:28 month:12 pages:99-108 https://doi.org/10.1007/s11265-006-0025-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_23 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_4307 GBV_ILN_4319 AR 48 2006 1-2 28 12 99-108 |
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Abstract A sequential data fusion approach via higher dimensional vector spaces is introduced. This is achieved by making use of the representation of directional signals within the field of complex numbers $$C$$. The concept of data fusion is next introduced and the place of the proposed approach within that framework is identified. The benefits of such an approach are illustrated and a range of possible applications is shown. The concept introduced is supported by a real world case study which focuses on simultaneous forecasting of wind speed and direction. The architectures and learning algorithms which support this concept are introduced and their distributed sequential fusion nature is highlighted. © Springer Science+Business Media, LLC 2006 |
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Abstract A sequential data fusion approach via higher dimensional vector spaces is introduced. This is achieved by making use of the representation of directional signals within the field of complex numbers $$C$$. The concept of data fusion is next introduced and the place of the proposed approach within that framework is identified. The benefits of such an approach are illustrated and a range of possible applications is shown. The concept introduced is supported by a real world case study which focuses on simultaneous forecasting of wind speed and direction. The architectures and learning algorithms which support this concept are introduced and their distributed sequential fusion nature is highlighted. © Springer Science+Business Media, LLC 2006 |
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