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
Autor*in: |
Mandic, Danilo P. [verfasserIn] Goh, Su Lee [verfasserIn] Aihara, Kazuyuki [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2006 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Journal of VLSI signal processing systems for signal, image and video technology - Springer Netherlands, 1989, 48(2006), 1-2 vom: 28. Dez., Seite 99-108 |
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Übergeordnetes Werk: |
volume:48 ; year:2006 ; number:1-2 ; day:28 ; month:12 ; pages:99-108 |
Links: |
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DOI / URN: |
10.1007/s11265-006-0025-6 |
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SPR018318975 |
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10.1007/s11265-006-0025-6 doi (DE-627)SPR018318975 (SPR)s11265-006-0025-6-e DE-627 ger DE-627 rakwb eng Mandic, Danilo P. verfasserin aut Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 spatio-temporal (dpeaa)DE-He213 complex-valued (dpeaa)DE-He213 vector spaces (dpeaa)DE-He213 complex nonlinearity (dpeaa)DE-He213 Goh, Su Lee verfasserin aut Aihara, Kazuyuki verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 48(2006), 1-2 vom: 28. Dez., Seite 99-108 (DE-627)SPR018308090 nnns volume:48 year:2006 number:1-2 day:28 month:12 pages:99-108 https://dx.doi.org/10.1007/s11265-006-0025-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 48 2006 1-2 28 12 99-108 |
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10.1007/s11265-006-0025-6 doi (DE-627)SPR018318975 (SPR)s11265-006-0025-6-e DE-627 ger DE-627 rakwb eng Mandic, Danilo P. verfasserin aut Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 spatio-temporal (dpeaa)DE-He213 complex-valued (dpeaa)DE-He213 vector spaces (dpeaa)DE-He213 complex nonlinearity (dpeaa)DE-He213 Goh, Su Lee verfasserin aut Aihara, Kazuyuki verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 48(2006), 1-2 vom: 28. Dez., Seite 99-108 (DE-627)SPR018308090 nnns volume:48 year:2006 number:1-2 day:28 month:12 pages:99-108 https://dx.doi.org/10.1007/s11265-006-0025-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 48 2006 1-2 28 12 99-108 |
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10.1007/s11265-006-0025-6 doi (DE-627)SPR018318975 (SPR)s11265-006-0025-6-e DE-627 ger DE-627 rakwb eng Mandic, Danilo P. verfasserin aut Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 spatio-temporal (dpeaa)DE-He213 complex-valued (dpeaa)DE-He213 vector spaces (dpeaa)DE-He213 complex nonlinearity (dpeaa)DE-He213 Goh, Su Lee verfasserin aut Aihara, Kazuyuki verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 48(2006), 1-2 vom: 28. Dez., Seite 99-108 (DE-627)SPR018308090 nnns volume:48 year:2006 number:1-2 day:28 month:12 pages:99-108 https://dx.doi.org/10.1007/s11265-006-0025-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 48 2006 1-2 28 12 99-108 |
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10.1007/s11265-006-0025-6 doi (DE-627)SPR018318975 (SPR)s11265-006-0025-6-e DE-627 ger DE-627 rakwb eng Mandic, Danilo P. verfasserin aut Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 spatio-temporal (dpeaa)DE-He213 complex-valued (dpeaa)DE-He213 vector spaces (dpeaa)DE-He213 complex nonlinearity (dpeaa)DE-He213 Goh, Su Lee verfasserin aut Aihara, Kazuyuki verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 48(2006), 1-2 vom: 28. Dez., Seite 99-108 (DE-627)SPR018308090 nnns volume:48 year:2006 number:1-2 day:28 month:12 pages:99-108 https://dx.doi.org/10.1007/s11265-006-0025-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 48 2006 1-2 28 12 99-108 |
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10.1007/s11265-006-0025-6 doi (DE-627)SPR018318975 (SPR)s11265-006-0025-6-e DE-627 ger DE-627 rakwb eng Mandic, Danilo P. verfasserin aut Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 spatio-temporal (dpeaa)DE-He213 complex-valued (dpeaa)DE-He213 vector spaces (dpeaa)DE-He213 complex nonlinearity (dpeaa)DE-He213 Goh, Su Lee verfasserin aut Aihara, Kazuyuki verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 48(2006), 1-2 vom: 28. Dez., Seite 99-108 (DE-627)SPR018308090 nnns volume:48 year:2006 number:1-2 day:28 month:12 pages:99-108 https://dx.doi.org/10.1007/s11265-006-0025-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 48 2006 1-2 28 12 99-108 |
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Mandic, Danilo P. |
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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. |
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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. |
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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. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR018318975</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124222358.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2006 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11265-006-0025-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR018318975</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11265-006-0025-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mandic, Danilo P.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2006</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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. 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