Embedded SVM on TMS320C6713 for signal prediction in classification and regression applications

Jaime Zabalza, Jinchang Ren, Carmine Clemente, Gaetano Di Caterina, John Soraghan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

10 Citations (Scopus)

Abstract

Support Vector Machine (SVM) is a very powerful tool for signal prediction including classification and regression. With Texas Instruments TMS320C6713 DSK, an embedded SVM is implemented, where a user friendly interface is provided via peripherals like the DIPs and LEDs. The C6713 processor in combination with the SDRAM block memory can solve the complex computation that SVM requires. Also a Real-Time utilisation of the device from Matlab environment is demonstrated. An exciting application framework is finally obtained, from which some conclusions related to the implementation and final usage are derived.
Original languageEnglish
Title of host publication2012 5th European DSP Education and Research Conference (EDERC)
PublisherIEEE
Pages90-94
Number of pages5
ISBN (Print)978-1-4673-4595-8
DOIs
Publication statusPublished - Sep 2012

Fingerprint

Support vector machines
Computer peripheral equipment
User interfaces
Light emitting diodes
Data storage equipment

Keywords

  • embedded svm
  • TMS320C6713
  • signal prediction
  • classification
  • regression applications

Cite this

Zabalza, Jaime ; Ren, Jinchang ; Clemente, Carmine ; Di Caterina, Gaetano ; Soraghan, John. / Embedded SVM on TMS320C6713 for signal prediction in classification and regression applications. 2012 5th European DSP Education and Research Conference (EDERC). IEEE, 2012. pp. 90-94
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abstract = "Support Vector Machine (SVM) is a very powerful tool for signal prediction including classification and regression. With Texas Instruments TMS320C6713 DSK, an embedded SVM is implemented, where a user friendly interface is provided via peripherals like the DIPs and LEDs. The C6713 processor in combination with the SDRAM block memory can solve the complex computation that SVM requires. Also a Real-Time utilisation of the device from Matlab environment is demonstrated. An exciting application framework is finally obtained, from which some conclusions related to the implementation and final usage are derived.",
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Zabalza, J, Ren, J, Clemente, C, Di Caterina, G & Soraghan, J 2012, Embedded SVM on TMS320C6713 for signal prediction in classification and regression applications. in 2012 5th European DSP Education and Research Conference (EDERC). IEEE, pp. 90-94. https://doi.org/10.1109/EDERC.2012.6532232

Embedded SVM on TMS320C6713 for signal prediction in classification and regression applications. / Zabalza, Jaime; Ren, Jinchang; Clemente, Carmine; Di Caterina, Gaetano; Soraghan, John.

2012 5th European DSP Education and Research Conference (EDERC). IEEE, 2012. p. 90-94.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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AB - Support Vector Machine (SVM) is a very powerful tool for signal prediction including classification and regression. With Texas Instruments TMS320C6713 DSK, an embedded SVM is implemented, where a user friendly interface is provided via peripherals like the DIPs and LEDs. The C6713 processor in combination with the SDRAM block memory can solve the complex computation that SVM requires. Also a Real-Time utilisation of the device from Matlab environment is demonstrated. An exciting application framework is finally obtained, from which some conclusions related to the implementation and final usage are derived.

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Zabalza J, Ren J, Clemente C, Di Caterina G, Soraghan J. Embedded SVM on TMS320C6713 for signal prediction in classification and regression applications. In 2012 5th European DSP Education and Research Conference (EDERC). IEEE. 2012. p. 90-94 https://doi.org/10.1109/EDERC.2012.6532232

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