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

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

doi:10.1109/EDERC.2012.6532232

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

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

Electronic And Electrical Engineering

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 language	English
Title of host publication	2012 5th European DSP Education and Research Conference (EDERC)
Publisher	IEEE
Pages	90-94
Number of pages	5
ISBN (Print)	978-1-4673-4595-8
DOIs	https://doi.org/10.1109/EDERC.2012.6532232
Publication status	Published - Sep 2012

Keywords

embedded svm
TMS320C6713
signal prediction
classification
regression applications

Access to Document

10.1109/EDERC.2012.6532232

Link to publication in Scopus

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Cite this

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title = "Embedded SVM on TMS320C6713 for signal prediction in classification and regression applications",

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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author = "Jaime Zabalza and Jinchang Ren and Carmine Clemente and {Di Caterina}, Gaetano and John Soraghan",

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doi = "10.1109/EDERC.2012.6532232",

language = "English",

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T1 - Embedded SVM on TMS320C6713 for signal prediction in classification and regression applications

AU - Zabalza, Jaime

AU - Ren, Jinchang

AU - Clemente, Carmine

AU - Di Caterina, Gaetano

AU - Soraghan, John

PY - 2012/9

Y1 - 2012/9

N2 - 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.

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.

KW - embedded svm

KW - TMS320C6713

KW - signal prediction

KW - classification

KW - regression applications

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M3 - Conference contribution book

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EP - 94

BT - 2012 5th European DSP Education and Research Conference (EDERC)

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