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

Author

Zabalza, J. ; Jinchang Ren ; Clemente, C. ; Di Caterina, G. ; Soraghan, J.

Author_Institution

Centre for Excellence in Signal & Image Process. (CeSIP), Univ. of Strathclyde, Glasgow, UK

fYear

2012

fDate

13-14 Sept. 2012

Firstpage

90

Lastpage

94

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.

Keywords

DRAM chips; Texas Instruments computers; signal classification; support vector machines; C6713 processor; DIP; LED; Matlab environment; Real-Time utilisation; SDRAM block memory; TMS320C6713; Texas Instruments TMS320C6713 DSK; classification applications; complex computation; embedded SVM; regression applications; signal prediction; support vector machine; TMS320C6713; embedded system; signal prediction; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Amsterdam

Print_ISBN

978-1-4673-4595-8

Electronic_ISBN

978-1-4673-4595-8

Type

conf

DOI

10.1109/EDERC.2012.6532232

Filename

6532232