Picture of athlete cycling

Open Access research with a real impact on health...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

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

Zabalza, Jaime and Ren, Jinchang and Clemente, Carmine and Di Caterina, Gaetano and Soraghan, John (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. ISBN 978-1-4673-4595-8

Full text not available in this repository. Request a copy from the Strathclyde author


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.