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In the recent years, virtual machine introspection (VMI) has become a valuable technique for developing security applications for virtualized environments. With the increasing popularity of the ARM architecture, and the recent addition of hardware virtualization extensions, there is a growing need for porting existing VMI tools. Porting these applications requires proper hypervisor support, which we have been implementing for the upcoming release of the Xen hypervisor.
This report focuses on improving classification accuracy and reducing computational complexity for human activity recognition problem on public datasets UCI and WISDM. We discussed the benefits of getting access to smartphones in the filed of HAR research. Our experiment indicates that combining AdaBoost M1 algorithm with C4.5 contributes to discriminating several common human activities. Moreover, we showed that it is feasible to reduce computational complexity and achieve high accuracy at the same time by applying correlation-based feature selection.
Power consumption is a troublesome design constraint for HPC systems. If current trends continue, future petaflop systems will require 100 megawatts of power to maintain high-performance. To address this problem the power and energy characteristics of high performance systems must be characterised. The main idea of this project is to design a methodology for the optimal selection (minimal number of systems that maximise performance and minimise energy consumption) of a network topology for high performance applications using ultra-low-voltage microprocessors platforms (Intel® AtomTM Processor E3825 - Minnowboard).