Localization of class-related mu-rhythm desynchronization in motor imagery based Brain-Computer Interface sessions

We localize the sources of class-dependent eventrelated desynchronisation (ERD) of the mu-rhythm related to different types of motor imagery in Brain-Computer Interfacing (BCI) sessions. Our approach is based on localization of singletrial Fourier coefficients using sparse basis field expansions (SFLEX). The analysis reveals focal sources in the sensorimotor cortices, a finding which can be regarded as a proof for the expected neurophysiological origin of the BCI control signal. As a technical contribution, we extend S-FLEX to the multiple measurement case in a way that the activity of different frequency bins within the mu-band is coherently localized

Energy efficient resource allocation strategy for cloud data centres

Cloud computing data centres are emerging as new candidates for replacing traditional data centres. Cloud data centres are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Making the data centre more energy efficient is a necessary task. In this paper, we focus on the organisation’s internal Infrastructure as a Service (IaaS) data centre type. An internal IaaS cloud data centre has many distinguished features with heterogeneous hardware, single application, stable load distribution, lived load migration and highly automated administration. This paper will propose a way of saving energy for IaaS cloud data centre considering all stated constraints. The basic idea is rearranging the allocation in a way that saving energy. The simulation results show the efficiency of the method