We propose a novel efficient and reliable gait authentication approach. It is based on the analysis of accelerometer signals using higher order statistics. Gait patterns are obtained by transformation of acceleration data in feature space represented with higher order cumulants. The proposed approach is able to operate on multichannel and multisensor data by combining feature-level and sensor-level fusion.
COBISS.SI-ID: 1536253635
We propose an Advanced Model for Efficient Workload Prediction in the Cloud (AME-WPC), which combines statistical and learning methods, improves accuracy of workload prediction for cloud computing applications and can be dynamically adapted to a particular system. We address the workload prediction problem with classification as well as regression. Experimental results demonstrate that combining statistical and learning methods makes sense and can significantly improve prediction accuracy of workload over time.
COBISS.SI-ID: 1536396227
We propose a new data-flow based approach for the identification of backbones in infinite clusters on 2-D perlocation site lattices. The infinite cluster is identified first, then a multi-step algorithm is applied for the reduction of the infinite cluster to its backbone. The algorithm is local and can therefore be efficiently implemented on data-flow parallel platforms. Algorithm performances are evaluated theoretically and experimentally.
COBISS.SI-ID: 29117223