This paper presents a scheduling framework that is configured for, and used in MedioGRID, a system for real-time processing of satellite images, operating in a grid environment. Our work addresses the problem of scheduling various computationally intensive and data intensive applications that are required for extracting information from satellite images. The proposed solution allows mapping of image processing applications onto available MedioGRID resources. The scheduling is done at the level of groups of concurrent applications. It demonstrates a very good behavior for scheduling and executing groups of applications, while also achieving a near-optimal utilization of the MedioGRID resources View full abstract»

The authors are proposing a transmission cost allocation mechanism for generators and consumers. The objects of analysis are the components of reactive power. The method allocates transmission cost using system matrix and heaving as object real and reactive power tracing and their losses. The results for the components associated with reactive power will be analyzed. In order to calculate the transmission cost for generators and consumers the authors use MVAr-km method. The case study refers to the 12 buses test power system, heaving 6 P-U buses and 9 P-Q buses. View full abstract»

Many existing works in face recognition are based solely on visible images. The use of bimodal systems based on visible and thermal images is seldom reported in face recognition, despite its advantage of combining the discriminative power of both modalities, under expressions or pose variations. In this paper, we investigate the combined advantages of thermal and visible face recognition on a Principal Component Analysis (PCA) induced feature space, with PCA applied on each spectrum, on a relatively new thermal/visible face database - OTCBVS, for large pose and expression variations. The recognition is done through k-nearest neighbors classification. Our findings confirm that the recognition results are improved by the aid of thermal images over the classical approaches on visible images alone, when a suitably chosen classifier score fusion is employed. We also propose a validation scheme for deriving the optimal fusion score between the two recognition modalities. View full abstract»

Prediction represents an important component of resource management, providing information about the future state, utilization and availability of resources. We propose a new prediction algorithm inspired from the decomposition of a complex wave into simpler waves with fixed frequencies (similar to Fourier decomposition). The partial results obtained from this decomposition stage are combined using approaches inspired from artificial intelligence models. The experimental results for different system parameters, used in Alice experiment, highlight the great improvement, discussed in terms of error reduction, offered by this new prediction algorithm. The tests were made using real-time monitoring data provided by a system monitoring tool, in the case of one-step and multi-step ahead prediction. The prediction's results can be used by the resource management systems in order to improve the scheduling decisions, assuring the load balancing and optimizing the resource utilization. View full abstract»