Abstract
Over the last few decades, surveillance applications have been an extremely useful tool to prevent dangerous situations and to identify abnormal activities. Although, the majority of surveillance videos are often subjected to different noises that corrupt structured patterns and fine edges. This makes the image processing methods even more difficult, for instance, object detection, motion segmentation, tracking, identification and recognition of humans. This paper proposes a novel filtering technique named robust bilateral and temporal (RBLT), which resorts to a spatial and temporal evolution of sequences to conduct the filtering process while preserving relevant image information. A pixel value is estimated using a robust combination of spatial characteristics of the pixel's neighborhood and its own temporal evolution. Thus, robust statics concepts and temporal correlation between consecutive images are incorporated together which results in a reliable and configurable filter formulation that makes it possible to reconstruct highly dynamic and degraded image sequences. The filtering is evaluated using qualitative judgments and several assessment metrics, for different Gaussian and Salt Pepper noise conditions. Extensive experiments considering videos obtained by stationary and non-stationary cameras prove that the proposed technique achieves a good perceptual quality of filtering sequences corrupted with a strong noise component.

Abstract
This article discusses the motion analysis based on dense optical flow fields and for a new generation of robotic moving systems with real-time constraints. It focuses on a surveillance scenario where an especially designed autonomous mobile robot uses a monocular camera for perceiving motion in the environment. The computational resources and the processing-time are two of the most critical aspects in robotics and therefore, two non-parametric techniques are proposed, namely, the Hybrid Hierarchical Optical Flow Segmentation and the Hybrid Density-Based Optical Flow Segmentation. Both methods are able to extract the moving objects by performing two consecutive operations: refining and collecting. During the refining phase, the flow field is decomposed in a set of clusters and based on descriptive motion properties. These properties are used in the collecting stage by a hierarchical or density-based scheme to merge the set of clusters that represent different motion models. In addition, a model selection method is introduced. This novel method analyzes the flow field and estimates the number of distinct moving objects using a Bayesian formulation. The research evaluates the performance achieved by the methods in a realistic surveillance situation. The experiments conducted proved that the proposed methods extract reliable motion information in real-time and without using specialized computers. Moreover, the resulting segmentation is less computationally demanding compared to other recent methods and therefore, they are suitable for most of the robotic or surveillance applications.

Abstract
This research presents an innovative mobile robotic system designed for active surveillance operations. This mobile robot moves along a rail and is equipped with a monocular camera. Thus, it enhances the surveillance capability when compared to conventional systems (mainly composed by multiple static cameras). In addition, the paper proposes a technique for multi-object tracking called MTMP (Multi-Tracking of Motion Profiles). The MTMP resorts to a formulation based on the Kalman filter and tracks several moving objects using motion profiles. A motion profile is characterized by the dominant flow vector and is computed using the optical flow signature with removal of outliers. A similarity measure based on the Mahalanobis distance is used by the MTMP for associating the moving objects over frames. The experiments conducted in realistic environments have proved that the static perception mode of the proposed robot is able to detect and track multiple moving objects in a short period of time and without using specialized computers. In addition, the MTMP exhibits a good computational performance since it takes less than 5 milliseconds to compute. Therefore, results show that the estimation of motion profiles is suitable for analyzing motion on image sequences.

Abstract

Abstract
The impact of large-scale Electric Vehicles (EVs) deployment in electric power systems is a current issue under study by the scientific community. The integration of this type of electric component requires robust planning solutions to mitigate possible consequences from such integration. This paper explores the quantification of the amount of renewable sources, namely wind and hydro power, which can be safely integrated into power systems in a scenario of a mass integration of EVs. The increase of renewable power in the generation portfolio is analyzed under the framework of generating system adequacy assessment considering several EVs deployment scenarios and an adequate charging strategy. The analysis is carried for the planning configurations of the Portuguese and Spanish generating systems and the results are focused on the potential benefits of EV integration in terms of hydro and wind power usage.

Abstract
This paper studies the impact of the energy upon electricity markets using Multidimensional Scaling (MDS). Data from major energy and electricity markets is considered. Several maps produced by MDS are presented and discussed revealing that this method is useful for understanding the correlation between them. Furthermore, theresults help electricity markets agents hedging against Market Clearing Price (MCP) volatility. © Springer Science+Business Media Dordrecht 2014.