Abstract
Data centers are large energy consumers and a substantial portion of this power consumption is due to the control of physical parameters, which bring the need of high efficiency environmental control systems. In this work, we describe a hardware sensing platform specifically tailored to collect physical parameters (temperature, pressure, humidity and power consumption) in large data centers. Our system architecture is composed of Smart Objects, the datacenter racks, that cooperate to contribute for the overall goal of finding opportunities to optimize energy consumption and achieving energy-efficient data centers.We also introduce an analysis of the delay to obtain the sensing data from the sensor network. This analysis provides an insight into the time scales supported by our platform, and also allows to study the delay for different data center topologies. Finally, we exemplify some capabilities of the system with a real deployment.

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
Media content recommendations for a mobile user based on his changing contextual preferences, otherwise called context-aware media recommendations, constitute a very important challenge. Context-aware media recommendation systems take context information such as user preferences, activities, time, location, device, and network capabilities as inputs for media recommendations, whereas the traditional recommendation systems use only user preferences in the form of ratings to deliver media recommendations. This paper presents a generic high-level architecture of context-aware recommendations, discussing its key techniques and solutions, which are based on context acquisition, recognition, and representations, using MPEG-21 and ontology model, and a contextual user profiling process, as well as MPEG-7 for media description model and media presentation adaptation.

Abstract
Several concurrent applications running on a sensor network may cause a node to transmit packets at distinct periods, which increases the radio-switching rate and has significant impact in terms of the overall energy consumption. We propose to batch the transmissions together by defining a harmonizing period to align the transmissions from multiple applications at periodic boundaries. This harmonizing period is then leveraged to design a distributed protocol called Network-Harmonized Scheduling (NHS) that coordinates transmissions across nodes and provides real-time guarantees in a multi-hop network. © 2014 IEEE.

Abstract
In this paper, an optic disk (OD) localization method is proposed for the retinal images based on a novel patch filtering approach. The patch filtering has been performed sequentially based on clustering in two stages. In the first stage, the patches are selected exploiting an 'isotropic' measure based on the ratio of maximum and minimum eigenvalues of the moment matrix representing the structure tensor. In the second stage, the patch filtering is based on the saliency measure. Finally, the optic disk is located from the centroids of the selected patches. Promising results are obtained for the low-contrast pathological retinal images using STARE database providing high localization accuracy.

Abstract
The incredible appeals of smartphones and the unprecedented progress in the development of mobile and wireless networks in recent years have enabled ubiquitous availability of myriad media contents. Consequently, it has become problematic for mobile users to find relevant media items. However, context awareness has been proposed as a means to help mobile users find relevant media items anywhere and at any time. The contribution of this paper is the presentation of a context-aware media recommendation framework for smart devices (CAMR). CAMR supports the integration of context sensing, recognition, and inference, using classification algorithms, an ontology-based context model and user preferences to provide contextually relevant media items to smart device users. This paper describes CAMR and its components, and demonstrates its use to develop a context-aware mobile movie recommendation on Android smart devices. Experimental evaluations of the framework, via an experimental context-aware mobile recommendation application, confirm that the framework is effective, and that its power consumption is within acceptable range. © 2014 Springer International Publishing Switzerland.