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 effciency environmental control systems. In this paper, we describe a hardware sensing platform specifi cally tailored to collect physical parameters (temperature, pressure, humidity and power consumption) in large data centers. This platform is an important enabler to find opportunities to optimize energy consumption. 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 datacenter topologies.

Abstract
While Cluster-Tree network topologies look promising for WSN applications with timeliness and energy-efficiency requirements, we are yet to witness its adoption in commercial and academic solutions. One of the arguments that hinder the use of these topologies concerns the lack of flexibility in adapting to changes in the network, such as in traffic flows. This paper presents a solution to provide these networks with the ability to self-adapt to different bandwidth and latency requirements, imposed by traffic flows, by changing the cluster's duty-cycle and scheduling. Importantly, our approach enables a network to change its cluster scheduling without requiring long inaccessibility times or the re-association of the nodes. We show how to apply our methodology to the case of IEEE 802.15.4/ZigBee cluster-tree WSNs without significant changes to the protocol. Finally, we analyze and demonstrate the validity of our methodology through a comprehensive simulation and experimental validation using commercially available technology on a Structural Health Monitoring application scenario.

Abstract
Current evaluation methods either rely heavily on reference information manually annotated or, by completely avoiding human input, provide only a rough evaluation of the performance of video object tracking algorithms. The main objective of this paper is to present a novel approach to the problem of evaluating video object tracking algorithms. It is proposed the use different types of reference information and the combination of heterogeneous metrics for the purpose of approximating the ideal error. This will enable a significant decrease of the required reference information, thus bridging the gap between metrics with different requirements concerning this type of data. As a result, evaluation frameworks can aggregate the benefits from individual approaches while overcoming their weaknesses, providing a flexible and powerful tool to assess and characterize the behavior of the tracking algorithms.

Abstract
Generation of side information for multi-view Distributed Video Coding in multi-camera environments (e.g., video surveillance) poses challenges in scenarios with temporary non-overlapping among views and consequently, no resources for generating the side information at some time instants. In this paper we extend our previous work (Ciobanu and Corte-Real, Multimed Tools Appl 48(3):411-436, 2010) (for scenarios with permanent complete-overlapping among views) and propose a solution to this problem by exploiting the past visual data associated with each view, gathered over time as a panoramic image (sprite). The entire collection of temporal data from all the cameras is subsequently used for generating the side information. We tackle several topics related to these scenarios and propose solutions for the encountered issues. Optimization techniques are also discussed, e.g., temporal tags and block alternatives associated with the sprite contents for an improved generation of side information. This paper also presents a post-processing technique for additional refinement of generated side information. Practical results show an overall significant enhancement of side information by over 2 dB.

Abstract
Automatic image registration is a process related to several application fields: remote sensing, medicine and computer vision, among others. Particularly in the field of remote sensing, the ever-increasing number of available satellite images requires automatic image registration methods, capable of correctly aligning a new image. An automatic image registration method - CHAIR (correlation-and Hough transform-based method of automatic image registration) - is proposed, the key concept of which relies on the 'correlation image' produced in both the horizontal and vertical directions. In particular, the computation of the distance of an identified diagonal brighter strip in the correlation image (through the Hough transform) to an offset (the main diagonal) allows for the determination of translational shifts and consequently control points. The set of obtained control points allows for the correction of several types of distortions. The geometric correction quality achieved by CHAIR was objectively evaluated through measures recently proposed, which allow for a more complete assessment of the obtained results. The CHAIR performance was evaluated on both synthetic and real data, with different spatial resolutions and spectral contents. CHAIR has been shown to be able to correctly align two images with a subpixel accuracy, having a priori a 'gold standard' image covering a considerable part of the image to be registered, and has also been shown to work for images of different sensors and/or different spectral bands, situations where traditional correlation methods often yield low and smooth peaks on the correlation surface. It is also able to account for elevation differences and to some extent for rotation and scale effects. Furthermore, it has been shown to have potential for registering synthetic aperture radar (SAR) with optical images.

Abstract
In this paper we present a complete system for object tracking over multiple uncalibrated cameras with or without overlapping fields of view. We employ an approach based on the bag-of-visterms technique to represent and match tracked objects. The tracks are compared with a global object model based on an ensemble of individual object models. The system can globally recognise objects and minimise common tracking problems such as track drift or split. The output is a timeline representing the objects present in a given multi-camera scene. The methods employed in the system are online and can be optimized to operate in real-time.