Abstract
Non-linear curvature wave-front sensing (nlCWFS) delivers outstanding sensitivity and high dynamic range by lifting the linearity constraint of standard curvature wave-front sensing and working in the non-linear Fresnel (near-field) regime [Guyon, 2010]. The goals of this paper are twofold: 1) revisit the phase-diversity PD formalism and attempt to use this framework, originally developed for the Fraunhofer (far-field) regime, with nlCWFS signals and 2) develop formulae making explicit use of the Fresnel regime for later use with gradient-based non-linear minimisation methods.

Abstract
Raven is a Multi-Object Adaptive Optics (MOAO) scientific demonstrator which will be used on-sky at the Subaru observatory from 2014. Raven is currently being built and tested at the University of Victoria AO Lab. This paper presents an overview of the optomechanical design and the software architecture of Raven, and gives the current status of this project. Raven includes three open loop wavefront sensors (WFSs), a laser guide star WFS and two figure/truth WFSs. Two science channels containing deformable mirrors (DMs) feed light to the Subaru IRCS spectrograph. Central to the Raven is a Calibration Unit which contains multiple sources, a telescope simulator including two phase screens and a ground layer DM that can be used to calibrate and test Raven in the lab. Preliminary results on calibration and open-loop AO correction using a tomographic reconstructor are presented.

Abstract
As the size and cost of embedded devices continue to decrease, it becomes economically feasible to densely deploy networks with very large quantities of such nodes, and thus enabling the implementation of networks with increasingly larger number of nodes becomes a relevant problem. In this paper we describe a novel algorithm to obtain the number of live nodes with a very low time-complexity. In particular, we develop a mechanism to estimate the number of nodes or the number of proposed values (COUNT), with a time complexity that increases sublinearly with the number of nodes. The approach we propose is based on the wise exploitation of dominance-based protocols and offers excellent scalability properties for emerging applications in dense Cyber Physical Systems.

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
The electromagnetic theory presents a unifying explanation of electric and magnetic phenomena underlying our technological society. It is a fundamental physical theory taught in engineering schools at university level. In this theory the electromagnetic field is a vector field permeating space. An important aspect relating to students difficulties and misconceptions is the difficulty in visualizing vector fields. With the goal of enhancing student understanding and studying student engagement we have developed high quality 3D visualizations of electromagnetic situations. These make use of accurate computation of the field lines, together with realistic rendering using the open source software Blender. We present examples of electrostatic situations with both an assessment of the student understanding and an evaluation of the students' perceptions of the importance of the visualizations. Complex interplay between visualization specific issues and the abstract notion of the field is identified in the students' conceptions. It is found that the visualizations are not used as substitutes of other learning resources. They are perceived as allowing a quick access to content and prompting motivation. The adequacy of the visualization to the subject content as well as the capacity to use it as self-assessment is valued by the students.