Abstract
The TERAPOD project aims to investigate and demonstrate the feasibility of ultra high bandwidth wireless access networks operating in the Terahertz (THz) band. The proposed TERAPOD THz communication system will be developed, driven by end user usage scenario requirements and will be demonstrated within a first adopter operational setting of a Data Centre. In this article, we define the full communications stack approach that will be taken in TERAPOD, highlighting the specific challenges and aimed innovations that are targeted.

Abstract
This paper focus on the use of unmanned aerial vehicle teams for performing cooperative perception using Data Distribution Service (DDS) Network. We develop a DDS framework to manage the incoming and out bounding network traffic of multiple types of data that is exchanged inside the UAV network. Experimental results both in laboratory and in actual flight are presented to help characterize the proposed system solution.

Abstract
Diabetes management is a complex and a sensible problem as each diabetic is a unique case with particular needs. The optimal solution would be a constant monitoring of the diabetic's values and automatically acting accordingly. We propose an approach that guides the user and analyses the data gathered to give individual advice. By using data mining algorithms and methods, we uncover hidden behaviour patterns that may lead to crisis situations. These patterns can then be transformed into logical rules, able to trigger in a particular context, and advise the user. We believe that this solution, is not only beneficial for the diabetic, but also for the doctor accompanying the situation. The advice and rules are useful input that the medical expert can use while prescribing a particular treatment. During the data gathering phase, when the number of records is not enough to attain useful conclusions, a base set of logical rules, defined from medical protocols, directives and/or advice, is responsible for advise and guiding the user. The proposed system will accompany the user at start with generic advice, and with constant learning, advise the user more specifically. We discuss this approach describing the architecture of the system, its base rules and data mining component. The system is to be incorporated in a currently developed diabetes management application for Android.

Abstract
This paper proposes an ontology-based approach to support the process of visualizing urban mobility data. The approach consists of building a visualization-oriented urban mobility ontology, focused on themes such as ridership, vehicle flows and the like. Existing ontologies focus on modelling the overall structure of transportation networks, and do not address the formalization of such themes. The ontology also allows characterizing visualization techniques with human perception factors, so that they can be used to automatically infer recommended techniques for a dataset. The ultimate goal is to benefit decision makers, by providing an ontology that can assist with the process of developing semantically-rich visualizations, with increased data interoperability and knowledge extraction capabilities. We provide an example with real data of the public transportation system of the city of Porto, Portugal. The example shows the semantic characterization of a visualization technique, and how semantics can assist the task of automatically recommending visualizations. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
We present a numerical implementation of a solver for the Maxwell-Bloch equations to calculate the propagation of a light pulse in a nonlinear medium composed of an atomic gas in one, two and three dimensional systems. This implementation solves the wave equation of light using a finite difference method in the time domain scheme, while the Bloch equations for the atomic population in each point of the simulation domain are integrated using splitting methods. We present numerical simulations of atomic-gas systems and performance benchmarks.

Abstract
This paper presents a context analysis methodology to improve the management of residential energy resources by making the decision making process adaptive to different contexts. A context analysis model is proposed and described, using a clustering process to group similar situations. Several clustering quality assessment indices, which support the decisions on how many clusters should be created in each run, are also considered, namely: the Calinski Harabasz, Davies Bouldin, Gap Value and Silhouette. Results show that the application of the proposed model allows to identify different contexts by finding patterns of devices' use and also to compare different optimal k criteria. The data used in this case study represents the energy consumption of a generic home during one year (2014) and features the measurements of several devices' consumption as well as of several contextual variables. The proposed method enhances the energy resource management through adaptation to different contexts. © 2017 IEEE.