Abstract
User experience is a key point for successful ubiquitous computing (ubicomp) environments. The envisaged design should be explored as soon as possible to anticipate potential user problems, thus reducing re-design costs. The development of ubicomp environments' prototypes might help, providing feedback on the users' reaction to the environments. This paper describes the design and evaluation of ubicomp environments using APEX, a rapid prototyping framework providing user experience via a 3D application server and connected physical devices. APEX prototypes allow users to explore and experience many characteristics of a proposed design, in a virtual world. The paper focus in particular the design and evaluation of a smart library in the APEX framework. © 2014 Springer International Publishing Switzerland.

Abstract
The paper presents the approach followed at the Faculty of Engineering of the University of Porto, to introduce design automation tools and structured design techniques in the first course on digital system design of our Integrated Master in Electrical and Computer Engineering. Digital Systems Laboratory is an introductory course on digital design, with the classical task of teaching Boolean algebra and combinational and sequential circuit design, using gates, flip-flops and medium complexity components/function blocks like counters and shift-registers. The need to cope with new curriculum requirements and modern digital design demands, motivated an extensive reformulation of the course contents and organization, leading to the introduction of the use of hardware description languages and synthesis tools, in order to implement small systems, of increasingly complex nature, on an FPGA platform. At the same time its coverage was extended to include low-level processor architecture issues, and to teach assembly programming for the MIPS processor. The paper describes how this reformulation was carried out. It presents the course contents and timeline, and discusses the main choices that were made. The paper also describes the laboratory experiments that were developed and discusses some of the challenges and results obtained so far. © 2014 IEEE.

Abstract

Abstract
Sparse coding techniques have seen an increasing range of applications in recent years, especially in the area of image processing. In particular, sparse coding using l(1)-regularization has been efficiently solved with the Augmented Lagrangian (AL) applied to its dual formulation (DALM). This paper proposes the decomposition of the dictionary matrix in its Singular Value/Vector form in order to simplify and speed-up the implementation of the DALM algorithm. Furthermore, we propose an update rule for the penalty parameter used in AL methods that improves the convergence rate. The SVD of the dictionary matrix is done as a pre-processing step prior to the sparse coding, and thus the method is better suited for applications where the same dictionary is reused for several sparse recovery steps, such as block image processing.

Abstract
This paper assesses the interference caused by uncontrolled traffic sources upon real-time traffic in IEEE 802.11s mesh networks. It was considered a mesh network transmitting real-time traffic and an external interference was injected in order to evaluate its impact over the real-time communication. A set of experiments were performed in ns-3 and different scenarios were evaluated, where the end-to-end delay, message loss and deadline miss ratios were chosen as performance metrics. It can be inferred that the real-time traffic in 802.11s is highly affected by external interferences, and therefore that must be taken into account when setting-up such type of networks.

Abstract
Regular services in telecommunications produce massive volumes of relational data. In this work the data produced in telecommunications is seen as a streaming network, where clients are the nodes and phone calls are the edges. Visualization techniques are required for exploratory data analysis and event detection. In social network visualization and analysis the goal is to get more information from the data taking into account actors at the individual level. Previous methods relied on aggregating communities, k-Core decompositions and matrix feature representations to visualize and analyse the massive network data. Our contribution is a group visualization and analysis technique of influential actors in the network by sampling the full network with a top-k representation of the network data stream.