Abstract
The sensors that acquire 3D data play an important role in many applications. In addition, they have been used in the robotic field for several purposes, for instance, enhancing the navigation of mobile robots, object detection, scene reconstruction, 3D inspection of parts and others. Moreover, a significant amount of devices with distinct cost, accuracy and features have been released in the recent years which increases the difficulty of comparing each sensor in a proper manner or choosing the most suitable device for a specific task and operation field. This paper compares the Kinect v1, Kinect v2, Structure Sensor and Mesa Imaging SR4000. The noise of each sensor is characterized for different distances and considering objects with different colors. Therefore, this paper proposes a simple but quantitative benchmark for evaluating 3D devices that characterizes the most relevant features for the robotic field and in accordance with different type of operations.

Abstract
This paper introduces the concept of tomography, a mechanism to lower management overhead for component-based IoT applications. Previous research has shown the advantages of component-based software engineering, wherein applications are built and reconfigured at runtime through the composition of components. While this approach promotes code-reuse and dynamic reconfiguration, the introspection and reconfiguration of distributed applications is cumbersome and inefficient. Tomography addresses this problem by reimagining the visitor design pattern for distributed component based compositions. We evaluate the performance of this approach in a case-study of discovering/introspecting and reconfiguring a real-world IoT application. We show that in comparison to classic management operations, tomography reduces both the number of explicit queries and the volume of network messages. This significantly reduces management effort and energy consumption.

Abstract

Abstract
The prospect of distributed generation investment planning (DGIP) is especially relevant in insular networks because of a number of reasons such as energy security, emissions and renewable integration targets. In this context, this paper presents a DGIP model that considers various DG types, including renewables. The planning process involves an economic analysis considering the costs of emissions, reliability and other relevant cost components. In addition, a comprehensive sensitivity analysis is carried out in order to investigate the effect of variability and uncertainty of model parameters on DG investment decisions. The ultimate goal is to identify the parameters that significantly influence the decision-making process and to quantify their degree of influence. The results show that uncertainty has a meaningful impact on DG investment decisions. In fact, the degree of influence varies from one parameter to another. However, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. The analyses made in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices.

Abstract

Abstract
In this paper, we present Social Network-based Education (SNE) and Learning Factory (LF) as emergent forms of education and training, including their application for the field of Quality Management. This novel learning method has some features of education 3.0 paradigm since participants have freedom to choose what, how and when to learn, as well with whom they coloborate. The actors, students and teachers, form a network, in which the network structure, communication, learning process and behavior exibit features of a social network. The training program content consists of a set of assignments (tasks) that are launched and realized in a crowdsourcing manner. The SNE and LF methodology was applied at the University of Minho in the Integrated Master on Industrial Engineering and Management course for different curricular units, including Quality Management. Within the concepts refered above, the set of assignments implemented for education and training for the field of Quality Management is also presented.