Abstract
In this work, we propose a lightweight approach for keyword extraction and ranking based on an unsupervised methodology to select the most important keywords of a single document. To understand the merits of our proposal, we compare it against RAKE, TextRank and SingleRank methods (three well-known unsupervised approaches) and the baseline TF. IDF, over four different collections to illustrate the generality of our approach. The experimental results suggest that extracting keywords from documents using our method results in a superior effectiveness when compared to similar approaches.

Abstract
Gesture recognition is very important for Human-Robot Interfaces. In this paper, we present a novel depth based method for gesture recognition to improve the interaction of a service robot autonomous shopping cart, mostly used by reduced mobility people. In the proposed solution, the identification of the user is already implemented by the software present on the robot where a bounding box focusing on the user is extracted. Based on the analysis of the depth histogram, the distance from the user to the robot is calculated and the user is segmented using from the background. Then, a region growing algorithm is applied to delete all other objects in the image. We apply again a threshold technique to the original image, to obtain all the objects in front of the user. Intercepting the threshold based segmentation result with the region growing resulting image, we obtain candidate objects to be arms of the user. By applying a labelling algorithm to obtain each object individually, a Principal Component Analysis is computed to each one to obtain its center and orientation. Using that information, we intercept the silhouette of the arm with a line obtaining the upper point of the interception which indicates the hand position. A Kalman filter is then applied to track the hand and based on state machines to describe gestures (Start, Stop, Pause) we perform gesture recognition. We tested the proposed approach in a real case scenario with different users and we obtained an accuracy around 89,7%.

Abstract

Abstract

Abstract
A brief review of new fiber microsphere geometries is presented. Simple microspheres working as Fabry-Perot cavities are interrogated in reflection and in transmission. Two microspheres were also spliced together, and subjected to different physical parameters. These structures are an alternative solution for load measurement and, when read in transmission, it is also possible to apply strain. Moreover, the structure is capable of being used under extreme ambient temperatures up to 900 degrees C. Random signal in cleaved microspheres was demonstrated with the possibility of using it for random laser or sensing applications. All this work was developed at the Centre for Applied Photonics, INESC TEC.

Abstract
This paper proposes a framework for the optimal operation of multi Micro Grids (multiMGs) based on Hybrid Stochastic/Robust optimization. MultiMGs with various characteristics are considered in this study. They are connected to different buses of their Up-Stream-Network (USN). Day-Ahead (DA) and Real-Time (RT) markets are contemplated. The proposed optimization structure in this paper is a bi-level one since both MGs operators' and USN operator's decisions are considered in the proposed model. The advantages of using time-of-use demand response programs on the optimal operation of USN in the presence of multiMGs are investigated. The uncertainty of different components, including wind units, photovoltaic units, plug-in electric vehicles, and DA market price is captured by using stochastic programming. In addition, robust programming is utilized for contemplating the uncertainty of the RT market price. Furthermore, the grid-connected and island modes of MGs' operation are investigated in this paper, discussing also the virtues of utilizing multiMGs over single MG. Finally, IEEE 18-bus and 30-bus test systems are considered for MGs and USN networks respectively to scrutinize the simulation results.