Abstract
This paper presents a new methodology to maximize the power output of Photovoltaic panels (PV), based on an adaptive duty-cycle methodology. The approach embeds the DC/DC converter characteristic in the cost function, allowing an optimization based on a single measured variable. Two cost functions, and respective learning rules, are derived. The first, more complex and comprehensive, traces the ground for the second which is less computational intensive and solves stability issues and implementation difficulties. It is also demonstrated that the system is asymptotically stable around the optimum duty-cycle, in the Lyapunov sense. Both methods are compared through simulations and deviations from the optimal solution are assessed.

Abstract
The duration of 802.11 user sessions has been widely studied in the context of analyzing user behavior and mobility. Short (smaller-than-5-minutes) sessions are never used or characterized in these analyses as they are unrelated to user behavior and considered as artifacts introduced by the wireless network. In this paper we characterize short 802.11 sessions as recorded through RADIUS authentication. We show that 50% of access points have 70% of smaller than 5 minutes sessions in a 5 months trace from the Eduroam academic wireless network in the University of Porto. Exactly because they are artifacts introduced by the network, short sessions are an important indicator for network management and the quality of the wireless access. Network managers typically do not collect and process session information but rely on SNMP to provide summaries of 802.11 usage data. We develop a modeling framework to provide predictions for the number of short sessions from SNMP data. We model the data stream of each access point using two methods of regression and one classification technique. We evaluate these models based on short session prediction accuracy. The models are trained on the 5 months data and the best results show prediction accuracy of 95.27% in polynomial regression at degree of 3.

Abstract
Optical Music Recognition (OMR) systems are an indispensable tool to transform the paper-based music scores and manuscripts into a machine-readable symbolic format. A system like this potentiates search, retrieval and analysis. One of the problematic stages is the musical symbols detection where operations to localize and to isolate musical objects are developed. The complexity is caused by printing and digitalization, as well as the paper degradation over time. Distortions inherent in staff lines, broken, connected and overlapping symbols, differences in sizes and shapes, noise, and zones of high density of symbols is even worst when we are dealing with handwritten music scores. In this paper the exploration of an optimization approach to support semantic and syntactic consistency after the music symbols extraction phase is proposed. The inclusion of this ongoing technique can lead to better results and encourage further experiences in the field of handwritten music scores recognition.

Abstract
The conservative treatment is now the preferred procedure to treat breast cancer mainly due to better aesthetical results obtained. However, the aesthetic outcome is diverse and very difficult to evaluate, which motivates the research on automatic methodologies. The use of three-dimensional (3D) methodologies is increasing; however, the high cost of the equipment and the need for specialised technicians to operate it are import setbacks. Consequently, the search for affordable and easy to perform equipments is highly desirable. This paper studies the application of a Kinect device in this field, addressing issues related to accuracy, resolution and quality of the data. The paper demonstrates a comparative study of state-of-the-art Super-Resolution (SR) algorithms applied to the Kinect depth data, and the importance to improve the quality of images is stressed. The results demonstrate that it is possible to measure volumetric information and that there is agreement between features and the subjective aesthetic evaluation.

Abstract
We present a complete and modular framework that extract trajectories in a real and complex retail scenario, under unconstrained video conditions. Two motion tracking algorithms that combine features from crowd motion detection and multiple tracking are presented to build motion patterns and understand customer's behavior. Their evaluation across several datasets show promising results.

Abstract
The rising challenges in the field of iris recognition, concerning the development of accurate recognition algorithms using images acquired under an unconstrained set of conditions, is leading to the a renewed interest in the area. Although several works already report excellent recognition rates, these values are obtained by acquiring images in very controlled environments. The use of such systems in daily security activities, such as airport security and bank account management, is therefore hindered by the inherent unconstrained nature under which images are to be acquired. The proposed work focused on mutual context information from iris centre and iris limbic contour to perform robust and accurate iris segmentation in noisy images. A random subset of the UBIRIS.v2 database was tested with a promising E1 classification rate of 0.0109.