Abstract
Reliable banknote recognition is critical for detecting counterfeit banknotes in ATMs and help visual impaired people. To solve this problem, it was implemented a computer vision system that can recognize multiple banknotes in different perspective views and scales, even when they are within cluttered environments in which the lighting conditions may vary considerably. The system is also able to recognize banknotes that are partially visible, folded, wrinkled or even worn by usage. To accomplish this task, the system relies on computer vision algorithms, such as image preprocessing, feature detection, description and matching. To improve the confidence of the banknote recognition the feature matching results are used to compute the contour of the banknotes using an homography that later on is validated using shape analysis algorithms. The system successfully recognized all Euro banknotes in 80 test images even when there were several overlapping banknotes in the same test image.

Abstract
The increasing penetration of wind generation in power systems to fulfil the ambitious European targets will make wind power producers to play an even more important role in the future power system. Wind power producers are being incentivized to participate in reserve markets to increase their revenue, since currently wind turbine/farm technologies allow them to provide ancillary services. Thus, wind power producers are to develop offering strategies for participation in both energy and reserve markets, accounting for market rules, while ensuring optimal revenue. We consider a proportional offering strategy to optimally decide upon participation in both markets by maximizing expected revenue from day-ahead decisions while accounting for estimated regulation costs for failing to provide the services. An evaluation of considering the same proportional splitting of energy and reserve in both day ahead and balancing market is performed. A set of numerical examples illustrate the behavior of such strategy. An important conclusion is that the optimal split of the available wind power between energy and reserve strongly depends upon prices and penalties on both market trading floors.

Abstract

Abstract
Smart Cities are a key application domain for the Internet of Things (IoT), and it is coming nearer everyday through pilot trials and deployments in various cities around the world. In Porto, Portugal, a city-wide IoT Living Lab emerged after we deployed several testbeds, e.g. harbour and a city-scale vehicular networks, and carried out various experiments with the SenseMyCity crowdsensor. In this paper, we discuss how a standard Machine-to-Machine (M2M) middleware is a key enabler of our e-health platform and SenseMyCity crowdsensor, powered by the use of smartphones as M2M gateways. M2M standards provided by ETSI/oneM2M are essential for a paradigm shift, aiming at making the IoT truly interoperable without the need for human intervention. In this work, we map two applications that rely on the role of a smartphone as a gateway, which acts as a proxy to connect legacy devices to the IoT using a standard middleware. We illustrate the advantages of using M2M, and, as a proof-of-concept, we measure and quantify the energy savings obtained, showing improvements of smartphones' battery life.

Abstract

Abstract
Agribusiness is an activity that generates huge amounts of temporal data. There are research centers that collect, store and create indexes of agricultural activities, providing multidimensional time series composed by years of data. In this paper, we are interested in studying the behavior of these time series, especially in what regards the evolution of agricultural price indexes over the years. We explore data mining techniques tailored to analyze temporal data, aiming to generate spatio-temporal trajectories of grains price indexes for six years of data. We propose the use of Tucker decomposition to both analyze the temporal patterns of these price indexes and map trajectories that represent their behavior over time in a concise and representative low-dimensional subspace. The case study presents an application of this methodology to real databases of price indexes of corn and soybeans in Brazil and the United States.