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.

Abstract
Spatial analyses and competition assessments can be used by firms to identify target geographic markets for entry. By integrating these two kinds of analysis, this paper presents an innovative method that identifies target geographic markets for health care providers. In these target markets, supply is potentially insufficient to satisfy demand and competition problems that make entry unsuccessful are not expected to occur. Considering the Portuguese hospital health care market, an application of the method in a case study illustrates how the method works in practice.

Abstract
Developing robust and universal methods for unsupervised segmentation of moving objects in video sequences has proved to be a hard and challenging task. State-of-the-art methods show good performance in a wide range of situations, but systematically fail when facing more challenging scenarios. Lately, a number of image processing modules inspired in biological models of the human visual system have been explored in different areas of application. This paper proposes a bio-inspired boosting method to address the problem of unsupervised segmentation of moving objects in video that shows the ability to overcome some of the limitations of widely used state-of-the-art methods. An exhaustive set of experiments was conducted and a detailed analysis of the results, using different metrics, revealed that this boosting is more significant when challenging scenarios are faced and state-of-the-art methods tend to fail.