INESC TEC Repository
Not a member yet
2130 research outputs found
Sort by
International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW)
Get PDFA voting method for stereo egomotion estimation
Get PDFThe development of vision-based navigation systems for mobile robotics applications in outdoor scenarios is a very challenging problem due to frequent changes in contrast and illumination, image blur, pixel noise, lack of image texture, low image overlap and other effects that lead to ambiguity in the interpretation of motion from image data. To mitigate the problems arising from multiple possible interpretations of the data in outdoor stereo egomotion, we present a fully probabilistic method denoted as probabilistic stereo egomotion transform. Our method is capable of computing 6-degree of freedom motion parameters solely based on probabilistic correspondences without the need to track or commit key point matches between two consecutive frames. The use of probabilistic correspondence methods allows to maintain several match hypothesis for each point, which is an advantage when ambiguous matches occur (which is the rule in image feature correspondence problems), because no commitment is made before analysing all image information. Experimental validation is performed in simulated and real outdoor scenarios in the presence of image noise and image blur. Comparison with other current state-of-the-art visual motion estimation method is also provided. Our method is capable of significant reduction of estimation errors mainly in harsh conditions of noise and blur. © 2017, © The Author(s) 2017
Inside packet sampling techniques: exploring modularity to enhance network measurements
Get PDFTraffic sampling is viewed as a prominent strategy contributing to lightweight and scalable network measurements. Although multiple sampling techniques have been proposed and used to assist network engineering tasks, these techniques tend to address a single measurement purpose, without detailing the network overhead and computational costs involved. The lack of a modular approach when defining the components of traffic sampling techniques also makes difficult their analysis. Providing a modular view of sampling techniques and classifying their characteristics is, therefore, an important step to enlarge the sampling scope, improve the efficiency of measurement systems, and sustain forthcoming research in the area. Thus, this paper defines a taxonomy of traffic sampling techniques resorting to a comprehensive analysis of the inner components of existing proposals. After identifying granularity, selection scheme, and selection trigger as the main components differentiating sampling proposals, the study goes deeper on characterizing these components, including insights into their computational weight. Following this taxonomy, a general-purpose architecture is established to sustain the development of flexible sampling-based measurement systems. Traveling inside packet sampling techniques, this paper contributes to a clearer positioning and comparison of existing proposals, providing a road map to assist further research and deployments in the area. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd
Ambulatory Assessment of Psychophysiological Stress among Police Officers: a Proof-of-Concept Study
No full textQuantification of Ethanol Concentration in Gasoline Using Cuprous Oxide Coated Long Period Fiber Gratings
No full textLabel Ranking Forests
Get PDFThe problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have been developed/adapted to treat rankings of a fixed set of labels as the target object, including several different types of decision trees (DT). One DT-based algorithm, which has been very successful in other tasks but which has not been adapted for label ranking is the Random Forests (RF) algorithm. RFs are an ensemble learning method that combines different trees obtained using different randomization techniques. In this work, we propose an ensemble of decision trees for Label Ranking, based on Random Forests, which we refer to as Label Ranking Forests (LRF). Two different algorithms that learn DT for label ranking are used to obtain the trees. We then compare and discuss the results of LRF with standalone decision tree approaches. The results indicate that the method is highly competitive
What catches the eye in class observation? Observers' perspectives in a multidisciplinary peer observation of teaching program
Get PDFPeer Observation of Teaching has raised a lot of interest as a device for quality enhancement of teaching. While much research has focused on its models, implementation schemes and feedback to the observed, little attention has been paid to what the observer actually sees and can learn from the observation. A multidisciplinary peer observation of teaching program is described, and its data is used to identify the pedagogical aspects to which lecturers pay more attention to when observing classes. The discussion addresses the valuable learning opportunities for observers provided by this program, as well as its usefulness in disseminating, sharing and clarifying quality teaching practices. The need for further research concerning teacher-student relationships and students' engagement is also suggested
Off the beaten track: A new linear model for interval data
Get PDFWe propose a new linear regression model for interval-valued variables. The model uses quantile functions to represent the intervals, thereby considering the distributions within them. In this paper we study the special case where the Uniform distribution is assumed in each observed interval, and we analyze the extension to the Symmetric Triangular distribution. The parameters of the model are obtained solving a constrained quadratic optimization problem that uses the Mallows distance between quantile functions. As in the classical case, a goodness-of-fit measure is deduced. Two applications on up-to-date fields are presented: one predicting duration of unemployment and the other allowing forecasting burned area by forest fires
An experimental analysis of the Trombe wall temperature fluctuations for high range climate conditions: Influence of ventilation openings and shading devices
No full textDespite the studies already developed about Trombe walls, more research work is needed to contribute to the knowledge about their behaviour and optimize it according to the specific characteristics of each climatic region. The ventilation openings and the shading device operation decisively influence the temperatures fluctuation along the system and that impact should be discussed. In this context, a test cell with a classical Trombe wall was submitted to real climatic conditions in a Portuguese city. The effect of ventilation openings and shading devices in the temperatures fluctuation was analysed. The temperatures in the air layer and along the massive wall presented a similar oscillation pattern and exceeded 60 °C without ventilation and shading devices. For this configuration, temperature values at the top of the air layer were always higher than those obtained at the base and a differential of 19 °C was achieved. The temperature fluctuation across the massive wall was not proportional to its thickness due to its heat storage capacity. When the ventilation system was closed and the shading device was not activated, the temperature inside the test cell exceeded the outside temperature value in 9 °C, showing the system ability to store and release heat. © 2016 Elsevier B.V