Behavioral characteristics of applied general equilibrium models with an Armington-Krugman-Melitz encompassing module

This paper explore how simulation results change with different choice of trade specification, and the strength of preference for traded variety by economic agent differs, utilizing two types of three-region, three-sector AGE model that includes the Armington-Krugman-Melitz Encompassing module based on Dixon and Rimmer (2012). Simulation experiments reveal that: (1) the Melitz-type specification does not always enhance effectiveness of a certain policy change more than the one obtained with the Krugman-type, especially when economic agents' preference for traded variety is not so strong; (2) there are likely to be points where the volumes of effects obtained with the Melitz-type exceed the ones with the Krugman-type; and (3) the preference of the producers, those who are in the sectors that exhibit increasing returns to scale, for traded variety might be the engine of explosive effects as suggested by Fujita, et al. (2000).

Enhancing Activity Recognition by Fusing Inertial and Biometric Information

Activity recognition is an active research field nowadays, as it enables the development of highly adaptive applications, e.g. in the field of personal health. In this paper, a light high-level fusion algorithm to detect the activity that an individual is performing is presented. The algorithm relies on data gathered from accelerometers placed on different parts of the body, and on biometric sensors. Inertial sensors allow detecting activity by analyzing signal features such as amplitude or peaks. In addition, there is a relationship between the activity intensity and biometric response, which can be considered together with acceleration data to improve the accuracy of activity detection. The proposed algorithm is designed to work with minimum computational cost, being ready to run in a mobile device as part of a context-aware application. In order to enable different user scenarios, the algorithm offers best-effort activity estimation: its quality of estimation depends on the position and number of the available inertial sensors, and also on the presence of biometric information.

Hand Image Segmentation by means of Gaussian Multiscale Aggregation for biometric applications

Applying biometrics to daily scenarios involves demanding requirements in terms of software and hardware. On the contrary, current biometric techniques are also being adapted to present-day devices, like mobile phones, laptops and the like, which are far from meeting the previous stated requirements. In fact, achieving a combination of both necessities is one of the most difficult problems at present in biometrics. Therefore, this paper presents a segmentation algorithm able to provide suitable solutions in terms of precision for hand biometric recognition, considering a wide range of backgrounds like carpets, glass, grass, mud, pavement, plastic, tiles or wood. Results highlight that segmentation accuracy is carried out with high rates of precision (F-measure 88%)), presenting competitive time results when compared to state-of-the-art segmentation algorithms time performance