Abstract

Abstract

Abstract
Electricity physical and financial contracts are common instruments used by generators to reallocate their risk among other market participants. Likewise, from the regulator point of view, contracts are understood and promoted as a way to mitigate the generators market power exercise. Therefore the analysis of the impact of these derivatives on the electricity market prices and on the agents' strategic behavior is of interest for both regulator and market participants. This paper focuses on how physical and financial electricity contracts affect the generators' strategies, but does not deal with determining contracts' quantities or prices, that are assumed to be fixed and known. Strategies are computed for selected agents and different physical or financial contracted quantities, assuming inelastic demand. A CSFE methodology for endogenous conjectures computation, developed by the authors in previous works, has been applied. The examples highlight the importance of computing the conjectures when the contracted quantities vary, since the variable agents' position can significantly change the market results. The main conclusion is that the common understanding that contracts can decrease the market prices may be called into question. © 2012 IEEE.

Abstract
Recent digital games have been developed not only for entertainment purposes, but also to promote learning. This paper presents MOW (Matching Objects and Words), which is an Augmented Reality (AR) game for learning words in different languages. Experimental tests were performed with Portuguese children during English classes. We have compared the results from using MOW along with traditional teaching methods. The results indicate that children who used the Augmented Reality games had a superior learning progress than those who used only traditional methods.

Abstract
Mammographic databases play an important role in the development of algorithms aiming to improve Computer-Aided Detection and Diagnosis systems (CAD). However, these often do not take into consideration all the requirements needed for a proper study, previously discussed at the Biomedical Image Processing Meeting in 1993. Case selection and annotation requirements are the most commonly referenced in literature, when describing a database used for the development of such algorithms. This work aims to assess the compliance and suitability of case selection and annotation requirements in the publicly available mammographic databases for development and optimization of CADs. A literature review has been made, applying proper selection criteria related to the research question. In the literature, we found citations to 3 publicly available mammographic databases and ten having restricted access. Through the analysis of the results attained, we noticed that none of the two requirements previously described is on its way to be fully complied in mammographic databases. We can conclude that researchers need a database that fulfils all the mentioned requirements in order to develop efficacious and effective CAD systems. We also believe that the requirements, discussed in 1993, need to be reviewed and updated. New paradigms and ideas to increase algorithms' performance are needed in order to improve CAD schemes.

Abstract
This work addresses a built-in self-test methodology for circuit cell identification under specific matching conditions. The proposed technique is applied to the CMOS realization of a reduced-KII network, which is a system model of the biological olfactory cortex. This model behaves as an associative memory, a useful tool for information and adaptive processes. Based on a mixed-signal approach, the test strategy makes proper use of the circuits comprising the network structure, and provides self reconfiguration as well. Both testing procedures and design of essential building blocks are described in this paper. Simulation results are presented for a reduced-KII network comprising 128-cells, sequentially tested for matching in terms of offsets and their dynamic performances.