Interações magnéticas e elétricas em GaMnAs

In this paper, we will address together the magnetic and electrical properties of a particular semiconductor, the GaMnAs. The treatment will be done analytically in the first part of the work, according to the computational method for simulation of physical systems through the implementation of the expressions obtained in the first part. All study of magnetic contribution will be made using an interaction Kondo type, using an approach by Green functions. The electrical part, which consists of the Coulomb interactions between carriers and Mn ions, will be treated within the approach of multiple scattering. The implementation of the proposed method will calculate the Green functions converged as multiple scattering solution and use them as a starting point for the calculation of the effective magnetic interactions between Mn ions mediated charge carriers. The concentration parameters were varied for Mn ions and carriers as well. The combination of these two parameters can lead to insulating, metal samples with carriers in Fermi level to low or high mobility. As a result a correlation between the obtained carrier mobility and the strength of magnetic interaction. The greater mobility, the greater the intensity of the interaction.

O uso da Divergência de Kullback-Leibler e da Divergência Generalizada como medida de similaridade em sistemas CBIR

The content-based image retrieval is important for various purposes like disease diagnoses from computerized tomography, for example. The relevance, social and economic of image retrieval systems has created the necessity of its improvement. Within this context, the content-based image retrieval systems are composed of two stages, the feature extraction and similarity measurement. The stage of similarity is still a challenge due to the wide variety of similarity measurement functions, which can be combined with the different techniques present in the recovery process and return results that aren’t always the most satisfactory. The most common functions used to measure the similarity are the Euclidean and Cosine, but some researchers have noted some limitations in these functions conventional proximity, in the step of search by similarity. For that reason, the Bregman divergences (Kullback Leibler and I-Generalized) have attracted the attention of researchers, due to its flexibility in the similarity analysis. Thus, the aim of this research was to conduct a comparative study over the use of Bregman divergences in relation the Euclidean and Cosine functions, in the step similarity of content-based image retrieval, checking the advantages and disadvantages of each function. For this, it was created a content-based image retrieval system in two stages: offline and online, using approaches BSM, FISM, BoVW and BoVW-SPM. With this system was created three groups of experiments using databases: Caltech101, Oxford and UK-bench. The performance of content-based image retrieval system using the different functions of similarity was tested through of evaluation measures: Mean Average Precision, normalized Discounted Cumulative Gain, precision at k, precision x recall. Finally, this study shows that the use of Bregman divergences (Kullback Leibler and Generalized) obtains better results than the Euclidean and Cosine measures with significant gains for content-based image retrieval.

Algorithms for super-resolution of images based on Sparse Representation and Manifolds

lmage super-resolution is defined as a class of techniques that enhance the spatial resolution of images. Super-resolution methods can be subdivided in single and multi image methods. This thesis focuses on developing algorithms based on mathematical theories for single image super­ resolution problems. lndeed, in arder to estimate an output image, we adopta mixed approach: i.e., we use both a dictionary of patches with sparsity constraints (typical of learning-based methods) and regularization terms (typical of reconstruction-based methods). Although the existing methods already per- form well, they do not take into account the geometry of the data to: regularize the solution, cluster data samples (samples are often clustered using algorithms with the Euclidean distance as a dissimilarity metric), learn dictionaries (they are often learned using PCA or K-SVD). Thus, state-of-the-art methods still suffer from shortcomings. In this work, we proposed three new methods to overcome these deficiencies. First, we developed SE-ASDS (a structure tensor based regularization term) in arder to improve the sharpness of edges. SE-ASDS achieves much better results than many state-of-the- art algorithms. Then, we proposed AGNN and GOC algorithms for determining a local subset of training samples from which a good local model can be computed for recon- structing a given input test sample, where we take into account the underlying geometry of the data. AGNN and GOC methods outperform spectral clustering, soft clustering, and geodesic distance based subset selection in most settings. Next, we proposed aSOB strategy which takes into account the geometry of the data and the dictionary size. The aSOB strategy outperforms both PCA and PGA methods. Finally, we combine all our methods in a unique algorithm, named G2SR. Our proposed G2SR algorithm shows better visual and quantitative results when compared to the results of state-of-the-art methods.

Ampliação vocabular: glossário de textos do livro didático de língua portuguesa "Vontade de saber português" do 9º ano

This research aimed to verify the vocabulary difficulties faced by 9th year students while understanding the didactic book of Portuguese Language (DBPL) “Vontade de Saber Português”, used at the Municipal School Sebastião Rangel. We noticed the students had some doubts concerning the unknown vocabulary in the texts and, therefore, in text comprehension. The hypothesis is that one “difficult” word and the lexicon used by DBPL author can disturb student comprehension. We adopted some action which could simplify the little vocabulary understanding and contributed to extend it. For that reason, the job was theoretically based on Biderman (1999), Barbosa (1989), Dias (2004), Krieger (2012), Coelho (1993) and on National Curriculum Parameters of Portuguese Language, aiming to ally theory and practice. The application methodology of the proposal was done in order to the students understand that the word needs to be adapted to its context. At the begging of the job, the students read the texts and took notes of the “difficult” words, selecting, corpus. We analyzed the doubts, registering them. Then, we showed to the students the classification of abbreviated words after each entry. The students separated the words for grammar classes – lexical words” (KRIEGER, 2012). Such words have a very significant meaning to the comprehension of the read texts, being interesting to take a look in online dictionaries. In the creative glossary, done by the students, the words were spread in alphabetical order. They transcript the part where was the word and copied again, substituting the word to a clearer word. Finally, we asked the students a writing production using five words from the glossary; we showed them that the meaning of the words is not found only in the dictionary, but they can be used in different contexts. In the analyzes, was discovered that there is one necessity of a pedagogic didactic work more effective with elementary school lexicon. Thus, this proposal is not a closed receipt, but the infield location allowed a reflexive pedagogic practice about lexicon education.