Ajustes de coeficientes integrantes de processos físicos e bioquímicos na autedepuração em curso de água de médio porte

The mathematical modeling in the simulation of self-purification capacity in lotic environment is an important tool in the planning and management of hydric resources in hydrographic basin scale. It satisfactorily deals with the self-purification process when the coefficients of physical and biochemical processes are calibrated from monitorated water quality data, which was the main focus of this study. The present study was conducted to simulate the behavior of the parameters OD, BOD5, total phosphorus, E. coli, ammonia, nitrite, nitrate and the total metals cadmium, chromium, copper, lead and zinc in the Uberabinha’s lower course (with an approximate annual growth flow between 4-35 m3/s), in a stretch of 19 km downstream of the treated effluent release by the WWTP of the city. The modelings, on the present study, show the importance of constant water quality parameters monitoration over the water course, based on the comparison of the simulations from calibrated coefficients and coefficients obtained in the literature for the period of June until November 2015. After coefficients calibration, there were good adjustments between simulated and measured data for the parameters OD, BOD, Ptotal, ammonia and nitrate and unsatisfactory adjust for the parameters nitrite and E. coli. About the total metals, the adjustments were not satisfactory on the reservoir’s vicinity of the Small Hydropower Plant Martins, due the considerable increase of the bottom sediment in lentic region. The greatest scientific contribution of this study was to calibrate the decay coefficient K and the quantification of the release by the fund S of total metals in watercourse midsize WWTP pollutant load receptor, justified by the lack of studies in the literature about the subject. For the metals cadmium, chromium, copper, lead and zinc, the borderline for K and S calibrated were: 0.0 to 13.0 day-1 and 0.0 to 1.7 g/m3.day; 0.0 to 0.9 day-1 and 0.0 to 7.3 g/m3.day; 0.0 to 25.0 day-1 and 0.0 to 1.8 g/m3.day; 0.0 to 7.0 day-1 and 0.0 to 40.3 g/m3.day; 0.0 to 30.0 day-1 and 0.0 to 70.1 g/m3.day.

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.