Publicações

Despite the significant advancements in drone sensory device reliability, data integrity from these devices remains critical in securing successful flight plans. A notable issue is the vulnerability of GNSS to jamming attacks or signal loss from satellites, potentially leading to incomplete drone flight plans. To address this, we introduce SiaN-VO, a Siamese neural network designed for visual odometry prediction in such challenging scenarios. Our preliminary studies have shown promising results, particularly for flights under static conditions (constant speed and altitude); while these findings are encouraging, they do not fully represent the complexities of real-world flight conditions. Therefore, in this paper, we have furthered our research to enhance SiaN-VO, improving data integration from multiple sensors and enabling more accurate displacement predictions in dynamic flight conditions, thereby marking a significant step forward in drone navigation technology.

In clustering problems where the objective is not based on specifically spatial proximity, but rather on feature patterns and the semantic description, traditional internal cluster validation indices might not be appropriate. This article proposes a novel validity index to suggest the most appropriate number of clusters based on a semantic description of categorical databases. To assess our index, we also propose a synthetic data generator specifically designed for this type of application. We tested data sets with different configurations to assess the performance of the proposed index compared to well-known indices in the literature. Thus, we demonstrate that the index has great potential for discovering the number of clusters for the type of application studied and the data generator is able to produce relevant data sets for the internal validation process.

There are currently large amounts of public databases on COVID-19 patients. Among them the FAPESP COVID-19 Data Sharing/BR has gathered laboratory tests and hospitalization data from large health centers in the São Paulo metropolitan area. This paper uses part of such a repository to assemble a set of networks of positive COVID-19 patients according to the similarity of their age and laboratory tests results. Next, popular complex network metrics such as clustering coefficient and average path length are extracted from such networks and compared to the expected values observed for classical networks. Similarities of the clustering coefficient values with those of Watts-Strogatz networks were observed, although there are no sustainable characteristics of Small World networks. There are also similarities to scale-free networks, such as high-degree variance and the presence of hubs of nodes. In addition, a partition of the networks based on the modularity measure using the Fast Greedy algorithm is obtained and analyzed. An structure with four clusters and modularity values greater than zero was found, indicating that the network has some community structure.