Roseli de Deus Lopes

Geovane Fedrecheski, Laisa C. P. Costa, Samira Afzal et al.

We present a low-overhead mechanism for self-sovereign identification and communication of IoT agents in constrained networks. Our main contribution is to enable native use of Decentralized Identifiers (DIDs) and DID-based secure communication on constrained networks, whereas previous works either did not consider the issue or relied on proxy-based architectures. We propose a new extension to DIDs along with a more concise serialization method for DID metadata. Moreover, in order to reduce the security overhead over transmitted messages, we adopted a binary message envelope. We implemented these proposals within the context of Swarm Computing, an approach for decentralized IoT. Results showed that our proposal reduces the size of identity metadata in almost four times and security overhead up to five times. We observed that both techniques are required to enable operation on constrained networks.

Maryana Alegro, Eduardo J. L. Alho, Maria da Graca Morais Martin et al.

Although the latest advances in MRI technology have allowed the acquisition of higher resolution images, reliable delineation of cytoarchitectural or subcortical nuclei boundaries is not possible. As a result, histological images are still required to identify the exact limits of neuroanatomical structures. However, histological processing is associated with tissue distortion and fixation artifacts, which prevent a direct comparison between the two modalities. Our group has previously proposed a histological procedure based on celloidin embedding that reduces the amount of artifacts and yields high quality whole brain histological slices. Celloidin embedded tissue, nevertheless, still bears distortions that must be corrected. We propose a computational pipeline designed to semi-automatically process the celloidin embedded histology and register them to their MRI counterparts. In this paper we report the accuracy of our pipeline in two whole brain volumes from the Brain Bank of the Brazilian Aging Brain Study Group (BBBABSG). Results were assessed by comparison of manual segmentations from two experts in both MRIs and the registered histological volumes. The two whole brain histology/MRI datasets were successfully registered using minimal user interaction. We also point to possible improvements based on recent implementations that could be added to this pipeline, potentially allowing for higher precision and further performance gains.