http://repositorio.jesuita.org.br/handle/UNISINOS/10029 Engenharia Eletrônica Tue, 21 Apr 2026 22:12:30 GMT 2026-04-21T22:12:30Z http://repositorio.jesuita.org.br/handle/UNISINOS/12134 Métodos de segmentação de máculas orais em imagens Kelsch, Carolina Rosa Every day, computational vision is growing and being used in health aid systems to improve performance and reduce the time of several processes. Despite that, the amount of research on oral lesions segmentation and classification is still very low. Oral and mouth cancers are the 16th most common form of cancer in the world and are presented with a high mortality rate when discovered late. One main problem that makes it hard to detect them in the early stages is the lack of specialized professionals, a gap that can be minimized by the use of telediagnosis and artificial intelligence. The segmentation process is already used in dermatology lesions, but there are still few works exploiting the oral cavity lesions. Such characteristics as borders and asymmetry can assist the diagnosis of cancer cases, but then a segmentation process is needed. Technologies such as artificial intelligence and image processing can be used to segment oral lesions, making the process quicker and allowing the assessment of more cases, thus helping more people. Of the few studies developed, the ones with the best results used deep learning to distinguish the lesions. Therefore, this work’s objective is to present and evaluate different methods for the automatic segmentation of oral macules and stains in photographic images using pixel-wise intensity features. Three methods to segment oral lesions were described in this research. They were evaluated in accuracy, precision, recall, and F1 score. The third method developed had the best performance in the tested images. It used a backprojection image created from the original inverted grayscale image and the Otsu binarization in two steps. This method resulted in an accuracy of 0.849, a precision of 0.701, a recall of 0.753, and an F1 score of 0.608. The results were satisfactory because they achieved values close to the related works, even without using complex algorithms or artificial intelligence. Mon, 21 Nov 2022 00:00:00 GMT http://repositorio.jesuita.org.br/handle/UNISINOS/12134 2022-11-21T00:00:00Z http://repositorio.jesuita.org.br/handle/UNISINOS/12133 Projeto e implementação de um protótipo de microinversor para geração fotovoltaica Bischoff, Patrício Adans do Canto Tue, 14 Jun 2022 00:00:00 GMT http://repositorio.jesuita.org.br/handle/UNISINOS/12133 2022-06-14T00:00:00Z http://repositorio.jesuita.org.br/handle/UNISINOS/12132 Segurança funcional aplicada a controladores industriais PID: um estudo de caso Gotardo, Guilherme Luiz Mon, 13 Jun 2022 00:00:00 GMT http://repositorio.jesuita.org.br/handle/UNISINOS/12132 2022-06-13T00:00:00Z http://repositorio.jesuita.org.br/handle/UNISINOS/11692 Sistema embarcado para aquisição de sinais eletroencefalográficos em tempo real com interface de acesso remoto Pinto, Murilo Machado Sat, 24 Jul 2021 00:00:00 GMT http://repositorio.jesuita.org.br/handle/UNISINOS/11692 2021-07-24T00:00:00Z http://repositorio.jesuita.org.br/handle/UNISINOS/11691 Classificação de sinais eletromiográficos utilizando redes neurais artificiais, análise discriminante linear e floresta aleatória Boelter, Josué da Silva There is a gap in the upper limb prosthesis market in relation to lower limb prostheses, this is due to the sum of a smaller market, after all, only approximately 20% of amputations performed are of upper limbs, added to a greater difficulty in developing these prostheses (ZIEGLER-GRAHAM, 2008), and to make it all worse, there is still a high acquisition cost in buying one. Modern advances in artificial intelligence and access to data processing, along with the rising of startups and scientists interested in using the best that data processing can offer, ensured a great technological advance in prosthesis models and a considerable reduction in costs. In this work, based on the public database Ninapro (Non-Invasive Adaptive Hand Prosthetics), three different artificial intelligence techniques were used, seeking to discover which one is the most promising in the classification of myoelectric signals. The algorithms used are Artificial Neural Networks, Linear Discriminant Analysis and Random Forest, with all its development, parameter adjustment, validation and testing being presented during the work. Based on the tests carried out, Random Forest was identified as the most promising of the three approaches, reaching an accuracy that ranged from 92% in sets of 5 movements to 84% in sets with all 52 movements. Wed, 23 Jun 2021 00:00:00 GMT http://repositorio.jesuita.org.br/handle/UNISINOS/11691 2021-06-23T00:00:00Z http://repositorio.jesuita.org.br/handle/UNISINOS/11690 Projeto de antena para etiqueta Rfid em Uhf para aplicação em objetos contendo líquidos e metais Kazu, Mena Simão Makengo Fri, 06 Dec 2019 00:00:00 GMT http://repositorio.jesuita.org.br/handle/UNISINOS/11690 2019-12-06T00:00:00Z http://repositorio.jesuita.org.br/handle/UNISINOS/11689 Projeto de circuito integrado para RF energy harvesting em tecnologia CMOS 180 NM Martins, Renan Daniel Dias Tue, 25 Jun 2019 00:00:00 GMT http://repositorio.jesuita.org.br/handle/UNISINOS/11689 2019-06-25T00:00:00Z http://repositorio.jesuita.org.br/handle/UNISINOS/11688 Processamento embarcado de sinais mioelétricos aplicado ao controle de próteses de mão Mirovski, Jonas Miguel Stanislau Beal Wed, 28 Nov 2018 00:00:00 GMT http://repositorio.jesuita.org.br/handle/UNISINOS/11688 2018-11-28T00:00:00Z http://repositorio.jesuita.org.br/handle/UNISINOS/11687 Sistema de monitoramento de parâmetros de processos industriais utilizando a tecnologia Lora® Farias, Daniel Mon, 03 Dec 2018 00:00:00 GMT http://repositorio.jesuita.org.br/handle/UNISINOS/11687 2018-12-03T00:00:00Z