Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
Page not in menu
This is a page not in th emain menu
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
MATHEMATICAL MORPHOLOGY AND ARTIFICIAL INTELLIGENCE APPLIED TO HELP GOLF-BALLS COLLECTING IN DRIVING RANGES
Published in International Journal of Applied Mathematics, 2014
Manual golf-balls collecting in indoor driving ranges is a tedious task. The objective of this paper is to propose a method that contributes to automate the golf-balls collecting in indoor driving ranges. This method applies Mathematical Morphology and Artificial Intelligence to help the balls collecting. The proposal is represented by a program based on Mathematical Morphology applied to balls detection and Genetic Algorithm applied to solve the Travelling Salesman Problem to conduct a virtual robot for balls collecting in a virtualized intelligent indoor driving range. Images of balls scattered on the ground were processed to obtain the results. The results show the relevance of our project as a method to help balls collecting in indoor driving ranges. Our proposal contributes to the implementation of a low computational-cost method for automatic organization of driving ranges and to low computational-cost intelligent environments.
Recommended citation: DE MELO, Maximilian Jaderson et al. MATHEMATICAL MORPHOLOGY AND ARTIFICIAL INTELLIGENCE APPLIED TO HELP GOLF-BALLS COLLECTING IN DRIVING RANGES. International Journal of Applied Mathematics, v. 27, n. 1, p. 73-88, 2014. www.diogenes.bg/ijam/contents/2014-27-1/8/8.pdf
Robust and adaptive chatter free formation control of wheeled mobile robots with uncertainties
Published in ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 2018
This paper addresses the robust formation control of non-holonomic mobile robots with homogeneous system architecture and decentralized control structure. Therefore, it was necessary the mathematical modeling of mobile robots, from which, the Separation-Bearing variant of Leader-Following control strategy was implemented. The stability proof were based on the Lyapunov theory. The sliding mode control (SMC) strategy was used in the controller design to make the control robust to the incidence of uncertainties and disturbances. The Fuzzy Adaptive Formation Control is designed to eliminate the previous bounding knowledge of these uncertainties and disturbances. The proposed control effectiveness is demonstrated by results obtained with simulations in Matlab/Simulink. The pure kinematic and kinematic with disturbances is also analyzed. The results shows the controllers effectiveness to formation of multi-robots systems to the eight-shaped trajectory.
Recommended citation: DE MELO, Maximilian Jaderson et al. Robust and adaptive chatter free formation control of wheeled mobile robots with uncertainties. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, v. 7, n. 2, p. 27-42, 2018. https://revistas.usal.es/cinco/index.php/2255-2863/article/download/ADCAIJ2018722742/19655
Automatic segmentation of cattle rib-eye area in ultrasound images using the UNet++ deep neural network
Published in Computers and Electronics in Agriculture, 2022
Ultrasound imaging is commonly used to estimate the size of various cuts of meat or quality traits in live animals. Unfortunately, ultrasound images are known for having large amount of visual noise, which can make it difficult to define the exact boundaries or shapes of the regions of the interest in these images. Therefore, new strategies related to the digital image processing field are required to improve the process of obtaining information from these groups of images. In this context, artificial intelligence, through deep learning methods particularly, has proved to be an optimized and efficient strategy, but that has not yet been investigated in the cattle rib-eye area. This paper aims to investigate the feasibility of applying the Unet++ deep neural network to automatic segmentation of cattle rib-eye area in ultrasound images. Additionally, several well established deep learning semantic segmentation models are compared with Unet++ performance. These architectures are FCN, U-Net, SegNet, and Deeplab v3+. The models were tested on a dataset composed of gray scale images of cattle ultrasound. All models showed excellent results in both location and boundaries. Best results showed 97.37% in IoU, 1.14cm2 in MAE and coefficient of determination (R2) of 0.999. The labeled rib-eye area dataset used in this study is available for future research.
Recommended citation: DE MELO, Maximilian Jaderson et al. Automatic segmentation of cattle rib-eye area in ultrasound images using the UNet++ deep neural network. Computers and Electronics in Agriculture, v. 195, p. 106818, 2022. https://www.sciencedirect.com/science/article/abs/pii/S0168169922001351
Semantic segmentation with labeling uncertainty and class imbalance applied to vegetation mapping
Published in International Journal of Applied Earth Observation and Geoinformation, 2022
Recently, Convolutional Neural Networks (CNN) methods achieved impressive success in semantic segmentation tasks. However, challenges like class imbalance around samples and the uncertainty in human pixel-labeling are not completely addressed. Here we present an approach that calculates a weight for each pixel considering its class and uncertainty during the labeling process. The pixel-wise weights are used at the training phase to increase or decrease the importance of the pixels accordingly. Experimental results were conducted adapting well-known CNN methods FCN and SegNet; however, this strategy can be applied to any segmentation method. We evaluated the experiments for semantic segmentation of urban trees in aerial imageries. The robustness of the approach was assessed using a dataset with terrestrial images from vegetation with a drastic imbalance condition. We achieved significant improvements in the tasks compared to the baseline methods. We also verified that the proposed strategy proved to be more invariant to noise. The approach presented in this paper could be used within a wide range of semantic segmentation methods to improve their robustness.
Recommended citation: BRESSAN, Patrik Olã et al. Semantic segmentation with labeling uncertainty and class imbalance applied to vegetation mapping. International Journal of Applied Earth Observation and Geoinformation, v. 108, p. 102690, 2022. https://www.sciencedirect.com/science/article/pii/S0303243422000162
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience UTFPR
Undergraduate course, Federal University of Technology of Paraná, Department of Computing, 2016
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience at IFMS (since 2017)
Certificate program, Federal Institute of Education, Science and Technology of Mato Grosso do Sul - IFMS, 2017
Information technology teacher since 2017. I worked mainly in the subjects of Object Oriented Programming and Information Security. I am an enthusiast of events related to competitive programming and educational robotics, as well as hackaflags. I have developed several computer research projects, as well as supervised several Undergraduate thesis.