Abstract
This paper describes the use of NAO robot, a humanoid robot and the use of Arduino in education for final engineering projects in the area of engineering. This learning approach was implemented in the final project of the Biomedical Engineering degree. The objective this project is the sensorial extension of the NAO platform, integrating a set of sensors that allow not only to analyze multiple vital parameters in a passive way but also to obtain a more precise contextual information. © CLAWAR Association.

Abstract
Over the years robotics has made great progress. Nowadays, robots begin to be part of the life of any person, designated social robotic. Humanoid robots are fascinating and have several advantages, such as they can work in places where there is a risk of contamination, risk of health, danger of life, places that are difficult to access. They also are able to access different types of terrain and to climb stairs. NAO robot is currently the humanoid platform with high sensory capacity that it has lower costs in the market. This robot is similar to human in order to have a more real and natural with society. Using the capabilities of the robot and adding other sensors, we can have a more powerful machine in our society. NAO robot is presented in this paper as a domestic robot. © CLAWAR Association.

Abstract
The challenge of noise attenuation in images has led to extensive research on improved noise reduction techniques, preserving important image characteristics, improving not only visual perception, but also enabling the use for special purposes, such as in medicine to increase clarity of medical images. In this paper, a technique for noise attenuation in medical images is proposed. Its operation takes place through the application of an adapted genetic algorithm. The results of experiments show that the proposed approach works best in suppressing artifacts and the preservation of the structure compared with several existing methods.

Abstract
Millions of people worldwide suffer from motor disabilities, which bring alongside many other related problems, such as self-esteem issues and their constant need for a carer. While there are treatments to reduce the impact of the impairment on the patient's life, they can be either very costly or be alternative methods that are not directly aimed at rehabilitation purposes, such as the usage of Kinect Adventures to make the patient perform favorable poses and stretching limbs to help the undergoing treatment. Even though a low-cost device, the Kinect can acquire valuable data and make the patient perform exercises. However, a more direct and targeted approach might achieve better results with a game aimed for them. To attend to the many forms and variations of motor impairments, a starting point may be necessary for the development of exergames that fulfill the needs of each patient and impairment. The result is a low-cost base project that can be easily used, created using only online free tools while giving the developer creative freedom and a properly aimed solution to the patients.

Abstract
The objective of this work is study, implementation and evaluation of compression techniques used in bioelectrical signals, applied to electroencephalography. For that, the fundamental concepts of Fast Walsh Hadamard Transform (FWHT), the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT), in essence, the mathematical models were studied. In these systems, the applicability and principles of operation were considered the Peak Signal to Noise Ratio (PSNR), Signal to Noise Ratio (SNR), Mean Absolute Error (MAE) and mean squared error. Later, it is proposed the implementation of the compression algorithms. For the implementation of the techniques, computational tools of tests were developed, and for the purposes of validation and comparison of the results were used, with the appropriate adaptations, and described in the work, being these among the most recognised in terms of evaluation of signal quality. Finally, we present the results and the conclusions, where we sought a compromise of the implementations between the estimated percentage of DCT and the level of degradation of the signal provided by the compression application. In this sense, it was verified that they presented satisfactory results.

Abstract
This paper presents a method capable of detecting and segmenting pulmonary nodules in clinical computed tomography images, using UNet convolutional neural network powered by The Lung Image Database Consortium image collection - LIDC-IDRI, that in the training process was submitted to different training tests, where for each of them, their hyper-parameters were modified so that the results could be collected from different media, getting quite satisfactory results in the segmentation task, highlighting the areas of interest almost perfectly, resulting in 91.61% on the IoU (Intersection over Union) metric.