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Publicações

Publicações por Nuno Miguel Ferreira

2019

Comparative Study of Compression Techniques Applied in Different Biomedical Signals

Autores
Saraiva, A; Castro, FMJ; Costa, NC; Sousa, JVM; Fonseca Ferreira, NMF; Valente, A; Soares, S;

Publicação
BIOSIGNALS: PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 4: BIOSIGNALS

Abstract
This paper aims to compare the compression of electro-oculographic signals, based on the (EOG) from MIT / BIH database, and the electromyographic signals, based on the (EMG) from MIT / BIH database, for that purpose, two compression techniques that can be used in electro-oculograms and electromyograms was approached, the two techniques mentioned above, were, the discrete cosine transform and Fast Walsh Hadamard Transform. For statistic the methods used was, the Mean squared error, mean absolute error, signal-to-noise ratio and peak signal-to-noise ratio as well, and for results, the techniques and they performance on each tested signal.

2019

Data Acquisition from the Integration of Kinect Quaternions and Myo Armband EMG Sensors to Aid Equinus Foot Treatment

Autores
Araújo, FMA; Ferreira, NMF; Soares, SFSP; Valente, A; Junior, GLS;

Publicação
PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 1 (BIODEVICES)

Abstract
This paper shows the advantage of using different sensors such the Microsoft Kinect and Myo Armband to acquire movement description of the plantarflexion and dorsiflexion of the foot with the help of the quaternions and the EMG Myo sensor. For the integration of these devices, it was chosen Python to develop the algorithm and create an interface to aid the signal acquisition. This integration, enabling an accurate motion description as well as a scale of EMG signal, allow the possibility of quantifying the treatment of the people with equinus foot.

2019

Models of Learning to Classify X-ray Images for the Detection of Pneumonia using Neural Networks

Autores
Saraiva, AA; Santos, DBS; Costa, NC; Sousa, JVM; Fonseca Ferreira, NMF; Valente, A; Soares, S;

Publicação
BIOIMAGING: PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 2

Abstract
This article describes a comparison of two neural networks, the multilayer perceptron and Neural Network, for the detection and classification of pneumonia. The database used was the Chest-X-Ray data set provided by (Kermany et al., 2018) with a total of 5840 images, with two classes, normal and with pneumonia. to validate the models used, cross-validation of k-fold was used. The classification models were efficient, resulting in an average accuracy of 92.16% with the Multilayer Perceptron and 94.40% with the Convolution Neural Network.

2019

Noise Attenuation using Genetic Algorithm in CT Image

Autores
Saraiva, AA; de Oliveira, MS; Sousa, JVM; Ferreira, NMF; Valente, A; Soares, S;

Publicação
PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 1 (BIODEVICES)

Abstract
The techniques of image filtering have undergone an explosive growth in the last years to make new advances and challenges. This is due to the fact, among several other reasons, the increase of the volume of images coming from several sources. Digital images have been used for a variety of purposes, from the storage of souvenirs to accurate medical exams. However, Images may be corrupted due to several factors. The challenge of suppression or noise attenuation has led to the search for improved techniques in order to preserve important characteristics of the image, but, on the other hand, there is no solution available to completely solve the problem, boosting the production of the work proposed here. In this paper proposes a method for noise attenuation in computed tomography images using a hybrid genetic algorithm, the proposed method seeks to optimize the results in the space of solutions composed by a series of techniques of noise filtering. At the end the proposed method is compared statistically with two other competing methods and after the resulting filtered images are shown.

2019

Learning Computer Vision using a Humanoid Robot

Autores
Vital, JPM; Fonseca Ferreira, NMF; Valente, A; Filipe, V; Soares, SFSP;

Publicação
PROCEEDINGS OF 2019 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON)

Abstract
This paper presents an innovative and motivating methodology to learn vision systems using a humanoid robot, NAO robot. Vision systems are an area of growing development and interest of engineering students. This approach to learning was applied in students of Master of Electrical Engineering. The goal is to introduce students the main approaches of visual object recognition and human face recognition using computer vision techniques to be embedded in a social robot and therefore he is able to iteract with human beings. NAO robot as an educational platform easy to learn how to program, and it has a high sensory ability and two cameras that can capture the images for processing.

2019

Scalable task clean-up assignment for multi-agents

Autores
Saraiva, AA; Costa, NJC; Sousa, JVM; De Araujo, TP; Fonseca Ferreira, NM; Valente, A;

Publicação
Robotics Transforming the Future - Proceedings of the 21st International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2018

Abstract
This paper describes a group of robots for cleaning a simulated environment and proposes an efficient algorithm for navigation based on Pathfinding A *. No need for vision sensors. As a result it was observed that the robots can work cooperatively to clear the ground and that the navigation algorithm is effective in cleaning. In order to test its efficiency it was compared the combination of the Pathfinding A* algorithm and the decision algorithm proposed in this paper with Pathfinding A* and Euclidean distance, resulted in an improvement in time and distance traveled. © CLAWAR Association.

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