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About

José Machado da Silva obtained a PhD degree on electrical and computer engineering in 1998 and is currently an Associated Professor at the Faculty of Engineering of the University of Porto, and a Senior Researcher at INESC Porto. His teaching and research interests include analogue, mixed-signal, and RF VLSI design and test, signal processing, and biomedical electronics.

Interest
Topics
Details

Details

003
Publications

2017

Beam for the steel fabrication industry robotic systems

Authors
Rocha, LF; Tavares, P; Malaca, P; Costa, C; Silva, J; Veiga, G;

Publication
ISARC 2017 - Proceedings of the 34th International Symposium on Automation and Robotics in Construction

Abstract
In this paper, we present a comparison between the older DSTV file format and the newer version of the IFC standard, dedicating special attention of its impact in the robotization of welding and cutting processes in the steel structure fabrication industry. In the last decade, we have seen in this industry a significant increase in the request for automation. These new requirements are imposed by a market focused on the productivity enhancement through automation. Because of this paradigm change, the information structure and workflow provided by the DSTV format needed to be revised, namely the one related with the plan and management of steel fabrication processes. Therefore, with this work we enhance the importance of the increased digitalization of information that the newer version of the IFC standard provide, by showing how this information can be used to develop advanced robotic cells. More in detail, we will focus on the automatic generation of robot welding and cutting trajectories, and in the automatic part assembly planning during components fabrications. Besides these advantages, as this information is normally described having as base a perfect CAD model of the metallic structure, the resultant robot trajectories will have some dimensional error when fitted with the real physical component. Hence, we also present some automatic approaches based on a laser scanner and simple heuristics to overcome this limitations.

2017

Correntropy applied to fault detection in analogue circuits

Authors
Da Silva, JM;

Publication
Proceedings of the 2017 IEEE 22nd International Mixed-Signals Test Workshop, IMSTW 2017

Abstract
Efficient test and diagnosis methods are required to ensure high levels of dependability of the electronic systems deployed to the market. These methods involve a trade-off in terms of accessibility to test nodes, test stimuli complexity, area overhead, and data processing that, altogether determine the impact that the involved operations have in the final cost, performance, and reliability presented by these systems. The work presented here describes preliminary results obtained with the application of correntropy as a means to efficiently analyse test responses in the fault detection decision process. © 2017 IEEE.

2016

Statistically Enhanced Analogue and Mixed-Signal Design and Test

Authors
Ramos, PL; da Silva, JM; Ferreira, DR; Santos, MB;

Publication
PROCEEDINGS OF THE 2016 IEEE 21ST INTERNATIONAL MIXED-SIGNALS TEST WORKSHOP (IMSTW)

Abstract
The design, manufacture and operational characteristics (e.g., yield, performance, and reliability) of modern electronic integrated systems exhibit extreme levels of complexity that cannot be easily modelled or predicted. Different mathematical methodologies have been explored to address this issue. Monte Carlo simulation is the most widely employed and straightforward approach to evaluate the circuits' performance statistics. However, the high number of trial cases and the long simulations times required to obtain results for complex circuits with a ppm resolution, lead to very long analysis times. The present work addresses the evaluation of alternative statistical inference methodologies which allow obtaining similar results departing from a smaller dimension data set of Monte Carlo simulations from which the overall population is estimated. These methodologies include the use of Bayesian inference, Expectation-inimization, and Kolmogorov-Smirnov tests. Results are presented which show the validity of these approaches.

2016

A Fuzzy Logic Approach for a Wearable Cardiovascular and Aortic Monitoring System

Authors
Oliveira, CC; Dias, R; da Silva, JM;

Publication
ICT INNOVATIONS 2015: EMERGING TECHNOLOGIES FOR BETTER LIVING

Abstract
A new methodology for fault detection on wearable medical devices is proposed. The basic strategy relies on correctly classifying the captured physiological signals, in order to identify whether the actual cause is a wearer health abnormality or a system functional flaw. Data fusion techniques, namely fuzzy logic, are employed to process the physiological signals, like the electrocardiogram (ECG) and blood pressure (BP), to increase the trust levels of the captured data after rejecting or correcting distorted vital signals from each sensor, and to provide additional information on the patient's condition by classifying the set of signals into normal or abnormal condition (e.g. arrhythmia, chest angina, and stroke). Once an abnormal situation is detected in one or several sensors the monitoring system runs a set of tests in a fast and energy efficient way to check if the wearer shows a degradation of his health condition or the system is reporting erroneous values.

2016

Fault Diagnosis in Highly Dependable Medical Wearable Systems

Authors
Oliveira, CC; da Silva, JM;

Publication
JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS

Abstract
High levels of dependability are required to promote the adherence by public and medical communities to wearable medical devices. The study presented herein addresses fault detection and diagnosis in these systems. The main objective resides on correctly classifying the captured physiological signals, in order to distinguish whether the actual cause of a detected anomaly is a wearer health condition or a system functional flaw. Data fusion techniques, namely fuzzy logic, artificial neural networks, decision trees and naive Bayes classifiers are employed to process the captured data to increase the trust levels with which diagnostics are made. Concerning the wearer condition, additional information is provided after classifying the set of signals into normal or abnormal (e.g., arrhythmia, tachycardia and bradycardia). As for the monitoring system, once an abnormal situation is detected in its operation or in the sensors, a set of tests is run to check if actually the wearer shows a degradation of his health condition or if the system is reporting erroneous values. Selected features from the vital signals and from quantities that characterize the system performance serve as inputs to the data fusion algorithms for Patient and System Status diagnosis purposes. The algorithms performance was evaluated based on their sensitivity, specificity and accuracy. Based on these criteria the naive Bayes classifier presented the best performance.

Supervised
thesis

2017

Self-testable and self-calibratable PLL

Author
Rui Andre Moutinho Teixeira

Institution
UP-FEUP

2017

LOW-COST OPEN SYSTEM FOR THE EFFICIENT USE OF WATER IN ORCHARDS AND VINEYARDS

Author
JUCILENE DE MEDEIROS SIQUEIRA

Institution
UP-FEUP

2017

Avaliação e Melhoria da Qualidade de Sinais ECG Adquiridos em Sistemas Vestíveis

Author
Cátia Rafaela Rodrigues Fernandes

Institution
UP-FEUP

2017

Dispositivo Médico baseado em Tecidos Inteligentes

Author
Filipa Daniela Amaral da Costa

Institution
UP-FEUP

2017

Statistical Enhanced Analog Design

Author
José Diogo Queiroz de Athayde Agorreta de Alpuim

Institution
UP-FEUP