A fractional calculus perspective in the evolutionary design of combinational circuits

This paper analyses the performance of a genetic algorithm (GA) in the synthesis of digital circuits using two novel approaches. The first concept consists in improving the static fitness function by including a discontinuity evaluation. The measure of variability in the error of the Boolean table has similarities with the function continuity issue in classical calculus. The second concept extends the static fitness by introducing a fractional-order dynamical evaluation.

On-line self-healing of circuits implemented on reconfigurable FPGAs

To boost logic density and reduce per unit power consumption SRAM-based FPGAs manufacturers adopted nanometric technologies. However, this technology is highly vulnerable to radiation-induced faults, which affect values stored in memory cells, and to manufacturing imperfections. Fault tolerant implementations, based on Triple Modular Redundancy (TMR) infrastructures, help to keep the correct operation of the circuit. However, TMR is not sufficient to guarantee the safe operation of a circuit. Other issues like module placement, the effects of multi- bit upsets (MBU) or fault accumulation, have also to be addressed. In case of a fault occurrence the correct operation of the affected module must be restored and/or the current state of the circuit coherently re-established. A solution that enables the autonomous restoration of the functional definition of the affected module, avoiding fault accumulation, re-establishing the correct circuit state in real-time, while keeping the normal operation of the circuit, is presented in this paper.

Implementação de um Array de detetores de radiação ionizante

Atualmente, as radiações ionizantes desempenham um papel fundamental nas áreas de diagnóstico e terapia, estando omnipresentes em ambientes hospitalares. Contudo, devido aos efeitos biológicos adversos da radiação, torna-se essencial a protecção dos profissionais de saúde e pacientes. Consequentemente, um array de detetores capazes de produzir um sinal acústico, aquando da presença de radiação ionizante excedendo determinados valores limite e transmissão via wireless das leituras para um sistema central _e de grande interesse prático. Nesta dissertação, foi implementado um sistema capaz de alimentar um array de sensores de radiação para monitorização de diferentes espaços e transmissão das leituras efetuadas via wireless. A aquisição de dados foi realizada, recorrendo à utilização de um conversor analógico-digital. Vários testes de validação foram realizados, através de vários passos para alcançar a concretização do sistema final, nomeadamente testes relativos ao circuito de detecção, módulos de comunicação wireless, bem como o uso de diferentes ambientes de desenvolvimento integrados (IDE). Os resultados destes testes mostram a visualização e gravação adequadas dos dados relativos aos níveis de radiação, bem como a transmissão de dados de forma viável, permitindo a monitorização de espaços sujeitos à presença de radiação ionizante. Desta forma, um array de contadores Geiger-Müller, ligados a módulos wireless XBee open-source e uma placa Arduino, possibilitou a implementação de um sistema viável e de baixo custo para monitorização de radiação ionizante e registar esses mesmos dados para posterior análise.

CardioML: integrating personal cardiac information for ubiquous diagnosis and analysis

The latest medical diagnosis devices enable the performance of e-diagnosis making the access to these services easier, faster and available in remote areas. However this imposes new communications and data interchange challenges. In this paper a new XML based format for storing cardiac signals and related information is presented. The proposed structure encompasses data acquisition devices, patient information, data description, pathological diagnosis and waveform annotation. When compared with similar purpose formats several advantages arise. Besides the full integrated data model it may also be noted the available geographical references for e-diagnosis, the multi stream data description, the ability to handle several simultaneous devices, the possibility of independent waveform annotation and a HL7 compliant structure for common contents. These features represent an enhanced integration with existent systems and an improved flexibility for cardiac data representation.

Academic effectiveness of VISIR remote lab in analog electronics

The effectiveness of VISIR is compared to other experimentation activities under the point of view presented by the professor Soysal in 2000. Advantages and limitations are discussed in terms of equipment availability, infrastructure cost, and contribution to various elements of experimental learning.

Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.