Myoelectric Signal Monitoring System

The Electromyography (EMG) is an important tool for gait analyzes and disorders diagnoses. Traditional methods involve equipment that can disturb the analyses, being gradually substituted by different approaches, like wearable and wireless systems. The cable replacement for autonomous systems demands for technologies capable of meeting the power constraints. This work presents the development of an EMG and kinematic data capture wireless module, designed taking into account power consumption issues. This module captures and converts the analog myoeletric signal to digital, synchronously with the capture of kinetic information. Both data are time multiplexed and sent to a PC via Bluetooth link. The work carried out comprised the development of the hardware, the firmware and a graphical interface running in an external PC. The hardware was developed using the PIC18F14K22, a low power family of microcontrollers. The link was established via Bluetooth, a protocol designed for low power communication. An application was also developed to recover and trace the signal to a Graphic User Interface (GUI), coordinating the message exchange with the firmware. Results were obtained which allowed validating the conceived system in static and with the subject performing short movements. Although it was not possible to perform the tests within more dynamic movements, it is shown that it is possible to capture, transmit and display the captured data as expected. Some suggestions to improve the system performance also were made.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Mode-Controlled Data-Flow Modeling of Real-Time Memory Controllers

Accepted in 13th IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia 2015), Amsterdam, Netherlands.

SNC / NCRF 24 – Acontecimentos após a data do balanço e suas implicações fiscais e de auditoria

Orientador Prof. Dr. João Domingues Costa

Geostatistical Mapping of Outfall Plume Dispersion Data gathered with an autonomous underwater vehicle

The main purpose of this study was to examine the applicability of geostatistical modeling to obtain valuable information for assessing the environmental impact of sewage outfall discharges. The data set used was obtained in a monitoring campaign to S. Jacinto outfall, located off the Portuguese west coast near Aveiro region, using an AUV. The Matheron’s classical estimator was used the compute the experimental semivariogram which was fitted to three theoretical models: spherical, exponential and gaussian. The cross-validation procedure suggested the best semivariogram model and ordinary kriging was used to obtain the predictions of salinity at unknown locations. The generated map shows clearly the plume dispersion in the studied area, indicating that the effluent does not reach the near by beaches. Our study suggests that an optimal design for the AUV sampling trajectory from a geostatistical prediction point of view, can help to compute more precise predictions and hence to quantify more accurately dilution. Moreover, since accurate measurements of plume’s dilution are rare, these studies might be very helpful in the future for validation of dispersion models.

An architecture for an effective usage of data mining in business intelligence systems

Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. Over the last years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. Therefore, a lack of an effective usage of DM in BI can be found in some BI systems. An architecture that pretends to conduct to an effective usage of DM in BI is presented.

Norma internacional de contabilidade [NIC 10 ]: acontecimento após a data de balanço

Revista Fiscal Maio 2006

O contributo da terapia ocupacional na análise dos ajustes posturais antecipatórios em crianças com 9 e 10 anos de idade, num sistema de captura de movimento - BioStage

O trabalho realizado teve como objetivo principal analisar os ajustes posturais antecipatórios que ocorrem durante o desempenho de uma tarefa motora fundamental (apanhar), em crianças entre os nove e os dez anos de idade, residentes no Porto e que apresentam um desenvolvimento normal com recurso ao sistema de captura e parametrização do movimento em tempo real BioStage ®. Como objetivo secundário pretendeu-se perceber de que forma este sistema pode ser uma ferramenta importante na prática clínica da terapia ocupacional. Para tal, realizou-se um estudo de natureza quantitativa e de carácter descritivo e recorreu-se a uma amostra de 12 crianças, utilizando o método de amostragem não probabilística por conveniência. A recolha de dados efetuou-se no sistema BioStage ® e foi pedido que realizassem quatro itens do subteste 5 do Bruininks-Oseretsky Test of Motor Proficiency (BOTMP) – receção bi e unilateral de uma bola com e sem ressalto no chão. Os resultados obtidos sugerem que as raparigas e as crianças mais novas demonstram ter menos estabilidade do tronco e pélvis ou menor capacidade de prever a trajetória da bola e que a receção unilateral foi mais difícil de efetuar pela maioria das crianças. Para concluir, refere-se que o BioStage ® mostra-se útil e é uma mais-valia, contribuindo de forma positiva para a prática da terapia ocupacional, uma vez que pode ser considerado como um complemento ao processo de avaliação pois faz uma análise detalhada, precisa e objetiva e identifica aspetos de difícil mensuração através da observação.

Análise dos padrões motores utilizados no desempenho de uma tarefa funcional, com recurso ao BioStage®, em crianças com 9 a 10 anos de idade, e seu contributo para a terapia ocupacional

O estudo do comportamento motor, nomeadamente as áreas do desenvolvimento e controlo motor, têm permitido fundamentar a prática da terapia ocupacional, proporcionando um entendimento mais abrangente de aspetos relacionados com a análise de movimento. Todavia, o processo de análise de atividades, por norma, é realizado de forma empírica, principalmente devido à carência de métodos que avaliem de forma objetiva e precisa o comportamento motor e, consequentemente, os movimentos realizados no desempenho de atividades. Neste sentido, este estudo pretendeu encontrar padrões motores em crianças entre os nove e os dez anos de idade, com desenvolvimento normal, que traduzam o desempenho de uma tarefa motora funcional, com recurso ao sistema de captura e parametrização do movimento em tempo real BioStage®. Por outro lado, tentou-se perceber se o sistema poderia revelar-se um contributo para a prática da terapia ocupacional, possibilitando a obtenção de dados que possam ser utilizados na clínica. As tarefas selecionadas para análise foram os cinco lançamentos propostos pelo Bruininks-Oseretsky Test of Motor Proficiency, que consistem no lançamento por baixo uni e bilateral, lançamento ao chão uni e bilateral e lançamento ao alvo (unilateral). Os resultados encontrados apontam que aos nove e dez anos existem padrões motores similares entre as crianças, no entanto ainda se nota uma ligeira variabilidade no comportamento. Aferiu-se, também, que a idade, sexo e prática de exercício físico podem influenciar os padrões utilizados, estando de acordo com a literatura. O sistema BioStage® mostrou-se uma ferramenta eficaz para a análise de movimento, providenciando informação detalhada sobre o comportamento motor das crianças, no decorrer das tarefas. Deste modo, pode ser uma mais-valia para a prática da terapia ocupacional, podendo contribuir para uma análise de atividades mais precisa, objetiva e fundamentada.

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Adaptive tool for automatic data collection of real electricity markets

The study of electricity markets operation has been gaining an increasing importance in last years, as result of the new challenges that the electricity markets restructuring produced. This restructuring increased the competitiveness of the market, but with it its complexity. The growing complexity and unpredictability of the market’s evolution consequently increases the decision making difficulty. Therefore, the intervenient entities are forced to rethink their behaviour and market strategies. Currently, lots of information concerning electricity markets is available. These data, concerning innumerous regards of electricity markets operation, is accessible free of charge, and it is essential for understanding and suitably modelling electricity markets. This paper proposes a tool which is able to handle, store and dynamically update data. The development of the proposed tool is expected to be of great importance to improve the comprehension of electricity markets and the interactions among the involved entities.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.

A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.