Removal of 60 Hz interference on the ECG signal using digital notch filter.

A interfer?ncia de 60 Hz AC pode ser um problema em qualquer situa??o de medi??o de biopotencial. A fonte dessa interfer?ncia ? o potencial AC da rede de alimenta??o de energia el?trica que est? inevitavelmente presente em qualquer situa??o cl?nica, ou para ilumina??o do ambiente ou como fonte de suprimento dos equipamentos de medi??o. A interfer?ncia causada pela rede el?trica, em 60 Hz, pode ser dif?cil de detectar visualmente em sinais tendo formas de onda n?o-regulares, como o EEG ou o EMG. N?o obstante, a interfer?ncia ? facilmente vis?vel quando presente em sinais com formas de onda bem definidas, como ? o caso do sinal de ECG (Eletrocardiograma). Em todo caso, o espectro de pot?ncia do sinal deve fornecer uma indica??o clara da presen?a da interfer?ncia da rede como um impulso em 60 Hz. Os harm?nicos, caso presentes, aparecem como impulsos adicionais em m?ltiplos inteiros da frequ?ncia fundamental. Neste trabalho ? demonstrada uma t?cnica de filtragem, empregando um filtro ?Notch? digital, o qual remove o artefato de 60 Hz do sinal de ECG, aumentando a confiabilidade do diagn?stico cl?nico a partir da interpreta??o do mesmo.60 Hz AC interference can be a problem in any biopotential measurement situation. The source of such interference is the AC potential of the electrical power supply network that is inevitably present in any clinical situation, either for lighting the environment or as a source of supply for the measuring equipment. Electrical interference at 60 Hz can be difficult to detect visually on signals having non-regular waveforms such as EEG or EMG. Nevertheless, the interference is easily visible when present in signals with well-defined waveforms, such as the ECG (Electrocardiogram) signal. In any case, the power spectrum of the signal shall provide a clear indication of the presence of the network interference as a 60 Hz pulse. The harmonics, if present, appear as additional pulses in integral multiples of the fundamental frequency. In this work, a filtering technique is demonstrated, using a digital Notch filter, which removes the 60 Hz artifact from the ECG signal, increasing the reliability of the clinical diagnosis from its interpretation

Fault location technique in transmission lines using the minimum square method.

A investiga??o de diferentes tipos de faltas em linhas de transmiss?o ? uma tarefa complexa e de extrema import?ncia para o Sistema El?trico de Pot?ncia (SEP). A modelagem da linha de transmiss?o deve ser estabelecida da forma mais rigorosa poss?vel, visando ? precis?o das dist?ncias de faltas simuladas a partir dos modelos levantados. Neste trabalho ser? modelada uma linha de transmiss?o em circuito simples usando o programa de c?lculo de transit?rios eletromagn?ticos ATPDraw? e, posteriormente, o algoritmo de localiza??o de falta baseado no emprego do m?todo dos m?nimos quadrados ser? implementado no MATLAB?. O desempenho do m?todo ser? discutido em termos de precis?o e robustez dos resultados.The investigation of different types of faults in transmission lines is a complex and extremely important task for the Electric Power System (EPS). The modeling of the transmission line should be established as rigorously as possible, aiming at the accuracy of simulated fault distances from the models surveyed.. In this work, a simple circuit transmission line will be modeled using the ATPDraw? electromagnetic transient program, and later the fault localization algorithm based on the use of the least squares method will be implemented in MATLAB?. The performance of the method will be discussed in terms of accuracy and robustness of the results

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost