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Most clinical measurements rely on bioelectromagnetic phenomena. These events allow us to record electric or magnetic signals during the activity of living tissues. In this paper, we put our attention on the bioelectric fields that occur in the muscle activity. In fact during the body movements, the muscle contractions produce a bioelectric potential distribution that can be measured by putting the electrodes on the skin. In clinical applications, the monitoring of muscle activity can be performed in a noninvasive way, by placing a fixed number of electrodes on the skin surface; this technique is called surface electromyography (sEMG), and it is able to reveal the electric field generated by each muscle activity. Unfortunately, the sEMG suffers from the external electromagnetic fields. In recent years, many authors investigated the correlation between muscle and brain activity by performing the sEMG during the functional magnetic resonance imaging (fMRI). The fMRI is the most reliable technique to evaluate the brain activity because it allows us to obtain some images of the human body with very high resolutions. Unfortunately, joint measurement is a very difficult task because of the high electromagnetic interference between the resonance coils (very high magnetic fields) and the sEMG electrodes. In this paper, we present a method based on wavelet analysis to reveal sEMG in voluntary contractions when the measurement is made in a fMRI environment. [ABSTRACT FROM AUTHOR]

Presents a study that developed a method for detecting and classifying defects in metallic plates. Background on nondestructive testing in the field of defect identification; Analysis of fuzzy entropy in measuring the depth of defects; Results and implications.

Presents a study that applied a fuzzy time series approach to the detection of disruption in Tokamak reactors. Background on the incidence of disruptions in Tokamaks; Analysis of the effectiveness of the disruption prediction approach; Results and implications.

Proposes a neural network model to treat inverse problems in electromagnetics which includes wavelet functions to improve local approximation capabilities. Wavelet approach; Novel combined processor; Details on test case.