• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.40 no.3
• /
• pp.181-188
• /
• 2003

In this paper, we introduce a magnetic noise shielding method to reduce the noise effects in permanent magnet based MRI systems. Through FEM electromagnetic analyses, we have shown that the magnetic noise component parallel to the main magnetic field is the major component that makes various artifacts in the images obtained with a permanent magnet based MRI. Based on the FEM analyses, we have developed an active magnetic noise shielding system composed of a magnetic field sensor, compensation coils, and a coil driving system. The shielding system has shown a noise rejection ratio of about 30dB at the frequency below several Hz. We have experimentally verified that the shielding system greatly improves the image quality in a 0.3 Tesla MRI system.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.1
• /
• pp.123-132
• /
• 1999

In this paper, a quadratic sigmoid neural equalizer(QSNE) is proposed to improve the performance of conventional neural equalizer in terms of bit error probability by using a quadratic sigmoid function as the activation function of neural networks. Conventional neural equalizers which have been used to compensate for nonlinear distortions adopt the sigmoid function. In the case of sigmoid neural equalizer, each neuron has one linear decision boundary. So many neurons are required when the neural equalizer has to separate complicated structure. But in case of the proposed QSNF and quadratic sigmoid neural decision feedback equalizer(QSNDFE), each neuron separates decision region with two parallel lines. Therefore, QSNE and QSNDFE have better performance and simpler structure than the conventional neural equalizers in terms of bit error probability. When the proposed QSNDFE is applied to communication systems and digital magnetic recording systems, it is an improvement of approximately 1.5dB~8.3dB in signal to moise ratio(SNR) over the conventional decision feedback equalizer(DEF) and neural decision feedback equalizer(NDFE). As intersymbol interference(ISI) and nonlinear distortions become severer, QSNDFE shows astounding SNR shows astounding SNR performance gain over the conventional equalizers in the same bit error probability.