According to studies confirming that robot assisted learning (RAL) can positively contribute to improving learners' motivation and achievement for language learning [1,4], RAL is facing its diffusion through the demand of parents and government. However, the biggest obstacle to the long-term interaction between humans and robots is the robots' lowsuccess rate of visual and voice recognition, as well as the limitation of artificial intelligence for the daily-life HRI [3]. This study demonstrated ROBOSEM's ability to sustain long term interaction between children and a robot in an elementary English class from the pilot studies with IROBIQ, called Langbot [1].

This paper presents a novel approach for removing noise from multi-echo knee magnetic resonance images using global intensity normalization and the averaging operation along the echo-time. Firstly, the global mean and standard deviation at the zero echo-time are estimated by applying the mono-exponential spin echo model to the means and standard deviations of multi-echo images. Secondly, the signal and noise levels at multi-echo images are normalized to the estimated mean and standard deviation at the zero echo-time. Then, the normalized multi-echo images are averaged along the echo-time into a noise-removed zero echo-time image. Finally, the multi-echo MR images are reconstructed from the noise-removed zero echo-time image by the inverse normalization. The experiments demonstrate that the proposed method effectively removes not only the noises of each multi-echo image but also noises of the Quantitative T2 image.

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