The error rates of multiferroic majority logic gate (MLG) in the presence of thermal noise fluctuation are evaluated via solving the Landau-Lifshitz-Gilbert equations. It is found that in the pre-stress condition, magnetisation orientations of nanomagnets in the MLG denote an initial 4°-10°departure from the easy axis direction due to random thermal noise field. This phenomenon may be the main origin of switching errors of multiferroic logic gates at high temperature. Specifically, in the post-stress condition, it has been seen that larger spacing between nanomagnets leads to a high error rate, up to 50%, which arises from weak dipolar coupling interactions and strong thermal fluctuations. However, opposed to their expected, small spacing between nanomagnets leads to a higher error rate, up to 90%, which mainly arises from spontaneous magnetisation reversal induced by weak stress anisotropy energy. Therefore, moderate spacing is very important to MLG switching at room temperature, for example the optimal spacing is 250-310 nm for MLG constructed with nanomagnets having shape anisotropy energy of 623 kT. These findings can provide guides on multiferroic logic circuit design.
Aiming at data fusion of Autonomous car multisensors experiment at many times on distances, a novel fusion method is proposed based on the approach degree and weights. The method calculate mean and variance based on the measured sensors data, Using the maximum and minimum approach degree of this fuzzy set , the approach degree of the measured data from various sensors is processed quantitatively ,eliminating outlier data by Grubbs method, assigned the weights of data measured in the fusion process reasonably , so that the final expression of the data fusion is obtained, thus the data fusion of multisensor is realized.Test results demonstrate that this method can bring higher fusion precision, and more suitable for microcontroller and embedded systems applications.
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