We predict high-velocity magnetic domain wall(DW)motion driven by out-of-plane acoustic spin in surface acoustic waves(SAWs).We demonstrate that the SAW propagating at a 30-degree angle relative to the x-axis of a 128...We predict high-velocity magnetic domain wall(DW)motion driven by out-of-plane acoustic spin in surface acoustic waves(SAWs).We demonstrate that the SAW propagating at a 30-degree angle relative to the x-axis of a 128∘Y-LiNbO_(3) substrate exhibits uniform out-of-plane spin angular momentum.This acoustic spin triggers the DW motion at a velocity exceeding 50 m/s in a way that is similar to the spin-transfer-torque effect.This phenomenon highlights the potential of acoustic spin in enabling rapid DW displacement,offering an innovative approach to developing energy-efficient spintronic devices.展开更多
脱机手写中文字符识别(handwritten Chinese character recognition,HCCR)在计算机视觉领域一直是一个巨大的挑战。相比传统方法,基于深度学习的网络通过训练大量数据在识别任务中取得了差异化的效果,但识别效果依旧处于发展过程中。基...脱机手写中文字符识别(handwritten Chinese character recognition,HCCR)在计算机视觉领域一直是一个巨大的挑战。相比传统方法,基于深度学习的网络通过训练大量数据在识别任务中取得了差异化的效果,但识别效果依旧处于发展过程中。基于此,结合DW卷积和残差连接设计了一种多分支残差模块,该模块通过DW卷积以较小的内存和参数量为代价来加深网络深度,增强网络的特征提取能力;再通过残差连接抑制网络梯度问题和退化问题;另外,提出了一种多分支权重算法,来改善多分支残差模块中各分支的权重分配问题;并将六个以多分支残差模块为主的结构线性连接,组成HCCR识别网络。该模型在CASIA-HWDB1.0、CASIA-HWDB1.1、ICDAR2013数据集上的识别准确率分别达到了97.77%、97.30%、97.64%,表现出高精度的识别效果。展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2022YFE0103300)the open research fund of Songshan Lake Materials Laboratory(Grant No.2023SLABFN26)the Natural Science Foundation of Hubei Province(Grant No.2022CFA088)。
文摘We predict high-velocity magnetic domain wall(DW)motion driven by out-of-plane acoustic spin in surface acoustic waves(SAWs).We demonstrate that the SAW propagating at a 30-degree angle relative to the x-axis of a 128∘Y-LiNbO_(3) substrate exhibits uniform out-of-plane spin angular momentum.This acoustic spin triggers the DW motion at a velocity exceeding 50 m/s in a way that is similar to the spin-transfer-torque effect.This phenomenon highlights the potential of acoustic spin in enabling rapid DW displacement,offering an innovative approach to developing energy-efficient spintronic devices.
文摘脱机手写中文字符识别(handwritten Chinese character recognition,HCCR)在计算机视觉领域一直是一个巨大的挑战。相比传统方法,基于深度学习的网络通过训练大量数据在识别任务中取得了差异化的效果,但识别效果依旧处于发展过程中。基于此,结合DW卷积和残差连接设计了一种多分支残差模块,该模块通过DW卷积以较小的内存和参数量为代价来加深网络深度,增强网络的特征提取能力;再通过残差连接抑制网络梯度问题和退化问题;另外,提出了一种多分支权重算法,来改善多分支残差模块中各分支的权重分配问题;并将六个以多分支残差模块为主的结构线性连接,组成HCCR识别网络。该模型在CASIA-HWDB1.0、CASIA-HWDB1.1、ICDAR2013数据集上的识别准确率分别达到了97.77%、97.30%、97.64%,表现出高精度的识别效果。
