图像分割是计算机视觉中基础且重要的一个问题.熵阈值图像分割作为一种有效的分割方法,被广泛应用于模式识别和图像处理中.传统的图像分割方法并不能获得足够有效的图像特征.为解决这个问题且进一步探究熵阈值在图像分割中的应用,引入一...图像分割是计算机视觉中基础且重要的一个问题.熵阈值图像分割作为一种有效的分割方法,被广泛应用于模式识别和图像处理中.传统的图像分割方法并不能获得足够有效的图像特征.为解决这个问题且进一步探究熵阈值在图像分割中的应用,引入一种GLLE(Gray Level and Local Entropy)二维直方图改进熵阈值图像分割模型,并提出了基于模糊熵的方法计算所建立的二维直方图模型.通过标准实验数据集上的对比实验表明,基于模糊熵的GLLE熵阈值分割方法可以得到更加准确的阈值,提高了分割精度.同时在处理不同类型图像的表现上优于往常的算法,具有更强的鲁棒性.展开更多
The segmentation effect of Tsallis entropy method is superior to that of Shannon entropy method, and the computation speed of two-dimensional Shannon cross entropy method can be further improved by optimization. The e...The segmentation effect of Tsallis entropy method is superior to that of Shannon entropy method, and the computation speed of two-dimensional Shannon cross entropy method can be further improved by optimization. The existing two-dimensional Tsallis cross entropy method is not the strict two-dimensional extension. Thus two new methods of image thresholding using two-dimensional Tsallis cross entropy based on either Chaotic Particle Swarm Optimization (CPSO) or decomposition are proposed. The former uses CPSO to find the optimal threshold. The recursive algorithm is adopted to avoid the repetitive computation of fitness function in iterative procedure. The computing speed is improved greatly. The latter converts the two-dimensional computation into two one-dimensional spaces, which makes the computational complexity further reduced from O(L2) to O(L). The experimental results show that, compared with the proposed recently two-dimensional Shannon or Tsallis cross entropy method, the two new methods can achieve superior segmentation results and reduce running time greatly.展开更多
The layered semiconducting transition metal dichaloogenides(S-TMDs)have attracted considerable interest as the channel material for field-effect transistors(FETs).However,the multilayer S-TMD transistors usually exhib...The layered semiconducting transition metal dichaloogenides(S-TMDs)have attracted considerable interest as the channel material for field-effect transistors(FETs).However,the multilayer S-TMD transistors usually exhibit considerable threshold voltage(Vn)shit and ambipolar behavior at high source-drain bias,which is undesirable for modern digital electronics.Here we report the design and fabrication of double feedback gate(FBG)transistors,i.e.,source FBG(S-FBG)and drain FBG(D-FBG),to combat these challenges.The FBG transistors differ from normal transistors by including an extra feedback gate,which is directly connected t0 the source/drain electrodes by extending and overlapping the source/drain electrodes over the yttrium oxide dielectrics on s-TMDs.We show that the S-FBG transistors based on mutilayer MoSg exhibit nearly negligible VIn rlloff at large source drain bias,and the D-FBG mutilayer WSe2 transistors could be tailored into either n-type or p-type transport,depending on the polarity of the drain bias.The double FBG structure offers an effective strategy to tailor multilayer s-TMD transistors with suppressed Vn roll-off and ambipolar transport for high-performance and low-power logic applications.展开更多
文摘图像分割是计算机视觉中基础且重要的一个问题.熵阈值图像分割作为一种有效的分割方法,被广泛应用于模式识别和图像处理中.传统的图像分割方法并不能获得足够有效的图像特征.为解决这个问题且进一步探究熵阈值在图像分割中的应用,引入一种GLLE(Gray Level and Local Entropy)二维直方图改进熵阈值图像分割模型,并提出了基于模糊熵的方法计算所建立的二维直方图模型.通过标准实验数据集上的对比实验表明,基于模糊熵的GLLE熵阈值分割方法可以得到更加准确的阈值,提高了分割精度.同时在处理不同类型图像的表现上优于往常的算法,具有更强的鲁棒性.
基金supported by National Natural Science Foundation of China under Grant No.60872065Open Foundation of State Key Laboratory for Novel Software Technology at Nanjing University under Grant No.KFKT2010B17
文摘The segmentation effect of Tsallis entropy method is superior to that of Shannon entropy method, and the computation speed of two-dimensional Shannon cross entropy method can be further improved by optimization. The existing two-dimensional Tsallis cross entropy method is not the strict two-dimensional extension. Thus two new methods of image thresholding using two-dimensional Tsallis cross entropy based on either Chaotic Particle Swarm Optimization (CPSO) or decomposition are proposed. The former uses CPSO to find the optimal threshold. The recursive algorithm is adopted to avoid the repetitive computation of fitness function in iterative procedure. The computing speed is improved greatly. The latter converts the two-dimensional computation into two one-dimensional spaces, which makes the computational complexity further reduced from O(L2) to O(L). The experimental results show that, compared with the proposed recently two-dimensional Shannon or Tsallis cross entropy method, the two new methods can achieve superior segmentation results and reduce running time greatly.
基金ONR through grant number N000141812707Y.H.acknowledges the financial support from National Science Foundation EFRI-1433541.
文摘The layered semiconducting transition metal dichaloogenides(S-TMDs)have attracted considerable interest as the channel material for field-effect transistors(FETs).However,the multilayer S-TMD transistors usually exhibit considerable threshold voltage(Vn)shit and ambipolar behavior at high source-drain bias,which is undesirable for modern digital electronics.Here we report the design and fabrication of double feedback gate(FBG)transistors,i.e.,source FBG(S-FBG)and drain FBG(D-FBG),to combat these challenges.The FBG transistors differ from normal transistors by including an extra feedback gate,which is directly connected t0 the source/drain electrodes by extending and overlapping the source/drain electrodes over the yttrium oxide dielectrics on s-TMDs.We show that the S-FBG transistors based on mutilayer MoSg exhibit nearly negligible VIn rlloff at large source drain bias,and the D-FBG mutilayer WSe2 transistors could be tailored into either n-type or p-type transport,depending on the polarity of the drain bias.The double FBG structure offers an effective strategy to tailor multilayer s-TMD transistors with suppressed Vn roll-off and ambipolar transport for high-performance and low-power logic applications.
