Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and pow...Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and power consumption overhead in the analog-to-digital conversion.In this work,we propose an analog-domain image correction architecture based on a proposed small-scale UNet,which implements a compact noise correction network within a one-transistor-one-memristor(1T1R)array.The statistical non-idealities of the fabricated 1T1R array(e.g.,device variability)are rigorously incorporated into the network's training and inference simulations.This correction network architecture leverages memristors for conducting multiply-accumulate operations aimed at rectifying non-uniform noise,defective pixels(stuck-at-bright/dark),and exposure mismatch.Compared to systems without correction,the proposed architecture achieves up to 50.13%improvement in recognition accuracy while demonstrating robust tolerance to memristor device-level errors.The proposed system achieves a 2.13-fold latency reduction and three orders of magnitude higher energy efficiency compared to conventional architecture.This work establishes a new paradigm for advancing the development of low-power,low-latency,and high-precision image processing systems.展开更多
In this paper,we focus on combining the theories of fuzzy soft sets with Γ-modules,and establishing a new framework for fuzzy soft Γ-submodules.The main contributions of the paper are 3-fold.First,we present the con...In this paper,we focus on combining the theories of fuzzy soft sets with Γ-modules,and establishing a new framework for fuzzy soft Γ-submodules.The main contributions of the paper are 3-fold.First,we present the concepts of（R,S）-bi-Γ-submodules,quasi-Γ-submodules and regular Γ-modules.Meanwhile,some illustrative examples are given to show the rationality of the definitions introduced in this paper.Second,several new kinds of generalized fuzzy soft Γ-submodules are proposed,and related properties and mutual relationships are also investigated.Third,we discover some intrinsic connections between the generalized fuzzy soft Γ-submodules presented in this paper and crisp Γ-submodules,and describe the relationships between regular Γ-modules and the generalized fuzzy soft Γ-submodules presented in this paper.展开更多
基金Project supported by the National Key Research and Development Program of China(Grant No.2024YFA1208800)the National Natural Science Foundation of China(Grant Nos.62404253,62304254,U23A20322)。
文摘Sensor noise is a critical factor that degrades the performance of image processing systems.In traditional computing systems,noise correction is implemented in the digital domain,resulting in redundant latency and power consumption overhead in the analog-to-digital conversion.In this work,we propose an analog-domain image correction architecture based on a proposed small-scale UNet,which implements a compact noise correction network within a one-transistor-one-memristor(1T1R)array.The statistical non-idealities of the fabricated 1T1R array(e.g.,device variability)are rigorously incorporated into the network's training and inference simulations.This correction network architecture leverages memristors for conducting multiply-accumulate operations aimed at rectifying non-uniform noise,defective pixels(stuck-at-bright/dark),and exposure mismatch.Compared to systems without correction,the proposed architecture achieves up to 50.13%improvement in recognition accuracy while demonstrating robust tolerance to memristor device-level errors.The proposed system achieves a 2.13-fold latency reduction and three orders of magnitude higher energy efficiency compared to conventional architecture.This work establishes a new paradigm for advancing the development of low-power,low-latency,and high-precision image processing systems.
基金Supported by the National Natural Science Foundation of China (61175055)the Innovation Term of Higher Education of Hubei Province,China (T201109)+1 种基金the Natural Science Foundation of Hubei Province (2012FFB01101)the Natural Science Foundation of Education Committee of Hubei Province (D20131903)
文摘In this paper,we focus on combining the theories of fuzzy soft sets with Γ-modules,and establishing a new framework for fuzzy soft Γ-submodules.The main contributions of the paper are 3-fold.First,we present the concepts of（R,S）-bi-Γ-submodules,quasi-Γ-submodules and regular Γ-modules.Meanwhile,some illustrative examples are given to show the rationality of the definitions introduced in this paper.Second,several new kinds of generalized fuzzy soft Γ-submodules are proposed,and related properties and mutual relationships are also investigated.Third,we discover some intrinsic connections between the generalized fuzzy soft Γ-submodules presented in this paper and crisp Γ-submodules,and describe the relationships between regular Γ-modules and the generalized fuzzy soft Γ-submodules presented in this paper.
文摘基于广义约化R矩阵理论,使用RAC程序(R-matrix analysis code)综合分析了^(6)He系统中所有可以利用的实验数据,给出了氚核入射10-2—20 MeV能量范围内主要反应道的评价核数据.其中积分截面包括T(t,2n)^(4)He,T(t,n)^(5)He,T(t,d)^(4)H;微分截面包括T(t,2n)^(4)He,T(t,n)^(5)He,T(t,d)^(4)H,T(t,t)T.结果表明,RAC的评价结果与实验数据和ENDF/B-Ⅷ.1的评价数据整体符合良好.重点关注T(t,2n)^(4)He反应,评价值在10^(-2)—20 MeV范围内与已有实验数据一致,在2.9 Me V附近出现由2+能级主导的共振,在1.9 Me V处,已有实验同时测量了积分截面和角分布,本工作的评价结果在两类数据上均表现出良好的一致性,积分截面与微分截面的联合约束有效提升了R矩阵参数的稳定性和评价结果的可靠性.基于6He系统的整体评价,进一步补充了T(t,n)^(5)He和T(t,d)^(4)H反应的截面数据.本工作完善了聚变反应相关的数据基础,并为后续与镜像系统6Be系统的联合分析奠定了基础.
