针对传统电容式触摸芯片的采样信号易受外部噪声干扰而引发注入锁定的现象,设计了一种多频采样电路。该电路引入5条充电电流支路,实现多频采样功能,减小采样信号受注入锁定现象的影响,提高采样精度。同时,设计了一种DOM(Delta over the ...针对传统电容式触摸芯片的采样信号易受外部噪声干扰而引发注入锁定的现象,设计了一种多频采样电路。该电路引入5条充电电流支路,实现多频采样功能,减小采样信号受注入锁定现象的影响,提高采样精度。同时,设计了一种DOM(Delta over the Mean)算法对采样信号进行处理,以此监测触摸输入引脚的电容值变化。仿真结果表明,多频采样电路输出的采样信号受噪声干扰的计数值偏差仅为0.46%;FPGA验证表明DOM算法的计算精度为1×10^(-8),最大绝对误差不超过0.4%;该电路具有强抗干扰、高可靠性、高精度特性,可应用于各类电容式触摸芯片。展开更多
背景:随着对运动健康影响的深入理解,运动干预机制的研究逐渐成为重点。传统研究依赖体内动物模型或多组学技术,间接推断运动干预机制,但研究不够深入,且许多疾病模型无法完成预定运动强度。因此,离体细胞运动环境模拟技术尤为重要。现...背景:随着对运动健康影响的深入理解,运动干预机制的研究逐渐成为重点。传统研究依赖体内动物模型或多组学技术,间接推断运动干预机制,但研究不够深入,且许多疾病模型无法完成预定运动强度。因此,离体细胞运动环境模拟技术尤为重要。现有技术多侧重单一信号复现,未能全面模拟运动中多维信号的交互作用,限制了对运动适应机制的理解。目的:探讨离体细胞运动环境模拟技术的进展,分析现有各信号模拟的技术优势,提出整合多维信号的新框架,以推动运动机制的精准复现和相关领域的应用研究。方法:在PubMed和Web of Science数据库中使用关键词如Exercise,Physiology,Molecular Signals,Myokines,Exerkines等进行检索,筛选研究原著和综述文献,初步检索并去除重复后共得到5 046篇相关文献,再次筛选最终纳入99篇文献。结果与结论:现有的离体细胞运动模拟技术在模拟运动的某些特定属性(如机械拉伸、电信号等)方面取得了一定进展,但仍未能全面复现运动过程中的多维信号交互作用。通过整合机械力、电生理刺激和生物因子等多种信号,未来的模拟技术有望更真实地再现运动对细胞代谢、基因表达和表型重塑的影响,为运动机制的研究提供更精准的实验平台。此外,离体运动模拟技术的创新和优化将为运动医学、药物开发及再生医学等领域提供重要支持。展开更多
Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technol...Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.展开更多
Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence archite...Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence architectures in terms of latency,energy efficiency,and computational power.To achieve this vision,it is of vital importance to scale up the PNNs while simultaneously reducing the high demand on the dimensions required by them.The underlying cause of this strategy is the enormous gap between the scales of photonic and electronic integrated circuits.Here,we demonstrate monolithically integrated optical convolutional processors on thin film lithium niobate(TFLN)that harness inherent parallelism in photonics to enable large-scale programmable convolution kernels and,in turn,greatly reduce the dimensions required by subsequent fully connected layers.Experimental validation achieves high classification accuracies of 96%(86%)on the MNIST(Fashion-MNIST)dataset and 84.6%on the AG News dataset while dramatically reducing the required subsequent fully connected layer dimensions to 196×10(from 784×10)and 175×4(from 800×4),respectively.Furthermore,our devices can be driven by commercial field-programmable gate array systems;a unique advantage in addition to their scalable channel number and kernel size.Our architecture provides a solution to build practical machine learning photonic devices.展开更多
为解决碳化硅金属氧化物半导体场效应晶体管(SiC Metal Oxide Semiconductor Field Effect Transistor,SiC MOSFET)硬开关故障(Hard Switch Fault,HSF)、负载故障(Fault Under Load,FUL)和过载故障(OverLoad fault,OL)的问题,本文提出...为解决碳化硅金属氧化物半导体场效应晶体管(SiC Metal Oxide Semiconductor Field Effect Transistor,SiC MOSFET)硬开关故障(Hard Switch Fault,HSF)、负载故障(Fault Under Load,FUL)和过载故障(OverLoad fault,OL)的问题,本文提出了一种基于SiC MOSFET漏极电压和源极电压检测的过流保护方法(OverCurrent Protection method based on the Drain-voltage and Source-voltage Detection,DSD-OCP).该方法通过检测电路实时监控SiC MOSFET的漏极电压和源极电压来准确识别短路故障和过载故障,并利用驱动电路控制SiC MOSFET的开通和关断,从而实现快速短路保护和自适应过载保护,同时还集成软关断功能.基于0.5μm双极型-互补金属氧化物半导体-双扩散金属氧化物半导体(Bipolar-CMOS-DMOS,BCD)工艺,设计了DSD-OCP电路并进行流片,芯片面积为2.8 mm^(2).采用研制的芯片搭建1200 V/80 mΩSiC MOSFET测试平台,并验证了DSD-OCP方法的有效性.实验结果表明,SiC MOSFET在DSD-OCP芯片保护下的HSF和FUL持续时间分别为88 ns和105 ns.在不同母线电压下,DSD-OCP芯片能够为SiC MOSFET提供自适应的过载保护.因DSD-OCP芯片具有软关断功能,SiC MOSFET在过流保护时的漏极电压过冲不超过110 V.展开更多
文摘针对传统电容式触摸芯片的采样信号易受外部噪声干扰而引发注入锁定的现象,设计了一种多频采样电路。该电路引入5条充电电流支路,实现多频采样功能,减小采样信号受注入锁定现象的影响,提高采样精度。同时,设计了一种DOM(Delta over the Mean)算法对采样信号进行处理,以此监测触摸输入引脚的电容值变化。仿真结果表明,多频采样电路输出的采样信号受噪声干扰的计数值偏差仅为0.46%;FPGA验证表明DOM算法的计算精度为1×10^(-8),最大绝对误差不超过0.4%;该电路具有强抗干扰、高可靠性、高精度特性,可应用于各类电容式触摸芯片。
文摘背景:随着对运动健康影响的深入理解,运动干预机制的研究逐渐成为重点。传统研究依赖体内动物模型或多组学技术,间接推断运动干预机制,但研究不够深入,且许多疾病模型无法完成预定运动强度。因此,离体细胞运动环境模拟技术尤为重要。现有技术多侧重单一信号复现,未能全面模拟运动中多维信号的交互作用,限制了对运动适应机制的理解。目的:探讨离体细胞运动环境模拟技术的进展,分析现有各信号模拟的技术优势,提出整合多维信号的新框架,以推动运动机制的精准复现和相关领域的应用研究。方法:在PubMed和Web of Science数据库中使用关键词如Exercise,Physiology,Molecular Signals,Myokines,Exerkines等进行检索,筛选研究原著和综述文献,初步检索并去除重复后共得到5 046篇相关文献,再次筛选最终纳入99篇文献。结果与结论:现有的离体细胞运动模拟技术在模拟运动的某些特定属性(如机械拉伸、电信号等)方面取得了一定进展,但仍未能全面复现运动过程中的多维信号交互作用。通过整合机械力、电生理刺激和生物因子等多种信号,未来的模拟技术有望更真实地再现运动对细胞代谢、基因表达和表型重塑的影响,为运动机制的研究提供更精准的实验平台。此外,离体运动模拟技术的创新和优化将为运动医学、药物开发及再生医学等领域提供重要支持。
基金supported by the Shenzhen Medical Research Fund(Grant No.A2303049)Guangdong Basic and Applied Basic Research(Grant No.2023A1515010647)+1 种基金National Natural Science Foundation of China(Grant No.22004135)Shenzhen Science and Technology Program(Grant No.RCBS20210706092409020,GXWD20201231165807008,20200824162253002).
文摘Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.
基金supported by the National Natural Science Foundation of China (Grant Nos.12192251,12334014,62335019,12134001,1230441812474378)+1 种基金the Quantum Science and Technology National Science and Technology Major Project(Grant No.2021ZD0301403)the Shanghai Municipal Science and Technology Major Project (Grant No.2019SHZDZX01)。
文摘Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence architectures in terms of latency,energy efficiency,and computational power.To achieve this vision,it is of vital importance to scale up the PNNs while simultaneously reducing the high demand on the dimensions required by them.The underlying cause of this strategy is the enormous gap between the scales of photonic and electronic integrated circuits.Here,we demonstrate monolithically integrated optical convolutional processors on thin film lithium niobate(TFLN)that harness inherent parallelism in photonics to enable large-scale programmable convolution kernels and,in turn,greatly reduce the dimensions required by subsequent fully connected layers.Experimental validation achieves high classification accuracies of 96%(86%)on the MNIST(Fashion-MNIST)dataset and 84.6%on the AG News dataset while dramatically reducing the required subsequent fully connected layer dimensions to 196×10(from 784×10)and 175×4(from 800×4),respectively.Furthermore,our devices can be driven by commercial field-programmable gate array systems;a unique advantage in addition to their scalable channel number and kernel size.Our architecture provides a solution to build practical machine learning photonic devices.
文摘为解决碳化硅金属氧化物半导体场效应晶体管(SiC Metal Oxide Semiconductor Field Effect Transistor,SiC MOSFET)硬开关故障(Hard Switch Fault,HSF)、负载故障(Fault Under Load,FUL)和过载故障(OverLoad fault,OL)的问题,本文提出了一种基于SiC MOSFET漏极电压和源极电压检测的过流保护方法(OverCurrent Protection method based on the Drain-voltage and Source-voltage Detection,DSD-OCP).该方法通过检测电路实时监控SiC MOSFET的漏极电压和源极电压来准确识别短路故障和过载故障,并利用驱动电路控制SiC MOSFET的开通和关断,从而实现快速短路保护和自适应过载保护,同时还集成软关断功能.基于0.5μm双极型-互补金属氧化物半导体-双扩散金属氧化物半导体(Bipolar-CMOS-DMOS,BCD)工艺,设计了DSD-OCP电路并进行流片,芯片面积为2.8 mm^(2).采用研制的芯片搭建1200 V/80 mΩSiC MOSFET测试平台,并验证了DSD-OCP方法的有效性.实验结果表明,SiC MOSFET在DSD-OCP芯片保护下的HSF和FUL持续时间分别为88 ns和105 ns.在不同母线电压下,DSD-OCP芯片能够为SiC MOSFET提供自适应的过载保护.因DSD-OCP芯片具有软关断功能,SiC MOSFET在过流保护时的漏极电压过冲不超过110 V.
