The accuracy and repeatability of the laser interferometer measurement system （LIMS） are often limited by the mirror surface error that comes from the mirror surface shape and distortion. This paper describes a new ...The accuracy and repeatability of the laser interferometer measurement system （LIMS） are often limited by the mirror surface error that comes from the mirror surface shape and distortion. This paper describes a new method to calibrate mirror map on ultraprecise movement stage （UPMS） with nanopositioning and to make a real-time compensation for the mirror surface error by using mirror map data tables with the software algorithm. Based on the mirror map test model, the factors affecting mirror map are analyzed through geometric method on the UPMS with six digrees of freedom. Dam processing methods including spline interpolation and spline offsets are used to process the raw sampling data to build mirror map tables. The linear interpolation as compensation method to make a real-time correction on the stage mirror unflatness is adopted and the correction formulas are illuminated. In this way, the measurement accuracy of the system is obviously improved from 40 nm to 5 nm.展开更多
All optical clock recovery from non return-to-zero （NRZ） data using an semiconductor optical amplifier （SOA） loop mirror and a mode-locked SOA fibcr lascr is firstly schematically explained and experimentally demo...All optical clock recovery from non return-to-zero （NRZ） data using an semiconductor optical amplifier （SOA） loop mirror and a mode-locked SOA fibcr lascr is firstly schematically explained and experimentally demonstrated at 10 Gb/s. Furthermore, the pulse quality of tile recovered cluck is cffcctivcly improved by using a continuous-wave （CW） assist light in the gain region of SOA, through which the amplitude modulation is reduced from 57.2% to 8.47%. This scheme is a promising method for clock recovery from NRZ data in the future all-optical communication networks.展开更多
The neural network proposed in this paper hierarchically processes time series data and has the function which becomes the basis of intellectual behavior of not only bacteria but also evolved animals.At first,time ser...The neural network proposed in this paper hierarchically processes time series data and has the function which becomes the basis of intellectual behavior of not only bacteria but also evolved animals.At first,time series data are divided into sequences of subsequences in which the same element does not appear multiple times.The neural network that recognizes/generates the obtained subsequence is familiar to basic behavior of nerve cells and is called a Basic Unit.General time series data have a hierarchical structure.The lowest level is the sequence of the subsequence obtained as a result of division.A neural network composed of hierarchically connected Basic Units processes time series data.Hierarchical processing is performed according to the context structure of time series data.The place where the Basic Units are activated moves from upper layer to lower layer or in the opposite direction as processing progresses.It is possible to predict the next processing by using the contextual position of the current executing process.There is a plurality of neural networks which process time series data according to the category of time series data.Isomorphism between neural networks brings about isomorphism of context structure of processing process.The behavior of mirror neurons is explained using the interaction between isomorphic neural networks.展开更多
文摘The accuracy and repeatability of the laser interferometer measurement system （LIMS） are often limited by the mirror surface error that comes from the mirror surface shape and distortion. This paper describes a new method to calibrate mirror map on ultraprecise movement stage （UPMS） with nanopositioning and to make a real-time compensation for the mirror surface error by using mirror map data tables with the software algorithm. Based on the mirror map test model, the factors affecting mirror map are analyzed through geometric method on the UPMS with six digrees of freedom. Dam processing methods including spline interpolation and spline offsets are used to process the raw sampling data to build mirror map tables. The linear interpolation as compensation method to make a real-time correction on the stage mirror unflatness is adopted and the correction formulas are illuminated. In this way, the measurement accuracy of the system is obviously improved from 40 nm to 5 nm.
基金This work was supported by the National Natural Sci-ence Foundation of China (No. 90401025)the Key Project of MOE (No. 105036).
文摘All optical clock recovery from non return-to-zero （NRZ） data using an semiconductor optical amplifier （SOA） loop mirror and a mode-locked SOA fibcr lascr is firstly schematically explained and experimentally demonstrated at 10 Gb/s. Furthermore, the pulse quality of tile recovered cluck is cffcctivcly improved by using a continuous-wave （CW） assist light in the gain region of SOA, through which the amplitude modulation is reduced from 57.2% to 8.47%. This scheme is a promising method for clock recovery from NRZ data in the future all-optical communication networks.
文摘The neural network proposed in this paper hierarchically processes time series data and has the function which becomes the basis of intellectual behavior of not only bacteria but also evolved animals.At first,time series data are divided into sequences of subsequences in which the same element does not appear multiple times.The neural network that recognizes/generates the obtained subsequence is familiar to basic behavior of nerve cells and is called a Basic Unit.General time series data have a hierarchical structure.The lowest level is the sequence of the subsequence obtained as a result of division.A neural network composed of hierarchically connected Basic Units processes time series data.Hierarchical processing is performed according to the context structure of time series data.The place where the Basic Units are activated moves from upper layer to lower layer or in the opposite direction as processing progresses.It is possible to predict the next processing by using the contextual position of the current executing process.There is a plurality of neural networks which process time series data according to the category of time series data.Isomorphism between neural networks brings about isomorphism of context structure of processing process.The behavior of mirror neurons is explained using the interaction between isomorphic neural networks.
