Discrete-Time Modeling of High Power Asymmetric Half-Bridge LED Constant-Current Driver Controlled by Digital Current Mode

The high-power Asymmetric half-bridge Converter (AHBC) LED constant current driver controlled by digital current mode is a fourth-order system. Static operating point, parasitic resistance, load characteristics, sampling effect, modulation mode and loop delay will have great influence on its dynamic performance. In this paper, the small-signal pulse transfer function of the driver is established by the discrete-time modeling method for the two operating points corresponding to the three modulation modes of the trailing edge, leading edge and double edge. And, the effects of parasitic parameters, delay effect, sampling effect and load effect are fully considered in modeling. For a large number of complex exponential matrix operations, the first order Taylor formula is used for approximate calculation after the coefficient matrix is obtained by substituting the data. Then, Matlab software is used to compare and analyze the discrete-time model and the discrete-average model. The results show that the proposed discrete-time model can more accurately characterize the resonant peak and high-frequency dynamic characteristics, and is very suitable for the design of high frequency digital controller.

Case study for sampling effect in nanometric surface roughness of ultra-precision grinding

Nanometric surface roughness (NSR) is commonly produced in ultra-precision grinding (UPG). However, the NSR would be significantly affected by the sampling frequency (SF), i.e. sampling effect, since it is governed by rich low, middle, and high spatial frequency features. Hereby, the case study focused on discussing sampling effect in the NSR of UPG. The theoretical and experimental results have been found that along with the SF increase the NSR rises and rapidly converges and the SF is over 10 times of the spatial frequency for the NSR within the 5% distortion. Moreover, the NSR is acceptable within its 5% variation ratio under two SFs. Further, the SFs should be provided as the measurement traceability, together. Significantly, the case study draws up a better insight into sampling effect in the NSR of UPG along with a feasible suggestion on its measurement.

MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions

scRNA-seq profiles each represent a highly partial sample of mRNA molecules from a unique cell that can never be resampled, and robust analysis must separate the sampling effect from biological variance. We describe a methodology for partitioning scRNA-seq datasets into metacells: disjoint and homogenous groups of profiles that could have been resampled from the same cell. Unlike clustering analysis, our algorithm specializes at obtaining granular as opposed to maximal groups. We show how to use metacells as building blocks for complex quantitative transcriptional maps while avoiding data smoothing. Our algorithms are implemented in the MetaCell R/C++ software package.