Gain and phase errors active calibration method based on neural network for arrays with arbitrary geometry

The manifold matrix of the received signals can be destroyed when the array is with the gain and phase errors,which will affect the performance of the traditional direction of arrival(DOA)estimation approaches.In this paper,a novel active array calibration method for the gain and phase errors based on a cascaded neural network(GPECNN)was proposed.The cascaded neural network contains two parts:signal-to-noise ratio(SNR)classification network and two sets of error estimation subnetworks.Error calibration subnetworks are activated according to the output of the SNR classification network,each of which consists of a gain error estimation network(GEEN)and a phase error estimation network(PEEN),respectively.The disadvantage of neural network topology architecture is changing when the number of array elements varies is addressed by the proposed group calibration strategy.Moreover,due to the data characteristics of the input vector,the cascaded neural network can be applied to arrays with arbitrary geometry without repetitive training.Simulation results demonstrate that the GPECNN not only achieves a better balance between calibration performance and calibration complexity than other methods but also can be applied to arrays with different numbers of sensors or different shapes without repetitive training.

Fast estimation of Michaelis-Menten constant of arylesterase with a pair of medium concentrations of substrate

Objective: To investigate the reliability for fast estimation of Michaelis-Menten constant (Km) with calibrated specific activity at only two medium concentrations of substrate by both simulation and experimentation with arylesterase (ArE)as model. Methods: Initial rates were simulated by randomly inserting uniform absolute error, and the experimental initial rates of ArE were determined by measuring the increaser of product absorbance. Calibrated specific activities at two substrate concentrations were obtained by regression analysis, and Km was calculated according to Michaelis-Menten equation. Results: By simulation with calibrated specific activities at two medium substrate concentrations, Km could be calculated according to Michaelis-Menten equation with reasonable precision and accuracy. By experimentation with substrates of 2-naphthyl acetate, phenyl acetate, and p-nitrophenyl acetate, there were no differences between the mean and SD of Km of ArE for either substrate by this linear kinetic method and the Lineweaver-Burk plot. Conclusion: This linear kinetic method was reliable for fast estimation of the Km of some specified enzyme on its substrate of lower solubility or lower sensitivity for quantification by common methods.