An improved arctangent algorithm based on phase-locked loop for heterodyne detection system

We present an ameliorated arctangent algorithm based on phase-locked loop for digital Doppler signal processing,utilized within the heterodyne detection system. We define the error gain factor given by the approximation of Taylor expansion by means of a comparison of the measured values and true values. Exact expressions are derived for the amplitude error of two in-phase & quadrature signals and the frequency error of the acousto-optic modulator. Numerical simulation results and experimental results make it clear that the dynamic instability of the intermediate frequency signals leads to cumulative errors, which will spiral upward. An improved arctangent algorithm for the heterodyne detection is proposed to eliminate the cumulative errors and harmonic components. Depending on the narrow-band filter, our experiments were performed to realize the detectable displacement of 20 nm at a detection distance of 20 m. The aim of this paper is the demonstration of the optimized arctangent algorithm as a powerful approach to the demodulation algorithm, which will advance the signal-to-noise ratio and measurement accuracy of the heterodyne detection system.

Privacy-Preserving Frank-Wolfe on Shuffle Model

In this paper,we design the differentially private variants of the classical Frank-Wolfe algorithm with shuffle model in the optimization of machine learning.Under weak assumptions and the generalized linear loss(GLL)structure,we propose a noisy Frank-Wolfe with shuffle model algorithm(NoisyFWS)and a noisy variance-reduced Frank-Wolfe with the shuffle model algorithm(NoisyVRFWS)by adding calibrated laplace noise under shuffling scheme in thel_(p)(p∈[1,2])-case,and study their privacy as well as utility guarantees for the H?lder smoothness GLL.In particular,the privacy guarantees are mainly achieved by using advanced composition and privacy amplification by shuffling.The utility bounds of the Noisy FWS and NoisyVRFWS are analyzed and obtained the optimal excess population risksO(n-(1+α/4α+log(d)√log(1/δ)/n∈and O(n-1+α/4α+log(d)√log1(+δ)/n^(2)∈with gradient complexity O(n(1+α)^(2)/4α^(2)forα∈[1/√3,1].It turns out that the risk rates under shuffling scheme are a nearly-dimension independent rate,which is consistent with the previous work in some cases.In addition,there is a vital tradeoff between(α,L)-H?lder smoothness GLL and the gradient complexity.The linear gradient complexity O(n)is showed by the parameterα=1.