A Probable Short Decimetric Type Ⅰ-like Noise Storm: Associated with Type Ⅲ Bursts?

A rare Type I-like noise storm was observed with the solar radio spectrometers (1.0-2.0 GHz and 2.60-3.8 GHz) at National Astronomical Observatories of China (NAOC) on September 23, 1998. We concentrate on checking the Type I-like noise storm occurred in the decay phase of a Type Ⅳ radio burst. This noise storm consists of many Type I bursts and isolated Type Ⅲ or Type Ⅲ pair bursts. It has a bandwidth of ≤0.5 GHz. The duration of each Type I burst is of the order of 100-300 ms. The total duration is greater than 11 minutes. The circular polarization degree of the components of Type Ⅰ and associated Type Ⅲ bursts are about 40%-100% and almost 100%, respectively, which is greater than that of the background continuum (nearly the precision of our instrument). This short decimetric Type Ⅰ-like storm may be another kind or the extension of the kind of metric Type Ⅰ storm, and may possess the duality of metric and decimetric radio emission. It may be in favor of an earlier emission mechanism of the fundamental plasma radiation due to the coalescence of Langmuir waves with low-frequency waves.

Spline adaptive filtering algorithm based on different iterative gradients:Performance analysis and comparison

Two novel spline adaptive filtering(SAF)algorithms are proposed by combining different iterative gradient methods,i.e.,Adagrad and RMSProp,named SAF-Adagrad and SAF-RMSProp,in this paper.Detailed convergence performance and computational complexity analyses are carried out also.Furthermore,compared with existing SAF algorithms,the influence of step-size and noise types on SAF algorithms are explored for nonlinear system identification under artificial datasets.Numerical results show that the SAF-Adagrad and SAFRMSProp algorithms have better convergence performance than some existing SAF algorithms(i.e.,SAF-SGD,SAF-ARC-MMSGD,and SAF-LHC-MNAG).The analysis results of various measured real datasets also verify this conclusion.Overall,the effectiveness of SAF-Adagrad and SAF-RMSProp are confirmed for the accurate identification of nonlinear systems.

Robust adaptive estimator based on a novel objective function—Using the L1-norm and L0-norm

To fully take advantage of LMS,LMAT,and SELMS,a novel adaptive estimator using the L1-norm and L0-norm of the estimated error is proposed in this paper.Then based on minimizing the mean-square deviation at the current time,the optimal step-size,parameters𝛿and𝜃of the proposed adaptive estimator are obtained.Besides,the stability and computational complexity of the mean estimation error is analyzed theoretically.Experimental results(both simulation and real mechanical system datasets)show that the proposed adaptive estimator is more robust to input signals and a variety of measurement noises(Gaussian and non-Gaussian noises).In addition,it is superior to LMS,LMAT,SELMS,the convex combination of LMS and LMAT algorithm,the convex combination of LMS and SELMS algorithm,and the convex combination of SELMS and LMAT algorithm.The theoretical analysis is consistent with the Monte-Carlo results.Both of them show that the adaptive estimator has an excellent performance in the estimation of unknown linear systems under various measurement noises.