scholarly journals High-Accuracy Parameter Estimation for Damped Signal via Three Weighted DFT Samples

2021 ◽  
Author(s):  
Haitao Xu ◽  
Ying Du ◽  
Shengxi Zhou ◽  
Hongwei Fan ◽  
Xuhui Zhang
Keyword(s):  
Parameter Estimation ◽  
Precise Measurement ◽  
State Of The Art ◽  
High Accuracy ◽  
Damping Factor ◽  
Estimation Variance ◽  
Theoretical Estimation ◽  
Single Tone ◽  
Accuracy Parameter ◽  
Complex Exponential

Abstract Recently, accurate parameter estimation of the damped complex exponential plays an increasingly important role in the field of precise measurement. However, the estimation variance of interpolation-based algorithms for the parameter estimation cannot be asymptotic to the Crámer-Rao lower bound (CRLB). This paper originally proposes a generalized, fast, and the accurate two-iteration estimator (TIE) based on the discrete Fourier transform (DFT). It can be operated by an arbitrary window (symmetric or asymmetric window). Theoretical estimation variances of the frequency and the damping factor for arbitrary windows are derived, respectively. Furthermore, extensive computer simulations are performed to compare the performance of the TIE with other state-of-the-art algorithms in the literature. The results support the theoretical findings and verify that high-accuracy parameter estimation can be ensured by the proposed algorithm. More importantly, the estimation variances returned by the TIE with the rectangle window exactly track the CRLB for a damped single tone.

scholarly journals Iterative High-Accuracy Parameter Estimation of Uncooperative OFDM-LFM Radar Signals Based on FrFT and Fractional Autocorrelation Interpolation

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3550 ◽  
Author(s):  
Yifei Liu ◽  
Yuan Zhao ◽  
Jun Zhu ◽  
Ying Xiong ◽  
Bin Tang
Keyword(s):  
Parameter Estimation ◽  
Fractional Fourier Transform ◽  
High Accuracy ◽  
Superior Performance ◽  
Spectral Leakage ◽  
Multiple Components ◽  
Orthogonal Frequency Division Multi ◽  
Accuracy Parameter ◽  
Finite Sampling ◽  
Radar Signals

To improve the parameter estimation performance of uncooperative Orthogonal Frequency Division Multi- (OFDM) Linear Frequency Modulation (LFM) radar signals, this paper proposes an iterative high-accuracy method, which is based on Fractional Fourier Transform (FrFT) and Fractional Autocorrelation (FA) interpolation. Two iterative estimators for rotation angle and center frequencies are derived from the analytical formulations of the OFDM-LFM signal. Both estimators are designed by measuring the residual terms between the quasi peak and the real peak in the fractional spectrum, which were obtained from the finite sampling data. Successful elimination of spectral leakage caused by multiple components of the OFDM-LFM signal is also proposed by a sequential removal of the strong coefficient in the fractional spectrum through an iterative process. The method flow is given and its superior performance is demonstrated by the simulation results.

scholarly journals The Degree of Oxidation of Graphene Oxide

Nanomaterials ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 560
Author(s):  
Alexandra Carvalho ◽  
Mariana C. F. Costa ◽  
Valeria S. Marangoni ◽  
Pei Rou Ng ◽  
Thi Le Hang Nguyen ◽  
...  
Keyword(s):  
Graphene Oxide ◽  
Ab Initio ◽  
State Of The Art ◽  
High Accuracy ◽  
Precise Determination ◽  
Photoemission Spectroscopy ◽  
Pristine Graphene ◽  
X Ray ◽  
Degree Of Oxidation

We show that the degree of oxidation of graphene oxide (GO) can be obtained by using a combination of state-of-the-art ab initio computational modeling and X-ray photoemission spectroscopy (XPS). We show that the shift of the XPS C1s peak relative to pristine graphene, ΔEC1s, can be described with high accuracy by ΔEC1s=A(cO−cl)2+E0, where c0 is the oxygen concentration, A=52.3 eV, cl=0.122, and E0=1.22 eV. Our results demonstrate a precise determination of the oxygen content of GO samples.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

scholarly journals Online Adaptive Parameter Estimation for Quadrotors

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 167 ◽  
Author(s):  
Jun Zhao ◽  
Xian Wang ◽  
Guanbin Gao ◽  
Jing Na ◽  
Hongping Liu ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Exponential Convergence ◽  
Estimation Error ◽  
Persistent Excitation ◽  
System Parameters ◽  
Adaptive Parameter ◽  
Adaptive Laws ◽  
Accuracy Parameter ◽  
The Stability ◽  
Quadrotor System

The stability and robustness of quadrotors are always influenced by unknown or immeasurable system parameters. This paper proposes a novel adaptive parameter estimation technology to obtain high-accuracy parameter estimation for quadrotors. A typical mathematical model of quadrotors is first obtained, which can be used for parameter estimation. Then, an expression of the parameter estimation error is derived by introducing a set of auxiliary filtered variables. Moreover, an augmented matrix is constructed based on the obtained auxiliary filtered variables, which is then used to design new adaptive laws to achieve exponential convergence under the standard persistent excitation (PE) condition. Finally, a simulation and an experimental verification for a typical quadrotor system are shown to illustrate the effectiveness of the proposed method.

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