Estimating wheat spike-leaf composite indicator(SLI)dynamics by coupling spectral indices and machine learning

The contribution of spike photosynthesis to grain yield(GY)has been overlooked in the accurate spectral prediction of yield.Thus,it’s essential to construct and estimate a yield-related phenotypic trait considering spike photosynthesis.Based on field and spectral reflectance data from 19 wheat cultivars under two nitrogen fertilization conditions in two years,our objectives were to(i)construct a yield-related phenotypic trait(spike–leaf composite indicator,SLI)accounting for the contribution of the spike to photosynthesis,(ii)develop a novel spectral index(enhanced triangle vegetation index,ETVI3)sensitive to SLI,and(iii)establish and evaluate SLI estimation models by integrating spectral indices and machine learning algorithms.The results showed that SLI was sensitive to nitrogen fertilizer and wheat cultivar variation as well as a better predictor of yield than the leaf area index.ETVI3 maintained a strong correlation with SLI throughout the growth stage,whereas the correlations of other spectral indices with SLI were poor after spike emergence.Integrating spectral indices and machine learning algorithms improved the estimation accuracy of SLI,with the most accurate estimates of SLI showing coefficient of determination,root mean square error(RMSE),and relative RMSE values of 0.71,0.047,and 26.93%,respectively.These results provide new insights into the role of fruiting organs for the accurate spectral prediction of GY.This high-throughput SLI estimation approach can be applied for wheat yield prediction at whole growth stages and may be assisted with agronomical practices and variety selection.

Enhanced Wideband Frequency Estimation via FFT: Leveraging Polynomial Interpolation and Array Indexing

Accurate frequency estimation in a wideband digital receiver using the FFT algorithm encounters challenges, such as spectral leakage resulting from the FFT’s assumption of signal periodicity. High-resolution FFTs pose computational demands, and estimating non-integer multiples of frequency resolution proves exceptionally challenging. This paper introduces two novel methods for enhanced frequency precision: polynomial interpolation and array indexing, comparing their results with super-resolution and scalloping loss. Simulation results demonstrate the effectiveness of the proposed methods in contemporary radar systems, with array indexing providing the best frequency estimation despite utilizing maximum hardware resources. The paper demonstrates a trade-off between accurate frequency estimation and hardware resources when comparing polynomial interpolation and array indexing.

Q-factor estimation in CMP gather and the continuous spectral ratio slope method

The attenuation factor or quality factor(Q-factor or Q) has been used to measure the energy attenuation of seismic waves propagating in underground media. Many methods are used to estimate the Q-factor. We propose a method to calculate the Q-factor based on the prestack Q-factor inversion and the generalized S-transform. The proposed method specifies a standard primary wavelet and calculates the cumulative Q-factors; then, it finds the interlaminar Q-factors using the relation between Q and offset(QVO) and the Dix formula. The proposed method is alternative to methods that calculate interlaminar Q-factors after horizon picking. Because the frequency spectrum of each horizon can be extracted continuously on a 2D time–frequency spectrum, the method is called the continuous spectral ratio slope(CSRS) method. Compared with the other Q-inversion methods, the method offers nearly effortless computations and stability, and has mathematical and physical significance. We use numerical modeling to verify the feasibility of the method and apply it to real data from an oilfield in Ahdeb, Iraq. The results suggest that the resolution and spatial stability of the Q-profile are optimal and contain abundant interlaminar information that is extremely helpful in making lithology and fluid predictions.

Spectral Estimation of Periodically Moving Part Modulation Based on AIDME Algorithm

The Periodicallg Moving Part Modulation (PMPM) for the moving parts in targetprovides important signatures for target recognition. However, most radars operate inmultiple-target mode and can only get discontinuous clusters of the returned pulses, which makes itextremely difficult to extract PMPM signature from the echoes. This paper puts forward theAlternative Iteration Deconvolution based on Minimum Entropy criteria (AIDME) for spectralestimation of extended target's echoes, utilizing the special feature that the PMPM spectra usuallyhave simple structures. Experimental results show that this method can effectively eliminate thesevere influence caused hy the convolution kernel and gain a satisfactory spectral estimation thatapproaches to the true spectrum.

Robust Spectral Estimation of Track Irregularity

Because the existing spectral estimation methods for railway track irregularity analysis are very sensitive to outliers, a robust spectral estimation method is presented to process track irregularity signals. The proposed robust method is verified using 100 groups of clean/contaminated data reflecting he vertical profile irregularity taken from Bejing-Guangzhou railway with a sampling frequency of 33 data every ~10 m, and compared with the Auto Regressive (AR) model. The experimental results show that the proposed robust estimation is resistible to noise and insensitive to outliers, and is superior to the AR model in terms of efficiency, stability and reliability.

Nonparametric Spectral Estimation Technique to Estimate Dominant Frequency for Atrial Fibrillation Detection

Atrial fibrillation (Afib) is related with heart failure, stroke, and high mortality rates. In frequency domain analysis, pre-requisite for Afib detection has been the estimation of reliable dominant frequency (DF) of atrial signals via different spectral estimation techniques. DF further characterizes Afib, and helps in its treatment. This paper aims at finding the most appropriate nonparametric FFT-based spectral estimation technique to estimate reliable DF for Afib detection. In this work, real-time intra-atrial electrograms have been acquired and pre-processed for frequency analysis. DF is estimated via Bartlett using Hanning window, and Welch methods. Regularity index (RI), a parameter to ensure reliability of DF, is calculated using Simpson 3/8 and Trapezoidal rules. The best method is declared based upon high accuracy of Afib detection using reliable DF. On comparison, Welch method is found to be more appropriate to estimate reliable DF for Afib detection with 98% accuracy.

A SPECTRAL ESTIMATION ALGORITHM USING THE HOUSEHOLDER TRANSFORM

Householder transform is used to triangularize the data matrix, which is basedon the near prediction error equation. It is proved that the sum of squared residuals for eachAR order can be obtained by the main diagonal elements of upper triangular matrix, so thecolumn by column procedure can be used to develop a recursive algorithm for AR modeling andspectral estimation. In most cases, the present algorithm yields the same results as the covariancemethod or modified covariance method does. But in some special cases where the numerical ill-conditioned problems are so serious that the covariance method and modified covariance methodfail to estimate AR spectrum, the presented algorithm still tends to keep good performance. Thetypical computational results are presented finally.

Iterative Reweighted <i>l</i>₁Penalty Regression Approach for Line Spectral Estimation

In this paper, we proposed an iterative reweighted l1?penalty regression approach to solve the line spectral estimation problem. In each iteration process, we first use the ideal of Bayesian lasso to update the sparse vectors;the derivative of the penalty function forms the regularization parameter. We choose the anti-trigonometric function as a penalty function to approximate the?l0? norm. Then we use the gradient descent method to update the dictionary parameters. The theoretical analysis and simulation results demonstrate the effectiveness of the method and show that the proposed algorithm outperforms other state-of-the-art methods for many practical cases.

A-Posteriori Error Estimation for the Legendre Spectral Galerkin Method in One-Dimension

In this paper, a-posteriori error estimators are proposed for the Legendre spectral Galerkin method for two-point boundary value problems. The key idea is to postprocess the Galerkin approximation, and the analysis shows that the postproeess improves the order of convergence. Consequently, we obtain asymptotically exact aposteriori error estimators based on the postprocessing results. Numerical examples are included to illustrate the theoretical analysis.

ERROR ESTIMATION OF SPECTRAL-DIFFERENCE METHOD FOR COMPRESSIBLE FLUID FLOW IN THREE-DIMENSIONAL SPACE

This paper is devoted to a combined Fourier spectral-finite difference method for solving 3-dimensional, semi-periodic compressible fluid flow problem. The error estimation, as well as the convergence rate, is presented.

Low-rank spectral estimation algorithm of learning Markov model

This paper proposes a low-rank spectral estimation algorithm of learning Markov model.First,an approximate projection algorithm for the rank-constrained frequency matrix set is proposed,and thereafter its local Lipschitzian error bound established.Then,we propose a low-rank spectral estimation algorithm for estimating the state transition frequency matrix and the probability matrix of Markov model by applying the approximate projection algorithm to correct the maximum likelihood estimation of the frequency matrix,and prove that there is only a multiplying constant difference in estimation errors between the low-rank spectral estimation and the maximum likelihood estimation under appropriate conditions.Finally,numerical comparisons with the prevailing maximum likelihood estimation,spectral estimation,and rank-constrained maxi-mum likelihood estimation show that the low-rank spectral estimation algorithm is effective.

Parameter Estimation of Time-Varying ARMA Model

The auto-regressive moving-average (ARMA) model with time-varying parameters is analyzed. The time-varying parameters are assumed to be a linear combination of a set of basis time-varying functions, and the feedback linear estimation algorithm is used to estimate the time-varying parameters of the ARMA model. This algorithm includes 2 linear least squares estimations and a linear filter. The influence of the order of basis time-(varying) functions on parameters estimation is analyzed. The method has the advantage of simple, saving computation time and storage space. Theoretical analysis and experimental results show the validity of this method.

ANALYSIS OF THE NEURAL OPTIMIZATION METHOD FOR DOA ESTIMATION

This paper gives a simple analysis of the method of using the Hopfield’s optimization neural network to solve the direction-of-arrival(DOA) estimation problem. Although this method can avoid the eigendecomposition of data autocorrelation matrix and the orthogonality search of parameter space, theoretical analysis and computer simulation results show that the construction of the DOA cost function is incorrect on the condition that there is no constraint on the number of outputs of the network.

A HIGH RESOLUTION TIME DELAY ESTIMATION BASED ON THE MAXIMUM ENTROPY POWER SPECTRUM ESTIMATON

Multipath time delay estimation is constrained by the width of the signal correlation function when using correlation based methods. This paper obtains a high resolution time delay estimation by introducing Burg algorithm and Marple algorithm of the maximum entropy power spectral estimation to non-resolvable multipath time delay estimatoin. The principles, the performaces and the results of computer simulation are given.

THE LARGE TIME ERROR ESTIMATES OF FOURIER SPECTRAL METHOD FOR GENERALIZED BENJAMIN-BONA-MAHONY EQUATIONS

In this paper, a spectral method to analyze the generalized Benjamin Bona Mahony equations is used. The existence and uniqueness of global smooth solution of these equations are proved. The large time error estimation between the spectral approximate solution and the exact solution is obtained.

NONPARAMETRIC SPECTRAL ESTIMATORS FORSECOND-ORDER (ALMOST) CYCLOSTATIONARYCOMPLEX PROCESSES

Second-order almost cycloststionary complex processes are complex random signals with almost periodically time-varying statistics. Smoothed periodograms are proposed for related to cyclic spectral estimation and are shown to be consistent. Asymptotic covariance expressions are derived along with their computable forms.

Optimal error estimates for Fourier spectral approximation of the generalized KdV equation

A Fourier spectral method for the generalized Korteweg-de Vries equation with periodic boundary conditions is analyzed, and a corresponding optimal error estimate in L^2-norm is obtained. It improves the result presented by Maday and Quarteroni. A modified Fourier pseudospectral method is also presented, with the same convergence properties as the Fourier spectral method.

Robust Continuous Quadratic Distance Estimation Using Quantiles for Fitting Continuous Distributions

Quadratic distance estimation making use of the sample quantile function over a continuous range is introduced. It extends previous methods which are based only on a few sample quantiles and it parallels the continuous GMM method. Asymptotic properties are established for the continuous quadratic distance estimators (CQDE) and the implementation of the methods are discussed. The methods appear to be useful for balancing robustness and efficiency and useful for fitting distribution with model quantile function being simpler than its density function or distribution function.

Use of Delayed Channel Estimation Results in Adaptive Modulation

In this paper,potential use of perfect but delayed channel estimates for variable-power discrete-rate adaptive modulation is explored.Research is concentrated on block by block adaptation.At first,a new quantity-TAUD(Tolerable Average Use Delay)is defined,it quantifies the performance of an adaptation scheme in tolerating the delay of channel estimates.Then,the research on TAUD shows that the delay tolerating performance declines with the increase in average power,the scheme working with more modulation modes can tolerate a longer delay,and such improvement will be more significant with the increase of average power.Finally,it shows that,as the delay tolerating performance determines the maximum block length,it has a great effect on the maximum spectral efficiency.The criterion for determining the block length appropriate for the target BER is described and a simple method of calculating the maximum block length is presented.

CROSS-SPECTRAL MOMENT AND SVD METHODS ESTIMATING THE PARAMETERS OF CLOSE SINUSOIDS IN COLORED NOISE

The cross-spectral estimation methods are efficient in estimating the parameters of sinusoidal signals embedded in colored noise. But up to now, only FPT and cross-periodogram methods are used in this field, the modern auto-spectral estimation method is introduced into cross-spectral estimation in this paper, meanwhile the cross-correlation based Yule-Walker equation is proposed theoretically and the moment and singular-value decomposition (SVD)) algorithms for cross-spectral estimation have been developed. Finally, a numerical example is given for comparing the presented methods with the well-known Cadzow’s SVD method.