Ensuring Quality of Random Numbers from TRNG: Design and Evaluation of Post-Processing Using Genetic Algorithm

Random numbers generated by pseudo-random and true random number generators (TRNG) are used in a wide variety of important applications. A TRNG relies on a non-deterministic source to sample random numbers. In this paper, we improve the post-processing stage of TRNGs using a heuristic evolutionary algorithm. Our post-processing algorithm decomposes the problem of improving the quality of random numbers into two phases: (i) Exact Histogram Equalization: it modifies the random numbers distribution with a specified output distribution;(ii) Stationarity Enforcement: using genetic algorithms, the output of (ii) is permuted until the random numbers meet wide-sense stationarity. We ensure that the quality of the numbers generated from the genetic algorithm is within a specified level of error defined by the user. We parallelize the genetic algorithm for improved performance. The post-processing is based on the power spectral density of the generated numbers used as a metric. We propose guideline parameters for the evolutionary algorithm to ensure fast convergence, within the first 100 generations, with a standard deviation over the specified quality level of less than 0.45. We also include a TestU01 evaluation over the random numbers generated.

Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

Improved Spectral Clustering Clothing Image Segmentation Algorithm Based on Sparrow Search Algorithm

In the process of clothing image researching,how to segment the clothing quickly and accurately and retain the clothing style details as much as possible is the basis of subsequent image analysis.Spectral clustering clothing image segmentation algorithm is a common method in the process of clothing image extraction.However,the traditional model requires high computing power and is easily affected by the initial center of clustering.It often falls into local optimization.Aiming at the above two points,an improved spectral clustering clothing image segmentation algorithm is proposed in this paper.The Nystrom approximation strategy is introduced into the spectral mapping process to reduce the computational complexity.In the clustering stage,this algorithm uses the global optimization advantage of the particle swarm optimization algorithm and selects the sparrow search algorithm to search the optimal initial clustering point,to effectively avoid the occurrence of local optimization.In the end,the effectiveness of this algorithm is verified on clothing images in each environment.

The measuring of spectral emissivity of object using chaotic optimal algorithm

There exist a considerable variety of factors affecting the spectral emissivity of an object. The authors have designed an improved combined neural network emissivity model, which can identify the continuous spectral emissivity and true temperature of any object only based on the measured brightness temperature data. In order to improve the accuracy of approximate calculations, the local minimum problem in the algorithm must be solved. Therefore, the authors design an optimal algorithm, i.e. a hybrid chaotic optimal algorithm, in which the chaos is used to roughly seek for the parameters involved in the model, and then a second seek for them is performed using the steepest descent. The modelling of emissivity settles the problems in assumptive models in multi-spectral theory.

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.

Fast identification of mural pigments at Mogao Grottoes using a LIBS-based spectral matching algorithm

To quickly identify the mineral pigments in the Dunhuang murals,a spectral matching algorithm(SMA)based on four methods was combined with laser-induced breakdown spectroscopy(LIBS)for the first time.The optimal range of LIBS spectrum for mineral pigments was determined using the similarity value between two different types of samples of the same pigment.A mineral pigment LIBS database was established by comparing the spectral similarities of tablets and simulated samples,and this database was successfully used to identify unknown pigments on tablet,simulated,and real mural debris samples.The results show that the SMA method coupled with the LIBS technique has great potential for identifying mineral pigments.

A blind modulation recognition algorithm based on cyclic spectral correlation

Cyclic spectral correlation above the bifrequency plane for the received signal was calculated by the strip spectral correlation algorithm （SSCA）and then was normalized. The result was expressed by matrix. The sum of error-square was computed between corresponding elements for the theoretical sampling matrix of all kinds of modulated signals and calculated matrix. The modulation type was recognized by exploiting the minimum value of the sum of error-square. No extracted characteristic parameter and prior information are needed for identifying the modulation type compared to the conventional methods. In addition, the new method extends the recognition scope and has high recognition probability at low SNR. The simulation results obtained by means of Monter-Carlo method proved the presented algorithm.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

Collaboration Filtering Recommendation Algorithm Based on the Latent Factor Model and Improved Spectral Clustering

Due to the development of E-Commerce, collaboration filtering (CF) recommendation algorithm becomes popular in recent years. It has some limitations such as cold start, data sparseness and low operation efficiency. In this paper, a CF recommendation algorithm is propose based on the latent factor model and improved spectral clustering (CFRALFMISC) to improve the forecasting precision. The latent factor model was firstly adopted to predict the missing score. Then, the cluster validity index was used to determine the number of clusters. Finally, the spectral clustering was improved by using the FCM algorithm to replace the K-means in the spectral clustering. The simulation results show that CFRALFMISC can effectively improve the recommendation precision compared with other algorithms.

Spectral matching based remote sensing identification of two main crop rotation patterns in a large irrigation district

The rapid identification of planting patterns for major crops in a large irrigated district has vital importance for irrigation management,water fee collection,and crop yield estimation.In this study,the OTSU algorithm and Mean-Shift algorithm were employed to automatically determine threshold values for mapping two main rotated crop patterns at the pixel scale.A time series analysis was conducted to extract the spatial distribution of rice-wheat and wheat-maize rotations in the Chuanhang irrigation district from 2016 to 2020.The results demonstrate that both threshold segmentation algorithms are reliable in extracting the spatial distribution of the crops,with an overall accuracy exceeding 80%.Additionally,both Kappa coefficients surpass 0.7,indicating better performance by OTSU method.Over the period from 2016 to 2020,the area occupied by rice-wheat rotation cropping ranged from 12500 to 14400 hm 2;whereas wheat-maize rotation cropping exhibited smaller and more variable areas ranging from 19730 to 34070 hm 2.These findings highlight how remote sensing-based approaches can provide reliable support for rapidly and accurately identifying the spatial distribution of main crop rotation patterns within a large irrigation district.

Cross-spectral root-min-norm algorithm for harmonics analysis in electric power system

To avoid drawbacks of classic discrete Fourier transform(DFT)method,modern spectral estimation theory was introduced into harmonics and inter-harmonics analysis in electric power system.Idea of the subspace-based root-min-norm algorithm was described,but it is susceptive to noises with unstable performance in different SNRs.So the modified root-min-norm algorithm based on cross-spectral estimation was proposed,utilizing cross-correlation matrix and independence of different Gaussian noise series.Lots of simulation experiments were carried out to test performance of the algorithm in different conditions,and its statistical characteristics was presented.Simulation results show that the modified algorithm can efficiently suppress influence of the noises,and has high frequency resolution,high precision and high stability,and it is much superior to the classic DFT method.

The Chebyshev spectral element method using staggered predictor and corrector for elastic wave simulations

Based on strong and weak forms of elastic wave equations, a Chebyshev spectral element method （SEM） using the Galerkin variational principle is developed by discretizing the wave equation in the spatial and time domains and introducing the preconditioned conjugate gradient （PCG）-element by element （EBE） method in the spatial domain and the staggered predictor/corrector method in the time domain. The accuracy of our proposed method is verified by comparing it with a finite-difference method （FDM） for a homogeneous solid medium and a double layered solid medium with an inclined interface. The modeling results using the two methods are in good agreement with each other. Meanwhile, to show the algorithm capability, the suggested method is used to simulate the wave propagation in a layered medium with a topographic traction free surface. By introducing the EBE algorithm with an optimized tensor product technique, the proposed SEM is especially suitable for numerical simulation of wave propagations in complex models with irregularly free surfaces at a fast convergence rate, while keeping the advantage of the finite element method.

Investigation of prior image constrained compressed sensing-based spectral X-ray CT image reconstruction

To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates（SPS） algorithm are proposed for the prior image constrained compressed sensing（PICCS）-based spectral X-ray CT image reconstruction. The PICCS-based image reconstruction takes advantage of the compressed sensing theory, a prior image and an optimization algorithm to improve the image quality of CT reconstructions.To evaluate the performance of the proposed method, three optimization algorithms and three prior images are employed and compared in terms of reconstruction accuracy and noise characteristics of the reconstructed images in each energy bin.The experimental simulation results show that the image xbins^W is the best as the prior image in general with respect to the three optimization algorithms; and the SPS algorithm offers the best performance for the simulated phantom with respect to the three prior images. Compared with filtered back-projection（FBP）, the PICCS via the SPS algorithm and xbins^W as the prior image can offer the noise reduction in the reconstructed images up to 80. 46%, 82. 51%, 88. 08% in each energy bin,respectively. M eanwhile, the root-mean-squared error in each energy bin is decreased by 15. 02%, 18. 15%, 34. 11% and the correlation coefficient is increased by 9. 98%, 11. 38%,15. 94%, respectively.

Adaptive inverse control of random vibration based on the filtered-X LMS algorithm

Random vibration control is aimed at reproducing the power spectral density （PSD） at specified control points. The classical frequency-spectrum equalization algorithm needs to compute the average of the multiple frequency response functions （FRFs）, which lengthens the control loop time in the equalization process. Likewise, the feedback control algorithm has a very slow convergence rate due to the small value of the feedback gain parameter to ensure stability of the system. To overcome these limitations, an adaptive inverse control of random vibrations based on the filtered-X least mean-square （LMS） algorithm is proposed. Furthermore, according to the description and iteration characteristics of random vibration tests in the frequency domain, the frequency domain LMS algorithm is adopted to refine the inverse characteristics of the FRF instead of the traditional time domain LMS algorithm. This inverse characteristic, which is called the impedance function of the system under control, is used to update the drive PSD directly. The test results indicated that in addition to successfully avoiding the instability problem that occurs during the iteration process, the adaptive control strategy minimizes the amount of time needed to obtain a short control loop and achieve equalization.

Wood species identification using spectral reflectance feature and optimal illumination radian design

We developed a scheme based on wood surface novel wood recognition spectral features that aimed to solve three problems. First was elimination of noise in some bands of wood spectral reflection curves. Second was improvement of wood feature selection based on analysis of wood spectral data. The wood spectral band is 350-2500 nm, a 2150D vector with a spectral sampling interval of 1 nm. We developed a feature selection proce- dure and a filtering procedure by solving the eigenvalues of the dispersion matrix. Third, we optimized the design for the indoor radian＇s mounting height. We used a genetic algorithm to solve the optimal radian＇s height so that the spectral reflection curves had the best classification infor- mation for wood species. Experiments on fivecommon wood species in northeast China showed overall recogni- tion accuracy 〉95 % at optimal recognition velocity.

Super spectral viscosity method for nonlinear conservation laws

In this paper, the super spectral viscosity （SSV） method is developed by introducing a spectrally small amount of high order regularization which is only activated on high frequencies. The resulting SSV approximation is stable and convergent to the exact entropy solution. A Gegenbauer-Chebyshev post-processing for the SSV solution is proposed to remove the spurious oscillations at the disconti-nuities and recover accuracy from the spectral approximation. The ssv method is applied to the scahr periodic Burgers equation and the one-dimensional system of Euler equations of gas dynamics. The numerical results exhibit high accuracy and resolution to the exact entropy solution,

Enhancing Clustering Stability in VANET: A Spectral Clustering Based Approach

Vehicles can establish a collaborative environment cognition through sharing the original or processed sensor data from the vehicular sensors and status map. Clustering in the vehicular ad-hoc network(VANET) is crucial for enhancing the stability of the collaborative environment. In this paper, the problem for clustering is innovatively transformed into a cutting graph problem. A novel clustering algorithm based on the Spectral Clustering algorithm and the improved force-directed algorithm is designed. It takes the average lifetime of all clusters as an optimization goal so that the stability of the entire system can be enhanced. A series of close-to-practical scenarios are generated by the Simulation of Urban Mobility(SUMO). The numerical results indicate that our approach has superior performance in maintaining whole cluster stability.

A seismic coherency method using spectral amplitudes

Seismic coherence is used to detect discontinuities in underground media. However, strata with steeply dipping structures often produce false low coherence estimates and thus incorrect discontinuity characterization results. It is important to eliminate or reduce the effect of dipping on coherence estimates. To solve this problem, time-domain dip scanning is typically used to improve estimation of coherence in areas with steeply dipping structures. However, the accuracy of the time-domain estimation of dip is limited by the sampling interval. In contrast, the spectrum amplitude is not affected by the time delays in adjacent seismic traces caused by dipping structures. We propose a coherency algorithm that uses the spectral amplitudes of seismic traces within a predefined analysis window to construct the covariance matrix. The coherency estimates with the proposed algorithm is defined as the ratio between the dominant the constructed covariance matrix. Thus, we eigenvalue and the sum of all eigenvalues of eliminate the effect of dipping structures on coherency estimates. In addition, because different frequency bands of spectral amplitudes are used to estimate coherency, the proposed algorithm has multiscale features. Low frequencies are effective for characterizing large-scale faults, whereas high frequencies are better in characterizing small-scale faults. Application to synthetic and real seismic data show that the proposed algorithm can eliminate the effect of dip and produce better coherence estimates than conventional coherency algorithms in areas with steeply dipping structures.

Cross-spectral recognition method of bridge deck aerodynamic admittance function

This study proposes a new identification algorithm about the admittance function, which can estimate the full set of six aerodynamic admittance functions considering cross power spectral density functions about the forces and the turbulence components. The method was first numerically validated through Monte Carlo simulations, and then adopted to estimate the aerodynamic admittance of a streamlined bridge deck. The identification method was further validated through a comparison between the numerical calculation and wind tunnel tests on a moving bridge section. © 2015, Institute of Engineering Mechanics, China Earthquake Administration and Springer-Verlag Berlin Heidelberg.

Super-resolution processing of passive millimeter-wave images based on adaptive projected Landweber algorithm

Passive millimeter wave （PMMW） images inherently have the problem of poor resolution owing to limited aperture dimension. Thus, efficient post-processing is necessary to achieve resolution improvement. An adaptive projected Landweber （APL） super-resolution algorithm using a spectral correction procedure, which attempts to combine the strong points of all of the projected Landweber （PL） iteration and the adaptive relaxation parameter adjustment and the spectral correction method, is proposed. In the algorithm, the PL iterations are implemented as the main image restoration scheme and a spectral correction method is included in which the calculated spectrum within the passband is replaced by the known low frequency component. Then, the algorithm updates the relaxation parameter adaptively at each iteration. A qualitative evaluation of this algorithm is performed with simulated data as well as actual radiometer image captured by 91.5 GHz mechanically scanned radiometer. From experiments, it is found that the super-resolution algorithm obtains better results and enhances the resolution and has lower mean square error （MSE）. These constraints and adaptive character and spectral correction procedures speed up the convergence of the Landweber algorithm and reduce the ringing effects that are caused by regularizing the image restoration problem.