Shaping the Wavefront of Incident Light with a Strong Robustness Particle Swarm Optimization Algorithm

We demonstrate a modified particle swarm optimization(PSO) algorithm to effectively shape the incident light with strong robustness and short optimization time. The performance of the modified PSO algorithm and genetic algorithm(GA) is numerically simulated. Then, using a high speed digital micromirror device, we carry out light focusing experiments with the modified PSO algorithm and GA. The experimental results show that the modified PSO algorithm has greater robustness and faster convergence speed than GA. This modified PSO algorithm has great application prospects in optical focusing and imaging inside in vivo biological tissue, which possesses a complicated background.

Adaptive Fault Estimation for Dynamics of High Speed Train Based on Robust UKF Algorithm

This paper proposes an adaptive unscented Kalman filter algorithm(ARUKF)to implement fault estimation for the dynamics of high⁃speed train(HST)with measurement uncertainty and time⁃varying noise with unknown statistics.Firstly,regarding the actuator and sensor fault as the auxiliary variables of the dynamics of HST,an augmented system is established,and the fault estimation problem for dynamics of HST is formulated as the state estimation of the augmented system.Then,considering the measurement uncertainties,a robust lower bound is proposed to modify the update of the UKF to decrease the influence of measurement uncertainty on the filtering accuracy.Further,considering the unknown time⁃varying noise of the dynamics of HST,an adaptive UKF algorithm based on moving window is proposed to estimate the time⁃varying noise so that accurate concurrent actuator and sensor fault estimations of dynamics of HST is implemented.Finally,a five-car model of HST is given to show the effectiveness of this method.

Novel robust approach for constructing Mamdani-type fuzzy system based on PRM and subtractive clustering algorithm

A novel approach for constructing robust Mamdani fuzzy system was proposed, which consisted of an efficiency robust estimator(partial robust M-regression, PRM) in the parameter learning phase of the initial fuzzy system, and an improved subtractive clustering algorithm in the fuzzy-rule-selecting phase. The weights obtained in PRM, which gives protection against noise and outliers, were incorporated into the potential measure of the subtractive cluster algorithm to enhance the robustness of the fuzzy rule cluster process, and a compact Mamdani-type fuzzy system was established after the parameters in the consequent parts of rules were re-estimated by partial least squares(PLS). The main characteristics of the new approach were its simplicity and ability to construct fuzzy system fast and robustly. Simulation and experiment results show that the proposed approach can achieve satisfactory results in various kinds of data domains with noise and outliers. Compared with D-SVD and ARRBFN, the proposed approach yields much fewer rules and less RMSE values.

A robust tensor watermarking algorithm for diffusion-tensor images

Watermarking algorithms that use convolution neural networks have exhibited good robustness in studies of deep learning networks.However,after embedding watermark signals by convolution,the feature fusion eficiency of convolution is relatively low;this can easily lead to distortion in the embedded image.When distortion occurs in medical images,especially in diffusion tensor images(DTIs),the clinical value of the DTI is lost.To address this issue,a robust watermarking algorithm for DTIs implemented by fusing convolution with a Transformer is proposed to ensure the robustness of the watermark and the consistency of sampling distance,which enhances the quality of the reconstructed image of the watermarked DTIs after embedding the watermark signals.In the watermark-embedding network,Ti-weighted(Tlw)images are used as prior knowledge.The correlation between T1w images and the original DTI is proposed to calculate the most significant features from the T1w images by using the Transformer mechanism.The maximum of the correlation is used as the most significant feature weight to improve the quality of the reconstructed DTI.In the watermark extraction network,the most significant watermark features from the watermarked DTI are adequately learned by the Transformer to robustly extract the watermark signals from the watermark features.Experimental results show that the average peak signal-to-noise ratio of the watermarked DTI reaches 50.47 dB,the diffusion characteristics such as mean diffusivity and fractional anisotropy remain unchanged,and the main axis deflection angleαAc is close to 1.Our proposed algorithm can effectively protect the copyright of the DTI and barely affects the clinical diagnosis.

Identification of dynamic systems using support vector regression neural networks

A novel adaptive support vector regression neural network （SVR-NN） is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is applied to determine the initial structure and initial weights of the SVR-NN so that the network architecture is easily determined and the hidden nodes can adaptively be constructed based on support vectors. Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN. To test the validity of the proposed method, it is demonstrated that the adaptive SVR-NN can be used effectively for the identification of nonlinear dynamic systems. Simulation results show that the identification schemes based on the SVR-NN give considerably better performance and show faster learning in comparison to the previous neural network method.

A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images

Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.

GA-BASED PID NEURAL NETWORK CONTROL FOR MAGNETIC BEARING SYSTEMS

In order to overcome the system non-linearity and uncertainty inherent in magnetic bearing systems, a GA（genetic algnrithm）-based PID neural network controller is designed and trained tO emulate the operation of a complete system （magnetic bearing, controller, and power amplifiers）. The feasibility of using a neural network to control nonlinear magnetic bearing systems with unknown dynamics is demonstrated. The key concept of the control scheme is to use GA to evaluate the candidate solutions （chromosomes）, increase the generalization ability of PID neural network and avoid suffering from the local minima problem in network learning due to the use of gradient descent learning method. The simulation results show that the proposed architecture provides well robust performance and better reinforcement learning capability in controlling magnetic bearing systems.

IMPLEMENTATION OF ADAPTIVE ROBUST POLE-PLACEMENT DESIGN

n this paper an adaptive robust algorithm for pole-placement design is proposed. It consists of the refined--optimal IV parameter estimator and a robust pole--placement controller. The robustness of the algorithm means that the output of the controlled plant can be stabilized in the presence of unmodelled dynamics and bounded unmeasurable output noise. Simulation results show the effeciency of the algorithm.

Efficient model building in active appearance model for rotated face

This paper proposes the efficient model building in active appearance model(AAM) for the rotated face.Finding an exact region of the face is generally difficult due to different shapes and viewpoints.Unlike many papers about the fitting method of AAM,this paper treats how images are chosen for fitting of the rotated face in modelling process.To solve this problem,databases of facial rotation and expression are selected and models are built using Procrustes method and principal component analysis(PCA).These models are applied in fitting methods like basic AAM fitting,inverse compositional alignment(ICA),project-out ICA,normalization ICA,robust normalization inverse compositional algorithm(RNIC)and efficient robust normalization algorithm(ERN).RNIC and ERN can fit the rotated face in images efficiently.The efficiency of model building is checked using sequence images made by ourselves.

IMPROVED RESULTS ON THE ROBUSTNESS OF STOCHASTIC APPROXIMATION ALGORITHMS

This paper is a continuation of the research carried out in [1]-[2], where the robustnessanalysis for stochastic approximation algorithms is given for two cases: 1. The regression functionand the Liapunov function are not zero at the sought-for x^0, 2. lim supnot zero, here {ξ_i} are the measurement errors and {a_n} are the weighting coefficients in thealgorithm. Allowing these deviations from zero to occur simultaneously but to remain small, thispaper shows that the estimation error is still small even for a class fo measurement errors moregeneral than that considered in [2].

A robust color image watermarking algorithm against rotation attacks

A robust digital watermarking algorithm is proposed based on quaternion wavelet transform(QWT) and discrete cosine transform(DCT) for copyright protection of color images. The luminance component Y of a host color image in YIQ space is decomposed by QWT, and then the coefficients of four low-frequency subbands are transformed by DCT. An original binary watermark scrambled by Arnold map and iterated sine chaotic system is embedded into the mid-frequency DCT coefficients of the subbands. In order to improve the performance of the proposed algorithm against rotation attacks, a rotation detection scheme is implemented before watermark extracting. The experimental results demonstrate that the proposed watermarking scheme shows strong robustness not only against common image processing attacks but also against arbitrary rotation attacks.

Rapid and robust medical image elastic registration using mean shift algorithm

In landmark-based image registration, estimating the landmark correspondence plays an important role. In this letter, a novel landmark correspondence estimation technique using mean shift algorithm is proposed. Image corner points are detected as landmarks and mean shift iterations are adopted to find the most probable corresponding point positions in two images. Mutual information between intensity of two local regions is computed to eliminate mis-matching points to improve the stability of corresponding estimation correspondence landmarks is exact. The proposed experiments of various mono-modal medical images. Multi-level estimation （MLE） technique is proposed Experiments show that the precision in location of technique is shown to be feasible and rapid in the

Entropy-like distance driven fuzzy clustering with local information constraints for image segmentation

To improve the anti-noise ability of fuzzy local information C-means clustering, a robust entropy-like distance driven fuzzy clustering with local information is proposed. This paper firstly uses Jensen-Shannon divergence to induce a symmetric entropy-like divergence. Then the root of entropy-like divergence is proved to be a distance measure, and it is applied to existing fuzzy C-means(FCM) clustering to obtain a new entropy-like divergence driven fuzzy clustering, meanwhile its convergence is strictly proved by Zangwill theorem. In the end, a robust fuzzy clustering by combing local information with entropy-like distance is constructed to segment image with noise. Experimental results show that the proposed algorithm has better segmentation accuracy and robustness against noise than existing state-of-the-art fuzzy clustering-related segmentation algorithm in the presence of noise.