Pre-filter that incorporates the noise model

A linear denoising filter is usually of lowpass type,and the main parameter in a lowpass filter is the cutoff frequency.The lowpass filters are normally shift invariant and can be implemented as convolution in the spatial domain or as multiplication in the Fourier domain.This paper presents a linear filter that is not characterized by its cutoff frequency but is characterized by the noise model.An example of such a linear filter is presented for lowdose X-ray computed tomography(CT).

Paradigm of Numerical Simulation of SpatialWind Field for Disaster Prevention of Transmission Tower Lines

Numerical simulation of the spatial wind field plays a very important role in the study of wind-induced response law of transmission tower structures.A reasonable construction of a numerical simulation method of the wind field is conducive to the study of wind-induced response law under the action of an actual wind field.Currently,many research studies rely on simulating spatial wind fields as Gaussian wind,often overlooking the basic non-Gaussian characteristics.This paper aims to provide a comprehensive overview of the historical development and current state of spatial wind field simulations,along with a detailed introduction to standard simulation methods.Furthermore,it delves into the composition and unique characteristics of spatial winds.The process of fluctuating wind simulation based on the linear filter AR method is improved by introducing spatial correlation and non-Gaussian distribution characteristics.The numerical simulation method of the wind field is verified by taking the actual transmission tower as a calculation case.The results show that the method summarized in this paper has a broader application range and can effectively simulate the actual spatial wind field under various conditions,which provides a valuable data basis for the subsequent research on the wind-induced response of transmission tower lines.

Anti-Occlusion Object Tracking Algorithm Based on Filter Prediction

Visual object tracking is an important issue that has received long-term attention in computer vision.The ability to effectively handle occlusion,especially severe occlusion,is an important aspect of evaluating the performance of object tracking algorithms in long-term tracking,and is of great significance to improving the robustness of object tracking algorithms.However,most object tracking algorithms lack a processing mechanism specifically for occlusion.In the case of occlusion,due to the lack of target information,it is necessary to predict the target position based on the motion trajectory.Kalman filtering and particle filtering can effectively predict the target motion state based on the historical motion information.A single object tracking method,called probabilistic discriminative model prediction(PrDiMP),is based on the spatial attention mechanism in complex scenes and occlusions.In order to improve the performance of PrDiMP,Kalman filtering,particle filtering and linear filtering are introduced.First,for the occlusion situation,Kalman filtering and particle filtering are respectively introduced to predict the object position,thereby replacing the detection result of the original tracking algorithm and stopping recursion of target model.Second,for detection-jump problem of similar objects in complex scenes,a linear filtering window is added.The evaluation results on the three datasets,including GOT-10k,UAV123 and LaSOT,and the visualization results on several videos,show that our algorithms have improved tracking performance under occlusion and the detection-jump is effectively suppressed.

IMPROVED METHOD FOR HILBERT INSTANTANEOUS FREQUENCY ESTIMATION

In the mechanical fault detection and diagnosis field, it is more and more important to analyze the instantaneous frequency （IF） character of complex vibration signal. The improved IF estimation method is put forward aiming at the shortage of traditional Hilbert transform. It is based on Hilbert transform in wavelet domain. With the help of relationship between the real part and the imaginary part obtained from the complex coefficient of continuous wavelet transform or the analyti- cal signal reconstructed in wavelet packet decomposition, the instantaneous phase function of the subcomponent is extracted. In order to improve the precise of IF estimated out, some means such as Linear regression, adaptive filtering, resampling are applied into the instantaneous phase obtained, then, the central differencing operator is used to get desired IF. Simulation results with synthetic and gearbox fault signals are included to illustrate the proposed method.

WIDELY LINEAR RLS CONSTANT MODULUS ALGORITHM FOR COMPLEX-VALUED NONCIRCULAR SIGNALS

Based on the constant modulus criterion, a new Widely Linear(WL) blind equalizer and a novel widely linear recursive least square constant modulus algorithm are proposed to improve the blind equalization performance for complex-valued noncircular signals. The new algorithm takes advantage of the WL filtering theory by taking full use of second-order statistical information of the complex-valued noncircular signals. Therefore, the weight vector contains the complete second-order information of the real and imaginary parts to decrease the residual inter-symbol interference effectively. Theoretical analysis and simulation results show that the proposed scheme can significantly improve the equalization performance for complex-valued noncircular signals compared with traditional blind equalization algorithms.

Widely linear UKF constant modulus algorithm for blind adaptive beamforming

Based on a uniform linear array, a new widely linear unscented Kalman filter-based constant modulus algorithm (WL-UKF-CMA) for blind adaptive beamforming is proposed. The new algorithm is designed according to the constant modulus criterion and takes full advantage of the noncircular property of the signal of interest (SOI), significantly increasing the output signal-to interference-plus-noise ratio (SINR), enhancing the convergence speed and decreasing the steady-state misadjustment. Since it requires no known training data, the proposed algorithm saves a large amount of the available spectrum. Theoretical analysis and simulation results are presented to demonstrate its superiority over the conventional linear least mean square-based CMA (L-LMS-CMA), the conventional linear recursive least square-based CMA (L-RLS-CMA), WL-LMS-CMA, WL-RLS-CMA and L-UKF-CMA.

A cascade model of information processing and encoding for retinal prosthesis

Retinal prosthesis offers a potential treatment for individuals suffering from photoreceptor degeneration diseases.Establishing biological retinal models and simulating how the biological retina convert incoming light signal into spike trains that can be properly decoded by the brain is a key issue.Some retinal models have been presented,ranking from structural models inspired by the layered architecture to functional models originated from a set of specific physiological phenomena.However,Most of these focus on stimulus image compression,edge detection and reconstruction,but do not generate spike trains corresponding to visual image.In this study,based on stateof-the-art retinal physiological mechanism,including effective visual information extraction,static nonlinear rectification of biological systems and neurons Poisson coding,a cascade model of the retina including the out plexiform layer for information processing and the inner plexiform layer for information encoding was brought forward,which integrates both anatomic connections and functional computations of retina.Using MATLAB software,spike trains corresponding to stimulus image were numerically computed by four steps：linear spatiotemporal filtering,static nonlinear rectification,radial sampling and then Poisson spike generation.The simulated results suggested that such a cascade model could recreate visual information processing and encoding functionalities of the retina,which is helpful in developing artificial retina for the retinally blind.

Improved Speech Denoising Algorithm Based on Discrete Fractional Fourier Transform

The speech signal and noise signal are the typical non-stationary signals,however the speech signa is short-stationary synchronously.Presently,the denoising methods are always executed in frequency domain due to the short-time stationarity of the speech signal.In this article,an improved speech denoising algorithm based on discrete fractional Fourier transform（DFRFT）is pre sented.This algorithm contains linear optimal filtering and median filtering.The simulation shows that it can easily eliminate the noise compared to Wiener filtering improve the signal to noise ratio（SNR）,and enhance the original speech signal.

A Novel Method of Edge Filter Linear Demodulation Using Long Period Grating in Fiber Sensor System

A novel method of linear demodulation based on edge filter is presented. An experimental system is built up in which LPG is used as the edge filter. We achieve linear demodulation with a bandwidth of 5nm.

Statistically robust design of linear filter for non-regenerative MIMO relay systems

This paper addresses the robust linear filter design issues for non-regenerative multiple input multiple output （MIMO） relay systems with imperfect channel state information （CSI） in both or partial hops. By considering statistical Kronecker channel model involving channel mean and antenna correlation, the robust linear processing schemes in imperfect CSI scenario for both hops are first derived based on mean squared error （MSE） criterion. In addition to this, the result is also extended to two practical scenarios, i.e. imperfect CSI for relay link with perfect CSI for access link and imperfect CSI for access link with perfect CSI for relay link. Simulation results show that the proposed scheme is capable of mitigating the performance degradation caused by the imperfect CSI.

On Optimal Dyadic Linear Prediction and Filtering in the Sequency Domain

In this paper dyadic linear prediction and dyadic linear filtering on a dyadic generalized stationary random process are dealt with via the Walsh transform. Taking the minimum of the mean-square errorof the dyadic system as the index of the working precision of the system, we consider and analyze theoptimal dyadic linear system, dyadic linear prediction and dyadic linear filtering. Finally, we study the precision of the optimal dyadic linear system.

Indoor Vehicle Positioning Based on Multi-Sensor Data Fusion

This study proposes a Kalman filter-based indoor vehicle positioning method for cases in which the steering angle and rotation speed of the vehicle’s wheels are unknown.By fusing the position and velocity data from the ultra-wideband sensors and acceleration and orientation data from the inertial measurement unit,we developed two algorithms to estimate the real-time position of the vehicle based on a linear Kalman filter and extended Kalman filter,respectively.We then conducted simulations and experiments to examine the performances of the algorithms.In the experiment,the Kalman filtering hyperparameters are configured,and we then ran the two algorithms to determine the positioning precision and accuracy with the ground truth produced via LiDAR.We verified that our method can improve precision and accuracy compared with the raw positioning data and can achieve desirable effects for indoor vehicle positioning when vehicles travel at low speeds.

Multi-scale image segmentation of coal piles on a belt based on the Hessian matrix

Segmenting images of coal piles on a belt is an unsolved problem in coal-based machine vision research, though it is an essential step for estimating size distribution and classifying coal. In this investigation, a new algorithm for segmenting images of coal piles on a belt is proposed. A multi^scale linear filter, constructed of a Hessian matrix and Gaussian function, forms the core of this algorithm and obtains an edge intensity image to form good seed regions for a watershed segmentation. Manual segmentation is used to define ground truth segmentation images to quantify the errors of the proposed method. Tests indicate that 12.76% of the visible regions are under- or over-segmented, and that this algorithm is feasible and effective in practical applications.

Determination of carbendazim in tea using surface enhanced Raman spectroscopy

Surface-enhanced Raman scattering（SERS） is applied to detect the concentration of carbendzim（CBZ） in tea leaves. Au colloid is selected and used for active surfaces, and the extraction conditions are optimized in the experiment. The linearity range for the SERS intensity and the concentration of CBZ is found to be0.5 to 8 mg kgà1. The detection limit for CBZ is 0.1 mg kgà1and its recovery in tea samples is 72.3%. The detection results for CBZ using this method are compared with those of HPLC, and no obvious difference can be found. In addition, by dripping the condensed Au colloid on the tea leaves, the proposed SERS approach could be used to the in-situ determination of the half life period of CBZ on tea leaves.

An approach for parameter estimation of combined CPPM and LFM radar signal

In this paper, the problem of parameter estimation of the combined radar signal adopting chaotic pulse position modulation （CPPM） and linear frequency modulation （LFM）, which can be widely used in electronic countermeasures, is addressed. An approach is proposed to estimate the initial frequency and chirp rate of the combined signal by exploiting the second-order cyclostationarity of the intra-pulse signal. In addition, under the condition of the equal pulse width, the pulse repetition interval （PRI） of the combined signal is predicted using the low-order Volterra adaptive filter. Simulations demonstrate that the proposed cyclic autocorrelation Hough transform （CHT） algorithm is theoretically tolerant to additive white Gaussian noise. When the value of signal noise to ratio （SNR） is less than 4 dB, it can still estimate the intra-pulse parameters well. When SNR = 3 dB, a good prediction of the PRI sequence can be achieved by the Volterra adaptive filter algorithm, even only 100 training samples.

A TV-Based Iterative Regularization Method for the Solutions of Thermal Convection Problems

Linear/nonlinear and Stokes based-stabilizations for the filter equations for damping out primitive variable(PV)solutions corrupted by uniformly distributed randomnoises are numerically studied through the natural convection(NC)aswell as the mixed convection(MC)environment.The most recognizable filter-scheme is based on a combination of the negative Laplace equation multiplied with the selection of the spatial scale and a linear function in order to preserve the uniqueness of the filtered solution.A more complicated filter-scheme,based on a Stokes problem which couples a filtered velocity and a filtered(artificial)pressure(or Lagrange multiplier)in order to enforce the incompressibility constraint,is also studied.Linear and Stokes basedfilters via nested iterative(NI)filters and the consistent splitting scheme(CSS)are proposed for the NC/MC problems.Inspired by the total-variation(TV)model of image diffusion,well preserved feature flow patterns from the corrupted NC/MC environment are obtained by TV-Stokes based-filters together with the CSS.Our experimental results show that our proposed algorithms are effective and efficient in eliminating the unwanted spurious oscillations and preserving the accuracy of thermal convective fluid flows.