Filtering White Noise Research Articles

Low-complexity and low-latency equalization technique - probabilistic noise cancellation.

Both inside data centers (DCs) and in short optical links between data centers (DC campuses), intensity-modulation and direct-detection (IMDD) systems using four-level pulse amplitude modulation (PAM4) will dominate this decade due to low transceiver price and power consumption. The next DC transceiver generation based on 100 Gbaud PAM4 will require advanced digital signal processing (DSP) algorithms and more powerful forward error correction (FEC) codes. Because of bandwidth limitations, the conventional DC DSP based on a few-tap linear feed-forward equalizer (FFE) is likely to be upgraded to more complex but still low-complexity Volterra equalizers followed by a noise whitening filter and either a maximum likelihood sequence estimation (MLSE) or a maximum a posteriori probability (MAP) algorithm. However, stringent power consumption and latency requirements may limit the use of complex algorithms such as decision feedback equalizer (DFE) or MLSE/MAP in DC networks (DCN). In this paper, we introduce a low-complexity, low-latency algorithm based on a feedforward structure, yielding a performance between DFE and MLSE. We call the novel equalization algorithm probabilistic noise cancellation (PNC), since it weights noise patterns based on their probabilities in the presence of bandwidth limitations. The probabilistic weighting is efficiently exploited in correcting correlated errors caused by noise coloring in the FFE.

A Computation-efficient Online Secondary Path Modeling Technique for Modified FXLMS Algorithm

This paper proposes an online secondary path modelling (SPM) technique to improve the performance of the modified filtered reference Least Mean Square (FXLMS) algorithm. It can effectively respond to a time-varying secondary path, which refers to the path from a secondary source to an error sensor. Unlike traditional methods, the proposed approach switches modes between adaptive ANC and online SPM, eliminating the use of destabilizing components such as auxiliary white noise or additional filters, which can negatively impact the complexity, stability, and noise reduction performance of the ANC system. The system operates in adaptive ANC mode until divergence is detected due to secondary path changes. At this moment, it switches to SPM mode until the path is remodeled and then returns to ANC mode. Furthermore, numerical simulations in the paper demonstrate that the proposed online technique effectively copes with the secondary path variations.

Working Mode Identification Method for High Arch Dam Discharge Structure Based on Improved Wavelet Threshold–EMD and RDT Algorithm

Prototype vibration response data of high arch dam discharge structures inevitably mix various noises under the discharge excitation, which adversely affects the accuracy of the working modal identification of the structure. To effectively filter noise and reduce modal aliasing for better identification accuracy, this study proposes an improved modal threshold identification method based on an improved wavelet threshold–empirical mode decomposition (EMD) and random decrement technique (RDT) algorithm for high arch dam discharge structures. On the basis of the measured vibration response data of the dam, the wavelet threshold is adopted to filter out most of the high-frequency white noise and to reduce the boundary accumulation effect of EMD decomposition. Detrended fluctuation analysis (DFA) is utilized to filter white noise and low-frequency flow noise after EMD decomposition. The natural frequency and damping ratio of the structure system are obtained by the improved RDT algorithm. The engineering examples show that the proposed method can accurately filter the measured vibration response signal noise of the discharge structure, retain the signal characteristic information, improve the accuracy of working modal recognition of the structural vibration response, avoid the complex ordering process of the system, and ease the working modal parameter identification of high arch dam discharge structures. This method can be applied to the mode identification of other large structures, as well.

Stochastic modeling of geometric imperfections for buckling analysis of suction buckets

Recently, suction buckets have become a very prominent foundation for bottom fixed and floating offshore wind turbines. They are embedded with an installation force that stems from water evacuation inside the bucket. This internal negative pressure leads to a high risk of structural buckling. The buckling strength is significantly reduced by geometric imperfections. In previous work, equivalent geometric imperfection forms were introduced and the lower bound was evaluated. However, it has not yet been possible to identify a generally appropriate imperfection form. A probabilistic design approach based on realistic imperfections was not yet considered for suction buckets. Therefore, in this work, a stochastic modeling approach is introduced, which bases on measured data. The imperfection is decomposed to the half-wave cosine Fourier representation. Realizations of the imperfection pattern are generated by filtering white noise with the amplitude spectrum. They are then applied as out of plane deviations on a geometrically and materially nonlinear finite element model and evaluated. The resulting buckling pressure distribution can then be evaluated for different reliability levels. By considering more realistic imperfections and a plastic soil model, the buckling pressure increases by up to a factor of two compared to the conservative stress-based buckling approach.

The Reduced-Order Model for Droplet Drift of Aerial Spraying under Random Lateral Wind

The droplet drift during aerial spraying process of oilseed rape, which is induced by complex flow field including random lateral wind, is difficult to predict and suppress. In this study, the high-fidelity computational fluid dynamics (CFD) technique is employed to simulate the two-phase flow of droplets in the rotor flow field, and the influence of main operation parameters on spraying effect is investigated numerically. Furthermore, the mechanism of droplet deposition in various operation conditions is discussed according to the analysis of unsteady flow field characteristics. However, the simulation via CFD technique is time-consuming, and it is not suitable for multidisciplinary work and optimization design. To address such issue, a filter white Gaussian noise signal is used to mimic the random lateral wind, and the droplet drift distance is obtained numerically. Based on the input and output dataset of CFD, the recursive algorithm including nonlinear autoregressive exogenous model and surrogate-based recurrence framework and the deep learning method for time-series prediction called long short-term memory neural network are used to build the efficient reduced-order model, respectively. Numerical simulations show that the droplet drift distance can be predicted by measurable lateral wind speed via the reduced-order model approach, which agreed well with the results obtained via the CFD method. In addition, the reduced-order model could decrease computation cost by 6 orders of magnitude with an acceptable accuracy, which indicates that the proposed method could be used for the design of off-line closed-loop controller of a variable spraying system.

Second-Order EKF White Noise Estimator Design for Hybrid Systems

The extended Kalman filter (EKF) has a wide range of applications (especially in power battery management systems) with a rapidly increasing market share. It aims to minimize the symmetric loss function (mean square error) and it has high accuracy and efficiency in battery state estimation. This study deals with the second-order extended Kalman filter-based process and the measurement white noise estimation problem for nonlinear continuous-discrete systems. The design of the white noise filter and smoother were, firstly, converted into a linear estimation problem by the second-order Taylor series expansion approximation and the function that makes the second-order term approximately equivalent to the estimation error variance. Secondly, based on the projection formula of the Kalman filtering (KF) theory and the Lemma of expectation for quadratic and quartic product traces of random vectors, the second-order EKF was derived. Then, to generate white noise estimators in the forms of filtering and smoothing, we derived a recursive solution, using an innovation method. Finally, a numerical example is given to show the effectiveness of the proposed method.

56 Gbit/s OOK Signal in C-band Over 20 km Dispersion-Uncompensated Link Transmission With Receiver-Side EDC Algorithm

In this paper, we propose and experimentally demonstrate a receiver-side electronic dispersion compensation (EDC) algorithm for direct-detection 56 Gbit/s on-off keying signal in C-band over 20 km dispersion-uncompensated standard single-mode fiber transmission. The proposed receiver-side EDC algorithm includes the feed-forward equalizer, post noise-whitening filter and modified iterative detection algorithm, which is originally used in spectrally efficient frequency division multiplexing systems for inter-carrier interference cancellation. Only with the aid of the proposed EDC algorithm for the compensation of power fading effect caused by bandwidth limit and chromatic dispersion, bit-error ratio below the 7% hard-decision forward error correction limit is achieved. This scheme can keep the system cost-effective and flexible, showing potentials for optical interconnects.

Dynamic Loading System of an Agricultural Vehicle Power Wagon Based on Complex Loading

A dynamic loading system of a power wagon is studied to solve the problems of low loading precision, slow response speed, and poor adaptive capability of a power wagon in an agricultural vehicle traction test. The load feature of an engine is analyzed under different gear locations, governor handle positions, and vehicle speeds. Meanwhile, the load feature of an eddy current retarder is examined based on a backpropagation (BP) neural network proportional-integral-derivative (PID) control algorithm. The mathematical model for the loading system is established. The time domain model for traction is refactored by a white noise filter according to the power spectral density function. To improve the dynamic loading performance of the eddy current retarder, the BP neural network PID control algorithm is used to change its exciting voltage to achieve an accurate simulation of the actual working load. The effectiveness and practicability of this control algorithm are validated by comparing its system output response dynamic characteristics with those of the traditional PID control algorithm. Simulation results show that the output error is less than 2.8% and the maximum delay is 56.5 ms when the BP neural network PID control is used. On the basis of this result, the traction property of a tractor with several wheels is tested on the road. The test and analysis results indicate that the output loads of the loading system accurately simulate the field actual load of the tractor. The maximum output load value of the system is 42-48 kN and the applicable speed range is 0-25 km/h. The output error is less than 3.5% and the maximum delay is 85.6 ms.

Design of a Short-Circuit Detection Intelligent Release Using Discrete Wavelet Algorithm

The short-circuit faults are one of the inevitable faults types in the power system. The frame circuit breaker can quickly break the transmission lines in case of the short-circuit faults to achieve the purpose of protecting the power system. At present, the method for detecting the short-circuit faults in the low-voltage distribution system by the intelligent release in the frame circuit breaker is mostly the rate of change of current or the effective value of current. Detection of the short-circuit faults by the rate of change of current has poor anti-interference ability. Detection of the short-circuit faults by the effective value will take a long time. In order to shorten the short-circuit fault detection time of the intelligent release, this paper proposes the wavelet packet decomposition as the short-circuit fault detection algorithm of the intelligent release. The multi-scale wavelet algorithm has a certain ability to filter white noise, and the singular value of the detail decomposition can be used to fast detection of the short-circuit faults when the short circuit occurs, which can shorten the detection time of the short-circuit faults. The results of the authoritative institutional tests prove that the intelligent release designed in this paper can detect the fault characteristics and send out the tripping signal within 0.5 ms after the short-circuit faults occur. The method proposed in this paper can be used in the low-voltage distribution system, as well as in medium-voltage and high-voltage situations to detect the short-circuit faults.

On Modelling Wind-Farm Wake Turbulence Autospectra and Coherence from a Database

This study addresses the feasibility of modeling wind-farm wake-turbulence autospectra and coherences from a database: flow velocity points from experimental and computational fluid dynamics (CFD) investigations. Specifically, it first applies an earlier-exercised framework to construct the autospectral models from a database and then it adopts a recently proposed framework to construct the coherence models from a database. While this proposed framework has not been tested against a database, the methodology has been completely formulated with a theoretical basis. These models of autospectrum and coherence are interpretive, and in closed form. Both frameworks basically involve the perturbation series expansion of the autospectra and coherences. The framework for modeling autospectra is tested against a demanding database of wake turbulence inside a wind farm over a complex terrain from a full-scale test. The suitability of these autospectral models for simulation through white-noise driven filters is also demonstrated. Finally, coherence models are generated for assumed values of the perturbation series constants, and these coherence models are used to demonstrate how the coherence models of homogeneous isotropic turbulence deviate from the coherence models of non-homogeneous non-isotropic turbulence such as wind-farm wake turbulence. This feasibility of extracting both the one-point statistics of autospectral models and the two-point statistics of coherence models from a database represents a research avenue that is new and promising in the treatment of wind-farm wake turbulence. This paper also demonstrates the feasibility of fruitfully exploiting the wake treatment methods developed in other fields.

Adaptive filtering‐based multi‐innovation gradient algorithm for input nonlinear systems with autoregressive noise

SummaryIn this paper, by means of the adaptive filtering technique and the multi‐innovation identification theory, an adaptive filtering‐based multi‐innovation stochastic gradient identification algorithm is derived for Hammerstein nonlinear systems with colored noise. The new adaptive filtering configuration consists of a noise whitening filter and a parameter estimator. The simulation results show that the proposed algorithm has higher parameter estimation accuracies and faster convergence rates than the multi‐innovation stochastic gradient algorithm for the same innovation length. As the innovation length increases, the filtering‐based multi‐innovation stochastic gradient algorithm gives smaller parameter estimation errors than the recursive least squares algorithm.

Adaptive random control of a two-axis redundantly actuated electro-hydraulic shaking table

An adaptive random control method is presented for a two-axis redundantly actuated electro-hydraulic shaking table system to replicate the reference acceleration power spectral density (PSD). The redundant force controller is developed to reduce the cross-coupling among the actuators. The offline iterations are implemented in conventional random control to compensate for the changes in the frequency response function of the system. Stability and responsiveness of control are determined by operator-selected parameters. In adaptive random control, the reference PSD is transformed into time domain histories by filtering white noise. The adaptive finite impulse response (FIR) filters are used to model and track the inverse system online. The weights of the FIR filters are updated by the forgetting factor recursive least-squares algorithm. The time histories are corrected by the copy of the inverse model. Experimental results obtained in step response and PSD replication demonstrate the effectiveness of the proposed control method.

Speech Identification using GFCC, Additive White Gaussian Noise (AWGN) and Wavelet Filter

his paper deals with the identification of speakers identity from the given set of values of speech from the database. The major problem during the identification of speech is noisy environment which degrades the system performance during its mismatch. So one can say identification using speaker recognition is the vital issue in research. This paper tells about the various used techniques like GFCC i.e. Gamma tone Frequency Cepstral Coefficients as its speech detection algorithm and Gaussian Mixture Model (GMM) to estimate the Gaussian model parameters. This paper basically focuses on improvement of speech identification in noisy environment using Wavelet filter which are added to de-noise the speech signals. These techniques are applied on store value of databases in Attendance system application. and the features of the speech are then matched from the database. Experiment are done 15 speech values saying phrases Present Mam,Present sir,Yes mam,Yes sirwith 4 types of utterance for each phase. This Experiment shows better results for stored database oriented system and gives 85% of the correct recognition rate i.e. CORR and 73% results are given when wavelet filter are not used .

Analytical reconstruction of isotropic turbulence spectra based on the Gaussian transform

The field of application of the Random Particle Mesh (RPM) method used to simulate turbulence-induced broadband noise in several aeroacoustic applications is improved to realise isotropic turbulence spectra. With this method turbulent fluctuations are synthesised by filtering white noise with a Gaussian filter kernel that in turn gives a Gaussian spectrum. The Gaussian filter is efficient and finds wide-spread applications in stochastic signal processing. However Gaussian spectra do not correspond to real turbulence spectra. Thus in turbo-machines the von Kármán, Liepmann, and modified von Kármán spectra are more realistic model spectra. In this note we analytically derive weighting functions to realise arbitrary isotropic solenoidal spectra using a superposition of weighted Gaussian spectra of different length scales. The analytic weighting functions for the von Kármán, the Liepmann, and the modified von Kármán spectra are derived subsequently. Finally a method is proposed to discretise the problem using a limited number of Gaussian spectra. The effectivity of this approach is demonstrated by realising a von Kármán velocity spectrum using the RPM method.

Evaluation of the physical forces exerted on a spherical bubble inside the nozzle in a cavitating flow with an Eulerian/Lagrangian approach

An Eulerian/Lagrangian approach is used to calculate the physical forces acting on a spherical bubble. Reynolds average Navier–Stokes (RANS) equations for the Eulerian approach are solved with a finite volume scheme. The SIMPLE algorithm is utilized for pressure and velocity linkage. To model convective fluxes, an upwind scheme is used. The Reynolds stress transport model (RSTM) is used to calculate the turbulent parameters. In the Lagrangian approach, a modified form of the Reyleigh–Plesset (RP) and Maxey equations are solved with MATLAB programming software for evaluation of bubble motion and bubble dynamics. The carrying fluid in this study is diesel fuel. Continuous filter white noise (CFWN) is solved parallel to the Maxey and RP equations to calculate fluctuating terms of velocity in x and y directions. Six forces exerted on the bubble during its motion are investigated inside the cavitating flow regime. The cavitating regime can be extremely effective on bubble force and increase bubble forces up to several thousand times. Added mass force in the y direction has the highest value among all forces exerted on the bubble during its motion inside the nozzle.

A hybrid LS-HE and LS-SVM model to predict time series of precipitable water vapor derived from GPS measurements

Precipitable water vapor (PWV) is one of the key parameters for the analysis of global climate systems, formation of clouds, and short-term forecasts of precipitation. Since water vapor is highly variable both spatially and temporally, it can be problematic, requiring modeling and efficient techniques in order to measure its quantity. Global Positioning System (GPS) stations provide total water column at high quality under all weather conditions with high temporal resolution. The technique used in this work is Precise Point Positioning (PPP) for single dual-frequency GPS receiver aided by precise orbit and clock product. Zenith wet delay (ZWD) is estimated through a stochastic process called random walk at 5-min intervals. The seasonal and diurnal variations of PWV for four GPS stations located in Europe are investigated. Latitude and station height are the most influential factors for the amount of water vapor when we compare their amplitudes and offsets to NASA Water Vapor Dataset-M (NVAP-M) and station heights. The area-averaged accumulated rain time series retrieved from Tropical Rainfall Measuring Mission (TRMM) product also shows that high PWV levels do not necessarily lead to rainfall. The amplitudes of diurnal and semi-diurnal variations are found to be of much lower amount than those of seasonal variations. In order to validate the capability of GPS-sensed PWV measurements, two episodes in wintertime and summertime are simulated using Weather Research Forecast (WRF) model. Both simulated PWV and observational PWV are consistent with each other. The main objective of this paper is to model and forecast the PWV time series which would be useful information on climatology and meteorology. Due to the distinct harmonic characteristics of PWV time series, the least squares harmonic estimation (LS-HE) is applied to time series of PWV derived from 4-year GPS station observations. Subsequently, least squares support vector machine (LS-SVM) optimized by cross-validation strategy is used to model non-harmonic components of the signal. The underlying motivation for using LS-SVM is the proficiency of this methodology to precisely model time series data when the underlying model is usually non-stationary, not defined a priori, and non-linear which are major characteristics of PWV data. The modeled time series shows that the hybrid approach (LS-HE and LS-SVM) can efficiently filter white noise in the observations and then perform forecasting. The bias (∼0.37 mm) and standard deviation (∼3 mm) of observed and predicted values presents the sound capability of the proposed model.

Sea wave modelling for motion control applications

The modelling of sea environment is important in designing an effective motion control system for any marine vehicle. Inadequate representation of the components of a typical random sea might lead to poor performance of the control system. A multiple output system such as the one having components of wave elevation and slope, facilitates designing the control system taking into account the different degrees of freedom. The method of modelling the sea environment presented here, provides the basis for the design of motion control systems for multiple degree of freedom cases, which give rise to excitation forces and moments acting on the marine vehicle. The method used here models the sea environment using Gaussian white noise and shaping filter to generate a multiple output form of the random sea state. In the first step a given standard wave spectrum is approximated using a rational polynomial, the coefficients of the polynomial are obtained by least square fitting method to best match the spectrum. The established rational polynomial is then decomposed to get the transfer function of the shaping filter. The wave slope spectrum is similarly approximated using the same rational polynomial. The transfer functions of the two components of amplitude and slope, representing the filters are combined to generate a state space form. Using the white noise as input, the state space form obtains the wave elevation and slope as outputs. By performing spectral analysis using Welch method, the quality of the obtained output is checked against the targetted spectrum. The application of the simulated wave slope spectrum in a closed loop state space model is demonstrated as applied to the roll stabilization characteristics of a stationary ship using a passive tank.DOI: http://dx.doi.org/10.3329/jname.v11i1.17768

Multi-input multi-output random vibration control of a multi-axis electro-hydraulic shaking table

An online multi-input multi-output (MIMO) random vibration control method is presented for a multi-axis hydraulic shaking table system to replicate the reference acceleration power spectral density (PSD) by the reproduction of the acceleration time domain histories that are proportional to the reference. The offline iterations are implemented using conventional random vibration control to compensate for the changes in the frequency response function (FRF) of the system. The successful test mainly depended on the experience of the operator. In online MIMO random vibration control, the reference PSD is transformed into time domain histories by filtering white noise. The impedance matrix of the shaking table system, which is the inverse of the FRF, is calculated and updated online. The time histories are compensated based on the system impedance matrix and then used as the drive signals to the shaking table. The overlap-save method and fast Fourier transformation are proposed to implement the transformation and compensation online. Two-axis random vibration experimental results obtained with the proposed control method show that both the reference PSD and the time domain histories can be replicated with high precision in the output of the shaking table without operator intervention.

SC-FDMA 수신기를 위한 잡음 백색화 판정궤환 등화기

이 논문에서는 SC-FDMA 수신기를 위한 잡음 백색화 판정궤환 등화기를 제안하였다. SC-FDMA 방식은 다중경로의 영향을 제거할 수 있는 OFDMA 방식의 장점을 유지하면서 OFDMA의 단점인 높은 PAPR 문제를 해결할 수 있는 장점을 가지고 있다. 또 SCFDMA 방식은 기본적으로 단일 반송파 전송방식이지만, 주파수 영역에서 채널 등화기를 구현함으로써 OFDMA 수신기와 마찬가지로 등화기의 복잡도를 크게 감소시킬 수 있다. 뿐만 아니라 시간 영역 신호가 디지털 변조된 복소심볼이므로 시간 영역에서 추가적으로 비선형 등화인 판정궤환 등화를 함으로써 주파수 영역의 선형 등화에 의해 잔류해 있는 심볼간 간섭을 더 제거할 수 있다. 한편 지금까지 제시된 SC-FDMA용 판정궤환 등화기는 판정기에 입력되는 신호에 포함된 잡음이 백색화되어 있지 않기 때문에 최적판정을 수행하고 있지 않다. 이 논문에서는 이러한 문제를 해결하기 위해 선형 잡음 백색화 필터를 판정기 앞에 연결하여 판정궤환 등화기의 성능을 더 향상 시킬 수 있는 방법을 제시한다. 또 컴퓨터 모의실험을 통해 기존의 등화기와 제안된 등화기의 비트 오율 성능을 비교한다. In this paper, we propose a noise whitening decision feedback equalizer for single carrier frequency division multiple access (SC-FDMA) receivers. SC-FDMA has the same advantage as that of orthogonal frequency division multiple access (OFDMA) in which the multipath effect can be removed easily, and also solves the problem of high peak to average power ratio (PAPR) which is the main drawback of OFDMA. Although SC-FDMA is a single carrier transmission scheme, a simple frequency domain linear equalizer (FD-LE) can be implemented as in OFDMA, which can dramatically reduce the equalizer complexity. Moreover, some residual intersymbol interference in the output of the FD-LE can be further removed by an additional nonlinear decision feedback equalizer (DFE) in time domain, because the time domain signal is a digitally modulated symbol. In the conventional DFE, however, the noise is not white at the input of the decision device and correspondingly the decision is not optimum. In this paper, we propose an improved DFE scheme for SC-FDMA systems where a linear noise whitening filter is inserted before the decision device of the conventional DFE scheme. Through computer simulations, we compare the bit error rate performance of the proposed DFE scheme with the conventional equalizers.

Optimal Detection in Flank Array

In underwater acoustic signal processing, flank enlarges the apertures of array and establishes a better foundation in weak signal detection; it includes two steps that are the processing in space domain and detection in time domain. Optimal detection is the basis of system design and can approximate the up limit performance of real ones. This paper presented an optimal detection method and derived its test statistics; in space domain, the maximum output SNR criterion is used and implemented by spatial whitening and match processing, in which the spatial difference between target signal and noise are utilized fully; in time domain, based on the N-P criteria, after the noise whitening and Wiener filter the output energy is then calculated, where the differences in spectral shape and spectral level between signal and noise are fully taken into account.