Raman spectroscopy de-noising based on EEMD combined with VS-LMS algorithm

The step size of least mean square (LMS) algorithm is significant for its performance. To be specific, small step size can get small excess mean square error but results in slow convergence. However, large step size may cause instability. Many variable step size least mean square (VSSLMS) algorithms have been developed to enhance the control performance. In this paper, a new VSSLMS was proposed based on Kwong’s algorithm to evaluate the robustness. The approximate analysis of dynamic and steady-state performance of this developed VSSLMS algorithm was given. An active vibration control system of piezoelectric cantilever beam was established to verify the performance of the VSSLMS algorithms. By comparing with the current VSSLMS algorithms, the proposed method has better performance in active vibration control applications.

This paper proposes a new application of fuzzy logic to variable step size least mean square (VS-LMS) adaptive beamforming algorithm in CDMA systems. The proposed algorithm adjusts the step size of the least mean square (LMS) by using the application of fuzzy logic in which the increase or decrease of step size depends on the fuzzy inference results of the mean square error (MSE). Computer simulation results show that the proposed algorithm has a better capacity of tracking compared with the conventional LMS algorithms and other variable step size LMS algorithms.

Electroencephalograph (EEG) is brain activity measurement technique that it is widely used by placing electrode on the scalp. The EEG recording signals are vulnerably contaminated by noise. Thus, it can lead to misperception in analyzing EEG signals. One of the noise that has major influence on the recorded EEG signals is ocular artifacts (OAs). In this paper, a new method to remove OAs automatically is proposed by combining between Complete Ensemble Empirical Mode Decomposition (CEEMD) and Independent Component Analysis (ICA). CEEMD-ICA-OD removes OAs based on the concentration of noise that is calculated by calculating entropy for every CEEMD decomposition. Every selected decomposition of CEEMD will be used as input to the ICA method. Then, ICA will decompose selected CEEMD decomposition into independent components (ICs). Then, Chauvenet Criterion (CC) as Outlier Detection (OD) method is used to detect OAs pattern. Every detected OAs pattern will be eliminated by zeroing process on OAs pattern so that the EEG signal information remains secure. The proposed method were compared with Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD) and Complete EEMD (CEEMD) with zeroing ICA combination for every comparison method. The performance measure of the proposed method and the benchmark method is calculated by using Root Mean Square Error (RMSE) and Signal to Noise Ratio (SNR) to show the performance quality in eliminating OAs. The experiment results show that the proposed method is successfully removing OAs better than the other methods with RMSE value and SNR value are 0.2491 and -0.3449, respectively.

A new strategy is proposed for deriving a variable step-size least mean squares (VSS-LMS) algorithm suited for the estimation of time-varying and frequency-selective communications channels modelled as wide sense stationary channels-uncorrelated scattering (WSS-US). The variable step size is obtained by minimising the analytical expression of the mean square weight estimation error as a function of time. Computer simulation results are presented which show that the new VSS-LMS algorithm so obtained produces performance gains in relation to the conventional LMS algorithm and for some channel models may also outperform the recursive least squares algorithm.

Vehicle interior noise is a harmful sound formed in the vehicle interior under the influence of engine noise, transmission system noise, and body radiation noise during vehicle operation. The intensity of noise in the vehicle increases with the increase of vehicle speed and engine speed, which worsens the operating environment of the vehicle and does harm to the mood, spirit, and physiology of drivers and passengers. Because the noise is difficult to identify in the process of vehicle vibration signal recognition, aiming at the problem of vehicle vibration signal detection under the background of strong noise, combined with the characteristics of vehicle vibration signal, the variable step size LMS algorithm is applied to vehicle noise and vibration signal recognition. In this paper, based on the signal eigenvalues obtained from the vibration signal characteristics in the noise and vibration signal, the eigenvalues of the vehicle noise can be extended from the frequency domain to the complex plane through the operation of the variable step size LMS, and the separation law corresponding to the extended vibration intensity can be used at the same time. It is used to obtain data by the signal variable step size LMS algorithm, and the obtained optimal separability feature is used as the characteristic parameter of the vibration signal. Finally, the results of an example analysis show that the algorithm proposed in this paper can be used to identify different types of vehicle noise and vibration signals, has a certain anti-interference performance against noise, and can improve the recognition rate of vehicle noise and vibration signals.

A novel variable step size least mean squares (VSS-LMS) algorithm employing cross correlation between channel output and error signal has been proposed as a solution to disadvantage of slow convergence of LMS algorithm. The new algorithm resolves the conflict between the convergence rate and precise of the fixed step-size conventional LMS algorithm. Computer simulations have been performed to verify the performance of the proposed method in frequency selective Rayleigh fading channels. The obtained simulation results using HIPERLAN/1 standard have demonstrated that the proposed VSS-LMS algorithm has considerably better performance than conventional LMS, RLS, normalized LMS (N-LMS) and the other VSS-LMS algorithms.

A variable step size Least Mean Square (LMS) algorithm based on the difference of the error signal (ESD-VSS) is proposed.The convergence and steady state behavior of the ESD-VSS is analyzed and the key parameters are given.Simulations are conducted and the results show that, compared with the Modified Variable Step Size (MVSS) algorithm, modified LMS algorithm and the standard LMS algorithm, the ESD-VSS provides faster convergence speed and smaller steady state misadjustment.Index Terms -LMS algorithm, variable step size, adaptive filter, MSE I . IntroductionThe LMS algorithm proposed by Widrow and Hoff has been widely used in channel equalization [1] and system identification [2], due to its robustness and simplicity.However the tradeoff between the convergence speed and the steady state misadjustment is a challenging problem.To achieve faster convergence speed and small misadjustment, a number of modified algorithms have been proposed.Ref. [3] utilized the self-correlation to control the step size, and suppress the interference of irrelevant noise effectively, but the convergence speed and the tracking capability are still expected to be improved.Ref. [4] gave a variable step size LMS algorithm using estimate signal error and thus it is of relatively faster tracking speed and smaller steady state error.However, the computational complexity of the algorithm is larger due to the tangent and exponent arithmetic, which makes it difficult to implement.

Artefacts created by thoracic compressions during cardiopulmonary resuscitation (CPR) prevent the proper classification of the cardiac rhythm by an automatic external defibrillator (AED), making a pause in CPR necessary for a correct rhythm analysis. Previously proposed adaptive filtering methods have produced satisfactory CPR cancellation results but involve complex out of hospital intervention scenarios. A new adaptive method requiring minimal modifications of the basic operation of an AED is proposed for the suppression of the CPR artefact from a ventricular fibrillation (VF) rhythm. A model of the CPR artefact, based on the instants of the thoracic compressions, is used as the reference signal of a variable step size least mean squares (LMS) algorithm. Emphasis is put on building a reference signal highly correlated to the CPR artefact and on the fine tuning of the LMS algorithm. Satisfactory results have been obtained for the average increase in signal to noise ratio (SNR) and the sensitivity of a commercially available AED

Delta Sigma Analog to Digital Converters (ADCs) are superior to their counterparts in achieving high resolution for low to medium bandwidth applications. This paper discusses an improved digital calibration technique for enhancing Signal to Noise ratio (SNR) of Delta Sigma ADC which is directly related to Effective Number of Bits (ENOB). The proposed method uses the Adaptive Line Enhancer (ALE) for improving the SNR of the modulator. Adaptive Line Enhancer filters out the estimate of signal of interest from noisy input signal by updating filter coefficients using Least Mean Square (LMS) Algorithm. Variable step size (VSS) LMS is used for updating filter coefficients in Adaptive Line Enhancer instead of Fixed step size (FSS) LMS to reduce the required number of iterations. The simulation results for second order single loop Delta Sigma modulator show approximately 20dB improvement in SNR after calibration and substitution of VSS LMS with FSS LMS has reduced approximately 4000 iterations.

A lot of work has been done recently to develop algorithms that utilize the distributed structure of anad hocwireless sensor network to estimate a certain parameter of interest. One such algorithm is called diffusion least-mean squares (DLMS). This algorithm estimates the parameter of interest using the cooperation between neighboring sensors within the network. The present work proposes an improvement on the DLMS algorithm by using a variable step-size LMS (VSSLMS) algorithm. In this work, first, the well-known variants of VSSLMS algorithms are compared with each other in order to select the most suitable algorithm which provides the best trade-off between performance and complexity. Second, the detailed convergence and steady-state analyses of the selected VSSLMS algorithm are performed. Finally, extensive simulations are carried out to test the robustness of the proposed algorithm under different scenarios. Moreover, the simulation results are found to corroborate the theoretical findings very well.

The sinc-1 model can reduce the LCD motion blur effectively, however, it can not restore the correct frequency components on the pole and is hard to implement on hardware. This paper presents a novel method which is trying to improve the LCD motion blur phenomenon from the view of system identification. The method uses the Volterra nonlinear system that is based on the VSSLMS (variable step size least mean square) algorithm to fit the sinc-1 model, which is a non-perfect inverse system to the LCD motion blur system for the pole problem. Simulation results demonstrate that the performance of the proposed simple method can avoid the pole problem of sinc-1 model and can be implemented on hardware easily.

Step size and tap length play critical roles in balancing the complexity and steady-state performance of an adaptive filter. For an impulse response with an exponential power decay profile, which models a wide range of practical systems, such as an acoustic echo path, this paper proposes a new variable step-size and tap-length least mean square (LMS) algorithm. In each iteration, the optimal step-size and tap-length are derived by minimizing the mean square deviation (MSD) between the true and the estimated filter coefficients. The proposed algorithm performs better in terms of both convergence rate and steady-state performance than the existing ones. Effectiveness of the proposed algorithm is demonstrated through computer simulations.

In this paper, we propose a novel ECG signal enhancement method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Higher Order Statistics (HOS). In our scheme, the noisy ECG signal is first decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs) by using Empirical Mode Decomposition (EMD) or its variants. Therefore, the obtained modes are separated into two groups of noisy signal modes and one group of useful signal modes, by using a novel criterion derived from the HOS namely the fourth order cumulant or kurtosis. After that, a modified shrinkage scheme based on Interval Thresholding technique is adaptively applied to each selected IMF from the noise-dominant groups in order to reduce the noise and to preserve the QRS complex. The overall filtered ECG signal is then reconstructed by combining the thresholded IMFs and the retained unprocessed lower frequency relevant IMFs. Various tests and simulations are investigated to evaluate the performance of our proposed approach in combination with the EMD, Ensemble EMD (EEMD) and CEEMDAN algorithms. The simulation results carried on MIT-BIH Arrhythmia database, show that CEEMDAN method gives better performance than the two other methods, and outperforms some state-of-the-art methods in terms of Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE).

In single-carrier aeronautical communication systems, the time-varying multipath propagation characteristics of the channel give rise to severe inter-code interference. Radio channel distortion compensation and inter-code interference cancellation are generally achieved by equalization techniques. In this paper, zero-forcing equalization, minimum mean square error equalization and minimum mean square error adaptive equalization algorithms for SC-FDE systems are investigated, but the traditional LMS algorithms cannot meet the performance requirements of convergence speed and steady-state error at the same time because the step size factor is fixed. This paper investigates the variable step size LMS algorithm and proposes an improved variable step size LMS algorithm, which uses the non-linear relationship between the error factor and the error function to construct a correlation correction function, to regulate the error factor in a small range, and to further correct the step size factor through the change of the error factor. The LMS algorithm is divided into a training mode and a tracking mode, and the two modes are combined to achieve a faster convergence rate. The computer simulation results are consistent with the theoretical analysis and confirm that the algorithm outperforms the original algorithm.

Historical Trends in Surface Air Temperature Estimated by Ensemble Empirical Mode Decomposition and Least Squares Linear Fitting