Like Least Mean Square Research Articles

A Novel Adaptive ANC Algorithm for Removal of Background Noise in Speech Applications

Noise is an unsafe mechanical toxin that causes serious hearing misfortune in the working environment of every nation. The working people in the military, mining, development, printing, and saw factories tend to lose their hearing performance due to the adverse effects of noise generated by the machines. They undergo elevated levels of noise, with various machinery producing greater levels of noise measured in decibels. These sounds may cause major health problems that may not allow the person to work in such conditions. Algorithms like Least Mean Square (LMS), Normalized Least Mean Squared (NLMS), Filtered-x Least Mean Squared (FxLMS) and Filtered-x Normalized Least Mean Squared (FxNLMS) are frequently being used for noise cancellation. Moreover, these filters have instability and poor noise reduction; slow convergence also requires a greater number of filter taps and less performance to identify the unknown system in the Active Noise Canceller (ANC). In this paper, a Précised FxNLMS (P-FxNLMS) algorithm is introduced for an ANC. This algorithm consists of dual adaptive filters, an updated Variable Step Size (VSS), a delay in the primary path, a slight improvement in the on-line secondary path, and a modified filter step size when compared to an existing ANC system, with the purpose of minimizing the demerits of existing algorithms. Initially, the P-FxNLMS algorithm was tested with Additive White Gaussian Noise (AWGN) and later tested with real noises from the NOISEUS dataset to check the noise reduction performance. The increase in Signal to Noise Ratio (SNR) segmentation for P-FxNLMS is around 1.45 dB to 4.07 dB and 38.46 % to 73.68 % of the Mean Square Error (MSE) as compared to the algorithms available for different sounds with different SNR input levels. From the performance results of MSE and SNR improvement (SNRi), we found improvements compared with existing algorithms

11.2 Gbps 100-meter free-space visible light laser communication utilizing bidirectional reservoir computing equalizer.

In this paper, we introduce an innovative post-equalization technique leveraging bidirectional reservoir computing (BiRC), and apply it to waveform-to-symbol level equalization for visible light laser communication for the first time. This strategy is more resistant to nonlinearities compared to traditional equalizers like least mean square (LMS) equalizer, while requiring less training time and fewer parameters than neural network (NN) -based equalizers. Through this approach, we successfully conduct a 100-meter transmission of a 32-amplitude phase shift keying (32APSK) signal using a green laser operating at a wavelength of 520 nm. Remarkably, our system achieves a high data rate of 11.2 Gbps, all while maintaining a satisfying bit error rate (BER) below the 7% hard decision forward error correction (HD-FEC) threshold of 3.8E-3.

Performance evaluation of dynamic voltage restorer using intelligent control algorithm

Power quality has become an important issue due to increasing applications of modern industrial and commercial sensitive equipment's. Due to high penetration of modern power electronics-based devices leads to voltage disturbances. To address the voltage disturbances the commonly employed series device called Dynamic Voltage Restorer (DVR) is used which requires an effective control technique. A control scheme namely Volterra adaptive expanded filter is developed which incorporate the higher ordered cost function. The conventional filters have like least mean square (LMS) algorithm is restricted to power error of second order. This restricts the computation of fundamental quantity from the disrupted signals and results in degradation under dynamical scenarios. The recommended approach surmount this aforesaid issue with a solution of a mixed learning of step size. This is derived from the error of second and fourth-order. The Volterra based LMS/Fourth (LMS/F) control has the merit of estimating the fundamental quantity under any power quality (PQ) scenario with less delay and low computational burden. The conventional controller like Proportional Integral (PI) has limited gain coefficients to achieve the optimized the voltage regulation. Furthermore, this is overcome by a Fractional Order PID (FOPID) with an additional coefficient of λ and γ obtain more stabilized DC and AC bus voltage by integrating with Kepler optimizer. For achieving optimal gain, an online tuning of FOPID is done with Kepler Optimizer. The Kepler is inspired by planet motion to achieve best gain values. The evaluation of FOPID is made by considering as a Fitness Function (FF) an Integral Time Square Error (ITSE). Owing to the superior performance of Kepler optimizer based FOPID under transitory state and an enhanced DVR performance is observed. The optimized FOPID has improved response in terms of less overshoot (2.9 %), undershoot (5.6 %), and fast settle time (0.104 s). To authenticate the simulating and experimental results dSPACE board is employed to validate the efficacy and accuracy of the proposed Volterra LMS/F technique.

Sparse echo cancellation using variants of least mean fourth and least mean square algorithms

Echo cancellation is the most essential and indispensable component of telephone networks. The impulse responses of most of the networks are sparse in nature; that is, the impulse response has a small percentage of its components with a significant magnitude (large energy), while the rest are zero or small. In these sparse environments, conventional adaptive algorithms like least mean square (LMS) and normalized LMS (NLMS) show substandard and inferior performances. In this paper, the performances of the normalized least mean square (NLMS) algorithm, the normalized least mean fourth (NLMF) and the proportionate normalized least mean fourth (PNLMF) are compared for sparse echo cancellation. The sparseness of both the echo response and the input signal is exploited in this algorithm to achieve improved results at a low computational cost. The PNLMF algorithm showed better results and faster convergence in sparse and non sparse systems, but its results in sparse environments are more impressive. The NLMF algorithm shows good results in sparse environments but not in non-sparse environments. The PNLMS algorithm can be considered superior to the NLMF and NLMS algorithms with respect to the error profile. A modified algorithm, the sparse controlled modified proportionate normalized LMF (SCMPNLMF) algorithm, is proposed, and its performances are compared with the other algorithms.

Adaptive Geman-McClure Estimator for Robust Distributed Channel Estimation

Communication systems are affected by channel distortions. Impulsive noise is one of the significant factors for channel impairments. The standard additive white Gaussian noise (AWGN) channel model and conventional estimation algorithms like least mean square (LMS) and its variants tend to be ineffective under such conditions. This paper presents a robust adaptive channel estimation algorithm using the Geman-McClure estimator in a diffusion-based distributed network. The analytical study on mean stability and mean square analysis is carried out under two separate noise statistics: Symmetric α-stable ( SαS) and Bernoulli-Gaussian (BG) distribution. The computer simulations confirm the proposed algorithm's competitive robustness compared to the Maximum Correntropy Criterion and Minimum Kernel Risk Sensitive Loss algorithms at a high impulsive noise environment without exponential cost function. Further, the efficiency is also verified by simulating the bit error rate by designing a minimum mean square error (MMSE) equalizer with the estimated coefficients.

Noise Cancellation in Electrocardiography Signal using Kalman LMS Algorithm in DSP Processor

Heart health becomes more important to monitor, which is analyzed using Electrocardiography (ECG) signal. ECG signal can be corrupted by different forms of noises, when it is acquired. These noises mislead the ECG analysis and reduce the chance of accurate identification of the ECG signal features. To remove these noises, various adaptive and non adaptive filter techniques are used. This paper proposes a methodology to remove power line interference noise, using various adaptive techniques, like least mean square (LMS) algorithm, normalized least mean square (NLMS) algorithm and Kalman least mean square (KLMS) algorithm. All three filters results are compared based on evaluation metrics like signal to noise ratio (SNR), Root mean square error and convergence. KLMS algorithm is analyzed, as it overcomes the drawback of LMS and NLMS algorithm. And the advantage of KLMS algorithm is that; it is step size independent, and has least root mean square error (RMSE) and converges quickly. The results have been concluded with the MIT-BIH arrhythmia data base and simulated using MATLAB software. The algorithms are implemented in TMS320C6713 DSP kit using Code composer studio v3. TMS320C6713 is use Very long instruction word (VLIW), which helps to compute numerically complex algorithms.

Design of Matched Wavelet Using Improved Genetic Algorithm for Heart Rate Variability Analysis of the Menstrual Cycle

The matched wavelet is designed in this paper using an improved genetic algorithm for detecting the Heart Rate Variability (HRV) variations within phases of the menstrual cycle accurately. The idea of an improved genetic algorithm is to use an optimization technique like least mean square (LMS) before the genetic algorithm. The advantage of using the LMS prior to the genetic algorithm is to optimize the data before giving to the genetic algorithm, thereby limiting the area of the search for an optimal solution. The results show that matched wavelets created using an improved genetic algorithm can detect the HRV variations accurately in the standing and laying postures.

Prediction of instantaneous heart rate using adaptive algorithms

In this paper, adaptive filter based on adaptive algorithms like least mean square (LMS), normalised least mean square (NLMS) and recursive least square (RLS) are used for the prediction of instantaneous heart rate in ECG signal. The adaptive algorithms work on the principle of optimising the least square error by achieving wiener solution. The weights of the filter coefficients are changing, as per the changes in the signal. The performance of adaptive filter is measured by mean square error (MSE) and the prediction accuracy is observed by mean absolute error (MAE). The simulation results show that the adaptive algorithms NLMS and RLS have faster convergence rate with less number of iteration but the forecasting accuracy is higher in LMS compared to NLMS and RLS algorithms.

Performance Analysis of Adaptive Algorithms for Speech Enhancement Applications

Background/Objectives: The improvement of the speech quality is an essential component in this modern world. Though different methods have been used by many researchers since a long period, still the research field is open to work further. In this paper, we have analyzed the same using adaptive algorithms. Methods/Statistical Analysis: The algorithms like Least Mean Square (LMS) and Recursive Least Square (RLS) are used to improve the quality. Further, State-Space Recursive Least Square (SSRLS) algorithm is developed for enhancement of the noisy speech. The existing Spectral Subtraction (SS) algorithm is verified for two types of noisy speech. Also the standard adaptive algorithms have been experienced for these signals. Findings: Spectral Subtraction method is implemented initially. Then LMS and RLS algorithms are tested for two different types of noisy speech signals. In case of LMS, the improvement of SNR is 8.2822 dB for random noise and 6.5245 dB for babble noise. Whereas for RLS algorithm, the SNR improvement is 11.7152 dB for random noise and 14.3883dB for babble noise. The SNR after enhancement is 43.5409 dB and 47.2456 dB for random noise and for babble noise respectively in case of SSRLS algorithm which is much higher than other algorithms as results suggest. Also the error minimization curve converges more rapidly than the others. So SSRLS algorithm provides better enhanced signal than the other three methods. Application/Improvements: The applications are in different field of communications like mobile telephony, video conferences as audio and video applications. Also the applications are of speech recognition, voice communications, information forensics and many more. The improvement of SNR in this proposed algorithm found better than SS, LMS and RLS algorithms as 31.2377 dB and 35.6381 dB.

CONTROL AND ESTIMATION OF BIOLOGICAL SIGNALS (ECG) USING ADAPTIVE SYSTEM

In this paper, we present a simple and efficient adaptive noise removal technique for de-noising the (ECG) signal. There are different techniques earlier used for de-noising the ECG signal ,adaptive filtration like least mean square (LMS), NLMS, BLMS , etc. In this paper we used recursive least square technique for adaptive filtration. The power line noises have been implemented according to their basic properties. After that, these noises have been mixed with ECG signal and nullify these noises using the LMS,NLMS and the RLS algorithms. Finally a performance study has been done between these algorithms based on their parameters and also discussed the effect of filter length and the corresponding signal to noise ratio. Results indicate that the noises cannot be handled by the LMS filtering whereas the RLS can handle these types of noises. Furthermore, most of the cases the RLS has achieved best effective noise cancellation performance although its computation time is slightly high. We are using the RLS Algorithm by matlab for simulation.