Adaptive Noise Cancellation Using NLMS Algorithm

Internet of things (IoT) has made possible the sharing of data wirelessly among everyday objects and physical devices. However, distortion and noise can significantly degrade the signaling performance of IoT. Adaptive Noise Cancellation (ACN) technology plays an important role not only in IoT but also in other numerous fields such as system identification, noise cancellation, communication, signal control, etc. At present, IoT is an advance trend and need to deal with upcoming issues in a way to deal properly in enjoying this technology. Due to the presence of noise in the signal, the quality of the signal is reduced, so the quality of communication (signal) can be improved by adaptive noise (acoustic) cancellation technology. This paper aims to provide the performance analysis of adaptive algorithm and the application of adaptive noise cancellation technique. Further, use cases of three-noise cancellation filter in IoT are introduced. In wireless communication, quality of services relies on signal quality and thus noise cancellation plays a vital role in the signaling of IoT. At the end of this paper performance analysis of different ANC algorithms including least mean square (LMS), Normalized least mean square (NLMS), recursive least square (RLS) will be done based on simulation results.

Among all adaptive noise cancellers, Widrow and Hoff's least mean square (LMS) algorithm has probably become the most popular because of its robustness, good tacking properties and simplicity of implementation. An important limitation of LMS algorithm is that the selection of a certain value for the step size implies compromise between speed of convergence and steady-state misadjustment, The variable step size normalized least-mean-square (VSS- NLMS) algorithm is appropriate to solve the conflicting requirement of fast convergence and low misadjustment of the LMS algorithm. To reduce noise in the output signal of the ANC, a step size for coefficient update is controlled according to the signal-to-noise ratio (SNR). This paper investigates the performance of a noise canceller with DSP processor (TI TMS320C6713) using the LMS algorithm, normalized least-mean-square (NLMS) algorithm, and VSS-NLMS algorithm. Results show the proposed combination of hardware and VSS-NLMS algorithm has not only a faster convergence rate but also lower distortion when compared with the fixed step size LMS algorithm and NLMS algorithm in real-time environments.

In the radio observation based on the large single antenna, in order to reduce the interference of navigation signal L 2 from observation of the red-shifted H I spectral line at L-band, we used the an auxiliary channel and normalized LMS algorithm, and proposed new evaluating indicators deduced by theoretical method. Finally, we verified the suppression performance and the indicators' utility by multiple simulations and statistical method.

Objectives: To enhance the quality of Cardiac Signal for perfect diagnosis by the doctor. Methods/Statistical Analysis: We are introducing some adaptive filter structures for Cardiac Signal (CS) enhancement for the extraction of high resolution cardiac signals and these structures were based on the Proportionate Normalized Least Mean Square (PNLMS) algorithm. The main advantage of PNLMS over the conventional techniques is extraction of sparse coefficients, suitably weighing them and fast convergence. These ANCs are tested using MIT-BIH database to compare the performance. Findings: We consider Signal to Noise Ratio (SNR), Excess Mean Square Error (EMSE), Misadjustment (MSD), convergence curves and residual error plots as performance measures. Among the ANCs tested, PMNSRLMA based ANC is found to be better with reference to the considered performance measures and computational complexity. The average SNRI achieved by this ANC is 18.8856dBs for PLI elimination, 8.7580dBs for BW elimination, 8.5106dBs for MA elimination and 8.5012dBs for EM elimination. From the above results it is clear that in practical biotelemetry applications to minimize the computational complexity of the noise canceller we combine PNLMS with signature algorithms. Again, to reduce the complexity in the denominator of the normalized recursion, we use maximum normalized version of PNLMS. Finally, these variations result in seven algorithms in addition to PNLMS. Based on these algorithms, we develop various Adaptive Noise Cancellers (ANCs) to eliminate artifacts present in the CS and to present best quality signal to the doctor for diagnosis. Application/ Improvement: The standard of Cardiac Signal can be enhanced by improving data acquisition methods.

Adaptive dual-sensor noise cancellation method for continuous wave mud pulse telemetry

Echo in telephones is generally undesirable but inevitable. Impedance mismatch and acoustic coupling between the receiver and microphone circuit(s) respectively produce electric and acoustic echoes in a telephone network. There is therefore a need to develop an echo canceller that can be used to reduce these echoes in telephone networks. In this work, a model for electrical echo in telephone networks for varied echo-path delay was designed. This was with a view to implementing an adaptive electrical echo canceller based on a combination of the normalized least mean square (NLMS) and Geigel speech detector algorithms (GSD). Subsequently, the performance indices (convergence rate and efficiency) of the echo canceller implemented were obtained using error function, echo return loss enhancement (ERLE) and the mean squared error (MSE) for various echoes with varied round-trip delay according to the minimum requirement of ITU-T (International Telecommunication Union) Recommendations. The results obtained show that there was a 3 dB improvement over the NLMS algorithm. The developed adaptive electrical echo canceller is therefore suitable for reduction of echoes in telephone networks.

It was previously demonstrated that it is feasible to simultaneously perform ultrasound therapy and imaging of a coagulated lesion during treatment with an integrated transducer that is capable of high intensity focused ultrasound (HIFU) and B-mode ultrasound imaging. It was found that coded excitation and fixed notch filtering upon reception could significantly reduce interference caused by the therapeutic transducer. During HIFU sonication, the imaging signal generated with coded excitation and fixed notch filtering had a range side-lobe level of less than −40 dB, while traditional short-pulse excitation and fixed notch filtering produced a range side-lobe level of −20 dB. The shortcoming is, however, that relatively complicated electronics may be needed to utilize coded excitation in an array imaging system. It is for this reason that in this paper an adaptive noise canceling technique is proposed to improve image quality by minimizing not only the therapeutic interference, but also the remnant side-lobe ‘ripples’ when using the traditional short-pulse excitation. The performance of this technique was verified through simulation and experiments using a prototype integrated HIFU/imaging transducer. Although it is known that the remnant ripples are related to the notch attenuation value of the fixed notch filter, in reality, it is difficult to find the optimal notch attenuation value due to the change in targets or the media resulted from motion or different acoustic properties even during one sonication pulse. In contrast, the proposed adaptive noise canceling technique is capable of optimally minimizing both the therapeutic interference and residual ripples without such constraints. The prototype integrated HIFU/imaging transducer is composed of three rectangular elements. The 6 MHz center element is used for imaging and the outer two identical 4 MHz elements work together to transmit the HIFU beam. Two HIFU elements of 14.4 mm × 20.0 mm dimensions could increase the temperature of the soft biological tissue from 55 °C to 71 °C within 60 s. Two types of experiments for simultaneous therapy and imaging were conducted to acquire a single scan-line and B-mode image with an aluminum plate and a slice of porcine muscle, respectively. The B-mode image was obtained using the single element imaging system during HIFU beam transmission. The experimental results proved that the combination of the traditional short-pulse excitation and the adaptive noise canceling method could significantly reduce therapeutic interference and remnant ripples and thus may be a better way to implement real-time simultaneous therapy and imaging.

One of the most important applications of adaptive filter is Interference or noise cancellation. The objective of adaptive interference cancellation is to obtain an estimate of the interfering signal and to subtract it from the corrupted signal and hence obtain a noise free signal. The tracking performances of the LMS and NLMS algorithms are compared when the input of the adaptive filter is no stationary For this purpose, the filter uses an adaptive algorithm to change the value of the filter coefficients, so that it acquires a better approximation of the signal after each iteration. The LMS (Least Mean Square), and its variant the NLMS (Normalized LMS) are two of the adaptive algorithms widely in use. This paper presents a comparative analysis of the LMS and the NLMS in case of interference cancellation from speech signals. For each algorithm, the effects of two parameters-filter length and step size have been analyzed. Finally, the performances of the two algorithms in different cases have been compared.

In Electrocardiogram (ECG) application, we face various types of artifacts added to the ECG signal. Different Researchers have been interested in this problem due to its importance. In this paper we introduce a study of different algorithms and their effects on the performance of the ECG noise canceller. We have used many kinds of algorithms such as: LMS (Least Mean Square), NLMS (Normalized Least Mean Square), Signed Regressor LMS (SRLMS), Sign LMS (SLMS), Sign-Sign LMS (SSLMS) and a new proposed modified LMS called Variable Step size Least Mean Square (VSLMS) using MATLAB software package as well as Unbiased Linear output Neural Network (ÜLNN) and Unbiased Non Linear output Neural Network (UNLNN). It is promising to clarify the difference among these algorithms with the aim of obtaining better performance.

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.

Energy detection (ED) receiver is one of two popular receivers for non-coherent (NC-) ultra-wideband (UWB) Green Communications. In this paper, weighted ED (WED) receivers are further investigated for NC-UWB systems using different adaptive filtering algorithms. In the conventional normalized least mean square (NLMS) algorithm, a fixed step-size is used to update the weighting coefficients of WED receivers, and therefore there must be a trade-off between the steady state error and the convergence speed. Correspondingly, an improved NLMS (INLMS) algorithm was recently proposed to address this issue. In this paper, parameter optimization is discussed for the INLMS algorithm and accordingly the parameter optimized (PO-) INLMS algorithm is presented. Moreover, convergence behavior and bit error rate (BER) performance are evaluated for adaptive WED receivers. Simulation results show that compared to the traditional NLMS algorithm, PO-INLMS possesses significant benefits in convergence rate together with about 0.3-dB-gain in terms of the BER performance, and therefore the PO-INLMS based WED receiver can be considered as a promising candidate for energy-aware NC-UWB Green Communications.

We note that some existing algorithms are based on the normalized least-mean square (NLMS) algorithm and aim to reduce the computational complexity of NLMS all inherited from the solution of the same optimization problem, but with different constraints. A new constraint is analyzed to substitute an extra searching technique in the set-membership partial-update NLMS algorithm (SM-PU-NLMS) which aims to get a variable number of updating coefficients for a further reduction of computational complexity. We get a closed form expression of the new constraint without extra searching technique to generate a novel set-membership variable-partial-update NLMS (SM-VPU-NLMS) algorithm. Note that the SM-VPU-NLMS algorithm obtains a faster convergence and a smaller mean-squared error (MSE) than the existing SM-PU-NLMS. It is pointed out that the closed form expression can also be applied to the conventional variable-step-size partial-update NLMS (VSS-PU-NLMS) algorithm. The novel variable-step-size variable-partial-update NLMS (VSS-VPU-NLMS) algorithm is also verified to get a further computational complexity reduction. Simulation results verify that our analysis is reasonable and effective.

We present an adaptive enhancement method for speech signal corrupted by the 3 Tesla (3T) functional Magnetic Resonance Imaging (fMRI) acoustic noise. The noisy speech signal is filtered into several frequency bands and noise cancellation is applied to each band independently. We use Normalized Least Mean Squares (NLMS) algorithm for updating the co-efficients of the adaptive filters in both wideband and in multi-band implementations. The NLMS algorithm is used to ensure faster convergence and stability. The multi-band method shows a better performance than the conventional wideband method resulting in a better noise reduction. It is shown that the distortion introduced by the band-pass filters limits the overall speech enhancement when the number of bands increases beyond an optimal value.

Impedance cardiography (ICG) is advantageous to identify various heart diseases. In recent times, mobile ICG has grabbed the attention of researchers and analysts for real-time supervision and automatic diagnosis. However, the attainment of ICG’s survey system was degraded due to many interrupts created by subject movement, which paves to wrong diagnosis. Several attempts have been performed to abolish the noise from clinical ICG signal using distinct digital signal processing techniques. Those approaches are not directly synced to be used for the mobile ICG environment and regular noises. Basically, motion artifact still is an open problem in mobile ICG. In this paper, an advanced process of adaptive artifact elimination proposed through impedance cardiography (ICG) signals. This is a composite exemplary based on wavelets and adaptive filter. The prime aspect of this methodology is the realization of adaptive noise canceller (ANC) without any reference signal. In the real-time medical environment during critical conditions due to heartbeat disorders, the filter coefficients become negative. This convergence unbalance leads to low filtering capability. In order to solve this issue, one may incorporate NN adaptive algorithms in the suggested ANC. To enhance the attainment of ANC, error normalization is adapted to change filter coefficients automatically. Again, in order to minimize computational complexity and to avoid overlapping of data samples at the input stage of the filter, a hybrid version of nonnegative and sign sign-based algorithms is considered for implementation. The resulting hybrid versions are exponential normalized nonnegative least mean square (eN3LMS) algorithm, exponential normalized nonnegative sign regressor LMS (eN3SRLMS) algorithm, exponential normalized nonnegative sign error LMS (eN3SELMS) algorithm, and exponential normalized nonnegative sign sign LMS (eN3SSLMS) algorithm. Finally, various ANCs are developed using these algorithms, and attainment measures are calculated and compared. Several implemented ANCs are verified on real impedance cardiogram signals.

This paper proposes a new variable step-size adaptive filtering algorithm based on the LMS algorithm and compares its performance with Least Mean Squares(LMS), Normalized Least Mean Squares(NLMS), Modified Sigmoid- LMS(MLMS) and Regularized NLMS(RNLMS) algorithms. The results indicate that compared to the other comparative algorithms, this algorithm can more effectively improve the signal-to-noise ratio of the filter output. Furthermore, within a wide input signal-to-noise ratio range, this algorithm can consistently enhance the filter signal-to-noise ratio output, effectively addressing the problem of inconsistent filtering effects caused by the input signal-to-noise ratio sensitivity in algorithms such as NLMS algorithms.