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

Noise cancellation for continuous wave mud pulse telemetry based on empirical mode decomposition and particle swarm optimization

MRS (Magnetic Resonance Sounding)) method is a direct method for underground water detecting which is easily disturbed by interferences according to its weak signal (nV). Time domain quantization methods require a good suppression of interferences for more accurate MRS signal parameter estimation. Of all interferences, power line harmonic interference is the most significant. To solve the problem, the paper analyzes the features of the MRS signal and the interference types and discusses two easily program integrated interference elimination method for MRS signal real time pretreatment. The stacking method based on random phase is an effective way to eliminate the interference but it requires massive stacks. Another method, Adaptive Noise Cancellation (ANC) method is also proposed which is a more effective method especially in power line harmonic interference cancellation. Here an improved bi- direction ANC method is proposed. The method can significantly eliminate the power line harmonic interference. Field test was carried out using JLMRS-I developed by Jilin University. With the method, the SNR of free induction decay (FID) records can be improved by 4 times with stacks 20 and nearly 100 times with stacks 160. The application of the bi-direction ANC method can improve the exploration efficiency under the same SNR of the FID records.

Line noise is artifact affecting a gamma band of the EEG. Conventional filters are used for removing the line noise artifact, but these filters, also remove physiological activity and could induce spurious oscillations. We tested two alternative methods for removing line noise artifact without removing a physiological activity, specifically Adaptive noise cancellation (ANC) method with Linear regression and modified Independent Component Analysis (ICA). We proposed a new protocol for statistical evaluation of line noise removal effectiveness. The line noise artifact was physically simulated and the protocol for statistical evaluation was verified. The ANC method shows good results according to statistical evaluation. The lowest residuum of simulated line noise artifact corresponds to 0.0005 \(\upmu \)V\(^2\)/Hz. Nevertheless, the ANC is very sensitive to the quality of the reference signal, which heavily depends on the reference electrode selection.

This chapter focuses on the theory and implementation of (Hayes in Statistical digital signal processing and modeling, Wiley, 1996; Hadei 2010) adaptive noise cancellation (ANC) method for dual-channel speech enhancement. Existing gradient-based approaches for adaptive filtering in speech enhancement are explained in detail. This chapter discusses about the limitations of the gradient-based algorithms for IIR filters. It also presents the advantages of the heuristic optimization technique. The organization of the chapter is as follows. Section 2.1 explains the concepts of ANC. Section 2.2 briefly reviews the different gradient-based algorithms that are proposed to speech enhancement. Section 2.3 discusses about how the heuristic optimization methods are advantageous over gradient-based approaches for ANC.

Dual-microphone active noise cancellation paved with Doppler assimilation for TADS

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.

This paper studies the behaviour of normalized least mean square (NLMS) adaptive filter algorithm-based noise canceller to eliminate intense background noise of high and low frequency from a desired signal. Noise signal filtration requires a filter which automatically adapts with the variation of input signal and noise signal. Performance is measured by optimizing rate of convergence and mean square error (MSE) using MATLAB. The experimental results indicate that adaptive noise canceller can remove low- and high-frequency noise of signals conveniently, and for small values of step size MSE decreases and for larger value of step size the rate of convergence increases. The computation time increases with the increase of filter length.

Objective: Aiming at the vibration control problem existing in the vector hydrophone under platform vibration and noise excitation, two methods, adaptive noise cancellation algorithm and vibration reduction design, are used to suppress vibration and noise. Methods: A simulation model of vibration and noise signals in the side area of the platform is established first, and the simulation model is simulated by adaptive noise cancellation method. Then noise reduction simulation is carried out by seismic mitigation and isolation method. Finally, the simulation results of the two methods are compared to check the effectiveness of the two noise suppression methods. Results: The results show that adaptive noise cancellation algorithm can effectively cancel the line spectra of platform vibration and noise, and the reduction of continuous spectra is about 10 dB; while the vibration reduction method can reduce the whole vibration and noise of the model, and the vibration and noise interference of the vector hydrophone is reduced by about 15 dB. Conclusion: The obtained results can serve as valuable reference for the performance optimization of suppression methos for vector hydrophones under platform’s vibration and noise excitation.

Pulse rate estimation from forehead photoplethysmograph signal using RLS adaptive filtering with dynamical reference signal

Speech enhancement is performed in a wide and varied range of instruments and systems. In this paper, a novel approach to Speech Enhancement using Adaptive Empirical Mode Decomposition (SEAEMD) is presented. Spectral analysis of non-stationary signals can be performed by employing techniques such as the STFT and the Wavelet transform (WT), which use predefined basis functions. Empirical Mode Decomposition (EMD) performs very well in such environments. EMD decomposes a signal into a finite number of data-adaptive basis functions, called Intrinsic Mode Functions (IMFs). The new SEAEMD system incorporates this multi-resolution approach with adaptive noise cancellation (ANC) for effective speech enhancement on an IMF level, in stationary and non-stationary noise environments. A comparative performance study is included that compares the competitive method of conventional ANC to the robust SEAEMD system. The results demonstrate that the new system achieves significantly improved speech quality with a lower level of residual noise.

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.

Chapter 9 Measurement While Drilling

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.

The measurement of space charge density caused by the corona discharge around the overhead transmission line is a tough problem till nowadays. For this problem, a system for measuring the one-dimensional (1D) distribution of space charge density in a coaxial cylinder model under negative corona discharge in ambient air is developed. This system is a scale model which can provide research basis for actual overhead line measurement. The system consists of acoustic emission, space charge generation, and signal-receiving systems. The sound wave of an ultrasonic transducer is used to stimulate space charge generated by a small corona cage, and the applied voltage ranges from −34 kV to −38 kV. Charge vibration produces an electrical field (E-field) that can be measured with an E-field antenna. After obtaining the E-field signal, the adaptive noise cancellation method is adopted to eliminate noise interference so that the accuracy of charge density can be improved. The Townsend's analysis solution is used to calculate the theoretical value of space charge density. Experimental results are consistent with the simulation value and thus confirm the validity of the proposed adaptive noise cancellation method and sensing system.

Adaptive Noise Cancellers (ANCs) are used to remove noise from the cardiac signals. In remote health monitoring system signals must be free from artifacts. In the proposed paper an attempt has been made to present a new ANC using Normalized Variable Step Size Least Mean Squared (NVLMS) algorithm. Sign Regressor Algorithm can reduce Computational Complexity and also to maximize the normalization of the algorithm. This type of implementation is suitable for remote health care monitoring system, as these systems require large Signal to Noise Ratio (SNR) and with the least computational complexity. The new ANC is tested on cardiac signals obtained from the MIT-BIH database. Simulation results confirm that the performance of the proposed algorithm is better than the conventional noise cancellers.