Signal Noise Reduction Research Articles

A Fractal Conservation Law for Simultaneous Denoising and Enhancement of Seismic Data

In this letter, we have applied a new filtering method for the simultaneous noise reduction and enhancement of seismic signals using a fractal conservation law, which is simply a partial differential equation (PDE) modified by a nonlocal fractional antidiffusive term of lower order. By using an integral formula, which is based on Taylor-Poisson's formula and Fubini's theorem for an antidiffusive term, and then taking the fast Fourier transform of the PDE, the filter in the frequency domain can be derived. The study showed that this filter eliminates the high frequencies, amplifies the medium frequencies, and preserves the low frequencies. Thus, some features of the signal, such as relative maxima or minima, can be preserved or even amplified. Usually, these features are flattened by other denoising methods. The properties of this novel method are tested on both synthetic seismic data and real common shot point seismic records. Additionally, we compared this method with time-frequency peak filtering (TFPF), which has a good performance under low signal-to-noise ratio. The experimental results illustrate the superior performance of our method versus TFPF in the recovery of seismic events by removing random noise and enhancing valid signal.

MACE: model based analysis of ChIP-exo.

Understanding the role of a given transcription factor (TF) in regulating gene expression requires precise mapping of its binding sites in the genome. Chromatin immunoprecipitation-exo, an emerging technique using λ exonuclease to digest TF unbound DNA after ChIP, is designed to reveal transcription factor binding site (TFBS) boundaries with near-single nucleotide resolution. Although ChIP-exo promises deeper insights into transcription regulation, no dedicated bioinformatics tool exists to leverage its advantages. Most ChIP-seq and ChIP-chip analytic methods are not tailored for ChIP-exo, and thus cannot take full advantage of high-resolution ChIP-exo data. Here we describe a novel analysis framework, termed MACE (model-based analysis of ChIP-exo) dedicated to ChIP-exo data analysis. The MACE workflow consists of four steps: (i) sequencing data normalization and bias correction; (ii) signal consolidation and noise reduction; (iii) single-nucleotide resolution border peak detection using the Chebyshev Inequality and (iv) border matching using the Gale-Shapley stable matching algorithm. When applied to published human CTCF, yeast Reb1 and our own mouse ONECUT1/HNF6 ChIP-exo data, MACE is able to define TFBSs with high sensitivity, specificity and spatial resolution, as evidenced by multiple criteria including motif enrichment, sequence conservation, direct sequence pileup, nucleosome positioning and open chromatin states. In addition, we show that the fundamental advance of MACE is the identification of two boundaries of a TFBS with high resolution, whereas other methods only report a single location of the same event. The two boundaries help elucidate the in vivo binding structure of a given TF, e.g. whether the TF may bind as dimers or in a complex with other co-factors.

WAVELET BASED DENOISIONG OF ACOUSTIC SIGNAL

This paper introduces the method of noise reduction in acoustic emission signal based on wavelet transform. Acquiring real time signal & analyzing it, is a very difficult task. In data acquisition (DAQ) system, the data can be acquired, measured and analyzed from electrical, mechanical and physical phenomena. In this paper, acoustic signal is generated from pencil lead break at different locations on metal sheet. Acoustic emission (AE) sensor is used to acquire the data of physical phenomenon. It converts the physical parameters into the electrical signal, which is very small (in mV). This has been amplified by using a preamplifier of suitable gain. Amplified signal is transformed into digital signal by using USB data acquisition module. But this signal contains noise. So for processing, digitized signal is connected to computer using USB and processing is done in Matlab software. This amplified signal is filtered using conventional filtering or wavelet filtering. In conventional filtering, Butterworth filter is used but it gives more attenuation. Hence wavelet filtering is better. In wavelet filtering, there are four methods of wavelet threshold denoising that are heursure, minimaxi, rigrsure and sqtwolog. These methods are compared based on the energy, standard deviation and percentage attenuation in filtered signal. The best choice for threshold noise reduction in case of AE signal obtained by pencil lead break is rigrsure. The filtered signal is good for analysis so that different parameters can be easily measured.

De-noising methods for NMR logging echo signals based on wavelet transform

The signal-to-noise ratio (SNR) of the echo signals in nuclear magnetic resonance (NMR) is one of the most important factors that affect the effective application of NMR logging. Wavelet transform can be used to remove the noise and improve the SNR of echo signals in NMR logging. This paper uses three de-noising methods to treat the NMR echo signals: modulus maxima, spatial correlation and wavelet threshold based on wavelet transform. The effects of the three methods in the noise reduction of NMR echo signals were compared by numerical simulation, core experiment and NMR logging data. The results show that while these three methods can all effectively improve the SNR of NMR echo signals and the NMR T2 inversion results, the most effective among them is the wavelet threshold method, which can obtain a higher SNR and provides more accurate formation porosity.

Fault Diagnosis of Transmission Based on EMD and Fractal Technology

As a result of the presence of noise in the measured vibration signal has a great influence on the results of calculation of fractal dimension, Therefore the empirical mode decomposition method for noise reduction of gear vibration signal is used, calculation fractal dimension, extraction fault feature of Gear in different conditions. The measured results show that: Different fault states have different fractal dimension, we can judge the fault type of gear effectively by the fractal dimension.

Noise Reduction of Electrocardiographic Signals using Wavelet Transforms

It has always been a critical issue to extract original signal having low signal-to-noise ratio (SNR) buried in heavy noise and interferences. Since the amplitude of the electrocardiogram (ECG) signal is smaller so while gathering and recording it may mix with various kinds of noises and interferences. In this paper, wavelet thresholding de-noising method based on stationary wavelet transform (SWT) is proposed in de-noising of ECG signal. In addition, this paper compares various de-noising methods to validate the proposed de-noising method. The improved de-noising method ensures that the geometrical characteristics of the original ECG are retained as well as efficiently suppresses additive noises. The experimental results reveal that the SWT method is better than traditional wavelet de-noising methods to maintain original shape of ECG waveform having improved SNR. DOI: http://dx.doi.org/10.5755/j01.eee.20.4.6886

Noise Reduction of Electrocardiographic Signals using Wavelet Transforms

It has always been a critical issue to extract original signal having low signal-to-noise ratio (SNR) buried in heavy noise and interferences. Since the amplitude of the electrocardiogram (ECG) signal is smaller so while gathering and recording it may mix with various kinds of noises and interferences. In this paper, wavelet thresholding de-noising method based on stationary wavelet transform (SWT) is proposed in de-noising of ECG signal. In addition, this paper compares various de-noising methods to validate the proposed de-noising method. The improved de-noising method ensures that the geometrical characteristics of the original ECG are retained as well as efficiently suppresses additive noises. The experimental results reveal that the SWT method is better than traditional wavelet de-noising methods to maintain original shape of ECG waveform having improved SNR. DOI: http://dx.doi.org/10.5755/j01.eee.20.3.6676

Noise Reduction of Chaotic Signal Based on Empirical Mode Decomposition

In view of the nonlinear characteristics of chaotic signal, a threshold denoising method of noisy chaotic signal based on empirical mode decomposition (EMD) is presented. Firstly, noisy chaotic signal is decomposed into several intrinsic mode function (IMF) by empirical mode decomposition. Secondly, the intrinsic mode functions of high frequency are respectively processed using threshold method. Finally, we add these IMFs with IMFs of low frequency to achieve denoising signal. The noisy Lorenz chaotic signal is chosen to perform noise reduction. Taking the noisy Lorenz chaotic signal as an example, the proposed method is used. The simulation results show that this method is an efficient method. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4015 Full Text: PDF

Efficient Use of Bi-orthogonal Wavelet Transform for Cardiac Signals

n the detection of various cardiac abnormalities the ECG finds its importance. ECG signal de-noising process in an embedded platform is a challenge which has to deal with several issues. Noise reduction in low amplitude ECG signals by various de-noising techniques is an important task of biomedical science. ECG signals are very low frequency signals of about 0.5Hz-100Hz. There are various types of artifacts which get added in ECG signals and change the original signal features, therefore there is a need of removal of these artifacts from the original ECG signal. The noises that commonly disturb the basic electrocardiogram signal are power line interference (PLI), electrode contact noise, motion artifacts, electromyography (EMG) noise and instrumentation noise. These noises can be classified according to their frequency content. In this paper, the wavelet transform based approach for removing noise is used. In this paper, the discrete wavelet transform (DWT) at level 8 was applied to noisy ECG signals and decomposition of these ECG signals was performed. After removal of noise component using thresholding technique, decomposed signal is again reconstructed using Inverse discrete wavelet transform (IDWT). Here for de-noising the ECG signal, bi-orthogonal wavelet transform is used and the most efficient idea for noise removal process is concluded with this wavelet transform. The simulation has been done in MATLAB environment with the help of SIMULINK. The experiments are carried out on MIT-BIH database. Performance analysis was performed by evaluating Mean Square Error (MSE), Signal-to-noise ratio (SNR), Peak Signal-to-noise ratio (PSNR) and visual inspection over the de-noised signal from each algorithm.

A Technique for Estimating the Occlusion Effect for Frequencies Below 125 Hz

The level of bone-conducted sound in the auditory meatus is increased at low frequencies by occlusion of the meatus, for example, by the earmold of a hearing aid (HA). Physical measures of this "occlusion effect" (OE) require vibration of the skull. In previous research, either self-voicing or audiometric bone conduction vibrators were used to produce this vibration, with the result that the OE could not be measured for frequencies below 125 Hz. However, frequencies below this can be important for music perception by HA users. The objective was to develop and evaluate a method that gives a lower-bound estimate of the OE for frequencies below 125 Hz. A low-noise amplifier with extended low-frequency response was used to record the output of a miniature microphone inserted into the meatus of participants. The signal came from sounds of the heartbeat and blood flow of the participant, transmitted via bone conduction through the walls of the meatus. A simultaneous recording was made of the carotid pulse to permit time-locked averaging (and hence noise reduction) of the microphone signal. Recordings were made from 7 otologically and audiometrically normal participants, using clinical probe tips to produce the occlusion. Recordings were also made from an overlapping group of 9 participants, using fast-setting impression material to provide a more consistent degree of occlusion. The difference in level of the recorded signal for unoccluded and occluded conditions provided a lower bound for the magnitude of the OE. The mean OE increased with decreasing frequency, reaching a plateau of about 40 dB for frequencies below 40 Hz. For some individual recordings, the OE reached 50 dB for frequencies below 20 Hz. With occlusion, the heartbeat became audible for most participants. The OE can be very large at low frequencies. The use of HAs with closed fittings, which may be used either to prevent acoustic feedback or to allow amplification of low frequencies, may lead to an unacceptable OE. The authors suggest reducing the OE with the use of a seal deep in the meatus, where the wall of the meatus is more rigid.

Rotor System Fault Diagnosis Based on Orbit Manifold Topological

A new fault diagnosis method based on orbit manifold topological space is put forward. Firstly, vibration signals from the working is reduced the noise by using EEMD for vibration signal noise reduction. Restored signal constitutes an orbit. An orbit of the orbit track points is defined as a dimension feature of the high-dimensional space; the high-dimensional characteristics space is reduced into lower-dimensional space based on LTSA algorithm, then orbit manifold sensitive characteristics in the topological space are obtained. It provides a new method for fault diagnosis of electrical system. Application of the method to test-bed of normal, misalignment, rotor rub-impact fault diagnosis of three types, and fault diagnosis of NC lathe spindle and eccentric properly, the correct rate is up to 99%. The validity of the algorithm is verified.

Zirconia potentiometric oxygen sensor activation using NEMCA

Abstract NEMCA known for the increasing the catalytic activity/selectivity of the gas exposed electrodes was investigated for the potentiometric zirconia oxygen sensor activation. Electrochemical promotion of O2-sensors with Pt-cermet electrodes aged in the field was significant with up to 5 times in the impedance reduction. Stability test on the NEMCA activated °2 -sensors was showing just a minor impedance increase. Sensor response time was reduced by NEMCA effect by ~5-10% with up to 3 times signal noise reduction on the aged O2-sensors.

Noise Reduction in Ultrasonic Signals for Identification of Weld Defects: A Review

In Ultrasonic Inspection, the first and foremost step to find the defects in welding materials used in industrial applications is Noise Reduction. It is used to improve the detect ability of the weld defects. It plays a vital role in Flaw Detection and Characterization of materials such as Austenitic Stainless Steels etc. This study gives a review of various noise reduction schemes or techniques for reducing the noise to detect the welding defects and aims at the usage of it.

Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals

To remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a threshold built on the analytic signal envelope. The algorithm was tested on simulated and real EEG signals that contain peak and spike artifacts with random amplitude and frequency occurrence. The performance of the filter was compared with commonly used adaptive filters. Three indexes were used for testing the performance of the filters: Correlation coefficient (ρ), mean of coherence function (C), and rate of absolute error (RAE). All these indexes were calculated between filtered signal and original signal without noise. It was found that the new proposed filter was able to reduce the amplitude of peak and spike artifacts with ρ>0.85, C>0.8, and RAE<0.5. These values were significantly better than the performance of LMS adaptive filter (ρ<0.85, C<0.6, and RAE>1).

Noise Reduction of Accelerometer Signal with Singular Value Decomposition and Savitzky-Golay Filter

Noise Reduction of Accelerometer Signal with Singular Value Decomposition and Savitzky-Golay Filter

METHOD FOR ENGINE WAVEFORM ANALYSIS AND FAULT DIAGNOSIS BASED ON SFB AND HHT

Traditional engine waveform analysis in time-domain fails to perform an accurate fault diagnosis when the fault waveform is very close to the normal waveform in time domain. A novel engine waveform analysis method is presented. In this paper, the aim is to perform fault diagnosis efficiently under such circumstances. This method is proposed by combining a new technique, called sensitive frequency band (SFB) selection, with the developed Hilbert–Huang transform (HHT). This can alleviate "mode mixing" by removing noise from the engine waveforms and reveal the time–frequency characteristics for a signal by deriving its time–frequency spectrum (TFS) distribution. The method is then applied to analyze the engine injector-pulse-width waveforms, and it works well for signal noise reduction and fault diagnosis.

An Optimized Particle Filter for Signal De-Noising Processing

The traditional particle filter (PF) algorithm is well known for signal noise reduction processing, but it exists problems of particle impoverishment and cumulation of estimation errors. An optimized PF algorithm called RBF-PF is proposed in this paper, which uses radial basis function network for training and optimizing the process of particle filter in the sampling. Experimental analysis verifies that the new method used to gain the signal-to-noise ratio is better than traditional PF algorithm during dealing with the added noise signal.

Design and implementation of frequency-following response recording system

Objective: The frequency-following response (FFR) is the compound phase-locked brainstem response to periodic components of sound stimuli, and is closely related to pitch perception. Its weak amplitude often prevents its measurement with a high signal-to-noise ratio (SNR). Recording of FFR using multichannel EEG is possible but expensive and it involves the manual screening of raw data. Design: We describe a new method to extract FFR features by prescreening the raw data using automatic monitoring of sound pressure in the ear canal. Removal of stimulus artifacts, noise reduction, and data selection were systematically studied. Study sample: The reliability of our new method was tested by comparing FFRs tracking accuracy and pitch perception in fifteen individuals with normal hearing. Results: The extracted FFRs tracking accuracy was significantly correlated with behavioral measures of pitch perception, indicating that FFR could be used to represent individual differences in pitch perception ability among a population with similar hearing characteristics. Conclusion: The designed system could extract FFR signals more accurately with high SNR after signal prescreen and noise reduction.

An Application of Minimum Statistical Noise Estimation in the High Rate of Fire Artillery Muzzle Velocity Measurement

The infrared starter is usually used to measure the artillery’s muzzle velocity, but due to the continued firelight near the muzzle produced by the continuous occurrence of the high rate of fire artillery projectile , so that the misjudgment and the Missing are easily produced by the infrared sensor . Continuous wave radar can effectively overcome the impact of the fire, but the detection accuracy of the leaving time is greatly affected echo because of the flame noise. Therefore the noise reduction of echo signal is an important step about the detection of the leaving time. For this problem, a noise reduction algorithm based on the minimum statistical noise estimation is proposed in this paper by the analysis of the echo signal. The noise of continuous muzzle flame is effectively reduced by the algorithm. It is verified correct and feasible by the processing of the measured signal. It lays the foundation for the measure of the artillery’s muzzle velocity and the engineering realization of muzzle velocity measurement.

Noise reduction in functional near-infrared spectroscopy signals by independent component analysis

Functional near-infrared spectroscopy (fNIRS) is used to detect concentration changes of oxy-hemoglobin and deoxy-hemoglobin in the human brain. The main difficulty entailed in the analysis of fNIRS signals is the fact that the hemodynamic response to a specific neuronal activation is contaminated by physiological and instrument noises, motion artifacts, and other interferences. This paper proposes independent component analysis (ICA) as a means of identifying the original hemodynamic response in the presence of noises. The original hemodynamic response was reconstructed using the primary independent component (IC) and other, less-weighting-coefficient ICs. In order to generate experimental brain stimuli, arithmetic tasks were administered to eight volunteer subjects. The t-value of the reconstructed hemodynamic response was improved by using the ICs found in the measured data. The best t-value out of 16 low-pass-filtered signals was 37, and that of the reconstructed one was 51. Also, the average t-value of the eight subjects' reconstructed signals was 40, whereas that of all of their low-pass-filtered signals was only 20. Overall, the results showed the applicability of the ICA-based method to noise-contamination reduction in brain mapping.