Line Noise Research Articles

Performance Evaluation of Wavelet De-Noising Schemes for Suppression of Power Line Noise in Electrocardiogram Signals

Power line noise introduces distortions to recorded electrocardiogram (ECG) signals. These distortions compromise the integrity and negatively affect the interpretation of the ECG signals. Despite the fact that the amplifiers used in biomedical signal processing have high common mode rejection ratio (CMRR), ECG recordings are still often corrupted with residual Power Line Interference (PLI) noise. Further improvement in the hardware solutions do not have significant achievements in PLI noise suppression but rather introduce other adverse effects. Software approach is necessary to refine ECG data. Evaluation of PLI noise suppression in ECG signal in the wavelet domain is presented. The performance of the Hard Threshold Shrinkage Function (HTSF), the Soft Threshold Shrinkage Function (STSF), the Hyperbola Threshold Shrinkage Function (HYTSF), the Garrote Threshold Shrinkage Function (GTSF), and the Modified Garrote Threshold Shrinkage Function (MGTSF) for the suppression of PLI noise are evaluated and compared with the aid of an algorithm. The optimum tuning constant for the Modified Garrote Threshold Shrinkage Function (MGTSF) is found to be 1.18 for PLI noise. GTSF is found to have best performance closely followed by MGTSF in term of filtering Gain. HTSF recorded the lowest Gain. Filtering against PLI noise in the wavelet domain preserves the key features of the signal such as the QRS complex.

Study on 10kV Line Characteristics Based on Medium Voltage Carrier

AbatractUsing medium voltage carrier communication to achieve full coverage of power information acquisition and distribution automation has become a hot spot in the domestic power industry. The characteristics of 10kV line will largely affect the reliability of medium voltage carrier communication, but due to the influence of voltage level and other factors, the research on 10kV line characteristics is not mature. In this paper, a research method of 10kV line characteristics based on medium voltage carrier is proposed. The improved medium voltage carrier communication machine and coupler can realize the collection of 10kV line impedance, attenuation and noise, accumulate useful data for the long-term study of 10kV line characteristics, help to improve the reliability of medium voltage carrier wave communication and assist 10kV line fault monitoring.

Frequency shift algorithm: Design of a baseband phase locked loop for frequency-domain multiplexing readout of x-ray transition-edge sensor microcalorimeters.

The Transition-Edge Sensor (TES) is an extremely sensitive device, which is used to measure the energy of individual x-ray photons. For astronomical spectrometry applications, SRON develops a frequency domain multiplexing readout system for kilopixel arrays of such TESs. Each TES is voltage biased at a specific frequency in the range of 1-5MHz. Isolation between the individual pixels is obtained through very narrow-band (high-Q) lithographic LC resonators. To prevent energy resolution degradation due to intermodulation line noise, the bias frequencies are distributed on a regular grid. The requirements on the accuracy of the LC resonance frequency are very high. The deviation of the resonance frequencies due to production tolerances is significant with respect to the bandwidth, and a controller is necessary to compensate for the LC series impedance. We present two such controllers: a simple orthogonal proportional-integral controller and a more complex impedance estimator. Both controllers operate in baseband and try to make the TES current in-phase with the bias voltage, effectively operating as phase-locked loops. They allow off-LC-resonance operation of the TES pixels while preserving the TES thermal response and energy resolution. Extensive experimental results-published in a companion paper recently-with the proposed methods show that these controllers allow the preservation of single pixel energy resolution in multiplexed operation.

Noise Elimination of Low-Voltage Power Line Communication Channel using Time-Frequency Peak Filtering Algorithm

There are a lot of noises in the low-voltage power line communication (LVPLC) channel, which seriously damages the LVPLC system. The noise in the low voltage power line can be divided into general background noise and random pulse noise. These two noises will cause serious interference to the communication process based on LVPLC, reduce the signal-to-noise ratio of LVPLC system, and the communication quality cannot meet the requirements. To ensure the communication quality, this paper uses the time-frequency peak filtering algorithm to eliminate the noise in the LVPLC in the experimental environment. Firstly, this paper studies the noise characteristics based on the measured LVPLC channel noise. Secondly, the memory noise model is established, and the time-frequency peak filtering algorithm is used to eliminate the noise. In order to analyze the denoising effect of time-frequency peak filtering algorithm, the algorithm is simulated. Finally, the application effect of the algorithm is verified by experimental test. The simulation and application results show that the time-frequency peak filtering algorithm can improve the signal-to-noise ratio by about 5 dB in the actual noise environment of LVPLC, which can adapt to the changeable environment of LVPLC channel noise, and has good noise suppression effect and good application value. The application in the solar panel data transmission system shows that the time-frequency peak filtering algorithm can meet the communication performance requirements of the laboratory, reduce the bit error rate by about 2 % under the general background noise interference, and reduce the bit error rate by about 3 % under the pulse interference environment, and improve the transmission quality of LVPLC system.

Multi-tracer intensity mapping: cross-correlations, line noise & decorrelation

Line intensity mapping (LIM) is a rapidly emerging technique for constraining cosmology and galaxy formation using multi-frequency, low angular resolution maps.Many LIM applications crucially rely on cross-correlations of two line intensity maps, or of intensity maps with galaxy surveys or galaxy/CMB lensing.We present a consistent halo model to predict all these cross-correlations and enable joint analyses, in 3D redshift-space and for 2D projected maps.We extend the conditional luminosity function formalism to the multi-line case, to consistently account for correlated scatter between multiple galaxy line luminosities.This allows us to model the scale-dependent decorrelation between two line intensity maps,a key input for foreground rejection and for approaches that estimate auto-spectra from cross-spectra.This also enables LIM cross-correlations to reveal astrophysical properties of the interstellar medium inacessible with LIM auto-spectra.We expose the different sources of luminosity scatter or “line noise” in LIM, and clarify their effects on the 1-halo and galaxy shot noise terms.In particular, we show that the effective number density of halos can in some cases exceed that of galaxies, counterintuitively.Using observational and simulation input, we implement this halo model for the Hα, [Oiii], Lyman-α, CO and [Cii] lines.We encourage observers and simulators to measure galaxy luminosity correlation coefficients for pairs of lines whenever possible.Our code is publicly available at https://github.com/EmmanuelSchaan/HaloGen/tree/LIM.In a companion paper, we use this halo model formalism and codeto highlight the degeneracies between cosmology and astrophysics in LIM, and to compare the LIM observables to galaxy detection for a number of surveys.

Performance Improvement in Electroencephalogram Signal by Using DWT

The nature of the electroencephalogram (EEG) signal is very fluid and spontaneous. Because of its lower amplitude, it is polluted by certain artifacts such as power line noise and baseline noise. Similarly, power line and baseline sounds have polluted electromyogram (EMG) and electrocardiogram (ECG) signals. These objects taint the initial signal's properties. Owing to the existence of objects, these signals cannot be accurately analyzed; hence, these sounds must be removed before analyzing the raw signal. The two important artifacts that corrupt a patient's EEG record are power line interference and baseline interference. This paper focuses on extracting power line and baseline artifacts from EEG signals using an effective de-noising algorithm called DWT (Discrete Wavelet Transform) to improve the signal's efficiency by increasing fidelity parameters including MSE, MAE, SNR, and PSNR. The MATLAB simulation is to be used for the implementation.

Design and implementation of VLC-based smart barrier gate systems

In this paper, an innovative and smart Visible Light Communication (VLC)-based Intelligent Transportation System (ITS) is designed. The proposed system uses vehicle LED headlamps as a remote controller. Differential Pulse Position Modulation (DPPM) is utilized for the sake of its asynchronicity. The system features an innovative optical interference cancellation mechanism to address the unwanted variable DC component and AC line noise in the received signal. The feasibility of the proposed system is demonstrated experimentally in day and night conditions using an off-the-shelf LED headlights. The Bit Error Rate (BER) performance of the system in terms of angular position of the vehicle with respect to the VLC receiver at different link ranges is evaluated. The prototype successfully demonstrates the smart control over the barrier gate using the ATmega microcontrollers family for the outdoor environment. BER of lower than 10-3 is achieved for a maximum link range of 4.4 meters.

Method of suppressing noise in a differential transmission line using a near-field noise-suppression device

Method of suppressing noise in a differential transmission line using a near-field noise-suppression device

Probabilistic Neural Network-Aided Fast Classification of Transmission Line Faults Using Differencing of Current Signal

Electrical power transmission lines are most vulnerable to different faults due frequent atmospheric hazards. Hence, fault detection and classification are imperative to restrict unwanted power outage by isolation of the faulted line. A probabilistic neural network (PNN)-based fault classification methodology is proposed here. This work uses differencing-based modulated fault signals as input to the PNN architecture to extract fault features in terms of three-phase fault intensity index, which are further analyzed for direct classification of faults aided by a decision tree like study. Simulation of faults has been carried out in practical alike simulation environment with variation of fault location, fault resistance and inherent power line noise, which helps to develop robustness of fault analyzer. Classifier accuracy of 99.33% is achieved here, and more importantly, using only (1/6)th fraction of post fault signals, which is considerably low compared to similar contemporary works. Furthermore, the proposed classifier is equipped with the ability of detecting ground fault without inspecting the neutral current. This method uses only 35.7% of the total fault locations for training, and the rest for validating the model, which is also an above average performance. Finally, the analyzer comprises of simple PNN model as the only classifier, hence possess considerably low computational complexity.

Frequency shift algorithm: Application to a frequency-domain multiplexing readout of x-ray transition-edge sensor microcalorimeters.

In the frequency-domain multiplexing (FDM) scheme, transition-edge sensors (TESs) are individually coupled to superconducting LC filters and AC biased at MHz frequencies through a common readout line. To make efficient use of the available readout bandwidth and to minimize the effect of non-linearities, the LC resonators are usually designed to be on a regular grid. The lithographic processes, however, pose a limit on the accuracy of the effective filter resonance frequencies. Off-resonance bias carriers could be used to suppress the impact of intermodulation distortions, which, nonetheless, would significantly affect the effective bias circuit and the detector spectral performance. In this paper, we present a frequency shift algorithm (FSA) to allow off-resonance readout of TESs, while preserving the on-resonance bias circuit and spectral performance, demonstrating its application to the FDM readout of an x-ray TES microcalorimeter array. We discuss the benefits in terms of mitigation of the impact of intermodulation distortions at the cost of increased bias voltage and the scalability of the algorithm to multi-pixel FDM readout. We show that with FSA, in the multi-pixel and frequencies shifted on-grid, the line noises due to intermodulation distortion are placed away from the sensitive region in the TES response and the x-ray performance is consistent with the single-pixel, on-resonance level.

Algorithms for Modulation Recognition of Narrowband Power Line Carrier Communication Signals

Automatic identification of modulated signals is an important technology of power line communication. Aiming at the problems of low signal recognition rate and characteristic parameter extraction in power line communication channel, this paper uses amplitude variance value of wavelet transform and higher order cumulant as the identification parameter, and designs a signal recognizer based on improved support vector machine. Under the condition of power line channel environment, the recognizer of this paper is less than the existing recognition method in the computational complexity, and has good robustness to power line noise. At the same time, it avoids the shortcomings of traditional neural network such as under-learning and over-learning. The simulation results show that when the SNR is 5 dB, power line communication signals through the recognizer, which the correct identification rate can reach 91%.

Transcranial Evoked Potentials Can Be Reliably Recorded with Active Electrodes.

Electroencephalographic (EEG) signals evoked by transcranial magnetic stimulation (TMS) are usually recorded with passive electrodes (PE). Active electrode (AE) systems have recently become widely available; compared to PE, they allow for easier electrode preparation and a higher-quality signal, due to the preamplification at the electrode stage, which reduces electrical line noise. The performance between the AE and PE can differ, especially with fast EEG voltage changes, which can easily occur with TMS-EEG; however, a systematic comparison in the TMS-EEG setting has not been made. Therefore, we recorded TMS-evoked EEG potentials (TEPs) in a group of healthy subjects in two sessions, one using PE and the other using AE. We stimulated the left primary motor cortex and right medial prefrontal cortex and used two different approaches to remove early TMS artefacts, Independent Component Analysis and Signal Space Projection—Source Informed Recovery. We assessed statistical differences in amplitude and topography of TEPs, and their similarity, by means of the concordance correlation coefficient (CCC). We also tested the capability of each system to approximate the final TEP waveform with a reduced number of trials. The results showed that TEPs recorded with AE and PE do not differ in amplitude and topography, and only few electrodes showed a lower-than-expected CCC between the two methods of amplification. We conclude that AE are a viable solution for TMS-EEG recording.

The Impact of Torso Signal Processing on Noninvasive Electrocardiographic Imaging Reconstructions

Goal:To evaluate state-of-the-art signal processing methods for epicardial potential-based noninvasive electrocardiographic imaging reconstructions of single-site pacing data.Methods:Experimental data were obtained from two torso-tank setups in which Langendorff-perfused hearts (n = 4) were suspended and potentials recorded simultaneously from torso and epicardial surfaces. 49 different signal processing methods were applied to torso potentials, grouped as i) high-frequency noise removal (HFR) methods ii) baseline drift removal (BDR) methods and iii) combined HFR+BDR. The inverse problem was solved and reconstructed electrograms and activation maps compared to those directly recorded.Results:HFR showed no difference compared to not filtering in terms of absolute differences in reconstructed electrogram amplitudes nor median correlation in QRS waveforms (p > 0.05). However, correlation and mean absolute error of activation times and pacing site localization were improved with all methods except a notch filter. HFR applied post-reconstruction produced no differences compared to pre-reconstruction. BDR and BDR+HFR significantly improved absolute and relative difference, and correlation in electrograms (p < 0.05). While BDR+HFR combined improved activation time and pacing site detection, BDR alone produced significantly lower correlation and higher localization errors (p < 0.05).Conclusion:BDR improves reconstructed electrogram morphologies and amplitudes due to a reduction in lambda value selected for the inverse problem. The simplest method (resetting the isoelectric point) is sufficient to see these improvements. HFR does not impact electrogram accuracy, but does impact post-processing to extract features such as activation times. Removal of line noise is insufficient to see these changes. HFR should be applied post-reconstruction to ensure over-filtering does not occur.

Classification and Fast Detection of Transmission Line Faults Using Signal Entropy

A simple, prompt and accurate method of fault identification and classification of faults in a long transmission system is presented here using entropy analysis of post-fault three-phase current signals, measured at the sending end only. Identification of the faulted phase is imperative for restricting unwanted outage of power through the faulted phase, as well as isolation of the same in order to retain the stability of the system. Transmission line fault classification, hence, has become one of the most vital topics of research. The high-frequency transient current oscillations, appearing immediately after the fault, are analyzed in this work. Signal entropy is computed for the modified waveform of each phase using the differentiation method to highlight the edges of oscillation. Entropy measures the randomness of a signal; hence, the sudden disturbance caused in the directly affected faulted line yields much higher entropy compared to the un-faulted lines. It is further observed that the extent of disturbance in the three phases primarily depends on the type of fault caused: ground fault or non-ground fault. An attempt has been made closely to monitor the entropy levels of the three-phase currents to distinguish between each phase in terms of the level of disturbance caused in a specific class of fault using the estimated signal entropy values, thereby aiding in the development of a threshold-based fault classifier entropy signatures. The major findings of the present work are primarily twofold: 100% accuracy of classification and requirement of only (1/20)th of post-fault signal, that is, detection within 1 ms, which is commendably fast compared to several contemporary works. Besides, the variation of fault parameters, such as fault location, fault resistance and power line noise, makes the design more practically suited.

Calculation of symbol error rate in a telecommunication system based on amplitude modulation of many components

The known analytical equations for calculating the symbol error rate (SER) in the M-ary telecommunication system are considered. The analytical equations for calculating SER in a telecommunication system based on M-ary amplitude modulation of many components (M-AMMC) and other varieties of amplitude-phase shift keying with arbitrary number and arbitrary location of signal points of the signal constellation, as well as under the action of additive white Gaussian noise in a communication line are proposed. According to the results of the research, it is found that the proposed equations allow us to increase the accuracy of calculating SER when using M-AMMC and other varieties of amplitude-phase shift keying compared to known analytical equations.

3D modeling of electron-beam lithographic process from scanning electron microscope images

Computational lithography is typically based on a model representing the lithographic process where a typical model consists of three components, i.e., line spread function, conversion formula (exposure-to-developing rate conversion), and noise process (exposure fluctuation). In our previous study, a practical approach to modeling the e-beam lithographic process by deriving the three components directly from SEM images was proposed. However, a 2D model of a substrate system was employed; i.e., the exposure variation along the resist-depth dimension was not considered. In this study, the possibility of improving the accuracy of modeling using a 3D model is investigated. The 3D model is iteratively determined by modeling the critical dimension estimated based on the model to those measured in SEM images. This paper describes the 3D modeling approach and new optimization procedures and discusses in detail the results from an extensive simulation for an accuracy analysis of the 3D modeling approach.

Study on CDM ESD Robustness Among On-Chip Decoupling Capacitors in CMOS Integrated Circuits

The integrated circuit (IC) products fabricated in the scaled-down CMOS processes with higher clock rate and lower power supply voltage (VDD) are more sensitive to the transient/switching noises on the power lines with the parasitic inductance induced by the bonding wire. The typical method to suppress the power line noise is to add on-chip decoupling capacitors. Meanwhile, electrostatic discharge (ESD) is also a challenging issue on IC reliability in advanced CMOS technology. For the ICs fabricated in an advanced process, with the thinner gate oxide, the circuits are particularly vulnerable to the charged-device model (CDM) ESD events. However, there was very limited research to investigate the ESD robustness on the decoupling capacitors, especially during the CDM ESD events. In this work, the CDM ESD robustness among different types of decoupling capacitors in ICs was investigated in a 0.18- ${\mu }\text{m}$ CMOS technology.

The effect of age on full-field electroretinograms recorded with skin electrodes.

The aim of this study was to determine whether age correlates with amplitude and latency, when full-field electroretinography (ERG) is performed using skin electrodes. The ability of pulse reference power line noise reduction (PURE) to dampen the noise associated with the use of skin electrodes, was also investigated. ERG was performed on 77 eyes in 77 healthy subjects (mean age: 55.6±19.0 years; age range: 9 to 86 years). Subjects with -5D or higher myopia, Emery-Little grade III or higher cataracts, retinal disease, uveitis, glaucoma, ≤5mm mydriasis, or a history of intraocular surgery other than cataract surgery, were excluded. The active, reference, and ground electrodes were placed on the lower eyelid, outer canthus, and earlobe, respectively. Responses were averaged 10 times for dark-adapted (DA) ERGs, and 32 to 64 times for light-adapted (LA) ERGs. Noise was removed using the PURE method. The DA ERGs without PURE were so noisy that the amplitude or latency could not be determined, whereas those with PURE were comparatively quieter. ERG with PURE demonstrated a significant negative correlation between age and amplitude and a significant positive correlation between age and latency. We could record the measurable ERG waveforms with skin electrodes by using the PURE method, especially in fewer averaged conditions. It is suggested that skin electrode with PURE is suitable to examine the pathological ERGs, and other types of electrodes. It is recommended that the aging effect should be taken into consideration when pathological ERGs are evaluated.

Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG.

Magneto-/Electro-encephalography (M/EEG) commonly uses (fast) Fourier transformation to compute power spectral density (PSD). However, the resulting PSD plot lacks temporal information, making interpretation sometimes equivocal. For example, consider two different PSDs: a central parietal EEG PSD with twin peaks at 10 Hz and 20 Hz and a central parietal PSD with twin peaks at 10 Hz and 50 Hz. We can assume the first PSD shows a mu rhythm and the second harmonic; however, the latter PSD likely shows an alpha peak and an independent line noise. Without prior knowledge, however, the PSD alone cannot distinguish between the two cases. To address this limitation of PSD, we propose using cross-frequency power–power coupling (PPC) as a post-processing of independent component (IC) analysis (ICA) to distinguish brain components from muscle and environmental artifact sources. We conclude that post-ICA PPC analysis could serve as a new data-driven EEG classifier in M/EEG studies. For the reader’s convenience, we offer a brief literature overview on the disparate use of PPC. The proposed cross-frequency power–power coupling analysis toolbox (PowPowCAT) is a free, open-source toolbox, which works as an EEGLAB extension.

Improved Tooth Crown Edge Smoothing Method Based on Noise Classification and Fitting

Because three-dimensional (3D) models of teeth are currently obtained via oral scanning, there are only the tooth crown and gingival surface part, lack of data on the roots of teeth, which is not conducive to the 3D reconstruction of teeth. In order to help doctors to carry out virtual tooth correction, this paper studies the edge characteristics of the tooth crown model, removes the edge noise, which can better carry out the 3D reconstruction of teeth. Therefore, this paper proposes an improved method of tooth crown edge smoothing based on noise classification and fitting. First, according to the characteristics of the tooth crown edge, the method of noise classification is proposed after fitting analysis. The noise can be divided into two types: the noise in the boundary line and the noise in the fitting curve. Then, the noise can be identified according to the Gaussian curvature. Finally, the improved Laplacian smoothing and least squares fitting methods are used to remove the two types of noise, and the denoised tooth crown model is the output. The smoothing effect of the method is verified in terms of the noise removal rate, the patch filling rate, and the patch deletion rate. Compared with the traditional Laplacian smoothig, the new method exhibited a noise removal rate increase of 86.0%, a probability of patch filling that approximately doubled, and a probability of patch deletion that basically remained the same. Compared with the least squares fitting method, the new method exhibited a noise removal rate increase of 75.9%, a patch filling reduction of 22.61%, and a patch deletion reduction of 22.14%.