Line Noise Research Articles

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%.

Grating Spectrometry and Spatial Heterodyne Fourier Transform Spectrometry: Comparative Noise Analysis for Raman Measurements

The desire for portable Raman spectrometers is continuously driving the development of novel spectrometer architectures where miniaturisation can be achieved without the penalty of a poorer detection performance. Spatial heterodyne spectrometers are emerging as potential candidates for challenging the dominance of traditional grating spectrometers, thanks to their larger etendue and greater potential for miniaturisation. This paper provides a generic analytical model for estimating and comparing the detection performance of Raman spectrometers based on grating spectrometer and spatial heterodyne spectrometer designs by deriving the analytical expressions for the performance estimator (signal-to-noise ratio, SNR) for both types of spectrometers. The analysis shows that, depending on the spectral characteristics of the Raman light and on the values of some instrument-specific parameters, the ratio of the SNR estimates for the two spectrometers () can vary as much as by two orders of magnitude. Limit cases of these equations are presented for a subset of spectral regimes which are of practical importance in real-life applications of Raman spectroscopy. In particular, under the experimental conditions where the background signal is comparable or larger than the target Raman line and shot noise is the dominant noise contribution, the value of is, to a first order of approximation, dependent solely on the relative values of each spectrometer’s etendue and on the number of row pixels in the detector array. For typical values of the key instrument-specific parameters (e.g., etendue, number of pixels, spectral bandwidth), the analysis shows that spatial heterodyne spectrometer-based Raman spectrometers have the potential to compete with compact grating spectrometer designs for delivering in a much smaller footprint (10–30 times) levels of detection performance that are approximately only five to ten times poorer.

Feasibility Study for the Evaluation of Chicken Meat Storage Time Using Surface Acoustic Wave Sensor

PurposeThe objectives of this study are to provide the detailed information of designing, fabrication process, and performance test results of a surface acoustic wave (SAW) sensor for evaluating chicken meat storage time (up to 15 days).MethodsThe aldehyde gas generated from chicken meat was selected as the reactive material, and Love wave was used among various SAWs due to its high sensitivity of the aldehyde gas particles. The SAW sensor was fabricated on the surface of LiNbO3 piezoelectric wafer with a cross-linked polydimethylsiloxane (PDMS)-sensitive layer using standard photolithographic techniques. To analyze the fabricated SAW sensor characteristics, the S21 parameters and base line noise were acquired by a vector network analyzer. Before measuring chicken meat gas samples, a reference gas (a mixture of 25, 50, 75, and 100 ppm acetaldehyde (a functional group with the structure –CHO, CH3CHO) and nitrogen (N2) gas) was measured to verify the reactive performance of the SAW sensor. Among the VOCs from chicken meat gas, CH3CHO was selected as an intrinsic biomarker due to its harmful effects on the human body. Then, aldehyde gas from chicken meat samples was measured and the phase changes of the response signals with increasing the chicken meat storage time were obtained by the SAW sensor.ResultsAbout − 4 dB insertion loss occurred due to the PDMS coating and the high electromechanical coupling factor of used substrate. The phases of the response signals decreased linearly with increase of both the reference gas concentration and the storage time of the chicken meat samples. The determination coefficients of the reference gas and the storage time were over 0.9. The results show that the developed SAW sensor can provide valuable information about evaluating the chicken meat storage time.ConclusionThe newly developed SAW sensor clearly demonstrates that chicken meat storage could be evaluated by detecting aldehyde gas. This opens a promising research avenue to explore.

Delayed LMS Algorithm for Ballistocardiogram Biomedical Signal

The Ballisto-cardiogram (BCG) is a biomedical signal which is basically a measure of ballistic forces on heart. Just like ECG to detect the abnormalities in heart Ballisto-cardiography technique is used extensively now a day in research to analyze the abnormalities of the patient. When the blood pumps from heart to different parts of the body is represented in the form of graph for each heart beat. The frequency of Ballisto-cardiography signal is 1-20Hz .Ballisto-cardiography is most emerging techniques which is used to test the diseases related to heart called as cardiovascular disease. Various devices [1] like chairs, beds and weighing scales are projected to improvise the extraction of the BCG, but noise is one of the main issues with this BCG signal processing, noise is generated because of motion artifacts, shaking of the devices or may be power line noise. This noise [2] affects the quality of signal which we need to test. In order to overcome such issue this paper proposes a new architecture making use of LMS filtering algorithm Here weight update algorithm is used to update the error in extracted signal. The architecture proposed here includes FIR filter and also error computation blocks. Here the author has implemented 5-tap filtering algorithm. MATLAB and system generators are used to carry out the work.

Additive Gaussian noise model and Kalman filter to enhance controllability of gesture-controlled teleoperated soft actuators

Soft actuators are used in bilateral systems as slave-side actuators. To broaden the applications and usage of these soft actuators in teleoperated applications required high accuracy and control. This article focuses on the improvement and control of the soft actuators operated through the Internet using a glove-based gesture control system. A hand-worn sensor glove (a glove with flex sensors and accelerometers) is used to detect hand gestures (specifically bending angle and force generated in a finger while gripping a spherical object) of an operator. The operator’s finger movements are mimicked in a remote location, through the Internet, in a soft actuator. The bending angles of the human finger have converted into the pressure variations inside the actuator using a linear calibration technique. A vertically mounted actuator and a spherical object are used to demonstrate the gripping action. The main problems that occurred during the controlling of this setup are noise and delays. Electronic noise (line noise and circuit noise), mechanical noise (vibration, nonuniformities in actuator fabrication, wear, and tear), elastic effect (energy absorbed during the state transformation), communication delays (delays occurred due to geography, telecommunication infrastructure, and round trip transmission), and other noises (other environmental effects) degraded the performance. This article considered a Brownian motion model, an additive Gaussian noise model, and a Kalman filter to solve these problems. The experimentations are performed in three different locations (to demonstrate the teleoperation) and the recorded improvement in the performance is approximately 17%.

Evaluation of Electrical Performance and Properties of Electroretinography Electrodes.

PurposeThe aim of this study was to evaluate and compare the electrical performance and properties of commercially available electroretinography (ERG) electrodes.MethodsA passive ionic model was used to measure impedance, noise, and potential drift in 10 types of ocular surface and skin ERG electrodes.ResultsThe impedance for silver-based ocular electrodes are generally lower (range, 65.35–343.3 Ω) with smaller phase angles (range, −6.41° to −33.91°) than gold-based electrodes (impedance ranged from 285.95 Ω to 2.913 kΩ, and phase angle ranged from −59.65° to −70.01°). Silver-based ocular electrodes have less noise (median line noise of 6.48 x 104nV2/Hz) than gold-based electrodes (median line noise of 2.26 x 105nV2/Hz). Although silver-based electrodes usually achieve a drift rate less than 5 µV/s within 15 minutes, gold-base ocular electrode cannot achieve a stable potential. The exception is the RETeval strip type of silver electrode, which had an unusual drift at 20 minutes. The noise spectral density showed no change over time indicating that noise was not dependent on the stabilization of the electrode.ConclusionsFrom the range of electrodes tested, lower impedance, lower capacitance, and lower noise was observed in silver-based electrodes. Stabilization of an electrode is effective against drift of the electrode potential difference but not the noise.Translational RelevanceApplication of electrodes with optimized materials improve the quality of clinical electrophysiology signals and efficiency of the recording.

The Elimination and Detection Method of Ballistic Missile Infrared Images Bright Line Noise in Middle Trajectory

The bright line noise of the infrared images in middle trajectory of the ballistic missile is similar with the track of the target. This will increase the difficulty in the detection and recognition of the target. So it is very important to eliminate the bright line in the infrared images. Firstly, the paper analyzes the characters of the bright line of the infrared images in the middle trajectory of the ballistic missile in the time domain and the frequency domain respectively. Secondly, the innovative method is put forward to detect the bright line automatically. Finally, the elimination and restoration method of the bright line is proposed in the time domain and the frequency domain. Numerical simulations are given to demonstrate the efficiency of the proposed control schemes and the experimental results show that the proposed method can eliminate the bright line effectively and improve the performance of the infrared images.

Characteristic of Electromagnetic Noise Based on the Theory of Multiconductor Communication Line

Electromagnetic noise has serious influence on the performance of electrical system. In addition, while multiconductor cable harnesses play an important role in transmitting electromagnetic energy or signal between devices of electrical system, they are also the major path to transmit electromagnetic noise. Crosstalk, as typical electromagnetic noise within multi-conductor cable harnesses, is an important factor which affects the efficiency of transmission. In this paper, the n+1 transmission lines (n=2, n>2) are taken as the object of research. Based on the theory of Multi-conductor Transmission Line (MTL), the transmission of electromagnetic noise in transmission line is studied, including noise of generating line (called G-line) and line receiving interference (called R-line), the latter called crosstalk. Transfer functions of electromagnetic noise of G-line and R-line are simulated using FEKO (FEldberechnung bei Korpern mit beliebiger Oberflache). Two transfer functions are obtained to investigate the severity of noise of G-line and R-line. The characteristics of the parameters are also studied. Simulation results indicate that transfer functions have tight relationship with the electrical length. When the electrical length is small, voltage loss of interference line along the transmission line is relatively small, so is the far-end crosstalk; however, when the electrical length is large, voltage loss and the far-end crosstalk is larger, and resonances at high frequency. Keywords: Electromagnetic noise; FEKO; electrical length;

Properties of different types of dry electrodes for wearable smart monitoring devices.

Wearable smart monitors (WSMs) applied for the estimation of electrophysiological signals are of utmost interest for a non-stressed life. WSM which records heart muscle activities could signalize timely a life-threatening event. The heart muscle activities are typically recorded across the heart at the surface of the body; hence, a WSM monitor requires high-quality surface electrodes. The electrodes used in the clinical settings [i.e. silver/silver chloride (Ag/AgCl) with the gel] are not practical for the daily out of clinic usage. A practical WSM requires the application of a dry electrode with stable and reproducible electrical characteristics. We compared the characteristics of six types of dry electrodes and one gelled electrode during short-term recordings sessions (≈30 s) in real-life conditions: Orbital, monolithic polymer plated with Ag/AgCl, and five rectangular shaped 10 × 6 × 2 mm electrodes (Orbital, Ag electrode, Ag/AgCl electrode, gold electrode and stainless-steel AISI304). The results of a well-controlled analysis which considered motion artifacts, line noise and junction potentials suggest that among the dry electrodes Ag/AgCl performs the best. The Ag/AgCl electrode is in average three times better compared with the stainless-steel electrode often used in WSMs.

E-Nose: Multichannel Analog Signal Conditioning Circuit With Pattern Recognition for Explosive Sensing

This paper presents E-Nose, a novel cost-effective, field-deployable portable system that constitutes a 4-channel signal conditioning circuit and multi-coated piezo-resistive micro-cantilever sensors for explosive sensing. E-Nose also features an embedded PCA and K-means based pattern recognition (PR) algorithm for the classification of explosives from non-explosives. The 4-channel configuration is a stack of two 2-channel circuits that are capable of measuring the change in the sensor resistance or capacitance in four optional modes of ΔR-Δ R,ΔR-ΔC,Δ C-ΔR, and ΔC-ΔC by using time multiplexing. The circuit uses a bidirectional AC current excitation method to drive the sensor bridge for significant reduction of DC offset errors, 1/f noise, line noise, and DC drifts. The proposed signal conditioning circuit uses the phase-sensitive synchronous rectification(PSSR) method for AC-to-DC conversion by using balanced demodulation. The circuit can measure a wide range of resistors that range from 100 Ω to 4 MΩ, with a sensitivity of 0.4mV/ppm and the worst relative error of 2.6%. The capacitive measurement range is from 100pF to 100 μF with the worst relative error of 3.3%. The entire data processing and the PR algorithms run on Raspberry Pi (R-Pi), which is integrated into the E-Nose system. The system performance is tested with MEMS cantilevers for the detection of explosive compounds, such as TNT and its derivatives, RDX and PETN in a controlled environment at a concentration that was as low as 16ppb TNT, 56ppb RDX and 134ppb of PETN. Measurements show that the E-Nose can detect explosives with 77% as true positive results without considering the environmental and mixed vapor effects.

Performance analysis of cooperative power‐line communication system with signal space diversity

AbstractPower‐line communication (PLC) is a cost‐effective and promising communication technology for many smart grid applications. However, the presence of additive and multiplicative power line noises deteriorate the performance of a PLC system. In this paper, we propose signal space diversity (SSD) technique for a cooperative PLC system. The additive PLC channel noise is assumed to be Bernoulli‐Gaussian–distributed. The multiplicative PLC channel noise gain is characterized by log‐normal distribution. The closed‐form expressions of the average symbol error rate (SER) and outage probability of cooperative PLC system with SSD have been derived in this paper. It has been shown that the proposed cooperative PLC system results in achieving better spectral efficiency, as well as the reliability of the received signal.

Effective Hybrid Method for the Detection and Rejection of Electrooculogram (EOG) and Power Line Noise Artefacts From Electroencephalogram (EEG) Mixtures

Electrooculogram (EOG) and power line noise artefact detection and rejection have commonly utilized Stone's blind source separation (Stone's BSS) algorithm. The paper suggests a hybrid method between particle swarm optimization (PSO) and Stone's BSS for the detection and rejection of electrooculogram (EOG) and power line noise in the single-channel without the use of a notch filter. The proposed method contains three major steps: centralizing and whitening of the input EEG signal, incorporating the processing EEG signal into the iterative algorithm of the particle swarm optimization (PSO) to randomly generate the optimal value of (h <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">short</sub> , h <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">long</sub> ) and weight vector W parameters, and applying Stone's BSS using the generalized eigenvalue decomposition (GEVD) method to eliminate electrooculogram (EOG) and power line noise artefacts to obtain a clean EEG signal. The authors assess the robustness of the suggested method evaluated using real and simulated electroencephalogram (EEG) data sets. The simulated electroencephalogram (EEG) data and electrooculogram (EOG) and line noise (LN) artefacts are produced and mixed randomly in the MATLAB program; two types of real EEG data are taken in 9 and 19 channels. Evaluation results show the proposed algorithms as effective techniques for extracting both the power line noise and electrooculogram (EOG) artefacts from brain mixtures compared to specific BSS algorithms (e.g., Stone's BSS, evolutionary fast independent component analysis (EFICA), fast independent component analysis (FastICA), and joint approximate diagonalization of Eigen matrices (JADE)) while preserving the clinical features of the reconstructed EEG signal.

Independent Components Analysis “Artifact Correction” Distorts EEG Phase in Artifact Free Segments

EEG artifact is defined as any electrical potential that is not produced by the brain, e.g., eye movement or head movement or muscle, 50 Hz-60 Hz line noise, etc. The most commonly used method of artifact elimination from an EEG recording is to delete the parts of the EEG recording that contain artifact and thereby leave the artifact free parts of the recording unchanged. Recently, Independent Components Analysis (ICA) has been used to decompose the original EEG into a set of components and then subjectively identify components that statistically load on one or more Independent Components (ICs) and using a smaller set of ICs then replace the original EEG recording with a different time series referred to as the ICA replacement or ICA-R. The purpose of this study is to mathematically and empirically test the distortion of the artifact free parts of the EEG when using ICA-R to replace the entire EEG digital record. The results of Joint-Time-Frequency-Analysis (JTFA) and the FFT spectral analyses demonstrated that ICA-Replacement of the original EEG produced phase distortions at each and every time point of the recording between all channel pairs. In contrast, the standard method of deleting the segments of an EEG recording that contain artifact did not distort the artifact free segments of the EEG recording. Conclusions are that ICA Replacement (ICA-R) is a severe distortion of the phase differences and time differences of the electrophysiology of the human scalp recorded Electroencephalogram (EEG) and invalidates all subsequent analyses that rely upon the imaginary part of the crossspectrum including scalp coherence, phase and network analyses that are dependent on the physics of electrical and magnetic fields.

Fast ROI Detection for Speed up in a CNN based Object Detection

Fast operation of a CNN based object detection is important in many application areas. It is an efficient approach to reduce the size of an input image. However, it is difficult to find an area that includes a target object with minimal computation. This paper proposes a ROI detection method that is fast and robust to noise. The proposed method is not affected by a flicker line noise that is a kind of aliasing between camera and LED light. Fast operation is achieved by using down-sampling efficiently. The accuracy of the proposed ROI detection method is 92.5% and the operation time for a frame with a resolution of 640 x 360 is 0.388msec.

A novel approach adaptive filtering method for electromyogram signal using Gray Wolf optimization algorithm

The proposed paper, presents the construction of adaptive noise cancellation filter based on gray wolf optimization (GWO) optimization technique.The relative investigation of different strategies uncovers that the presentation of GWO calculation is better in boisterous condition. The objective of proposed paper is structure ANC channel utilizing GWO method that improves association involving output with pure EMG signal.The results of proposed strategy are contrasted through gray wolf optimizer (GWO) and other evolutionary algorithms.The presentation of these calculations is assessed regarding signal-to-noise ratio (SSNR), mean square error (SMSE), maximum error (SME) mean, convergence rate (CR) plus correlation feature (Sr). The noise attenuation capability is tested on EMG signal contaminated with power line and ECG noise at different SNR levels. The ANC filter based on GWO technique provides 28 dB improvement in output SNR, 81% reduction in MSE, and 84% lower ME as compared to reported ANC filter based on RLS algorithm. Further, ANC filter based on GWO technique provides 7 dB improvement in output SNR, 59.5% reduction in MSE, and 69.2% lower ME as compared to recently reported ANC filter based on ABC-MR algorithm.

Review on bio-inspired algorithms approach to solve assembly line balancing problem

Bio-inspired algorithms that have been introduced by mimicking the biological phenomenon of nature have widely implemented to cater various real-world problems. As example, memetic algorithm, EGSJAABC3 is applied for economic environmental dispatch (EED) optimization, Hybrid Pareto Grey Wolf Optimization to minimize emission of noise and carbon in U-shaped robotic assembly line and Polar Bear Optimization to optimize heat production. The results obtained from their research have clearly portrayed the robustness of bio-inspired algorithms to cater complex problems. This paper highlights the efficiencies of bio-inspired algorithms implemented to cater problem relate to assembly line balancing. This kind of problem is very crucial to counter since it involves minimizing the time of the machines and operators or cost that required optimal task distribution. The outcome of this paper shows the effectiveness of bio-inspired algorithms in solving assembly line balancing problem compared to traditional method.

Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies

Significant achievements have been made in the fMRI field by pooling statistical results from multiple studies (meta-analysis). More recently, fMRI standardization efforts have focused on enabling the joint analysis of raw fMRI data across studies (mega-analysis), with the hope of achieving more detailed insights. However, it has not been clear if such analyses in the EEG field are possible or equally fruitful. Here we present the results of a large-scale EEG mega-analysis using 18 studies from six sites representing several different experimental paradigms. We demonstrate that when meta-data are consistent across studies, both channel-level and source-level EEG mega-analysis are possible and can provide insights unavailable in single studies. The analysis uses a fully-automated processing pipeline to reduce line noise, interpolate noisy channels, perform robust referencing, remove eye-activity, and further identify outlier signals. We define several robust measures based on channel amplitude and dispersion to assess the comparability of data across studies and observe the effect of various processing steps on these measures. Using ICA-based dipolar sources, we also observe consistent differences in overall frequency baseline amplitudes across brain areas. For example, we observe higher alpha in posterior vs anterior regions and higher beta in temporal regions. We also detect consistent differences in the slope of the aperiodic portion of the EEG spectrum across brain areas. In a companion paper, we apply mega-analysis to assess commonalities in event-related EEG features across studies. The continuous raw and preprocessed data used in this analysis are available through the DataCatalog at https://cancta.net.

Visual Evoked Potentials Used to Evaluate a Commercially Available Superabsorbent Polymer as a Cheap and Efficient Material for Preparation-Free Electrodes for Recording Electrical Potentials of the Human Visual Cortex.

The aim of this study was to investigate the use of inexpensive and easy-to-use hydrogel “marble” electrodes for the recording of electrical potentials of the human visual cortex using visual evoked potentials (VEPs) as example. Top hat-shaped holders for the marble electrodes were developed with an electrode cap to acquire the signals. In 12 healthy volunteers, we compared the VEPs obtained with conventional gold-cup electrodes to those obtained with marble electrodes. Checkerboards of two check sizes—0.8° and 0.25°—were presented. Despite the higher impedance of the marble electrodes, the line noise could be completely removed by averaging 64 single traces, and VEPs could be recorded. Linear mixed-effect models using electrode type, stimulus, and recording duration revealed a statistically significant effect of the electrode type on only VEP N75 peak latency (mean ± SEM: 1.0 ± 1.2 ms) and amplitude (mean ± SEM: 0.8 ± 0.9 µV) The mean amplitudes of the delta, theta, alpha, beta, and gamma frequency bands of marble electrodes were statistically significantly different and, on average, 25% higher than those of gold-cup electrodes. However, the mean amplitudes showed a statistically significant strong correlation (Pearson’s r = 0.8). We therefore demonstrate the potential of the inexpensive and efficient hydrogel electrode to replace conventional gold-cup electrodes for the recording of VEPs and possibly other recordings from the human cortex.

Development of A Low-Cost Arduino-Based 12-Lead Ecg Acquisition System And Accompanied Labview Application

In this paper, the design of a real-time digital multi--channel ECG signal acquisition system is presented. With the purpose of fabrication towards a simple, compact and low-cost tool for bioelectrical signal processing laboratories, the system is developed to acquire the 12 leads EGC signals and converted to numerical data based on an Arduino module named as Leonardo equipped 12 channels ADC. To observe the EGC waves, the ECG signals are amplified through designed amplifiers with the gain of 60 dB. To reduce the effects from the DC component as well as the baseline wandering and the high frequency noise, the active analog bandpass filter ranged in 0,05 Hz to 100 Hz was designed. The power line noise of 50 Hz also decreased with an active analog bandstop filter with attenuation -38 dB. Under the PC application was built using Labview programing, the low-cost digital ECG signal acquisition system was demonstrated with the requirement of observation in real-time. To clarify the small wave in the digital EGG signal, the limitation of the analog signal processing is improved through the digital filters parameterized in the software to increase the SNR from 1.4 dB to 27.6 dB. Practically, the system is evaluated through a series of experiments on a volunteer person resulting the ECG data is recorded and stored in a TDMS file. Since the system is designed as opened-system, a series of developments towards various applications in biomedical diagnosis based on digital signal analysis techniques is promised to be feasible in the near future.

A study of asynchronous noise in indoor power line with applications to power line communication

Power line channels do not have standard models for noise simulation. As such, study and analysis of different types of noise is imperative to developing channel simulator where communication systems can be tested and optimized for maximum efficiency. This is an indispensable part in developing power line communication systems that uses the available power line as a communication channel. This paper analyzes the asynchronous impulsive noise in indoor power line channels. The experimental setup for noise acquisition is described in detail. It is found that the impulse amplitude and the impulse durations follow the t-location scale and the log-logistic distribution better than other distribution tested. Such a distribution is easier to handle in terms of random number generation using the inverse cumulative distribution method. This can be used in the random noise simulation of impulsive noise in the generation of overall power line noise for system testing and optimization.