Noise Rejection Method Research Articles

I see artifacts: ICA-based EEG artifact removal does not improve deep network decoding across three BCI tasks

Abstract \textit{Objective.}
In this paper, we conduct a detailed investigation on the effect of IC-based noise rejection methods in neural network classifier-based decoding of electroencephalography (EEG) data in different task datasets.
\textit{Approach.}
We apply a pipeline matrix of two popular different Independent Component (IC) decomposition methods (Infomax, AMICA) with three different component rejection strategies (none, ICLabel, and MARA) on three different EEG datasets (Motor imagery, long-term memory formation, and visual memory). We cross-validate processed data from each pipeline with three architectures commonly used for EEG classification (two convolutional neural networks (CNN) and one long short term memory (LSTM) based model. We compare decoding performances on within-participant and within-dataset levels. 
\textit{Main Results.}
Our results show that the benefit from using IC-based noise rejection for decoding analyses is at best minor, as component-rejected data did not show consistently better performance than data without rejections---especially given the significant computational resources required for ICA computations.
\textit{Significance.}
With ever growing emphasis on transparency and reproducibility, as well as the obvious benefits arising from streamlined processing of large-scale datasets, there has been an increased interest in automated methods for pre-processing EEG data. One prominent part of such pre-processing pipelines consists of identifying and potentially removing artifacts arising from extraneous sources. This is typically done via Independent Component (IC) based correction for which numerous methods have been proposed, differing not only in the decomposition of the raw data into ICs, but also in how they reject the computed ICs. While the benefits of these methods are well established in univariate statistical analyses, it is unclear whether they help in multivariate scenarios, and specifically in neural network based decoding studies. As computational costs for pre-processing large-scale datasets are considerable, it is important to consider whether the tradeoff between model performance and available resources is worth the effort.

Performance of the RF Detectors of the Astroneu Array

Since 2014, the university campus of Hellenic Open University (HOU) has hosted the Astroneu array, which is dedicated to the detection of extensive air showers (EAS) induced by high-energy cosmic rays (CR). The Astroneu array incorporates 9 large particle scintillation detectors and 6 antennas sensitive to the radio frequency (RF) range 1–200 MHz. The detectors are adjusted in three autonomous stations operating in an environment with a strong electromagnetic background. As shown by previous studies, EAS radio detection in such environments is possible using innovative noise rejection methods, as well as advanced analysis techniques. In this work, we present the analysis of the collected radio data corresponding to an operational period of approximately four years. We present the performance of the Astroneu radio array in reconstructing the EAS axis direction using different RF detector geometrical layouts and a technique for the estimation of the shower core by comparing simulation and experimental data. Moreover, we measure the relative amplitudes of the two mechanisms that give rise to RF emission (the Askaryan effect and geomagnetic emission) and show that they are in good agreement with previous studies as well as with the simulation predictions.

Power Transformer Voltages Classification with Acoustic Signal in Various Noisy Environments.

Checking the stable supply voltage of a power distribution transformer in operation is an important issue to prevent mechanical failure. The acoustic signal of the transformer contains sufficient information to analyze the transformer conditions. However, since transformers are often exposed to a variety of noise environments, acoustic signal-based methods should be designed to be robust against these various noises to provide high accuracy. In this study, we propose a method to classify the over-, normal-, and under-voltage levels supplied to the transformer using the acoustic signal of the transformer operating in various noise environments. The acoustic signal of the transformer was converted into a Mel Spectrogram (MS), and used to classify the voltage levels. The classification model was designed based on the U-Net encoder layers to extract and express the important features from the acoustic signal. The proposed approach was used for its robustness against both the known and unknown noise by using the noise rejection method with U-Net and the ensemble model with three datasets. In the experimental environments, the testbeds were constructed using an oil-immersed power distribution transformer with a capacity of 150 kVA. Based on the experimental results, we confirm that the proposed method can improve the classification accuracy of the voltage levels from 72 to 88 and to 94% (baseline to noise rejection and to noise rejection + ensemble), respectively, in various noisy environments.

Study on noise and disturbance issues of generalized predictive speed control for permanent magnet synchronous machines

Generalized predictive control (GPC) is promising for permanent magnet synchronous machine drive systems, due to its outstanding dynamic performance and disturbance rejection capability. However, the speed measurement noise introduced by the encoder and the unmodelled disturbances introduced by the non-ideal machine characteristics deteriorate the performance of GPC. The noise and disturbance issues are studied in detail, and an enhanced GPC method is proposed to address them. For the noise issue, the mathematical model of the speed measurement noise is derived, and the noise spectra at different speeds are analysed. Then, a novel GPC with internal low-pass filter is proposed to reject the influence of the noise. For the disturbance issue, the influence of the unmodelled disturbances on the speed performance is assessed when applying different noise rejection methods, and a resonant controller-based speed ripple mitigation method is proposed and integrated to the GPC. Experimental results show that the proposed methods can effectively deal with the noise and disturbance issues, as well as providing better dynamic performance than the conventional PI based speed control.

Impulse Noise Rejection Method in LFM Radars

A method is proposed for rejecting impulse noise in LFM radars, based on sharing of information from the frequency and time domains of the signal representation. The method is implemented on a dual-core processor with an ARM (Advanced RISC Machine) architecture.

Noise Rejection for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks

Progress in wearable techniques makes the long-term daily electrocardiogram (ECG) monitoring possible. However, the long-term wearable ECGs can be significantly contaminated by various noises, which affect the detection and diagnosis of cardiovascular diseases (CVDs). The situation becomes more serious for wearable ECG screening, where the data are huge, and doctors have no way to visually check the signal quality episode-by-episode. Therefore, automatic and accurate noise rejection for the wearable big-data ECGs is craving. This paper addressed this issue and proposed a noise rejection method for wearable ECGs based on the combination of modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN). Wearable ECGs were recorded using the newly developed 12-lead Lenovo smart ECG vest with a sample rate of 500 Hz and a resolution of 16 bit. One thousand 10-s ECG segments were picked up and were manually labeled into three quality types: clinically useful segments with good signal quality (type A), clinically useful segments with poor signal quality (type B), and clinically useless segments (pure noises, type C). Each of the 1,000 10-s ECG segments were transformed into a 2-D time-frequency (T-F) image using the MFSWT, with a pixel size of $200\times 50$ . Then, the 2-D grayscale images from MFSWT were fed into a 13-layer CNN model for training the classification models. Results from the standard 5-folder cross-validation showed that the proposed combination method of MFSWT and CNN achieved a highest classification accuracy of 86.3%, which was higher than the comparable methods from continuous wavelet transform (CWT) and artificial neural networks (ANN). The combination of MFSWT and CNN also had a good calculation efficiency. This paper indicated that the combination of MFSWT and CNN is a potential method for automatic identification of noisy segments from wearable ECG recordings.

Algebraic Parameter Estimation Using Kernel Representation of Linear Systems

This work makes a contribution to algebraic parameter estimation as it proposes a simple alternative to the derivation of the algebraic estimation equations. The idea is based on a system representation in the form of an evaluation functional which does not exhibit any singularities in the neighbourhood of zero. Implied is the fact that algebraic estimation of parameters as well as system states can then truly be performed in arbitrary time and with uniform accuracy over the entire estimation interval. Additionally, the result offers a geometric representation of a linear system as a finite dimensional subspace of a Hilbert space, that readily suggests powerful noise rejection methods in which invariance plays a central role.

Extracting and Mathematical Identifying Form of Stationary Noise in X-ray Images

Image noise may prevent proper diagnostic X-ray imaging. This study is aimed at developing new noise rejection methods using a mathematical model that describes the form of X-ray image noise. Stationary noise is one type of noise found in X-ray images. Stationary noise is nonstochastic and appears independent of the radiographic factors. In this paper, we verify methods for identifying stationary noise using a polynomial regression model, and extracting such noise from X-ray images obtained from a CR system. The results of this study demonstrate that stationary noise can be extracted with high precision using a particular low-pass filter frequency. We found that a regression model for greater than second-degree polynomials can be applied for roughly identifying stationary noise. However, the fitting accuracy of the regression curve is not significantly improved in terms of the amount of multiplication required when increasing the degree of the polynomial regression model. Ke yw ords: X-ray Image, Nonstochastic Noise, Stationary Noise, Polynomial Regression Model and CR System

A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing. Application: Stock price prediction

In this paper, a new fuzzy functions (FFs) model is presented and its main parameters are optimized with simulated annealing (SA) approach. For this purpose, a new hybrid clustering algorithm for model structure identification is proposed. This model is based on hybridization of extended version of possibilistic c-mean (PCM) clustering with mahalonobise distance measure and a noise rejection method. In this research, Multivariate Adaptive Regression Splines (MARS) is applied for selecting variables and approximating fuzzy functions in each cluster. A metaheuristic Imperialist Competitive Algorithm (ICA) is used to initialize the clustering parameters. The proposed FFs model is validated using two well-known standard artificial datasets and two real datasets, Tehran stock exchange and ozone level. It is shown that using the proposed FFs model can lead to promising results.

A combined noise-rejection method for UHF PD detection on-site

An important condition monitoring technology of power apparatus is ultra-high-frequency (UHF) partial discharge (PD) detection. This has been widely used in gas insulated substations, power transformers, etc. It benefits from its high sensitivity and low overlap with noise frequency. However, lots of field practices indicate that some interference, such as the mobile phone communication signals, the PD signals propagated from adjacent equipments, or some pulse-type interferences, do mix with the to-be-detected PD signals and generate great difficulties in UHF diagnosis. Based on various field experiences, this paper presents a combined UHF noise rejection method, which integrates the waveform correlation analysis, the time domain windowing method based on external noise signals, and the clustering method based on the magnitude ratio from two consecutive sensors. Experimental results show that this method can distinguish real PD signals from environmental noises from various sources effectively, and can separate different PD signals within the same apparatus in test.

Impulsive noise rejection method for compressed measurement signal in compressed sensing

Abstract The Lorentzian norm of robust statistics is often applied in the reconstruction of the sparse signal from a compressed measurement signal in an impulsive noise environment. The optimization of the robust statistic function is iterative and usually requires complex parameter adjustments. In this article, the impulsive noise rejection for the compressed measurement signal with the design for image reconstruction is proposed. It is used as the preprocessing for any compressed sensing reconstruction given that the sparsified version of the signal is obtained by utilizing octave-tree discrete wavelet transform with db8 as the mother wavelet. The presence of impulsive noise is detected from the energy distribution of the reconstructed sparse signal. After the noise removal, the noise corrupted coefficients are estimated. The proposed method requires neither complex optimization nor complex parameter adjustments. The performance of the proposed method was evaluated on 60 images. The experimental results indicated that the proposed method effectively rejected the impulsive noise. Furthermore, at the same impulsive noise corruption level, the reconstruction with the proposed method as the preprocessing required much lower measurement rate than the model-based Lorentzian iterative hard thresholding.

Study of Parameter Decision for a Noise Rejection Method Using ICA for a Magnetocardiogram

We studied an environmental magnetic noise rejection method using independent component analysis (ICA) for a magnetocardiogram (MCG). In the ICA, when the SN ratio of the data is reduced, the accuracy of the noise rejection becomes low and it becomes difficult to reach a decision regarding the contraction dimension. In order to solve these problems, we examined the initial value of the transformation matrix W of the ICA. We proposed an initial value decision procedure for the transformation matrix W, and as a result the accuracy of the noise rejection was improved. Verification was carried out for MCGs with various SN ratios. It was shown that this method was effective for improving noise rejection.

Noise rejection method for magnetic noise from wire using ICA for magnetocardiogram

AbstractIn magnetocardiogram (MCG) measurements, magnetic noise from the wire used to suture the sternum after heart surgery becomes a problem. Independent component analysis is an effective method of noise rejection.In this study, MCG measurements were made on a normal subject with the wire attached and without the wire. We performed signal processing by independent component analysis in order to reduce the effect of magnetic noise from the wire. Comparison of the waveforms after this signal processing with waveforms without the wire clearly showed that magnetic noise caused by the wire was reduced.This result clearly shows that independent component analysis is effective for the removal of magnetic noise from the wire. ©2007 Wiley Periodicals, Inc. Electr Eng Jpn, 162(1): 7–14, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20589

Further noise rejection in linear associative memories

It is well known that linear associative memories are sensitive to input noise. In this paper, a new noise rejection method for linear associative memories is proposed. It is shown that with a little cost in recall bias the model is much more resistant to noise than previous models, especially as the number of stored vector pairs approaches the number of the key vector components. Using the singular value decomposition (SVD) of matrices, parameter estimations in white or colour noise environments are discussed.

Real‐time data acquisition and interpretation capabilities of the Dynasonde: 1. Data acquisition and real‐time display

Dynasonde software for data acquisition in three modes is described. The ‘I’ mode corresponds to an ionogram format but includes complete echo polarization and angle‐of‐arrival information. K mode provides time series and high time resolution at freely selected sounding frequencies. B mode combines features of the other two modes. All share a real‐time noise rejection method which is described statistically. The system includes a digitally driven CRT (cathode ray tube) display and a variety of display formats for real time or deferred examination of data; in particular, dynamic time‐lapse displays of ionospheric activity can be maintained by the Dynasonde.