Noise-free Signal Research Articles

Methodology of Filtering the Noise and Utilization of STEMI in the Myocardial Infarction

In order to detect the Myocardial Infarction from ECG records of the patients, the physicians study the electrical motion of the heart. A Myocardial Infarction is an illness condition related to the heart and it is recognized when the pathway to the heart is blocked. These blocks interrupt the regular functioning of the heart; which is spotted through the deviations in the readings of ECG signals. For the sake of detecting Myocardial Infarction, it is essential to detect ST-Elevation followed by the removal of noise in ECG signals with the help of the filtering process. ECG signals are getting affected by various noises with high and low frequencies that will originate the incorrect interpretation. The methodologies for ECG signal filtering using filtering algorithms and for STEMI feature selection from the resultant noise free ECG signals are presented in this paper by employing the MATLAB tool.

Classification of Sleep Disturbance and Deep Sleep using FFT, PCA, and Neural Network

This paper proposes a method to classify sleep disturbance and deep sleep using electroencephalogram (EEG) signals at sleep stage 2, fast Fourier transforms (FFT), and principal component analysis (PCA). In order to extract the initial features, the FFT was carried out to remove noise from EEG signals at sleep stage 2 in the first step. In the second step, the noise-free EEG signal extracted in the first step was reduced to five dimensions using the PCA. In the final step, the classification performance was measured using the five dimensions as input to a neural network with weighted fuzzy membership functions (NEWFM). In classification performance, accuracy, specificity, and sensitivity were all 100%.

Noise-free simulation of an FT-IR imaging hyperspectral dataset of pancreatic biopsy core bound by experiment

A noise-free hyperspectral FT-IR imaging dataset of a pancreatic tissue core was simulated based on experimental data that allows to test the performance of various data analysis and processing algorithms. A set of experimental noise levels was also added and used for denoising approaches comparison, which due to the noise-free reference signal enables to truly observe signal distortion caused by different approaches.

A modified multiple OLS (m2OLS) algorithm for signal recovery in compressive sensing

Orthogonal least square (OLS) is an important sparse signal recovery algorithm in compressive sensing, which enjoys superior probability of success over other well known recovery algorithms under conditions of correlated measurement matrices. Multiple OLS (mOLS) is a recently proposed improved version of OLS which selects multiple candidates per iteration by generalizing the greedy selection principle used in OLS and enjoys faster convergence than OLS. In this paper, we present a refined version of the mOLS algorithm where at each step of iteration, we first preselect a submatrix of the measurement matrix suitably and then apply the mOLS computations to the chosen submatrix. Since mOLS now works only on a submatrix and not on the overall matrix, computations reduce drastically. Convergence of the algorithm, however, requires to ensure passage of true candidates through the two stages of preselection and mOLS based identification successively. This paper presents convergence conditions for both noisy and noise free signal models. The proposed algorithm enjoys faster convergence properties similar to mOLS, at a much reduced computational complexity.

Improved supervised learning methods for EoR parameters reconstruction

ABSTRACT Within the next few years, the Square Kilometre Array (SKA) or one of its pathfinders will hopefully detect the 21-cm signal fluctuations from the Epoch of Reionization (EoR). Then, the goal will be to accurately constrain the underlying astrophysical parameters. Currently, this is mainly done with Bayesian inference. Recently, neural networks have been trained to perform inverse modelling and, ideally, predict the maximum-likelihood values of the model parameters. We build on these by improving the accuracy of the predictions using several supervised learning methods: neural networks, kernel regressions, or ridge regressions. Based on a large training set of 21-cm power spectra, we compare the performances of these methods. When using a noise-free signal generated by the model itself as input, we improve on previous neural network accuracy by one order of magnitude and, using a local ridge kernel regression, we gain another factor of a few. We then reach an accuracy level on the reconstruction of the maximum-likelihood parameter values of a few per cents compared the 1σ confidence level due to SKA thermal noise (as estimated with Bayesian inference). For an input signal affected by an SKA-like thermal noise but constrained to yield the same maximum-likelihood parameter values as the noise-free signal, our neural network exhibits an error within half of the 1σ confidence level due to the SKA thermal noise. This accuracy improves to 10$\, {\rm per\, cent}$ of the 1σ level when using the local ridge kernel. We are thus reaching a performance level where supervised learning methods are a viable alternative to determine the maximum-likelihood parameters values.

An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition

To extract partial discharge (PD) signals from white noise efficiently, this paper proposes a denoising method for PD signals, named adaptive short-time singular value decomposition (ASTSVD). First, a sliding window was moved along the time axis of a PD signal to cut a whole signal into segments with overlaps. The singular value decomposition (SVD) method was then applied to each segment to obtain its singular value sequence. The minimum description length (MDL) criterion was used to determine the number of effective singular values automatically. Then, the selected singular values of each signal segment were used to reconstruct the noise-free signal segment, from which the denoised PD signal was obtained. To evaluate ASTSVD, we applied ASTSVD and two other methods on simulated, laboratory-measured, and field-detected noisy PD signals, respectively. Compared to the other two methods, the denoised PD signals of ASTSVD contain less residual noise and exhibit smaller waveform distortion.

A New Quality-Aware Quality-Control Data Compression Framework for Power Reduction in IoT and Smartphone PPG Monitoring Devices

In this article, we propose a new real-time quality-aware quality-control data compression framework for maximizing the battery life of Internet of Things (IoT) and Smartphone based health monitoring devices. The proposed framework is implemented on Arduino Due with a 32-bit Atmel SAM3X8E ARM Cortex-M3 processor and validated using four standard databases and real-time signals obtained by using our sensing hardware. The proposed framework achieves compression ratios between seven and 28 with energy saving between 83% and 92%. Results demonstrate that our framework provides a promising energy saving solution by discarding the noisy photoplethysmogram (PPG) signals and pulse-free signals before performing data compression and compressing the noise-free PPG signals efficiently with very small feature parameter error.

Use of zero-frequency resonator for automatically detecting systolic peaks of photoplethysmogram signal.

This work investigates the application of zero-frequency resonator (ZFR) for detecting systolic peaks of photoplethysmogram (PPG) signals. Based on the authors’ studies, they propose an automated noise-robust method, which consists of the central difference operation, the ZFR, the mean subtraction and averaging, the peak determination, and the peak rejection/acceptance rule. The method is evaluated using different kinds of PPG signals taken from the standard MIT-BIH polysomnographic database and Complex Systems Laboratory database and the recorded PPG signals at their Biomedical System Lab. The method achieves an average sensitivity (Se) of 99.95%, positive predictivity (Pp) of 99.89%, and overall accuracy (OA) of 99.84% on a total number of 116,673 true peaks. Evaluation results further demonstrate the robustness of the ZFR-based method for noisy PPG signals with a signal-to-noise ratio (SNR) ranging from 30 to 5 dB. The method achieves an average Se = 99.76%, Pp = 99.84%, and OA = 99.60% for noisy PPG signals with a SNR of 5 dB. Various results show that the method yields better detection rates for both noise-free and noisy PPG signals. The method is simple and reliable as compared with the complexity of signal processing techniques and detection performance of the existing detection methods.

Mode shape identification based on Gabor transform and singular value decomposition under uncorrelated colored noise excitation

Abstract A new mode shape identification method incorporating Gabor transform and singular value decomposition (SVD) is proposed based on the power spectral density transmissibility (PSDT) function at the system poles. The Gabor transform and SVD are used for the extraction of noise free Gabor component signals for an improved accuracy. The Gabor spectral transmissibility (GST) function is then derived to identify the mode shapes. The vibration modes of the structure are assumed well separated and the excitation is spatially incoherent. Both the GST function and the PSDT function are approximated by the ratio of amplitudes of vibration modes at the system poles. The proposed method is numerically verified using a six-degrees-of-freedom system under uncorrelated colored excitations. Results show that the proposed method can give more accurate results for higher modes and is robust to high noise effect compared to the PSDT function. Further comparison with experimental results from a seventeen-storey space frame building also demonstrates that the proposed method can better identify the mode shapes of large-scale structure.

Comparative Analysis of the Crosstalk Effects in Multilayer Graphene Nanoribbon and MWCNT Interconnects in Sub-10 nm Technologies

In this paper, the crosstalk effects in multilayer graphene nanoribbon (MLGNR) interconnects are evaluated and are compared with those in multiwalled carbon nanotube (MWCNT) interconnects in ternary integrated circuits at the 10 and 7 nm technologies. For a more precise evaluation, the five-line bus architecture is considered. Furthermore, the effects of the top and bottom aggressors are considered for a more accurate analysis in sub-10 nm dimensions. The active and passive shielding techniques are also applied to reduce the crosstalk effects. Our results indicate that the MLGNR interconnects outperform the MWCNTs, especially in the more realistic seven-line case, which also considers the effects of the bottom and top layer aggressors. Also, the advantages of the MLGNR interconnects become more significant when the node shrinks from 10 to 7 nm. The crosstalk delays of normal, passively shielded, and actively shielded five-line MLGNRs at 7 nm are on average 49%, 56%, and 54% lower compared to their MWCNT counterparts, respectively. These improvements are 36%, 36%, and 41% regarding the power. Moreover, the normal and actively shielded MLGNRs have 25% and 72% smaller crosstalk noise area than their MWCNT counterparts, respectively. The smaller crosstalk noise for MLGNR leads to noise-free signals at the end of both actively and passively shielded victim lines in ternary logic at the 10 and 7 nm technologies.

Low Cost Portable Solution for Real-Time Complete Detection and Analysis of Heart Sound Components

Detection of heart sound and analysis is required to determine heart rate, heart sound components (S1, S2 and murmurs), and is an important step to diagnose cardiac disorder. The most important part of detection method is to achieve a noise free signal staring from detection to separation of heart components to improve the accuracy of signal analysis. Though several innovative individual methods are found in the literature related to detection, noise filter or analyse the characteristic patterns of the heart signals, but some of them have computationally complex algorithms, some are equipped with expensive instruments and to the best of our knowledge, it is rare to found a low cost complete solution starting from detection to analysis with simple instruction based methodology to be handled by the literate ordinary people. There is also difficulty to implement them on real-time portable systems and also require medical expertise to handle the equipment and perform the operations. In this work, we propose an innovative simple solution for complete heart sound detection and analysis system with low cost portable heart sound detection devices, best effort wavelet de-noising and computationally less complex start and end point detection algorithms to separate and analyse heart sound components. MATLAB and DSP processor (TMS320C6713) has been used for the real time implementation of the algorithms. This process will be very helpful in a place with low medical establishment to take mass scale measurement and analysis to be finally sent to doctor for advise and treatment. We also provide frequency domain power spectrum density of the detected heart sound components for better diagnosis. At the end, we provide a comparative table with the available literature.

Investigation of velocity and temperature measurement sensitivities in cross-correlation filtered Rayleigh scattering (CCFRS)

The cross-correlation filtered Rayleigh scattering method is presented for planar measurements of time-averaged velocity, temperature, density, and pressure using laser scanning, Rayleigh scattering, and cross-correlation-based signal processing. Of particular note, the methods and analyses presented show that velocity and temperature measurements are obtainable at acceptable uncertainties for many aerodynamic applications with or without particles present. In the current work, velocity and temperature measurement uncertainties for this method are analysed by Monte Carlo simulations and evaluation of the Cramér–Rao lower bounds for the signals generated in this approach with aerodynamic applications in mind. A key aspect considered is the relative strength of Rayleigh (molecular) scattering and Mie (particle) scattering contributions to the simulated flow signal. Compared to particle-based velocity estimation, the effects due to convolution between the molecular filter and the broadband Rayleigh scattering spectrum are found to increase the estimator variance of the velocity. For given flow conditions, the velocity error associated with the signal processing routine with noise-free signals is found to be less than , and bias error associated with discrete sampling in the laser frequency domain is within . For these same conditions, temperature measurement simulations indicate bias error bounds of 0. 014% with random uncertainty bounds of less than ±1.4% for a signal with no Mie scattering contribution, and bias error bounds of less than ±0.14% with random uncertainty bounds of less than 3.2% for flows contaminated with Mie scattering sources at a signal-to-noise ratio of 25 dB or greater. This technique is also being further developed to measure density and pressure using the principles of laser Rayleigh scattering in flows containing particles of unknown sizes and concentrations.

Transformer Health Management Based on Self-Powered RFID Sensor and Multiple Kernel RVM

Health management for transformer in service is of great significance to the reliable operation of power system. This paper presents a cost-efficient transformer condition prognosis methodology. First, a power management circuit is designed to guarantee the stable performance of self-powered RFID sensor. Moreover, the sensor data storage mechanism is modified by embedding the sensor data into the ID information of RFID sensor to realize less response time and lower power consumption compared with the traditional approach. Second, a denoising method based on band restricted empirical mode decomposition and a fuzzy threshold is introduced to extract noise-free vibration signal from the measured raw signal. Third, the multiple kernel relevance vector machine (MKRVM) is imported to predict the future condition of transformer. The kernels of MKRVM keep diversity for higher prediction accuracy by using the heterogeneous kernel learning approach. Moreover, the double chain quantum genetic algorithm is employed as optimization tool to find the optimal sparse weights of basic kernel functions in MKRVM. The experimental results indicate that the modified RFID sensor can stably operate even when the load of the transformer is abruptly decreased to zero. The response time and power consumption of RFID sensor are markedly decreased by using the improved sensor data storage mechanism. Furthermore, the prediction experiment results demonstrate that the proposed approach is effective in term of health monitoring for transformer.

Online Estimation of Missing Data Using Sparse Optimization Techniques With Applications to Classical Control

This paper proposes and describes a method based on sparse optimization techniques for online estimation of missing data. The basic idea of the proposed method is to represent the noise-free signal of interest sparsely in a composite dictionary. The proposed algorithm offers three prime advantages: 1) handles nonlinear processes that are locally linearizable; 2) requires lower computational power than the existing algorithms; and 3) adapts to the changing process conditions by optimally selecting the most relevant feature for that window of time. Results show that the proposed algorithm outperforms the existing neural network-based method. An application of the proposed algorithm to classical (PID) control using measurements with missing data is presented. In this context, an added benefit of the proposed method is that it serves as a feature selection operator for the process, which can be useful in other applications. Numerical (simulation) studies are carried out to study the influence of user-defined parameter in the algorithm and two stochastic factors, namely, the random sampling scheme and noise , on the stability and performance of the closed-loop system. Results show that the closed-loop system with the proposed algorithm is stable and yields a satisfactory performance.

Fitting Intravoxel Incoherent Motion Model to Diffusion MR Signals of the Human Breast Tissue using Particle Swarm Optimization

Intravoxel incoherent motion (IVIM) modeling offers the parameters f, D and D* as biomarkers for different lesion types and cancer stages from diffusion MR signals. Challenges with the available optimization algorithms in fitting the model to the signals motive new studies for improved parameter estimations. In this study, one thousand value sets of f, D, D* for human breast are assembled and used to generate five thousand diffusion MR signals considering noise-free and noisy situations exhibiting signal-to-noise ratios (SNR) of 20, 40, 60 and 80. The estimates of f, D, D* are obtained using Levenberg-Marquardt (LM), trust-region (TR) and particle swarm (PS) algorithms. On average, the algorithms provide the highest fitting performance for the noise-free signals (R2adj=1.000) and great fitting performances on the noisy signals with SNR>20 (R2adj>0.988). TR algorithm performs slightly better for SNR=20 (R2adj=0.947). TR and PS algorithms achieve the highest parameter estimation performance for all the parameters while LM algorithm reveals the highest performance for f and D only on the noise-free signals (r=1.00). For the noisy signals, performances increase while SNR increases. All algorithms accomplish poor performances for D* (r=0.01-0.20) while TR and PS algorithms perform same for f (r=0.48-0.97) and D (r=0.85-0.99) but remarkably better than LM algorithm for f (r=0.08-0.97) and D (r=0.53-0.99). Overall, TR and PS algorithms demonstrate better but indistinguishable performances. Without requiring any user-given initial value, PS algorithm may facilitate improved estimation of IVIM parameters of the human breast tissue. Further studies are needed to determine its benefit in clinical practice.

SAR Image Denoising Algorithm Based on Bayes Wavelet Shrinkage and Fast Guided Filter

To remove the speckle noise of synthetic aperture radar (SAR) images, a novel denoising algorithm based on Bayes wavelet shrinkage and a fast guided filter is proposed. According to the statistical properties of SAR images, the noise-free signal and speckle noise in the wavelet domain are modeled as Laplace and Fisher-Tippett distributions respectively. Then a new wavelet shrinkage algorithm is obtained by adopting the Bayes maximum a posteriori estimation. Speckle noise in the high-frequency domain of SAR images is shrunk by this new wavelet shrinkage algorithm. As the wavelet coefficients of the low-frequency domain also contain some speckle noise, speckle noise in the low-frequency domain can be further filtered by the fast guided filter. The result of the denoising experiments of simulated SAR images and real SAR images demonstrate that the proposed algorithm has the ability to better denoise and preserve edge information.

Dependency of Output Signal to Noise Ratio on Output Power and Signal to Noise Ratio at Reference Path in the Adaptive Noise Cancellation Technique

In Adaptive noise cancelling method uses two inputs one is primary input and other is the reference input. A “primary” input the combination of main signal and noise whereas a “reference” input contains correlated noise within the primary signal. The difference of reference input from primary gives the noise free signal. It is verified that signal to noise ratio at reference path impact the signal to noise ratio at output, and by decreasing the signal to noise ratio at reference path increase in signal to noise ratio achieved. Also, by decreasing changing the output power a SIR changes immensly.

Response probability density of a system with cross-correlated parametric and additive input noises

Data processing and subsequent mining is a widely followed task. Employment of suitable evaluation and interpretation procedures can significantly improve the effective resolution of measuring facility using an identical hardware equipment. Recording of time variable processes is accompanied by various internal disturbing effects as a rule. They influence parameters of the measuring facility, transducer-device transmitting, etc. These parasitic processes are usually of the random character and, consequently, they exercise as parametric noises. Moreover, the input signal mostly consists of a useful signal, which can be taken for deterministic, and of a random additive part. Due to interaction of additive noises with the device itself, the cross-correlation of both additive and multiplicative noises cannot be neglected as a rule. Various combinations of noises are the origin of random and also systematic measuring errors which can have under certain circumstances a cumulative character. Their influence deteriorates the output signal quality and can lead finally to the stochastic stability loss. These effects can be theoretically described using differential systems with stochastic coefficients and a stochastic right hand side considering all input and output processes to be of the Markov type. A direct investigation of the relevant Fokker-Planck equation is employed as the main tool. Two first stochastic moments (mathematical mean value and variance) as evolutionary processes are investigated for a general deterministic useful signal and subsequently for two special cases of this one. Both types of input random noises are considered. Conditions of stochastic stability with respect to intensities of input random processes are formulated. The probability density function is deduced as well, in order to illustrate the probabilistic character of the system response as a whole. The stochastic asymmetry of the output signal is identified. Limitation procedures show a smooth transition from a general stochastic problem to deterministic noise free input signal and its processing.

Study of structural complexity of optimal order digital filters for de-noising ECG signal

Selection and implementation of optimal order digital filter for denoising ECG signal on FPGA, based on Signal to Noise Ratio (SNR), error and accuracy using wavelet toolbox is a tedious task. To overcome this problem, an attempt has been made in MATLAB to obtain a noise-free ECG signal based on SNR and mean square error to select optimal order digital filter. An ECG sample from MIT-BIH arrhythmia database is artificially corrupted by adding noise and is filtered through different low-pass IIR and FIR digital filters. The hardware complexity of optimal order digital filters structure is checked in terms of multipliers, adders and delays in MATLAB and their performance is compared based on the number of basic elements and visual inspections. It is found that the elliptic IIR filter and Kaiser window FIR filter require low-filter order for a given specification and hence, requires less number of basic elements.

Ultra-low Noise EEG at LSBB: Effective Connectivity Analysis

In this study, we further investigate electroencephalographic (EEG) data recorded during October 2014 in the ultra-shielded capsule at LSBB, with a focus on the study of task-specific Granger-causal effective connectivity pat-terns. In previous studies, we showed that noise-free EEG signals acquired in LSBB are suitable for analysis of activity patterns in high frequency bands, i.e. 30 Hz and above. We previously demonstrated that increases in task/rest gamma band (30-70 Hz) energy ratios during ankle and wrist movements are more prominent in the LSBB capsule than in an above-ground hospital environ-ment. The present study extends previous analyses by examining gamma-band connectivity, i.e. the functional patterns of interaction between 64 channels of EEG within the gamma band during motor tasks. We use parameters from a MultiVariate Auto-Regressive (MVAR) model to estimate effective connectivity in 10-second batches of EEG and report the average patterns across all batches in which subjects repetitively move their ankle/wrist. We report the gamma-band connectivity results in a reduced form as strength of hemispheric and inter-regional connections. The analysis reveals that for some subjects, significant channel-wise connections in the LSBB capsule outnumber those in the hospital, suggesting that patterns of gamma-band connectivity are better reflected in low-noise environments. This study again demonstrates the poten-tial of the ultra-shielded capsule and motivates further protocol enhancements and analysis methods for conducting future high-frequency EEG studies within LSBB.