Showing papers on "Noise (signal processing) published in 2016"
TL;DR: The axial double probe (ADP) instrument on the magnetospheric multiscale (MMS) spacecraft has been used to measure DC electric field with a precision of ∼ 1.1mV/m, a resolution of ∼ 25μV/μ, and a range of ∼±1 V/m in most of the plasma environments MMS will encounter.
Abstract: The Axial Double Probe (ADP) instrument measures the DC to ∼100 kHz electric field along the spin axis of the Magnetospheric Multiscale (MMS) spacecraft (Burch et al., Space Sci. Rev., 2014, this issue), completing the vector electric field when combined with the spin plane double probes (SDP) (Torbert et al., Space Sci. Rev., 2014, this issue, Lindqvist et al., Space Sci. Rev., 2014, this issue). Two cylindrical sensors are separated by over 30 m tip-to-tip, the longest baseline on an axial DC electric field ever attempted in space. The ADP on each of the spacecraft consists of two identical, 12.67 m graphite coilable booms with second, smaller 2.25 m booms mounted on their ends. A significant effort was carried out to assure that the potential field of the MMS spacecraft acts equally on the two sensors and that photo- and secondary electron currents do not vary over the spacecraft spin. The ADP on MMS is expected to measure DC electric field with a precision of ∼1 mV/m, a resolution of ∼25 μV/m, and a range of ∼±1 V/m in most of the plasma environments MMS will encounter. The Digital Signal Processing (DSP) units on the MMS spacecraft are designed to perform analog conditioning, analog-to-digital (A/D) conversion, and digital processing on the ADP, SDP, and search coil magnetometer (SCM) (Le Contel et al., Space Sci. Rev., 2014, this issue) signals. The DSP units include digital filters, spectral processing, a high-speed burst memory, a solitary structure detector, and data compression. The DSP uses precision analog processing with, in most cases, >100 dB in dynamic range, better that −80 dB common mode rejection in electric field (E) signal processing, and better that −80 dB cross talk between the E and SCM (B) signals. The A/D conversion is at 16 bits with ∼1/4 LSB accuracy and ∼1 LSB noise. The digital signal processing is powerful and highly flexible allowing for maximum scientific return under a limited telemetry volume. The ADP and DSP are described in this article.
531 citations
TL;DR: The transient noise backgrounds used to determine the significance of the event (designated GW150914) are described and the results of investigations into potential correlated or uncorrelated sources of transient noise in the detectors around the time of theevent are presented.
Abstract: On 14 September 2015, a gravitational wave signal from a coalescing black hole binary system was observed by the Advanced LIGO detectors. This paper describes the transient noise backgrounds used to determine the significance of the event (designated GW150914) and presents the results of investigations into potential correlated or uncorrelated sources of transient noise in the detectors around the time of the event. The detectors were operating nominally at the time of GW150914. We have ruled out environmental influences and non-Gaussian instrument noise at either LIGO detector as the cause of the observed gravitational wave signal.
308 citations
TL;DR: In this paper, an empirical wavelet transform (EWT) is used to extract inherent modulation information by decomposing signal into mono-components under an orthogonal basis, which is seen as a powerful tool for mechanical fault diagnosis.
290 citations
TL;DR: The characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal are reviewed and approaches for distinguishing signal from noise are reviewed.
223 citations
TL;DR: In this article, the authors show that DMD is biased to sensor noise, and quantify how this bias depends on the size and noise level of the data, and present three modifications to DMD that can be used to remove this bias.
Abstract: Dynamic mode decomposition (DMD) provides a practical means of extracting insightful dynamical information from fluids datasets. Like any data processing technique, DMD’s usefulness is limited by its ability to extract real and accurate dynamical features from noise-corrupted data. Here, we show analytically that DMD is biased to sensor noise, and quantify how this bias depends on the size and noise level of the data. We present three modifications to DMD that can be used to remove this bias: (1) a direct correction of the identified bias using known noise properties, (2) combining the results of performing DMD forwards and backwards in time, and (3) a total least-squares-inspired algorithm. We discuss the relative merits of each algorithm and demonstrate the performance of these modifications on a range of synthetic, numerical, and experimental datasets. We further compare our modified DMD algorithms with other variants proposed in the recent literature.
221 citations
TL;DR: The first theoretical accuracy guarantee for 1-b compressed sensing with unknown covariance matrix of the measurement vectors is given, and the single-index model of non-linearity is considered, allowing the non- linearity to be discontinuous, not one-to-one and even unknown.
Abstract: We study the problem of signal estimation from non-linear observations when the signal belongs to a low-dimensional set buried in a high-dimensional space. A rough heuristic often used in practice postulates that the non-linear observations may be treated as noisy linear observations, and thus, the signal may be estimated using the generalized Lasso. This is appealing because of the abundance of efficient, specialized solvers for this program. Just as noise may be diminished by projecting onto the lower dimensional space, the error from modeling non-linear observations with linear observations will be greatly reduced when using the signal structure in the reconstruction. We allow general signal structure, only assuming that the signal belongs to some set $K \subset \mathbb {R} ^{n}$ . We consider the single-index model of non-linearity. Our theory allows the non-linearity to be discontinuous, not one-to-one and even unknown. We assume a random Gaussian model for the measurement matrix, but allow the rows to have an unknown covariance matrix. As special cases of our results, we recover near-optimal theory for noisy linear observations, and also give the first theoretical accuracy guarantee for 1-b compressed sensing with unknown covariance matrix of the measurement vectors.
216 citations
TL;DR: The proposed method is based on the synchrosqueezed continuous wavelet transform (SS-CWT) and custom thresholding of single-channel data and incorporates a detection procedure that uses the thresholded wavelet coefficients and detects an arrival as a local maxima in a characteristic function.
Abstract: Typical microseismic data recorded by surface arrays are characterized by low signal-to-noise ratios (S/Ns) and highly nonstationary noise that make it difficult to detect small events. Currently, array or crosscorrelation-based approaches are used to enhance the S/N prior to processing. We have developed an alternative approach for S/N improvement and simultaneous detection of microseismic events. The proposed method is based on the synchrosqueezed continuous wavelet transform (SS-CWT) and custom thresholding of single-channel data. The SS-CWT allows for the adaptive filtering of time- and frequency-varying noise as well as offering an improvement in resolution over the conventional wavelet transform. Simultaneously, the algorithm incorporates a detection procedure that uses the thresholded wavelet coefficients and detects an arrival as a local maxima in a characteristic function. The algorithm was tested using a synthetic signal and field microseismic data, and our results have been compared wit...
216 citations
TL;DR: A novel signal denoising method that combines variational mode decomposition (VMD) and detrended fluctuation analysis (DFA), named DFA-VMD, is proposed in this paper and shows the superior performance of this proposed filtering over EMD-based denoisings and discrete wavelet threshold filtering.
214 citations
01 Jan 2016
TL;DR: The advanced digital signal processing and noise reduction is universally compatible with any devices to read and can be downloaded instantly from the authors' digital library.
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197 citations
01 Jan 2016
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195 citations
TL;DR: In this paper, a damping factor was introduced into traditional multichannel singular spectrum analysis (MSSA) to dampen the singular values to distinguish between signal and noise in seismic data.
Abstract: Multichannel singular spectrum analysis (MSSA) is an effective algorithm for random noise attenuation in seismic data, which decomposes the vector space of the Hankel matrix of the noisy signal into a signal subspace and a noise subspace by truncated singular value decomposition (TSVD). However, this signal subspace actually still contains residual noise. We have derived a new formula of low-rank reduction, which is more powerful in distinguishing between signal and noise compared with the traditional TSVD. By introducing a damping factor into traditional MSSA to dampen the singular values, we have developed a new algorithm for random noise attenuation. We have named our modified MSSA as damped MSSA. The denoising performance is controlled by the damping factor, and our approach reverts to the traditional MSSA approach when the damping factor is sufficiently large. Application of the damped MSSA algorithm on synthetic and field seismic data demonstrates superior performance compared with the conve...
TL;DR: The new method is applied to continuous wave electron spin resonance spectra and it is found that it increases the signal-to-noise ratio (SNR) by more than 32 dB without distorting the signal, whereas standard denoising methods improve the SNR by less than 10 dB and with some distortion.
Abstract: A new method is presented to denoise 1-D experimental signals using wavelet transforms. Although the state-of-the-art wavelet denoising methods perform better than other denoising methods, they are not very effective for experimental signals. Unlike images and other signals, experimental signals in chemical and biophysical applications, for example, are less tolerant to signal distortion and under-denoising caused by the standard wavelet denoising methods. The new method: 1) provides a method to select the number of decomposition levels to denoise; 2) uses a new formula to calculate noise thresholds that does not require noise estimation; 3) uses separate noise thresholds for positive and negative wavelet coefficients; 4) applies denoising to the approximation component; and 5) allows the flexibility to adjust the noise thresholds. The new method is applied to continuous wave electron spin resonance spectra and it is found that it increases the signal-to-noise ratio (SNR) by more than 32 dB without distorting the signal, whereas standard denoising methods improve the SNR by less than 10 dB and with some distortion. In addition, its computation time is more than six times faster.
TL;DR: In this paper, a multivariate empirical mode decomposition (multivariate EMD) is used to simultaneously analyze the multivariate signal to extract fault information, especially for weak fault characteristics during the period of early failure.
TL;DR: The VARTOOLS program is designed especially for batch processing of light curves, including built-in support for parallel processing, making it useful for large time-domain surveys such as searches for transiting planets.
TL;DR: A new approach to depth and reflectivity estimation that emphasizes the unmixing of contributions from signal and noise sources is introduced, and improved performance of both reflectivity and depth estimates over state-of-the-art methods, especially at low SBR.
Abstract: Conventional LIDAR systems require hundreds or thousands of photon detections to form accurate depth and reflectivity images. Recent photon-efficient computational imaging methods are remarkably effective with only 1.0 to 3.0 detected photons per pixel, but they are not demonstrated at signal-to-background ratio (SBR) below 1.0 because their imaging accuracies degrade significantly in the presence of high background noise. We introduce a new approach to depth and reflectivity estimation that focuses on unmixing contributions from signal and noise sources. At each pixel in an image, short-duration range gates are adaptively determined and applied to remove detections likely to be due to noise. For pixels with too few detections to perform this censoring accurately, we borrow data from neighboring pixels to improve depth estimates, where the neighborhood formation is also adaptive to scene content. Algorithm performance is demonstrated on experimental data at varying levels of noise. Results show improved performance of both reflectivity and depth estimates over state-of-the-art methods, especially at low signal-to-background ratios. In particular, accurate imaging is demonstrated with SBR as low as 0.04. This validation of a photon-efficient, noise-tolerant method demonstrates the viability of rapid, long-range, and low-power LIDAR imaging.
TL;DR: Numerical and experimental results show accurate identification of the natural frequencies and damping ratios even when the signal is embedded in high-level noise demonstrating that the proposed methodology provides a powerful approach to estimate the modal parameters of a civil structure using ambient vibration excitations.
TL;DR: The proposed approach can effectively detect and extract the useful features of bearing outer race and inner race defect and is applied to single fault diagnosis of a locomotive bearing and compound faults diagnosis of motor bearings.
TL;DR: In this method, a de-noising algorithm of second generation wavelet transform using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal-noise ratio (SNR).
Abstract: In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal-noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method.
TL;DR: A novel method to suppress low-frequency noise in microseismic data based on mathematical morphology theory that aims at distinguishing useful signals and noise according to their tiny differences of waveform is developed.
Abstract: The frequency of microseismic data is higher than that of conventional seismic data. The range of effective frequency is usually from 100 to 500 Hz, and low-frequency noise is a common disturbance in downhole monitoring. Conventional signal analysis techniques, such as band-pass filters, have their limitation in microseismic data processing when the useful signals and noise share the same frequency band. We have developed a novel method to suppress low-frequency noise in microseismic data based on mathematical morphology theory that aims at distinguishing useful signals and noise according to their tiny differences of waveform. By choosing suitable structure elements, we have extracted low-frequency noise from a original data set. We first developed the fundamental principle of mathematical morphology and the formulation of our approach. Then, we used a synthetic data example that was composed of a Ricker wavelet and low-frequency noise to test the feasibility and performance of the proposed appro...
Australia Telescope National Facility1, Curtin University2, University of California, San Diego3, Bielefeld University4, Max Planck Society5, California Institute of Technology6, Goddard Space Flight Center7, ASTRON8, Albert Einstein Institution9, INAF10, National Radio Astronomy Observatory11, Cornell University12, University of Orléans13, Centre national de la recherche scientifique14, Peking University15, Hillsdale College16, McGill University17, University of British Columbia18, University of Edinburgh19, Vancouver Coastal Health20, University of Amsterdam21, Columbia University22, University of Manchester23, Monash University24, West Virginia University25, Lafayette College26, University of Virginia27, Paris Diderot University28, Swinburne University of Technology29, University of Birmingham30, University of Wisconsin–Milwaukee31, Oberlin College32, University of New Mexico33, Chinese Academy of Sciences34, Huazhong University of Science and Technology35, Southwest University36, University of Western Australia37
TL;DR: In this paper, the authors analyse the stochastic properties of the 49 pulsars that comprise the first International Pulsar Timing Array (IPTA) data release and use Bayesian methodology, performing model selection to determine the optimal description of the signal present in each pulsar.
Abstract: We analyse the stochastic properties of the 49 pulsars that comprise the first International Pulsar Timing Array (IPTA) data release. We use Bayesian methodology, performing model selection to determine the optimal description of the stochastic signals present in each pulsar. In addition to spin-noise and dispersion-measure (DM) variations, these models can include timing noise unique to a single observing system, or frequency band. We show the improved radio-frequency coverage and presence of overlapping data from different observing systems in the IPTA data set enables us to separate both system and band-dependent effects with much greater efficacy than in the individual PTA data sets. For example, we show that PSR J1643−1224 has, in addition to DM variations, significant band-dependent noise that is coherent between PTAs which we interpret as coming from time-variable scattering or refraction in the ionised interstellar medium. Failing to model these different contributions appropriately can dramatically alter the astrophysical interpretation of the stochastic signals observed in the residuals. In some cases, the spectral exponent of the spin noise signal can vary from 1.6 to 4 depending upon the model, which has direct implications for the long-term sensitivity of the pulsar to a stochastic gravitational-wave (GW) background. By using a more appropriate model, however, we can greatly improve a pulsar's sensitivity to GWs. For example, including system and band-dependent signals in the PSR J0437−4715 data set improves the upper limit on a fiducial GW background by ∼ 60% compared to a model that includes DM variations and spin-noise only.
TL;DR: In this article, an improved harmonic product spectrum (IHPS) was proposed to detect and identify the multiple modulation sources buried in a vibration signal, and a harmonic significance index was further established to quantify the diagnostic information contained in a narrow band signal.
TL;DR: An incipient fault detection method that does not need any a priori information on the signals distribution or the changed parameters is proposed and an analytical model of the fault detection performances (False Alarm Probability and Missed Detection Probability) is developed.
TL;DR: A noise reduction DCSK system as a solution to reduce the noise variance present in the received signal in order to improve performance, and computer simulation results are compared to relevant theoretical findings to validate the accuracy of the proposed system and demonstrate the performance improvement.
Abstract: One of the major drawbacks of the conventional differential chaos shift keying (DCSK) system is the addition of channel noise to both the reference signal and the data-bearing signal, which deteriorates its performance. In this brief, we propose a noise reduction DCSK system as a solution to reduce the noise variance present in the received signal in order to improve performance. For each transmitted bit, instead of generating $\beta$ different chaotic samples to be used as a reference sequence, $\beta/P$ chaotic samples are generated and then duplicated $P$ times in the signal. At the receiver, $P$ identical samples are averaged, and the resultant filtered signal is correlated to its time-delayed replica to recover the transmitted bit. This averaging operation of size $P$ reduces the noise variance and enhances the performance of the system. Theoretical bit error rate expressions for additive white Gaussian noise and multipath fading channels are analytically studied and derived. Computer simulation results are compared to relevant theoretical findings to validate the accuracy of the proposed system and to demonstrate the performance improvement compared to the conventional DCSK, the improved DCSK, and the differential-phase-shift-keying systems.
TL;DR: In this paper, a hybrid fault diagnosis approach is developed for the denoising and non-stationary feature extraction in this work, which combines well with the variational mode decomposition (VMD) and majoriation-minization based total variation denoizing (TV-MM) approach to remove stochastic noise in the raw signal and to enhance the corresponding characteristics.
Abstract: Feature extraction plays an essential role in bearing fault detection. However, the measured vibration signals are complex and non-stationary in nature, and meanwhile impulsive signatures of rolling bearing are usually immersed in stochastic noise. Hence, a novel hybrid fault diagnosis approach is developed for the denoising and non-stationary feature extraction in this work, which combines well with the variational mode decomposition (VMD) and majoriation–minization based total variation denoising (TV-MM). The TV-MM approach is utilized to remove stochastic noise in the raw signal and to enhance the corresponding characteristics. Since the parameter is very important in TV-MM, the weighted kurtosis index is also proposed in this work to determine an appropriate used in TV-MM. The performance of the proposed hybrid approach is conducted through the analysis of the simulated and practical bearing vibration signals. Results demonstrate that the proposed approach has superior capability to detect roller bearing faults from vibration signals.
TL;DR: In the current paper several approaches to improve the analytical figures-of-merit are reviewed and the respective advantages and drawbacks are discussed.
Abstract: Laser Induced Breakdown Spectroscopy (LIBS) has become a very popular technique for elemental analysis thanks to its ease of use. However, LIBS users often report poor repeatability of the signal, due to shot-to-shot fluctuations, and consequent not satisfactory limits of detection. In many practical cases, these shortcomings are difficult to control because the signal is affected by several noise sources that cannot be reduced simultaneously. Hopefully, there is a large amount of knowledge, accumulated during several decades, that can provide guidelines to reduce the effect of the single sources of fluctuations. Experimental setup and measurement settings can be optimized on purpose. Spectral data can be processed in order to better exploit the information contained. In the current paper several approaches to improve the analytical figures-of-merit are reviewed and the respective advantages and drawbacks are discussed.
TL;DR: Simulation and experimental results show that the proposed weak signal detection method can enhance the signal amplitude, can effectively detect multi-frequency weak signals buried under heavy noise and is valuable and usable for bearing fault signal analysis.
TL;DR: A new technique using singular spectrum analysis (SSA) and adaptive noise canceler (ANC) to remove the EOG artifact from the contaminated EEG signal, which outperforms the existing techniques.
Abstract: The electroencephalogram (EEG) signals represent the electrical activity of the brain. In applications, such as brain–computer interface (BCI), features of the EEG signals are used to control the devices. However, while recording, EEG signals often contaminated by electrooculogram (EOG) artifacts; such artifacts degrade the performance of the BCI. In this paper, we proposed a new technique using singular spectrum analysis (SSA) and adaptive noise canceler (ANC) to remove the EOG artifact from the contaminated EEG signal. In this technique, first, we proposed a novel grouping technique for SSA to construct the reference signal (EOG) for ANC. Later, using the extracted reference signal, the adaptive filter was employed to remove EOG artifact from the contaminated EEG signal. To quantify the performance of the proposed technique, we carried out simulations on synthetic and real-life EEG signals. In terms of relative root mean square error and mean absolute error, the proposed SSA-ANC method outperforms the existing techniques.
TL;DR: This paper proposes a novel preprocessing approach for attenuating the influence of the non-unique artifacts on the reference SPN to reduce the false identification rate through detecting and suppressing the peaks according to the local characteristics, aiming at removing the interfering periodic artifacts.
Abstract: Although sensor pattern noise (SPN) has been proved to be an effective means to uniquely identify digital cameras, some non-unique artifacts, shared among cameras undergo the same or similar in-camera processing procedures, often give rise to false identifications. Therefore, it is desirable and necessary to suppress these unwanted artifacts so as to improve the accuracy and reliability. In this paper, we propose a novel preprocessing approach for attenuating the influence of the non-unique artifacts on the reference SPN to reduce the false identification rate. Specifically, we equalize the magnitude spectrum of the reference SPN through detecting and suppressing the peaks according to the local characteristics, aiming at removing the interfering periodic artifacts. Combined with six SPN extractions or enhancement methods, our proposed spectrum equalization algorithm is evaluated on the Dresden image database as well as our own database, and compared with the state-of-the-art preprocessing schemes. The experimental results indicate that the proposed procedure outperforms, or at least performs comparable with, the existing methods in terms of the overall receiver operating characteristic curves and kappa statistic computed from a confusion matrix, and tends to be more resistant to JPEG compression for medium and small image blocks.
Patent•
01 Aug 2016
TL;DR: In this paper, a system and methods for cancelling noise caused by the flicker of ambient lights are provided. But the system is not suitable for the use of a laser-based barcode scanner.
Abstract: Systems and methods for cancelling noise caused by the flicker of ambient lights are provided Such noise cancelling systems and methods may be incorporated in a laser-based barcode scanning device In one example, a barcode scanning device includes a light source, a first sensor, a second sensor, and a noise cancelling circuit The light source is configured to emit a beam of light The first sensor is configured to detect a first optical signal indicative of light reflecting off of a barcode The reflected light may originate from the light source and from at least one ambient light source in the vicinity of the barcode scanning device The second sensor is configured to detect a second optical signal indicative of light originating from the at least one ambient light source The noise cancelling circuit is configured to obtain a noise-cancelled scanning signal from the first and second optical signals