Nonlinear Noise Reduction Research Articles

Method of feature extraction of abnormal communication signal in network based on nonlinear technology

Abstract Accurate extraction of abnormal communication signal features in the network is the basis to ensure the completion of network communication. Therefore, a method of feature extraction of abnormal communication signal in network based on nonlinear technology is proposed. In this method, wavelet transform is adopted to decompose the abnormal network communication signals in the high- and low-frequency bands. According to the distribution characteristics of noise and signal in the frequency band, the corresponding parameters are selected for phase space reconstruction and nonlinear dimension reduction of local tangent space mainstream shape recognition algorithm, and the decomposition coefficients of wavelet packet after noise reduction are reconstructed to realize the nonlinear noise reduction of abnormal signal; the denoised abnormal communication signal in network is mapped to the high-dimensional feature space. The principal component is analyzed in accordance with the nonlinear function in the mapped feature space, and the nonlinear function is solved by self-organizing neural network to output the principal component extraction result. According to test results, this method has a significant signal noise reduction effect, results are more than 92% for different abnormal communication signals, and the features of abnormal signals are accurately extracted.

An Unstructured Mesh Approach to Nonlinear Noise Reduction for Coupled Systems

An Unstructured Mesh Approach to Nonlinear Noise Reduction for Coupled Systems

Visualization of the distortion induced by nonlinear noise reduction in computed tomography.

We developed a method to visualize the image distortion induced by nonlinear noise reduction algorithms in computed tomography (CT) systems. Nonlinear distortion was defined as the induced residual when testing a reconstruction algorithm by the criteria for a linear system. Two types of images were developed: a nonlinear distortion of an object () image and a nonlinear distortion of noise () image to visualize the nonlinear distortion induced by an algorithm. Calculation of the images requires access to the sinogram data, which is seldomly fully provided. Hence, an approximation of the image was estimated. Using simulated CT acquisitions, four noise levels were added onto forward projected sinograms of a typical CT image; these were noise reduced using a median filter with the simultaneous iterative reconstruction technique or a total variation filter with the conjugate gradient least-squares algorithm. The linear reconstruction technique filtered back-projection was also analyzed for comparison. Structures in the image indicated contrast and resolution reduction of the nonlinear denoising. Although the approximated image represented the original image well, it had a higher random uncertainty. The image for the median filter indicated both stochastic variations and structures reminding of the object while for the total variation filter only stochastic variations were indicated. The developed images visualize nonlinear distortions of denoising algorithms. The object may be distorted by the noise and vice versa. Analyzing the distortion correlated to the object is more critical than analyzing a distortion of stochastic variations. The absence of nonlinear distortion may measure the robustness of the denoising algorithm.

Kurtosis-Limited Sphere Shaping for Nonlinear Interference Noise Reduction in Optical Channels

Nonlinear interference (NLI) generated during the propagation of an optical waveform through the fiber depends on the fourth order standardized moment of the channel input distribution, also known as kurtosis. Probabilistically-shaped inputs optimized for the linear Gaussian channel have a Gaussian-like distribution with high kurtosis. For optical channels, this leads to an increase in NLI power and consequently, a decrease in effective signal-to-noise ratio (SNR). In this work, we propose kurtosis-limited enumerative sphere shaping (K-ESS) as an algorithm to generate low-kurtosis shaped inputs. Numerical simulations at a shaping blocklength of 108 amplitudes demonstrate that with K-ESS, it is possible to increase the effective SNRs by 0.4 dB in a single-span single-channel scenario at 400 Gbit/s. K-ESS offers also a twofold decrease in frame error rate with respect to Gaussian-channel-optimal sphere shaping.

Estimating subjective evaluation of low-contrast resolution using convolutional neural networks.

To develop a convolutional neural network-based method for the subjective evaluation of computed tomography (CT) images having low-contrast resolution due to imaging conditions and nonlinear image processing. Fourradiological technologists visually evaluated CT images that were reconstructed using three nonlinear noise reduction processes (AIDR 3D, AIDR 3D Enhanced, AiCE) on a CT system manufactured by CANON. The visual evaluation consisted of two items: low contrast detectability (score: 0-9) and texture pattern (score: 1-5). Four AI models with different convolutional and max pooling layers were constructed and trained on pairs of CANON CT images and average visual assessment scores of four radiological technologists. CANON CT images not used for training were used to evaluate prediction performance. In addition, CT images scanned with a SIEMENS CT system were input to each AI model for external validation. The mean absolute error and correlation coefficients were used as evaluation metrics. Our proposed AI model can evaluate low-contrast detectability and texture patterns with high accuracy, which varies with the dose administered and the nonlinear noise reduction process. The proposed AI model is also expected to be suitable for upcoming reconstruction algorithms that will be released in the future.

Composite test inclusive of Benford’s law, noise reduction and 0–1 test for effective detection of chaos in rotor–stator rub

Segregating noise from chaos in dynamic systems has been one of the challenging work for the researchers across the globe due to their seemingly similar statistical properties. Even the most used tools such 0-1 test and Lyapunov exponents fail to distinguish chaos when signal is mixed with noise. This paper addresses the issue of segregating the dynamics in a rotor-stator rub system when the vibrations are subjected to different levels of noise. First, the limitation of 0-1 test in segregating chaos from signal mixed with noise has been established. Second, the underexplored Benfords Law and its application to the vibratory dynamical rotor-stator rub system has been introduced for the first time. Using the Benfords Law Compliance Test (BLCT), successful segregation of not only noise from chaos but also very low Signal to Noise Ratio (SNR) signals which are mainly stochastic has been achieved. The Euclidean Distance concept has been used to explore the scale-invariant probability distribution of systems that comply with Benfords Law to separate chaos from noise. Moreover, for moderate bands of noise in signals, we have shown that the Schreibers Nonlinear Noise Reduction technique works effectively in reducing the noise without damaging the dynamic properties of the system. Combining these individual layers (0-1 Test, BLCT and Noise reduction) on a rotor system, a Decision Tree based method to effectively segregate noise from chaos and identify the correct dynamics of any system with time series data set has been proposed.

Nonlinear Noise Reduction for the Airborne Transient Electromagnetic Method based on Kernel Minimum Noise Fraction

The airborne transient electromagnetic method has become a powerful tool to explore deep resource and tectonic structures. However, aircraft vibrations and flight environments produce very strong and complex nonlinear noise and result in poor data quality compared to ground transient electromagnetic methods. Consequently, the reduction of airborne electromagnetic noises is of vital importance to data inversion and imaging. To suppress and remove the nonlinear noise, we propose using kernel minimum noise fraction (KMNF), which is a nonlinear generalized method of minimum noise fraction. First, an adaptive variable window-width filtering algorithm is used to evaluate the noises and perform the preliminary denoising. Then, we adopt the two filter methods, which are minimum noise fraction (MNF) and KMNF to suppress the noise. The results show that these two methods can both suppress noise and make the decay curves smooth, but kernel MNF is more effective for the nonlinear characteristics of noise and it does not weaken the anomaly. Finally, field data from the Qinling mine area is processed, using the MNF and KMNF methods. The results show that nonlinear noise is suppressed by both methods but the results of KMNF are better than those of the linear MNF method.

Baud rate and shaping blocklength effects on the nonlinear performance of super-symbol transmission.

In this paper we study the nonlinear performance of super-symbol (SUP) transmission. We analyze the spectral dip, a unique feature of SUP, as a function of various system parameters and discuss how this dip and the associated nonlinear benefit would vary with different baud rates and shaping blocklengths. We then conduct simulations to verify our analysis, by which we confirm that the SUP performance can be optimized via a judicious choice on the baud rate and/or blocklength. Furthermore, a nonlinear noise study confirms that the nonlinear benefit of SUP mainly comes from its significant nonlinear phase noise (NLPN) reduction.

A grid-based nonlinear approach to noise reduction and deconvolution for coupled systems

To varying degrees, all experimental measurements are corrupted by real-world noise sources including electronic noise in the acquisition system, far-field perturbations in the surrounding environment, and local physical phenomena. This paper presents a grid-based nonlinear analysis technique for causally coupled systems which leverages the availability of a high-fidelity reference signal to effectively denoise a target measurement signal. The foundation of this approach, essentially an ensemble-averaging procedure in multidimensional phase space, is strongly motivated by work from dynamical systems theory. Furthermore, a straightforward extension of this technique allows for recovery of a time-resolved representation from the underresolved measurement signal. Both the nonlinear noise reduction and this temporal deconvolution extension are applied to signals from three different coupled dynamic systems (sine wave system, Lorenz system, and Hall-effect thruster) to demonstrate effectiveness on periodic, chaotic, and experimental systems.

Multiple degradation mode analysis via gated recurrent unit mode recognizer and life predictors for complex equipment

In order to ensure the reliability and safety of industrial complex equipment, it is necessary to predict and manage the health status of the equipment. Remaining useful life (RUL) prediction is the decision basis of condition-based maintenance (CBM) and one of the main tasks in prognostics and health management (PHM). Complex systems tend to have multiple degradation modes, while similar degradation features may have significantly different RUL labels in different degradation modes, which can be called feature multi-label problem. To solve the problem, a novel RUL prediction method was proposed, which first analyzed the degradation mode and then utilized the predictor for RUL prediction under the specific mode. In particular, a modified de-noising auto-encoder (DAE) was proposed for nonlinear feature extraction and noise reduction. Mode recognizer and life predictors based on gated recurrent unit (GRU) and fuzzy k-means were proposed as the core modules. Case studies of commercial modular aero-propulsion system simulation data and the life cycle data of bearing were conducted to verify the effectiveness of the proposed method. Results show that the proposed method achieved much higher prediction accuracy than other methods.

An Investigation Into Fault Diagnosis of Planetary Gearboxes Using A Bispectrum Convolutional Neural Network

To improve the efficiency and accuracy of fault diagnostics of planetary gearboxes, an intelligent diagnosis approach is proposed based on deep convolutional neural networks (CNNs) and vibration bispectrum (BSP). Rather than using raw vibration signals, BSP is appreciated as the input for the CNN models (denoted as BSP-CNN) because the BSP allows nonlinear feature enhancement and noise reduction. In addition, transfer learning (TL) is accompanied to address the challenges of CNN difficulties. The proposed BSP-CNN is verified firstly to diagnose a number of common faults including gear states: normal, tooth wear, tooth root crack, tooth breakage and missing tooth, achieving an accuracy of 97.36% in identifying different faults. Then, its TL capability is evaluated based on the sun gear faults datasets. The classification accuracy of the planet gear faults is over 95.1%. After the transfer learning, the classification accuracy of the sun gear fault is still higher than 97.9%, and the computational time consumed by proposed method is also less compared to other diagnosis methods. This article has twofold contributions: first, the development of a BSP-based CNN model for fault diagnosis; andsecond, the extensive evaluation of CNN-TL methods for monitoring and diagnosing planetary gearboxes.

Image quality comparison of a nonlinear image-based noise reduction technique with a hybrid-type iterative reconstruction for pediatric computed tomography

PurposeTo compare computed tomography (CT) image properties between a vendor-independent image-based noise reduction technique, Image-space Noise Reduction (iNoir) and a hybrid-type iterative reconstruction technique, Adaptive Statistical Iterative Reconstruction (ASIR). MethodsA cylindrical water phantom, corresponding to pediatric body size, containing soft-tissue-equivalent rod and 12-mg iodine/ml rod was scanned at size-specific dose estimates of 8.4 and 16.7 mGy. For assessments of image quality and noise texture change, task-based system performance function (SPF) and peak frequency difference (PFD) were compared, respectively, among filtered back projection (FBP), IR image with 50%-blending rate (50%ASIR), 100%ASIR, 50%iNoir, and 100%iNoir. Human observer test for pediatric CT images was performed by radiologists. ResultsFor the soft-tissue contrast, SPF2 of 100%iNoir was the highest. The average SPF2 between 0.1 and 0.5 cycles/mm for 100%iNoir increased by approximately 70% compared with FBP, while ASIR indicted slight increases in the frequency region of >0.2 cycles/mm. For the iodine contrast, 100%iNoir indicated highest values at the spatial frequencies corresponding pediatric artery diameters. The PFDs of iNoir were negligible and lower than that of ASIR. The results of human observer test supported results of SPF2 and PFD. ConclusionsCompared with ASIR, iNoir provided better image quality for pediatric abdominal CT without compromising noise texture change.

Interference Elimination of Wall Fluctuation Pressure Test Signal of High-Speed Train Based on Phase Space Reconstruction and Vibration Interference Model

Because of the complexity of high-speed train operation, the measured wall pressure of high-speed train will be affected by many factors, such as train vibration and environmental changes, which make the test signal inaccurate. The main interferences are the electromagnetic interference caused by the complicated electromagnetic environment and the sensor output interference caused by the train body vibration. In this paper, ensemble empirical mode decomposition (EEMD) combined with singular value decomposition (SVD) and the local projection de-noise algorithm with better effect on non-linear time series noise reduction are used to eliminate electromagnetic interference in test signal. Because of the frequency band aliasing of test signal and interference cause by vibration, it is difficult to eliminate vibration interference by the signal processing method. In this paper, a vibration interference model is established by model-train experiment and computational fluid dynamics (CFD) software to eliminate the vibration interference in the test signal. Moreover, the theoretical guidance for accurately extracting the wall pressure of high-speed train has been put forward.

Fundamental study on low-dose imaging of CT colonography examination using nonlinear noise reduction filter

Fundamental study on low-dose imaging of CT colonography examination using nonlinear noise reduction filter

A New Method of Two-stage Planetary Gearbox Fault Detection Based on Multi-Sensor Information Fusion

Due to their high transmission ratio, high load carrying capacity and small size, planetary gears are widely used in the transmission systems of wind turbines. The planetary gearbox is the core of the transmission system of a wind turbine, but because of its special structure and complex internal and external excitation, the vibration signal spectrum shows strong nonlinearity, asymmetry and time variation, which brings great trouble to planetary gear fault diagnosis. The traditional time-frequency analysis technology is insufficient in the condition monitoring and fault diagnosis of wind turbines. For this reason, we propose a new method of planetary gearbox fault diagnosis based on Compressive sensing, Two-dimensional variational mode decomposition (2D-VMD) and full-vector spectrum technology. Firstly, the nonlinear reconstruction and noise reduction of the signal is carried out by using compressed sensing, and then the signal with multiple degrees of freedom is adaptively decomposed into multiple sets of characteristic scale components by using 2D-VMD. Then, Rényi entropy is used as the optimization index of 2D-VMD analysis performance to extract the effective target intrinsic mode function (IMF) component, reconstruct the dynamics signal in the planetary gearbox, and improve the signal-to-noise ratio. Then, using the full-vector spectrum technique, the homologous information collected by numerous sensors is data layer fused in the spatial domain and the time domain to increase the comprehensiveness and certainty of the fault information. Finally, the Teager–Kaiser energy operator is used to demodulate the potential low-frequency dynamics frequency characteristics from the high-frequency domain and detect the fault characteristic frequency. Furthermore, the correctness and validity of the method are verified by the fault test signal of the planetary gearbox.

Fault Classification of Rotary Machinery Based on Smooth Local Subspace Projection Method and Permutation Entropy

Collected mechanical signals usually contain a number of noises, resulting in erroneous judgments of mechanical condition diagnosis. The mechanical signals, which are nonlinear or chaotic time series, have a high computational complexity and intrinsic broadband characteristic. This paper proposes a method of gear and bearing fault classification, based on the local subspace projection noise reduction and PE. A novel nonlinear projection noise reduction method, smooth orthogonal decomposition (SOD), is proposed to denoise the vibration signals of various operation conditions. SOD can decompose the reconstructed multiple strands to identify smooth local subspace. In the process of projection from a high dimension to a low dimension, a new weight matrix is put forward to achieve a better denoising effect. Afterwards, permutation entropy (PE) is applied in the detection of time sequence randomness and dynamic mutation behavior, which can effectively detect and amplify the variation of vibration signals. Hence PE can characterize the working conditions of gear and bearing under different conditions. The experimental results illustrate the effectiveness and superiority of the proposed approach. The theoretical derivations, numerical simulations and experimental studies, all confirm that the proposed approach based on the smooth local subspace projection method and PE, is promising in the field of the fault classification of rotary machinery.

A novel scheme of cascaded four-wave mixing for phase-sensitive amplification in nonlinear optical fibre

We propose and numerically verify a scheme of phase-sensitive amplifier (PSA) using four-wave mixing (FWM) in cascaded highly nonlinear fibres (HNLFs), without requiring initial phase-locking between signal and pump. The first HNLF is used to generate two phase-conjugate waves, which act as two pumps for FWM process in second HNLF. We feed the two pumps from opposite ends of second HNLF and a signal co-propagating with one of the pumps. We keep the signal frequency in the middle of two pump frequencies to obtain phase-conjugate wave at the same frequency as the signal by FWM process in second HNLF. Signal and phase-conjugate wave appear at opposite ends of the second HNLF and combined to obtain PSA. The frequency-shift-free operation of phase conjugation helps in preserving the frequency of input signal during phase-sensitive amplification. We derive the expression for PSA signal output and PSA gain and show analytically that PSA gain depends upon signal phase only, as the two pumps are phase conjugate to each other. Thus, eliminating the need of phase locking between signal and pump waves. We show that PSA provides high gain for in-phase component and almost cancellation for quadrature-phase component of signal. We show the broadband nature of PSA due to minimum effect of group velocity and group velocity dispersion owing to counter-propagating nature of signal and conjugate waves. We study the performance of PSA under the effects of pump-signal detuning, amplifier length and input signal phase. Simulation results show that PSA output is forced to attain 0 or phase regardless of large variation of phase in the input signal. Nonlinear phase noise reduction of 100 Gbps DPSK signal transmitted over 1000-km standard single-mode fibre confirms phase regeneration by PSA.

Performance evaluation of an efficient RZ-MQAM modulation scheme in all-optical OFDM transmission systems

All optical orthogonal frequency division multiplexing (AO-OFDM) technique is a promising technique and employed in many military applications for data transmission over long distance. But AO-OFDM signals are very sensitive to nonlinear distortion and these systems suffer from nonlinear impairments. This paper describes a novel nonlinear phase noise reduction technique by decreasing interaction time between subcarriers in AO-OFDM transmission systems. This technique mitigates the effects of nonlinearity on all-optical OFDM transmission systems and improves performance of transmission systems. The proposed AO-OFDM transmission system has been successfully simulated and analyzed for estimating phase noise due to cross-phase modulation, self-phase modulation and four-wave mixing. The proposed RZ-8QAM AO-OFDM system with 29 subcarriers is examined and the transmission distance is fixed at 550km.

Nonlinear Short-term Prediction of Aluminum Foil Thickness via Global Regressor Combination

ABSTRACTIn this study, short-term prediction of aluminum foil thickness time-series data recorded during cold-rolling process was investigated. The locally projective nonlinear noise reduction was applied in order to improve the predictability of the time series. The higher-order statistics methods (bispectrum and bicoherence) were used to detect the nonlinearity. The embedding vectors with appropriate embedding dimension and time delay were obtained via the false nearest neighbors and mutual information methods, respectively. The maximum prediction horizon was determined depending on the maximal Lyapunov exponent. For various prediction horizons, the embedding vector and corresponding thickness value pairs were used as the dataset to assess the prediction performance of various machine learning algorithms (i.e., multilayer perceptron neural network, support vector machines with Pearson VII function-based kernel, and radial basis function network). The n-step ahead prediction outputs of the machine learning algorithms were globally combined with simple voting in favor of the one having minimum absolute error. The accuracy of our proposed method was compared with nonlinear autoregressive exogenous model for various thickness time-series data using mean absolute percentage error measure.

The Effects of misregistration on the dead leaves crosscorrelation texture blur analysis

The dead leaves image model is often used for measurement of the spatial frequency response (SFR) of digital cameras, where response to fine texture is of interest. It has a power spectral density (PSD) similar to natural images and image features of varying sizes, making it useful for measuring the texture-blurring effects of non-linear noise reduction which may not be well analyzed by traditional methods. The standard approach for analyzing images of this model is to compare observed PSDs to the analytically known one. However, recent works have proposed a cross-correlation based approach which promises more robust measurements via full-reference comparison with the known true pattern. A major assumption of this method is that the observed image and reference image can be aligned (registered) with subpixel accuracy. In this paper we study the effects of registration errors on the calculation of texture-based SFR and its derivative metrics (such as MTF50), in order to determine how accurate this registration must be for reliable results. We also propose a change to the dead leaves cross-correlation algorithm, recommending the use of the absolute value of the transfer function rather than its real part. Simulations of registration error on both real and simulated observed images reveal that small amounts of misregistration (as low as 0.15px) can cause large variability in MTF curves derived using the real part of the transfer function, while MTF curves derived from the absolute value are significantly less affected.