Distribution Characteristics Of Noise 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.

Traffic noise distribution characteristics of high-rise buildings along ultra-wide cross section highway with multiple noise reduction measures.

Nowadays, ultra-wide crosssection highway is a hotspot in construction and brings some unique noise distribution characteristics. In this work, we further investigate noise distribution characteristics of diverse building layouts along ultra-wide cross section highwayin Guangdong Province with multiple noise mitigation measures. By the aid of vehicle noise emission model and noise mapping, the influence of high-rise building layouts and shielding in the urban planning on noise mitigation is also considered. Some key findings are summarized as follows: (1) Under the same distance, the noise level of non-frontage building facades is higher than frontage building facades. After taking noise reduction measures, the noise reduction effect of non-street-facing building facades, buildings facing the road, and buildings at a long distance to the road is greater than street-facing building facades, buildings sideways to the road, and buildings at a short distance; (2) the distribution trend of insertion loss (IL) of non-frontage buildings is influenced by the height of the frontage buildings. Specifically, the trend of insertion loss first increases and then decreases as the floor rises when the height of non-frontage buildings is higher than frontage buildings. Comparatively, the trend of insertion loss decreases as the floor rises when the height of non-frontage buildings is equal to frontage buildings; (3) when double noise reduction measures are implemented, the noise distribution trend in buildings is similar to that observed with individual noise reduction measure, where the difference between both is only 0.6dB(A). Thanks to the high representativeness of the case area, this work can provide some design guidance for the urban planning and the selection of noise reduction measures along the ultra-wide cross section highway.

Comparative Study on Noise-Estimation-Based Fuzzy C-Means Clustering for Image Segmentation.

Since a noisy image has inferior characteristics, the direct use of Fuzzy C -Means (FCM) to segment it often produces poor image segmentation results. Intuitively, using its ideal value (noise-free image) benefits FCM's robustness enhancement. Therefore, the realization of accurate noise estimation in FCM is a new and important task. To date, only two noise-estimation-based FCM algorithms have been proposed for image segmentation, that is: 1) deviation-sparse FCM (DSFCM) and 2) our earlier proposed residual-driven FCM (RFCM). In this article, we make a thorough comparative study of DSFCM and RFCM. We demonstrate that an RFCM framework can realize more accurate noise estimation than DSFCM when different types of noise are involved. It is mainly thanks to its utilization of noise distribution characteristics instead of noise sparsity used in DSFCM. We show that DSFCM is a particular case of RFCM, thus signifying that they are the same when only impulse noise is involved. With a spatial information constraint, we demonstrate RFCM's superior effectiveness and efficiency over DSFCM in terms of supporting experiments with different levels of single, mixed, and unknown noise.

Single-Image Simultaneous Destriping and Denoising: Double Low-Rank Property

When a remote sensing camera work in push-broom mode, the obtained image usually contains significant stripe noise and random noise due to differences in detector response and environmental factors. Traditional approaches typically treat them as two independent problems and process the image sequentially, which not only increases the risk of information loss and structural damage, but also faces the situation of noise mutual influence. To overcome the drawbacks of traditional methods, this paper leverages the double low-rank characteristics in the underlying prior of degraded images and presents a novel approach for addressing both destriping and denoising tasks simultaneously. We utilize the commonality that both can be treated as inverse problems and place them in the same optimization framework, while designing an alternating direction method of multipliers (ADMM) strategy for solving them, achieving the synchronous removal of both stripe noise and random noise. Compared with traditional approaches, synchronous denoising technology can more accurately evaluate the distribution characteristics of noise, better utilize the original information of the image, and achieve better destriping and denoising results. To assess the efficacy of the proposed algorithm, extensive simulations and experiments were conducted in this paper. The results show that compared with state-of-the-art algorithms, the proposed method can more effectively suppress random noise, achieve better synchronous denoising results, and it exhibits a stronger robustness.

U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data

Road traffic noise is a special kind of high amplitude noise in seismic or acoustic data acquisition around a road network. It is a mixture of several surface waves with different dispersion and harmonic waves. Road traffic noise is mainly generated by passing vehicles on a road. The geophones near the road will record the noise while receiving the seismic signal. The amplitude of the traffic noise is much larger than the signal, which masks the effective information and degrades the quality of acquired data. At the same time, the traffic noise is coupled with the effective signal, which makes it difficult to separate them. Therefore, attenuating traffic noise is the key to improving the quality of the final processing results. In recent years, denoising methods based on convolution neural networks (CNN) have shown good performance in noise attenuation. These denoising methods can learn the potential characteristics of acquired data, thus establishing the mapping relationship between the original data and the effective signal or noise. Here, we introduce a method combining UNet networks with asymmetric convolution blocks (ACBs) for traffic noise attenuation, and the network is called the ACB-UNet. The ACB-UNet is a supervised deep learning method, which can obtain the distribution characteristics of noise and effective signal through learning the training data and then effectively separate the two to achieve noise removal. To validate the performance of the proposed method, we apply it to synthetic and real data. The data tests show that the ACB-UNet can obtain good results for high amplitude noise attenuation and is practical and efficient.

Noise modelling of perfusion CT images for robust hemodynamic parameter estimations

Objective. The radiation dose of cerebral perfusion computed tomography (CPCT) imaging can be reduced by lowering the milliampere-second or kilovoltage peak. However, dose reduction can decrease image quality due to excessive x-ray quanta fluctuation and reduced detector signal relative to system electronic noise, thereby influencing the accuracy of hemodynamic parameters for patients with acute stroke. Existing low-dose CPCT denoising methods, which mainly focus on specific temporal and spatial prior knowledge in low-dose CPCT images, not take the noise distribution characteristics of low-dose CPCT images into consideration. In practice, the noise of low-dose CPCT images can be much more complicated. This study first investigates the noise properties in low-dose CPCT images and proposes a perfusion deconvolution model based on the noise properties. Approach. To characterize the noise distribution in CPCT images properly, we analyze noise properties in low-dose CPCT images and find that the intra-frame noise distribution may vary in the different areas and the inter-frame noise also may vary in low-dose CPCT images. Thus, we attempt the first-ever effort to model CPCT noise with a non-independent and identical distribution (i.i.d.) mixture-of-Gaussians (MoG) model for noise assumption. Furthermore, we integrate the noise modeling strategy into a perfusion deconvolution model and present a novel perfusion deconvolution method by using self-relative structural similarity information and MoG model (named as SR-MoG) to estimate the hemodynamic parameters accurately. In the presented SR-MoG method, the self-relative structural similarity information is obtained from preprocessed low-dose CPCT images. Main results. The results show that the presented SR-MoG method can achieve promising gains over the existing deconvolution approaches. In particular, the average root-mean-square error (RMSE) of cerebral blood flow (CBF), cerebral blood volume, and mean transit time was improved by 40.3%, 69.1%, and 40.8% in the digital phantom study, and the average RMSE of CBF can be improved by 81.0% in the clinical data study, compared with tensor total variation regularization deconvolution method. Significance. The presented SR-MoG method can estimate high-accuracy hemodynamic parameters andachieve promising gains over the existing deconvolution approaches.

Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs.

An important problem in many fields dealing with noisy time series, such as psychophysiological single trial data during learning or monitoring treatment effects over time, is detecting a change in the model underlying a time series. Here, we present a new method for detecting a single changepoint in a linear time series regression model, termed residuals permutation-based method (RESPERM). The optimal changepoint in RESPERM maximizes Cohen’s effect size with the parameters estimated by the permutation of residuals in a linear model. RESPERM was compared with the SEGMENTED method, a well-established and recommended method for detecting changepoints, using extensive simulated data sets, varying the amount and distribution characteristics of noise and the location of the change point. In time series with medium to large amounts of noise, the variance of the detected changepoint was consistently smaller for RESPERM than SEGMENTED. Finally, both methods were applied to a sample dataset of single trial amplitudes of the N250 ERP component during face learning. In conclusion, RESPERM appears to be well suited for changepoint detection especially in noisy data, making it the method of choice in neuroscience, medicine and many other fields.

Vibration and Noise Characteristics of Air-Core Reactor Used in HVDC Converter Stations

Air-core reactors used in filters are significant equipment in HVDC converter stations. They are among the most serious noise sources under the action of multi-frequency magnetic forces. The vibration and noise characteristics of air-core reactors are investigated in this paper, which is the basis for noise control of HVDC projects. Firstly, based on the characteristics of magnetic forces, the generation process of vibration is analyzed theoretically. Then, Experimental Modal Analysis (EMA) is conducted and the results indicate that multiple natural frequencies exist and their mode shapes contain distinct peaks and troughs. Vibration shapes are also measured under different excitation frequencies with dense measuring points over the winding surface. The measured vibration shapes are consistent with the corresponding mode shapes. Furthermore, the noise distribution characteristics are analyzed by Boundary Element Method (BEM). The simulation results show that the noise radiation has obvious directional effect, which are mainly dominated by the corresponding vibrations and mode shapes. Based on the results of this paper, some suggestions for noise reduction are given in the last.

지하철 승강장에서 소음 및 신호안내음의 분포 특성

As the elderly frequently use subway stations, there is a need for the acoustical improvement of subway platforms from a barrier-free perspective. In this study, the distribution characteristics of noise and signal guide sounds on subway platforms are determined for different locations. The entering and leaving noise of the train and the signal guide sounds were measured for thirteen and fourteen points along the two subway platforms. As a result, the maximum difference (L<SUB>Aeq</SUB>) between the noise values for the train entering and leaving for the measurement points ranged from 5.0 dBA ~ 9.0 dBA and 9.2 dBA ~ 10.9 dBA, respectively. Additionally, the maximum difference (L<SUB>Aeq</SUB>) between the signal and guide sounds for the measurement points ranged from 6.2 dBA ~ 6.5 dBA and 4.7 dBA ~ 6.5 dBA, respectively. The maximum difference (L<SUB>Aeq</SUB>) in the signal to noise ratio (S/N ratio) between the noise and signal guide sounds for the measurement points ranged from 5.1 dBA ~ 14.6 dBA. It was also found that noise had more influence on the S/N ratio than the signal guide sound. These results suggest that there is a need for noise reduction in various positions of a subway platform to enhance speech intelligibility.

Bayesian Forward-Looking Superresolution Imaging Using Doppler Deconvolution in Expanded Beam Space for High-Speed Platform

Deconvolution technique can be utilized in the forward-looking radar (FLR). However, the forward-looking imaging performance degenerates greatly due to the effect of high-speed movement of the platform. In this article, an efficient Bayesian forward-looking superresolution imaging algorithm based on Doppler deconvolution in expanded beam space is proposed. First, the Doppler phase information caused by the high-speed platform is fully exploited and the Doppler matrix is integrated with the antenna pattern. The Doppler convolution model of the echo signal for forward-looking is derived in this article. Then, the Doppler phase information is adopted to perform the Doppler deconvolution. Moreover, an expanded beam space is constructed to enhance the sparsity of the imaging scene. The complex Gaussian distribution and the Laplace distribution have been used to model the distribution characteristics of noise and targets in the imaging scene, respectively. Finally, based on the Bayesian framework, the forward-looking imaging problem is converted into the convex optimization problem. The performance assessment based on simulated and experimental data, also in comparison to the conventional real beam, truncated singular value decomposition (TSVD), iterative adaptive approach (IAA) methods, has demonstrated the effectiveness of our proposed algorithm under high-speed platform scenarios.

Indoor Positioning Algorithm Based on Maximum Correntropy Unscented Information Filter

In view of the fact that indoor positioning systems are usually affected by non-Gaussian noise in complex indoor environments, this paper tests the data in the actual scene and analyzes the distribution characteristics of noise, and proposes a new indoor positioning algorithm based on maximum correntropy unscented information filter (MCUIF). The proposed indoor positioning algorithm includes three steps: First, the estimation of the state matrix and the corresponding covariance matrix are predicted through the unscented transformation (UT). Second, the observed information is reconstructed by using a nonlinear regression method on the basis of the maximum correntropy criterion (MCC). Third, the contribution of information vector is gained by non-Gaussian measurement and the predicted information vector is corrected by the contribution of information vector. Finally, the gain of information filtering is got by the information entropy state matrix and the information entropy measurement matrix to calculate the position coordinates of the unknown nodes. This algorithm enhances the robustness of the MCUIF to non-Gaussian noise in complex indoor environments. The results from the indoor positioning experiments show that MCUIF is better than the traditional methods in state estimation and position location of the unknown nodes.

Numerical simulation on near-field aerodynamic noise of an adjustable-blade axial-flow fan

A numerical simulation method based on the Ffowcs-Williams and Hawkings model is employed to predict the mechanisms of the near-field aerodynamic noise distribution characteristics of an adjustable-blade axial-flow fan with different installation angles of moving blades (Δβ). The simulated results reveal that with Δβ changing from −12° to 12°, the changing curves of the maximum total sound pressure level (MTSPL) at the tip clearance region (A region), the leading edge region (B region), and the trailing edge region (C region) exhibit an apparently rising trend, which increase by 4.0 dB, 5.7 dB, and 4.3 dB, respectively. Besides, the MTSPL at the C region is always smaller than that at A and B regions within the studied installation angles. Additionally, the acoustic energy ratio (Cpi) is the ratio of the sound energy density of a certain frequency to the total sound energy density, which shows the various frequency distribution characteristics under studied angles. It is found that when Δβ deflects from −12° to 0°, Cp1 (the acoustic energy ratio at the low-frequency in the range of 20–500) decreases from 0.71 to 0.59, Cp2 (the acoustic energy ratio at the intermediate-frequency in the range of 500–2000) increases from 0.18 to 0.25, and Cp3 (the acoustic energy ratio at the high-frequency in the range of 2000–3000) rises from 0.1 to 0.16. This study derives the aerodynamic distribution characteristics of the TSPL and acoustic energy in the near field of moving blades, which reveals its changing rules and frequency distribution under various installation angles. The conclusions may provide guidance for the research regarding the technology of the noise control of the adjustable-blade axial-flow fan.

A quality enhancement framework with noise distribution characteristics for high efficiency video coding

Video coding effectively reduces the amount of video data while unavoidably producing compression noise. Compression noise can cause significant artifacts in compressed video, such as blocking, ringing, and blurring, which seriously affects the visual quality of videos and the value of videos for content analysis. In compressed video quality enhancement, few methods based on deep learning fully consider the relationship between video content and compression noise or the possibility of uniting the encoder or the decoder to enhance the quality of compressed video. In an approach different from existing methods, we propose a video quality enhancement framework based on the distribution characteristics of compression noise. The proposed framework consists of two parts: at the encoder, we propose a convolutional neural network (CNN)-based in-loop filtering network combined with noise distribution (IFN-ND) characteristics for the I frame instead of high efficiency video coding (HEVC) standard in-loop filters; at the decoder, we propose a CNN-based quality enhancement network combined with the noise distribution characteristics (PQEN-ND) for the P frames. The noise characteristics are extracted from the code stream to further improve the performance of the proposed networks. The experiments show that the proposed method can significantly improve the quality of HEVC compressed video, achieving an average 12.84% reduction in the BD rate and up to a 1.0476 dB increase in the peak signal-to-noise ratio (PSNR).

A Bootstrap‐Based Approach for Improving Measurements by Retarding Potential Analyzers

AbstractRetarding potential analyzers are frequently flown on small satellites as in situ ion probes, from which can be derived a number of ion plasma parameters from a current‐voltage relationship (I‐V curve). The traditional method of analyzing retarding potential analyzer data produces inaccuracies in derived estimates when there is significant noise present in the instrument measurements. In this study we investigate the dependencies between parameters that produce uncertainties in noisy I‐V curves. It is found that multiple combinations of ion velocity and spacecraft floating potential can produce I‐V curves that lie within the noise envelope, which renders it difficult for a traditional curve fitting technique to objectively and accurately estimate parameters from a noisy I‐V curve. In this paper we propose BATFORD—a bootstrap resampling‐based technique to improve the accuracies of parameter estimates. It is particularly useful when signal‐to‐noise ratios are low. The algorithm is tested against a traditional curve fitting method for a simulated data set comprising I‐V curves for the middle‐ and low‐latitude ionosphere at low Earth orbit altitudes around 450 km, where O+ is the predominant species. BATFORD is found to provide more robust and reliable estimates assuming generalized noise distribution characteristics. As further validation, the algorithm is applied to satellite data from an orbit with deep plasma bubbles and hence low signal levels.

Robust Low-rank subspace segmentation with finite mixture noise

Subspace segmentation or clustering remains a challenge of interest in computer vision when handling complex noise existing in high-dimensional data. Most of the current sparse representation or minimum-rank based techniques are constructed on ℓ1-norm or ℓ2-norm losses, which is sensitive to outliers. Finite mixture model, as a class of powerful and flexible tools for modeling complex noise, becomes a must. Among all the choices, exponential family mixture is extremely useful in practice due to its universal approximation ability for any continuous distribution and hence covers a broader scope of characteristics of noise distribution. Equipped with such a modeling idea, this paper focuses on the complex noise contaminated subspace clustering problem by using finite mixture of exponential power (MoEP) distributions. We then harness a penalized likelihood function to perform automatic model selection and hence avoid over-fitting. Moreover, we introduce a novel prior on the singular values of representation matrix, which leads to a novel penalty in our nonconvex and nonsmooth optimization. The parameters of the MoEP model can be estimated with a Maximum A Posteriori (MAP) method. Meanwhile, the subspace is computed with joint weighted ℓp-norm and Schatten-q quasi-norm minimization. Both theoretical and experimental results show the effectiveness of our method.

A Research on MBES data classification denoising algorithm based on Octree index

For the Multi-Beam Echo Sounder (MBES) data denoising problem, the noise is divided into near-ground noise and outlier noise away from the seabed. The algorithm in this paper considers the spatial distribution characteristics of noise and removes two kinds of noise by initial denoising and precise denoising. The algorithm first uses the octree index to organize the point cloud and removes the outlier noise. Then combined with Bayesian estimation theory and statistical methods to remove near-ground noise. This paper designs experiments to process MBES data and compare it with the statistical filter of PCL point cloud library. The experimental results show that compared with the PCL statistical filter, the algorithm used in this paper has good denoising effect and can better preserve the information such as regional boundaries.

An improved infrared image processing method based on adaptive threshold denoising

This paper combines the image adaptive threshold denoising algorithm and performs double threshold mapping processing to the infrared image, which effectively reduces the influence of these phenomena to the infrared image and improves the quality of the image. In this paper, the infrared image denoising technology is studied, and an infrared image denoising method based on the wavelet coefficient threshold processing is proposed. This method is based on the noise distribution characteristics of infrared images, the multiplicative noise in the infrared image is transformed into an additive noise, and the wavelet transform coefficient of the transformed infrared image is processed to denoise the image. On this basis, the advantages and disadvantages of the soft and hard threshold functions are deeply analyzed, and an adaptive threshold function with adjustable parameter is constructed. At the same time, in order to suppress the Gibbs visual distortion caused by the absence of translation invariance of the orthogonal wavelet transform, the two-input wavelet transform with translation invariance is introduced, and a double threshold mapping infrared image processing method based on the adaptive threshold denoising algorithm based on the two-input wavelet transform is formed. Simulation results show that the method proposed in this paper has a better suppression of noise, maintains the integrity of image details, and improves the image quality to a certain extent.

变电站降噪方法研究

采用穿孔板共振吸声结构用于变电站噪声治理，根据变电站噪声频率分布特性设计了共振吸声结构参数，并对共振结构吸声系数进行了数值仿真，得到其吸声谱在低频具有相对较大的吸声系数。对设计的穿孔板共振吸声结构进行了工程实验，利用设计的软件得到变电站的可听噪声倍频A计权声压级。分析了噪声治理前后的不同频带噪声的变化特性，测试结果表明所采用穿孔板共振吸声结构具有较好频率选择吸声特性，该结构大幅度地降低了变电站中的低频噪声，使变电站的噪声得到有效的控制。 This paper discusses the use of perforated plate resonant sound absorption structure for substation noise control. Based on the noise distribution characteristics of the substation, the resonant sound absorption structure parameters are designed, and the sound absorption coefficient of the resonant structure is numerically simulated. It is shown that the structure has a relatively large sound absorption coefficient at low frequency. Experiments are conducted to verify the perforated plate resonant sound absorption structure and a software is designed to obtain the substation audible noise frequency A weighted sound pressure level. The results show that the resonant sound absorption structure of the perforated plate has better frequency selection sound absorption characteristics, which greatly reduces the low frequency noise in the substation and makes the noise of the substation get effective control.

Laser test studies on the single-sided dual-column 3D stripixelsilicon detector

The single-sided full-3D stripixel detector and the laser test system were introduced in this paper. The detector responses to different conditions (including detector bias voltage, laser illumination position, pulse generator parameters setting for driving laser device, etc) were measured and analyzed. Main detector parameters, including the depletion voltage, 2D position sensitivity response, charge collection characteristics with different bias voltages, noise distribution characteristics influenced by the different coupling modes of n+-strips and p+-strips electrodes to the readout chip channels, were obtained with 1060nm laser irradiation tests. The measurement results showed that the detector was position sensitive. Its depletion voltage was about 5V. The measured equivalent noise charge(ENC) of the detector system was about 730±10e.

Numerical computation of aerodynamic noise of two tandem circular cylinders and flapping wing motion based on immersed boundary method

With the continuous development of theories, numerical computations and computational conditions, computational aero-acoustics presents huge advantages when they are used to solve the aerodynamic noise. Under low Mach number, objects with complex geometric profiles were selected as the research objects to research their aerodynamic noises at stationary and motion conditions by means of hybrid flow and sound separation algorithm for aerodynamic noise based on immersed boundary method (IBM). Firstly, the incompressible flow field was solved based on immersed boundary method, in order to obtain the flow field parameters as the input values, and solve the linearized acoustic perturbation compressible equation under non-uniform Cartesian meshes. As a result, the generation and diffusion of acoustic waves can be simulated. The circumferential flow field of two tandem circular cylinders was firstly completed and compared with the published test results to verify its reliability of the method proposed in this paper. And at different observation points, the noise distribution characteristics of two tandem circular cylinders were studied, showing that the noise in the vertical plane was distributed symmetrically and its noise intensity was greater than that in the horizontal plane. Moreover, the effect of different cylindrical diameters on radiation noise distribution was also studied, showing that the larger the cylindrical diameter was, the radiation noise close to the cylinder was smaller. Sound radiation problems of flapping wing motion were further studied by IBM, and this model was featured with obvious directivity in terms of its acoustic radiation, similar to the dipole sound source, and obvious periodicity regarding its acoustic pressure distribution. With good generality and practicability, this method can be also used for solving aerodynamic noise problems of other machines.