Suppress Noise Interference Research Articles

A local binary patterns/variance operator based on guided filtering for seismic fault detection

Aiming at suppressing noise interference, improving the fault detection ability of seismic data, fully excavating the effective information in seismic data, and further improving the accuracy of fault detection, this study proposes a seismic fault detection method that combines the local binary pattern/variance (LBP/VAR) operator with guided filtering. The proposed method combines the advantages of LBP/VAR and guided filtering to remove noise from seismic data, and can simultaneously smooth the data and preserve linear features. When compared with several existing methods (coherent operator, LBP/VAR operator, LBP/VAR operator based on median filtering, and Canny operator based on guided filtering), the proposed method exhibits a better SNR, a better ability to identify small faults, and robustness to noise. This novel algorithm can control the balance between noise attenuation and effective signal preservation as well as effectively detect faults in seismic data. Therefore, the proposed method effectively improves the fault identification accuracy, facilitates the gas-bearing analysis of the structure, provides guidance for the actual well location deployment of the project, and has important practical significance for oil and gas exploration and development.

Design of dynamic load balancing algorithm for heterogeneous clusters based on energy consumption

In order to reduce energy waste and improve the dynamic load balancing of heterogeneous clusters, sensor technology is introduced into the Internet of Things technology to build a channel transmission model for heterogeneous clusters. Then use the dynamic weighting method to configure the output channel to complete the decomposition of the channel characteristics. In addition, based on the establishment of the channel fuzzy recombination structure model, the noise interference suppression method is used to suppress the multipath interference of heterogeneous cluster communication channels, and the equalization of the channel output is controlled by the baud interval equalization sampling method. At the same time, through the fuzzy balance configuration and spatial balance scheduling process, the dynamic load balancing processing of heterogeneous clusters is realized. The simulation results show that the method proposed in this paper has better balance for the load scheduling of heterogeneous clusters and reduces the bit error rate of the output signal. More importantly, the output balance and adaptive control capabilities of heterogeneous clusters have also been improved.

PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris

Current segmentation methods have limitations for multi-source heterogeneous iris segmentation since differences of acquisition devices and acquisition environment conditions lead to images of greatly varying quality from different iris datasets. Thus, different segmentation algorithms are generally applied to distinct datasets. Meanwhile, deep-learning-based iris segmentation models occupy more space and take a long time. Therefore, a lightweight, precise, and fast segmentation network model, PFSegIris, aimed at the multi-source heterogeneous iris is proposed by us. First, the iris feature extraction modules designed were used to fully extract heterogeneous iris feature information, reducing the number of parameters, computation, and the loss of information. Then, an efficient parallel attention mechanism was introduced only once between the encoder and the decoder to capture semantic information, suppress noise interference, and enhance the discriminability of iris region pixels. Finally, we added a skip connection from low-level features to catch more detailed information. Experiments on four near-infrared datasets and three visible datasets show that the segmentation precision is better than that of existing algorithms, and the number of parameters and storage space are only 1.86 M and 0.007 GB, respectively. The average prediction time is less than 0.10 s. The proposed algorithm can segment multi-source heterogeneous iris images more precisely and quicker than other algorithms.

A recursive approach for aeroacoustic phased array measurements in wind tunnels.

Noise-interference suppression and data-processing acceleration are crucial to aeroacoustic measurements with phased array in wind tunnels. In this paper, we develop a "multi-window" beamforming algorithm that recursively processes data based on an array-acquisition model. This spatial filtering algorithm is derived from the Kalman filter theory for signal processing. The simulated results show that by using recursive operations, accurate signal estimation is acquired with incoherent and coherent background noise removed in the presence of both channel noise and phase noise. The convergence rate of this recursive algorithm is faster than the existing algorithms. As a result, considerable storage space and computational resources are saved, while testing defects in wind tunnel measurement are revealed and corrected immediately on-site. It has a great potential for real-time localization of sound sources in a noisy environment.

Monopulse Feature Extraction and Fault Diagnosis Method of Rolling Bearing under Low-Speed and Heavy-Load Conditions

According to the rolling bearing local fault vibration mechanism, a monopulse feature extraction and fault diagnosis method of rolling bearing under low-speed and heavy-load conditions based on phase scan and CNN is proposed. The synchronous collected speed signal is used to calculate bearing phase function and divide fault monopulse periods. The monopulse waveforms of multiple fault periods are scanned and ensemble averaged to suppress noise interference and detail feature loss at the same time of feature extraction. By iteratively calibrating phase function, the feature matrix containing bearing fault information can be obtained. Finally, CNN is used to recognize and classify different bearing states. The experimental and analysis results show that bearing fault diagnosis can be achieved. The total recognition rates of constant and variable speed samples are 99.67% and 99.89%, respectively. The trained network has fast convergence speed and good generalization ability for different fault sizes and working conditions. Further experiments show that the method can also accurately identify different bearing degradation states. The total recognition rates of constant and variable speed samples are 96.67% and 95.56%, respectively. The limited errors are concentrated between the degradation states with the same type weak fault. The experimental results using Case Western Reserve University bearing data show that feature extraction and network training are better, and the recognition rates of 5 bearing states are all 100%. Therefore, the proposed method is an effective rolling bearing feature extraction and fault diagnosis technology.

An Improved Impedance Measurement Method Based on Multi-Sine Signal Considering the Suppression of Noise Interference

Impedance measurement is a practical method to obtain the impedance characteristic of the power electronic equipment (PEE), which is important for the small-signal stability analysis. In the impedance measurement, the measurement results may be influenced by the external noise. Therefore, it is essential to study how to avoid the measurement errors caused by the noise. To address this issue, this paper analyzes the method of suppressing the noise interference by enhancing signal-to-noise ratio (SNR) and concludes that there is limitation on improving the measurement accuracy by merely enhancing SNR due to the practical limitation on the perturbation amplitudes. And then this paper proposes an improved impedance measurement method, by which the measurement errors caused by the noise can be decreased effectively. By investigating the influence mechanism of the noise on the impedance measurement results, the measurement error can be divided into average component and fluctuant component. The average component can be eliminated by averaging two groups of measurement results with the phase difference of $\pi $ rad while the influence of the fluctuant component can be minimized by employing complex-linear fitting algorithm. Finally, experiments are conducted to verify the effectiveness of proposed impedance measurement method.

Application of VMD in Pipeline Leak Detection Based on Negative Pressure Wave

Leakage problems are common in the water supply pipeline system, which will threaten the health of residents and cause economic losses. Negative pressure wave (NPW) technology calculates the time difference through the inflection point to locate the leak. However, due to the nonlinear and nonstationary characteristics of the pressure signal, it is difficult to obtain an accurate inflection point of the NPW by the traditional method. Therefore, the advantages of applying variational mode decomposition (VMD) in NPW technology are explored. Firstly, the correlation coefficient and permutation entropy (PE) are used for effective intrinsic mode function (IMF) component selection and parameter optimization. Thus, an adaptive denoising method based on VMD (AD‐VMD) is presented. Then, to effectively separate the detail features of the NPW, a novel inflection point extraction method based on VMD (IPE‐VMD) is proposed. Simulation and experimental results show that AD‐VMD can effectively suppress noise interference and conserve the mutation characteristic of the leakage. IPE‐VMD can obtain a distinct maximum peak at the inflection point and has good robustness to noise interference. This method can calculate the time difference precisely and stably. In addition, the accuracy of the leak location is verified. The average relative positioning error is 5.13%.

Base Line Wander, Breathing, Power Line Interference Noise Suppression and QRS Detection in Scilab

Base Line Wander, Breathing, Power Line Interference Noise Suppression and QRS Detection in Scilab

The Broken Wires Identification of Wire Rope Based on Multilevel Filtering Method Using EEMD and Wavelet Analysis

In view of the shortcomings of current wire rope magnetic flux leakage (MFL) detection devices such as large size and low sensitivity, as well as the poor noise reduction effect of MFL data and the low recognition rate of defects, this paper designs a wire rope three-dimensional MFL detection device based on unsaturated magnetic excitation (UME) and proposes a multilevel filtering algorithm. Six types of small gap broken wire defects can be effectively detected by the three-dimensional tunnel magnetoresistive sensor array. The multilevel filtering method based on EEMD and optimal wavelet is applied to three-dimensional UME signal processing, effectively suppressing noise interference. And color processing technology is applied to enhance the characterization of the defect images. A support vector machine recognition network based on the color moment feature vector as input is designed to classify broken wire defects, and the quantitative recognition rate of 98.572% is achieved when the limit error is 0.901%. The experimental results show that the detection device has the advantages of high sensitivity and simple structure and can collect MFL information in three directions, and the filtering algorithm effectively improves the signal-to-noise ratio and can realize the preliminary quantification of broken wire defects.

Research on Multi-Order Anti-Aliasing Filter in Bearing Profile Detection

In the process of bearing profile detection, an analog displacement sensor is used, but there is noise interference in the process of data acquisition. In order to suppress noise interference, the paper designs three-order and fifth-order anti-aliasing filters according to the actual interference signal source. The resistance value and capacitance value are calculated according to the transfer function. The real object is made by the porous simulation board. The comparison between the input and output signals shows that the fifth-order anti-aliasing filter is better than the third-order anti-aliasing filter. Experiments show that the higher order anti-aliasing filter has a certain suppression effect on the noise signal, but the effect is not obvious. The signal distortion would happen and the system would become more and more complicated. For this reason, the fifth-order anti-aliasing filter is finally selected, and the actual sampling is completed, and the effect reaches the requirements of the detection system.

Lower Limb Motion Recognition Method Based on Improved Wavelet Packet Transform and Unscented Kalman Neural Network

Exoskeleton robot is a typical application to assist the motion of lower limbs. To make the lower extremity exoskeleton more flexible, it is necessary to identify various motion intentions of the lower limbs of the human body. Although more sEMG sensors can be used to identify more lower limb motion intention, with the increase in the number of sensors, more and more data need to be processed. In the process of human motion, the collected sEMG signal is easy to be interfered with noise. To improve the practicality of the lower extremity exoskeleton robot, this paper proposed a wavelet packet transform- (WPT-) based sliding window difference average filtering feature extract algorithm and the unscented Kalman neural network (UKFNN) recognition algorithm. We established an sEMG energy feature model, using a sliding window difference average filtering method to suppress noise interference and extracted stable feature values and using UKF filtering to optimize the neural network weights to improve the adaptability and accuracy of the recognition model. In this paper, we collected the sEMG signals of three muscles to identify six lower limb motion intentions. The average accuracy of 94.83% is proposed in this paper. Experiments show that the algorithm improves the accuracy and anti-interference of motion intention recognition of lower limb sEMG signals. The algorithm is superior to the backpropagation neural network (BPNN) recognition algorithm in the lower limb motion intention recognition and proves the effectiveness, novelty, and reliability of the method in this paper.

Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection.

Automated electrocardiogram (ECG) analysis for arrhythmia detection plays a critical role in early prevention and diagnosis of cardiovascular diseases. Extracting powerful features from raw ECG signals for fine-grained diseases classification is still a challenging problem today due to variable abnormal rhythms and noise distribution. For ECG analysis, the previous research works depend mostly on heartbeat or single scale signal segments, which ignores underlying complementary information of different scales. In this paper, we formulate a novel end-to-end Deep Multi-Scale Fusion convolutional neural network (DMSFNet) architecture for multi-class arrhythmia detection. Our proposed approach can effectively capture abnormal patterns of diseases and suppress noise interference by multi-scale feature extraction and cross-scale information complementarity of ECG signals. The proposed method implements feature extraction for signal segments with different sizes by integrating multiple convolution kernels with different receptive fields. Meanwhile, joint optimization strategy with multiple losses of different scales is designed, which not only learns scale-specific features, but also realizes cumulatively multi-scale complementary feature learning during the learning process. In our work, we demonstrate our DMSFNet on two open datasets (CPSC_2018 and PhysioNet/CinC_2017) and deliver the state-of-art performance on them. Among them, CPSC_2018 is a 12-lead ECG dataset and CinC_2017 is a single-lead dataset. For these two datasets, we achieve the F1 score [Formula: see text] and [Formula: see text] which are higher than previous state-of-art approaches respectively. The results demonstrate that our end-to-end DMSFNet has outstanding performance for feature extraction from a broad range of distinct arrhythmias and elegant generalization ability for effectively handling ECG signals with different leads.

VIF-Net: An Unsupervised Framework for Infrared and Visible Image Fusion

Visible images provide abundant texture details and environmental information, while infrared images benefit from night-time visibility and suppression of highly dynamic regions; it is a meaningful task to fuse these two types of features from different sensors to generate an informative image. In this article, we propose an unsupervised end-to-end learning framework for infrared and visible image fusion. We first construct enough benchmark training datasets using the visible and infrared frames, which can address the limitation of the training dataset. Additionally, due to the lack of labeled datasets, our architecture is derived from a robust mixed loss function that consists of the modified structural similarity (M-SSIM) metric and the total variation (TV) by designing an unsupervised learning process that can adaptively fuse thermal radiation and texture details and suppress noise interference. In addition, our method is an end to end model, which avoids setting hand-crafted fusion rules and reducing computational cost. Furthermore, extensive experimental results demonstrate that the proposed architecture performs better than state-of-the-art methods in both subjective and objective evaluations.

Combining Chrominance Features and Fast ICA for Noncontact Imaging Photoplethysmography

Video-based noncontact detection of heart rate has a wide range of applications in the field of medicine and health. However, this method is susceptible to noise interference, making it difficult to effectively extract blood volume pulse (BVP) signals. To overcome this problem, a new method of noncontact heart rate estimation that can suppress noise interference is proposed in this paper. First, the established data acquisition system conducts video collection, and the captured videos are divided into multiple small regions. Subsequently, the initial signals of BVP are extracted in accordance with the chrominance features extracted through multi-channel data fusion. The BVP signals are separated using the FastICA algorithm. The kurtosis value and signal-to-noise ratios of the power spectrum of the separated signals are analyzed to determine the effective separation component. Results show that this method can extract and process pulse signals, effectively suppressing non-periodic interference. The experiment also proves that the method has good consistency with the measurement of pulse oximeter and has good stability and accuracy in the detection of heart rate of the human body.

Intelligent Control of Urban Lighting System Based on Video Image Processing Technology

The rapid iterative development of information technology, computer technology and Internet of things technology promotes the construction of smart city, and the related concepts of smart city gradually move from theory to practice. As an important part of the development of smart city, the intelligent lighting and intelligent control of urban lighting system become increasingly urgent with the development of the latest Internet of things technology and video image processing technology. Based on the current Internet of things data communication and related processing technology as the background, this article constructs the overall control model of smart city lighting system, including GPRS wireless transmission technology and ZigBee technology. In the core algorithm, this article innovatively presents an improved video image processing technology based on Gaussian mixture algorithm, which mainly describes the background of a specific scene in multi-dimensional model, so as to improve the reliability of the whole scene judgment. At the level of noise suppression, the video image processing algorithm proposed in this article designs interference noise suppression technology based on the principle of mean filtering, so as to achieve the accuracy and integrity of urban traffic scene extraction. In order to solve the security problems of the whole video image processing technology in information extraction, storage and analysis, this article adds the electromagnetic information leakage prevention algorithm in the video image processing technology, which mainly processes the video image at the source, so as to reduce the electromagnetic radiation of the overall information. Based on this, this article completed the construction of the software and hardware of the city intelligent lighting system. On this basis, the software and hardware construction of urban intelligent lighting system is completed. At the same time, the experiment was carried out in a small-scale scene of a city. The experimental results show that the intelligent urban lighting system based on video image processing technology is feasible and can achieve the desired effect. At the same time, the lighting energy consumption level can be reduced by about 30%, and the corresponding failure rate can be reduced by about 30%.

Non-local weighted regularization for optical flow estimation

Global optical flow characterizes the overall motion while local optical flow characterizes the individual's movement. Therefore, both global and local optical flow is important to analyze the motion of each object in an image and of the camera. In order to robustly and accurately estimate the optical flow which includes both global and local motion information, this paper proposes a novel variational framework with non-local weighted regularization for optical flow estimation (NLWOF). The proposed NLWOF strategy includes the following two key parts: Firstly, non-local prior is used a regularization to robustly estimate the local optical flow due to that the similar structure patches using in non-local weight can suppress noise interference. Secondly, the solution of the NLWOF model can be approximated by the employment of the Lagrange–Euler formula and the approximation of the Laplace operator. Experimental results in both quantitation and qualitation verify the effectiveness of the NLWOF scheme, and even better than those of the state-of-the-arts.

Design and development of an EOG-based simplified Chinese eye-writing system

Eye movement information provides an alternative way for patients with muscular and neurological disorders to communicate with other people or devices. Electrooculography (EOG) is an effective eye movement recording method that has been widely applied to design human–computer interaction systems. In this paper, we propose a simplified Chinese eye-writing system based on EOG (EsCew) by identifying the basic strokes of Chinese characters. Specifically, we first use a bandpass digital filter to preprocess the raw EOG signals to suppress noise interference. Then, we determine the effective eye movement segments according to different strokes by detecting blink signals using the sliding window technique. On this basis, we establish basic stroke templates in terms of the handwriting characteristics of Chinese characters. To reduce the computational complexity, the DTW algorithm is adopted to classify the EOG segments. Finally, we match the stroke sequence with the encoded Chinese characters to obtain the final recognition results. In the lab environment, the recognition experiments are performed on 10 most representative Chinese characters, i.e., ▪. The average accuracies of the basic strokes and Chinese characters are 93.998% and 94.52%, respectively. The experimental results validate the feasibility of the proposed EsCew system. [Display omitted]

Wideband Noise Interference Suppression for Sparsity-Based SAR Imaging Based on Dechirping and Double Subspace Extraction

Sparsity-based synthetic aperture radar (SAR) imaging has attracted much attention since it has potential advantages in improving the image quality and reducing the sampling rate. However, it is vulnerable to deliberate blanket disturbance, especially wideband noise interference (WBNI), which severely damages the imaging quality. This paper mainly focuses on WBNI suppression for SAR imaging from a new perspective—sparse recovery. We first analyze the impact of WBNI on signal reconstruction by deducing the interference energy projected on the real support set of the signal under different observation parameters. Based on the derived results, we propose a novel WBNI suppression algorithm based on dechirping and double subspace extraction (DDSE), where the signal of interest (SOI) is reconstructed by exploiting the known geometric prior and waveform prior, respectively. The experimental results illustrate that the DDSE-based WBNI suppression algorithm for sparsity-based SAR imaging is effective and outperforms the other algorithms.

High-efficient and low-coupling spoof surface plasmon polaritons enabled by V-shaped microstrips.

We propose a novel variety of V-shaped microstrips for highly efficient and strongly confined spoof surface plasmon polaritons (SSPPs) propagation. We analyze the dispersion characteristics of the V-shaped SSPPs microstrip units and find that the asymptotic frequency of the dispersion curve can be significantly reduced by adding the folded stub without increasing the lateral dimension of the structure. The V-shaped microstrip possesses the advantage of being compatible with a conventional microstrip without the need for complicated and bulky mode conversion structures in other typical grooved SSPP waveguides. Then, broadband transitions with a tapered microstrip and an array of graded height V-shaped units with good impedance matching and high mode conversion efficiency are designed. The simulated and measured results demonstrate that the proposed V-shaped microstrip has excellent broadband lowpass filter characteristics with the reflection coefficient (S11) less than -10 dB and the transmission coefficient (S21) higher than -3 dB in the frequency range from 0 to 10.3 GHz. Furthermore, the coupling characteristics of the parallel and symmetrically arranged V-shaped microstrips are investigated. Compared to conventional parallel microstrips with a separation of 2.8 mm, the proposed parallel V-shaped microstrips with 2 mm inner-overlapping have significantly lower coupling effects in the frequency ranging from 0 to 10 GHz. The low coupling, strong field confinement, and flexible dispersion manipulation of the proposed microstrip make it possible to achieve device miniaturization and noise interference suppression, which may have great potentials in the development of various highly integrated microwave plasmonic circuits, devices, and systems.

Improved Non-local Means Algorithm for Image Denosing

In order to solve the problem that Non-local means method (NLM) results in a weaker retention and noise immunity, an improved non-local means (INLM) is proposed. This paper introduces bilateral structure tensor combined with the spatial distance and the gradient distance to weight the structure tensor matrix. It has stronger noise immunity and can extract the texture structure more accurately. Mahalanobis distance is applied as a measure of the similarity of image pixels to suppress noise interference. Analyses and simulation results prove the validity of the proposed algorithm.