Obvious Noise Research Articles

A noise-reduction algorithm for raw 3D point cloud data of asphalt pavement surface texture

High-precision 3D point cloud data have various analyses and application use cases. This study aimed to achieve a more precise noise reduction of the raw 3D point cloud data of asphalt pavements obtained using 3D laser scanning. Hence, a noise-reduction algorithm integrating improved Gaussian filtering and coefficient of variation was developed. A portable laser scanner was used to collect raw, high-precision 3D point cloud data of surface textures from pavement slab samples prepared with three different types of asphalt mixtures: AC-13, SMA-13, and OGFC-13, as well as asphalt from the test sections of the Yakang Expressway. An improved Gaussian filtering and Gaussian filtering that extracts noise using the coefficient of variation were used to filter out the obvious outlier noise and small-scale burr noise, respectively. Finally, the filtering effect of the proposed algorithm, Gaussian filtering, median filtering, and mean filtering on raw 3D point cloud data of pavement textures was evaluated through subjective visual quality and objective index evaluations. The results showed that the proposed algorithm filters out noise while preserving the micro-texture structure information, outperforming Gaussian filtering, median filtering, and mean filtering.

Cotton seed cultivar identification based on the fusion of spectral and textural features.

The mixing of cotton seeds of different cultivars and qualities can lead to differences in growth conditions and make field management difficult. In particular, except for yield loss, it can also lead to inconsistent cotton quality and poor textile product quality, causing huge economic losses to farmers and the cotton processing industry. However, traditional cultivar identification methods for cotton seeds are time-consuming, labor-intensive, and cumbersome, which cannot meet the needs of modern agriculture and modern cotton processing industry. Therefore, there is an urgent need for a fast, accurate, and non-destructive method for identifying cotton seed cultivars. In this study, hyperspectral images (397.32 nm-1003.58 nm) of five cotton cultivars, namely Jinke 20, Jinke 21, Xinluzao 64, Xinluzao 74, and Zhongmiansuo 5, were captured using a Specim IQ camera, and then the average spectral information of seeds of each cultivar was used for spectral analysis, aiming to estab-lish a cotton seed cultivar identification model. Due to the presence of many obvious noises in the < 400 nm and > 1000 nm regions of the collected spectral data, spectra from 400 nm to 1000 nm were selected as the representative spectra of the seed samples. Then, various denoising techniques, including Savitzky-Golay (SG), Standard Normal Variate (SNV), and First Derivative (FD), were applied individually and in combination to improve the quality of the spectra. Additionally, a successive projections algorithm (SPA) was employed for spectral feature selection. Based on the full-band spectra, a Partial Least Squares-Discriminant Analysis (PLS-DA) model was established. Furthermore, spectral features and textural features were fused to create Random Forest (RF), Convolutional Neural Network (CNN), and Extreme Learning Machine (ELM) identification models. The results showed that: (1) The SNV-FD preprocessing method showed the optimal denoising performance. (2) SPA highlighted the near-infrared region (800-1000 nm), red region (620-700 nm), and blue-green region (420-570 nm) for identifying cotton cultivar. (3) The fusion of spectral features and textural features did not consistently improve the accuracy of all modeling strategies, suggesting the need for further research on appropriate modeling strategies. (4) The ELM model had the highest cotton cultivar identification accuracy, with an accuracy of 100% for the training set and 98.89% for the test set. In conclusion, this study successfully developed a highly accurate cotton seed cultivar identification model (ELM model). This study provides a new method for the rapid and non-destructive identification of cotton seed cultivars, which will help ensure the cultivar consistency of seeds used in cotton planting, and improve the overall quality and yield of cotton.

Characterizing Timing Noise in Normal Pulsars with the Nanshan Radio Telescope

We present a decade of observations of pulse arrival times for 85 pulsars using the Nanshan radio telescope from July 2002 to March 2014. The Cholesky method can accurately estimate the covariance function of the timing residuals, significantly improving the parameter’s estimation accuracy when red noise is prominent. We utilize the Cholesky method to determine positions and basic timing parameters of these pulsars, as well as to obtain timing residuals. Most of these sources showed evidence of significant timing irregularities, which are described. The spectral analyses of timing residuals are presented for pulsars showing obvious red noise. Our results show that timing residuals in half of these pulsars are attributed to rotational irregularities. The red noise in normal pulsars may originate from a random walk in spin frequency or spin-down rate.

Target positioning of dual forward looking sonars based on orthogonal detection

Accurate positioning of underwater targets is an important basis for many underwater tasks. As an imagery equipment, forward-looking sonar (FLS) can overcome the problem of poor brightness in underwater environment, and has a longer detection distance than the camera, which plays an important role in oceanic target positioning. However, there remain two challenging problems: (1) due to the complex underwater environment and the propagation characteristics of sound waves in the water, there are a lot of noises in the FLS images, which impair the accuracy of positioning; (2) FLS can only obtain two-dimensional information, which is insufficient for target positioning. Aiming at these two problems, a three-dimensional underwater target positioning algorithm based on dual FLSs is proposed in this paper. Firstly, a Threshold-Mask-Guided Filter (TMGF) image denoising algorithm is designed to deal with the obvious background noises and same-pitch noises in FLS images, which greatly improves the denoising effect compared with the original guided filter algorithm. Secondly, a target positioning algorithm based on orthogonal vertical placement of dual sonars is proposed to calculate the three-dimensional position of the target. Finally, pool experiments and sea trial tests are carried out respectively to verify the effectiveness of TMGF algorithm in denoising and the accuracy and stability of positioning algorithm.

SilentTrig: An imperceptible backdoor attack against speaker identification with hidden triggers

Speaker identification based on deep learning is known to be susceptible to backdoor attacks. However, the current research on audio backdoor attacks is limited, and these attacks often use obvious noises as triggers, which can raise suspicion among users. In this paper, we introduce SilentTrig, a novel and imperceptible backdoor attack method targeted at speaker identification. Our approach involves utilizing an optimized steganographic network to embed triggers into benign audio samples and implementing a two-stage adversarial optimization process. This ensures that the poisoned samples are acoustically indistinguishable from their benign counterparts, resulting in a substantially improved attack imperceptibility. We evaluate SilentTrig on two datasets and four state-of-the-art models. The results demonstrate a high Attack Success Rate (ASR) of up to 99.2%, a Just Noticeable Difference (JND) of only 0.3, and resistance to typical defense methods such as Neural Cleanse and Fine-Pruning.

Improvement of low-frequency ultrasonic image quality using a enhanced convolutional neural network

Ultrasound has become an indispensable clinical scanning and diagnostic tool because of its convenience, rapidity, and radiation-free properties. In ultrasound imaging, the resolution and detection depth of ultrasound images are closely related to the frequency of the transmitted ultrasound. Ultrasound with low transmission frequency has a wide detection range, but its images have problems such as low resolution, blurred edge contours and obvious background noise. In contrast, the higher transmission frequency, the smaller the detection range, but the resolution has improved. The rapid development of deep learning provides new approaches and enormous potential for improving resolution of ultrasound images and acquiring high-resolution images with an extensive detection range. In this paper, the attention mechanism is combined into a convolutional neural network based on the encoder-decoder, and the feature maps extracted at different scales are merged by skip connections to preserve more structure and contrast details, thus realizing the improvement of lower-frequency ultrasound image resolution. This work utilizes the capacitive micromechanical ultrasound transducer (CMUT) probe for dataset acquisition. The network's performance is validated by simulated datasets for point targets and experimental datasets for breast body models. The experimental results show that for ultrasound images, the network output map can improve by 0.2 in structural similarity and at least 11 dB in peak signal-to-noise ratio. The proposed network can effectively preserve ultrasound images' structural and textural information, improve edge contour definition, and significantly suppress noise and imaging artifacts, which has significant development potential in ultrasound imaging.

MAPMaN: Multi‐Stage U‐Shaped Adaptive Pattern Matching Network for Semantic Segmentation of Remote Sensing Images

AbstractRemote sensing images (RSIs) often possess obvious background noises, exhibit a multi‐scale phenomenon, and are characterized by complex scenes with ground objects in diversely spatial distribution pattern, bringing challenges to the corresponding semantic segmentation. CNN‐based methods can hardly address the diverse spatial distributions of ground objects, especially their compositional relationships, while Vision Transformers (ViTs) introduce background noises and have a quadratic time complexity due to dense global matrix multiplications. In this paper, we introduce Adaptive Pattern Matching (APM), a lightweight method for long‐range adaptive weight aggregation. Our APM obtains a set of pixels belonging to the same spatial distribution pattern of each pixel, and calculates the adaptive weights according to their compositional relationships. In addition, we design a tiny U‐shaped network using the APM as a module to address the large variance of scales of ground objects in RSIs. This network is embedded after each stage in a backbone network to establish a Multi‐stage U‐shaped Adaptive Pattern Matching Network (MAPMaN), for nested multi‐scale modeling of ground objects towards semantic segmentation of RSIs. Experiments on three datasets demonstrate that our MAPMaN can outperform the state‐of‐the‐art methods in common metrics. The code can be available at https://github.com/INiid/MAPMaN.

Noise Reduction of Cable Partial Discharge Vibration Signal based on EEMD-CIIT method

Abstract The cable partial discharge vibration signal is often polluted by noise. It is necessary to denoise the signal, improve the signal-to-noise ratio, and then realize the feature extraction of the vibration signal. Therefore, this paper builds a cable partial discharge test platform, collects the time-domain waveform of vibration signals under different working conditions, analyzes the noise reduction effect of ensemble empirical mode decomposition clearing repetition interval threshold noise reduction method under different threshold functions, and analyzes the difference and approximation degree between the simulated signal and the real signal after noise reduction by using signal-to-noise ratio and correlation, By comparing the difference of statistical characteristics in different defects before and after noise reduction, the wavelet threshold denoising method is established synchronously and compared with the proposed method. The results show that the high-frequency part of the vibration signal has obvious random noise components, and the effective frequency is mainly concentrated in the low-frequency band; The smoothing shear absolute deviation penalty threshold function has the smoothest curve after noise reduction and the highest approximation to the real signal. The high-frequency noise component is eliminated, and the low-frequency band frequency component is effectively retained. The method proposed in this paper can be extended to the noise reduction of vibration signals of other industrial equipment and provide technical support for the signal noise reduction of various industrial equipment.

Passive control on deep cavity noise at subsonic speeds by leading-edge grooves

Control effects of leading-edge grooves on deep cavity noise are investigated numerically. The length-to-depth ratio of the grooves are 0.5, 1, and 4, respectively. The freestream Mach number is equal to 0.16, corresponding to the Re based on the cavity length of 7.7 × 105. The Detached Eddy Simulations (DES) combined with Ffowcs Williams–Hawkings (FW-H) acoustic analogy are adopted to simulate the characteristic information of the flow field and the acoustic field. The analysis results show that all grooves investigated in this paper have a certain noise control effect, and the groove with the most obvious noise reduction effect is the groove with a length-to-depth ratio of 0.5, namely, the deep groove. Narrowband noise generated by the flow-acoustic feedback (100–700 Hz) and the acoustic resonance mechanism (above 300 Hz) and broadband noise caused by the turbulent disturbance in the shear layer of the cavity mouth significantly reduce when the deep groove is used for the noise control. The reason for the noise reduction is that the leading-edge grooves can effectively change the flow characteristics near the mouth of the downstream cavity. When the flow passes the grooves, the groove flow reduces the energy in the fluid, resulting in the significant decrease of flow velocity of the boundary layer of the incoming flow of the cavity. In addition, the use of the deep groove also promotes the vortex concentration position to move towards the bottom of the cavity, pushes the energy in the shear layer of the downstream cavity towards the front-edge as well as the bottom of the cavity, and stabilizes the development of the shear layer near the cavity mouth.

One-bit SAR imaging algorithm based on MC function and TV norm

Synthetic Aperture Radar (SAR) imaging is generally characterized by a large amount of data and a high sampling rate. The traditional one-bit compressed sensing will generate virtual targets when recovering images at a low SNR, resulting in low reconstruction accuracy and obvious noise impact of the algorithm. In this paper, a one-bit SAR imaging algorithm based on the Minimax Concave (MC) penalty function and the Total Variation (TV) norm is proposed, which can potentially improve the reconstruction accuracy. At the same time, to solve the problem of speckle noise generated in the imaging process, this paper proposes a Fast Low-rank Sparse Decomposition (FLRSD) algorithm based on the one-bit contaminated image, which potentially improves the anti-noise performance and reconstruction efficiency. The results of simulation and measured data show that the proposed algorithms have better reconstruction accuracy and focusing performance than other algorithms. Even at low SNR, they also have good reconstruction effect.

Current Zero-Crossing Prediction-Based Critical Conduction Mode Control of Totem-Pole PFC Rectifiers

Critical conduction mode (CRM) is widely used in totem-pole Boost power factor correction converters due to its compatibility with soft-switching and high switching frequency. Conventionally, a current sensor or zero-current detector is required to realize CRM operation. The system performances highly rely on the behaviors of peripheral circuits. Additional power loss and delay are also introduced. Moreover, the inductor current contains obvious differential mode noise, which brings interferences to the sensing signals. To address this issue, a novel CRM realization method is proposed. It utilizes an inductor current estimator model to estimate the averaged current and to predict the current zero-crossings. The noisy sensing signal is replaced by estimated values. Therefore, the zero-current detection circuit is removed, which simplifies the peripheral circuit design. Valley-switching and zero-voltage switching can also be achieved. Operation principles, digital implementation, and error suppression of the proposed control are analyzed. The proposed concepts are validated on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm{550}~W$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$150\mathrm{k}Hz-1.6\mathrm{M}Hz$</tex-math></inline-formula> , GaN-based prototype. Experimental results record 98.96% peak efficiency with a 0.9972 power factor.

An Acceleration Denoising Method Based on an Adaptive Kalman Filter for Trajectory in Merging Zones

Vehicle trajectory data can reveal naturalistic driving behaviour trends. However, owing to measurement and processing errors, the trajectory data extracted from videos often contain obvious noise. In merging zones, vehicles tend to accelerate and decelerate frequently, leading to poor denoising performance of the linear Kalman filter (KF). To address this issue, this study proposes a new denoising method based on the adaptive Kalman filter, which automatically switches between KF and Unscented KF to accommodate car-following and merging behaviours, respectively. A merging behaviour detection method was designed based on the PELT method and normalized innovation squared (NIS). The F1 score of 92.9% shows the accuracy of behaviour detection. According to our results, the proposed method minimizes the range of jerk compared with other methods, reducing it from −4927.78 to 4960.72 of raw data to −44.92 to 47.14, indicating a significant improvement in denoising and trajectory smoothing. The goal of this study is to achieve high-precision trajectory data under complex real traffic scenarios.

New hide-in-dark image encryption algorithm

In order to protect image information, existing encryption methods pay more attention to transforming images into noise-like images with random pixels. It is acknowledged that attackers can easily notice these encrypted images for there is a striking difference between them and unencrypted ones visible to the naked eye. As a result, these noise-like images may suffer a lot of attacks which could increase the probability of information leakage. For this problem, a method based on discrete wavelet transform and previous encryption methods has been proposed which can make the encrypted images finally look like other pictures. However, the results do not have a high quality, for some obvious noise can be found in the details of the final images. In this paper, a new method called HID is introduced in which the information is hidden in a dark area. Both experimental results and security analysis prove that our method can not only transform images full of noise into meaningful images but also produce images with higher quality.

A Rolling Bearing Fault Diagnosis Method Based on the WOA-VMD and the GAT.

In complex industrial environments, the vibration signal of the rolling bearing is covered by noise, which makes fault diagnosis inaccurate. In order to overcome the effect of noise on the signal, a rolling bearing fault diagnosis method based on the WOA-VMD (Whale Optimization Algorithm-Variational Mode Decomposition) and the GAT (Graph Attention network) is proposed to deal with end effect and mode mixing issues in signal decomposition. Firstly, the WOA is used to adaptively determine the penalty factor and decomposition layers in the VMD algorithm. Meanwhile, the optimal combination is determined and input into the VMD, which is used to decompose the original signal. Then, the Pearson correlation coefficient method is used to select IMF (Intrinsic Mode Function) components that have a high correlation with the original signal, and selected IMF components are reconstructed to remove the noise in the original signal. Finally, the KNN (K-Nearest Neighbor) method is used to construct the graph structure data. The multi-headed attention mechanism is used to construct the fault diagnosis model of the GAT rolling bearing in order to classify the signal. The results show an obvious noise reduction effect in the high-frequency part of the signal after the application of the proposed method, where a large amount of noise was removed. In the diagnosis of rolling bearing faults, the accuracy of the test set diagnosis in this study was 100%, which is higher than that of the four other compared methods, and the diagnosis accuracy rate of various faults reached 100%.

Auditory perception architecture with spiking neural network and implementation on FPGA

Spike-based perception brings up a new research idea in the field of neuromorphic engineering. A high-performance biologically inspired flexible spiking neural network (SNN) architecture provides a novel method for the exploration of perception mechanisms and the development of neuromorphic computing systems . In this article, we present a biological-inspired spike-based SNN perception digital system that can realize robust perception. The system employs a fully paralleled pipeline scheme to improve the performance and accelerate the processing of feature extraction. An auditory perception system prototype is realized on ten Intel Cyclone field-programmable gate arrays, which can reach the maximum frequency of 107.28 MHz and the maximum throughput of 5364 Mbps. Our design also achieves the power of 5. 148 W/system and energy efficiency of 845.85 μJ. Our auditory perception implementation is also proved to have superior robustness compared with other SNN systems. We use TIMIT digit speech in noise in accuracy testing. Result shows that it achieves up to 85.75% speech recognition accuracy under obvious noise conditions (signal-to-noise ratio of 20 dB) and maintain small accuracy attenuation with the decline of the signal-to-noise ratio. The overall performance of our proposed system outperforms the state-of-the-art perception system on SNN.

Three-Dimensional Point Cloud Data Pre-Processing for the Multi-Source Information Fusion in Aircraft Assembly

Wing-body assembly is a key part of aircraft manufacturing, and during the process of wing assembly, the 3D point cloud data of the components are an important basis for attitude adjustment. The large amount of measured point cloud data and the obvious noise affect the quality and efficiency of the final assembly. To address this problem, research on the pre-processing method of the component point cloud data is carried out. Firstly, a feature-enhanced point cloud resampling method is proposed to preserve key features such as part contours in the resampling process. Then, a multi-scale point cloud data noise filtering method is proposed, which can effectively filter out the outliers. The experimental results show that the proposed method improves the speed and accuracy of the subsequent point cloud analysis effectively and is successfully applied to the assembly process of a large passenger aircraft, laying the foundation for high-quality assembly.

Speech Enhancement for Laser Doppler Vibrometer Dealing with Unknown Irradiated Objects

The acquisition of distant sound has always been a hot research topic. Since sound is caused by vibration, one of the best methods for measuring distant sound is to use a laser Doppler vibrometer (LDV). This laser has high directivity, that enables it to acquire sound from far away, which is of great practical use for disaster relief and other situations. However, due to the vibration characteristics of the irradiated object itself and the reflectivity of its surface (or other reasons), the acquired sound is often lacking frequency components in certain frequency bands and is mixed with obvious noise. Therefore, when using LDV to acquire distant speech, if we want to recognize the actual content of the speech, it is necessary to enhance the acquired speech signal in some way. Conventional speech enhancement methods are not generally applicable due to the various types of degradation in observed speech. Moreover, while several speech enhancement methods for LDV have been proposed, they are only effective when the irradiated object is known. In this paper, we present a speech enhancement method for LDV that can deal with unknown irradiated objects. The proposed method is composed of noise reduction, pitch detection, power spectrum envelope estimation, power spectrum reconstruction, and phase estimation. Experimental results demonstrate the effectiveness of our method for enhancing the acquired speech with unknown irradiated objects.

Application of Near Infrared Hyperspectral Imaging Technology in Purity Detection of Hybrid Maize

Seed purity has an important impact on the yield and quality of maize. Studying the spectral characteristics of hybrid maize and exploring the rapid and non-destructive detection method of seed purity are conducive to the development of maize seed breeding and planting industry. The near-infrared spectral data of five hybrid maize seeds were collected in the laboratory. After eliminating the obvious noises, the multiple scattering correction (MSC) was applied to pretreat the spectra. PLS-DA, KNN, NB, RF, SVM-Linear, SVM-Polynomial, SVM-RBF, and SVM-Sigmaid were used as pattern recognition methods to classify five different types of maize seeds. The recognition accuracy of the models established by different algorithms was 84.4%, 97.6, 100%, 96.4, 99.2%, 100%, 98.4%, and 91.2%, respectively. The results indicated that hyperspectral imaging technology could be used for variety classification and the purity detection of maize seeds. To improve the calculation speed, using the principal component analysis (PCA) to reduce the dimension of hyperspectral data, we then established classification models based on characteristic wavelengths. The recognition accuracy of the models established by different algorithms was 80.8%, 86.8%, 98%, 94%, 96.8%, 98.4%, 94.4%, and 88.2%, respectively. The results showed that the selected sensitive wavelengths could be used to detect the purity of maize seeds. The overall results indicated that it was feasible to use near-infrared hyperspectral imaging technology for the variety identification and purity detection of maize seeds. This study also provides a new method for rapid and non-destructive detection of seed purity.

Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review.

Continuous blood pressure (BP) monitoring is of great significance for the real-time monitoring and early prevention of cardiovascular diseases. Recently, wearable BP monitoring devices have made great progress in the development of daily BP monitoring because they adapt to long-term and high-comfort wear requirements. However, the research and development of wearable continuous BP monitoring devices still face great challenges such as obvious motion noise and slow dynamic response speeds. The pulse wave transit time method which is combined with photoplethysmography (PPG) waves and electrocardiogram (ECG) waves for continuous BP monitoring has received wide attention due to its advantages in terms of excellent dynamic response characteristics and high accuracy. Here, we review the recent state-of-art wearable continuous BP monitoring devices and related technology based on the pulse wave transit time; their measuring principles, design methods, preparation processes, and properties are analyzed in detail. In addition, the potential development directions and challenges of wearable continuous BP monitoring devices based on the pulse wave transit time method are discussed.

Noise reduction by perforated cascades in annular ducts

One of the latest trends in noise control relating to aeroacoustics is to mimic the silent flight capability of owls. Particularly, porosity is most often applied on cascades in ducts with axial flows, such as stator structures in an aero-engine. However, current acoustic scattering models of perforated cascades are based on two-dimensional methods without including the three-dimensional effects. In this paper, we present a fully three-dimensional acoustic scattering model for perforated cascades based on the lifting surface theory in which the dominant sound source reduces to dipoles alone under the thin airfoil assumption. Accordingly, the acoustic scattering of perforated cascades with single-mode incident wave was studied and obvious noise reduction was observed. The optimum Rayleigh conductivity and the maximum noise-reducing capability of the porosity varied substantially with different incident duct modes, whilst a larger cascade chord-length could achieve more noise reduction at optimum porosity. With a background flow, the Kutta condition can greatly influence the overall distribution of the unsteady loading on vanes. Additionally, the unsteady vortex shedding at the trailing edge offers extra sound energy dissipation mechanism. Therefore, the implementation of porosity on cascades is much different to the design of a traditional acoustic liner.