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A Signal Separation Method Based on Instantaneous Frequency Embedded Continuous Wavelet Transform and Short-Time Fourier Transform 认领 引用
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作者 Qingtang Jiang 《Analysis in Theory and Applications》 CSCD 2026年第1期15-31,共17页
Modeling a non-stationary,multicomponent signal as a superposition of frequency components,each with a well-defined instantaneous frequency(IF),is crucial for extracting information,such as the underlying dynamics hid... Modeling a non-stationary,multicomponent signal as a superposition of frequency components,each with a well-defined instantaneous frequency(IF),is crucial for extracting information,such as the underlying dynamics hidden within the signal.The synchrosqueezing transform(SST)has emerged as an alternative to empirical mode decomposition(EMD)for separating non-stationary signals.However,because the SST estimates the IFs of all frequency components based on a single phase transformation,its accuracy can be limited.To address this,SST variants based on the IFembedded short-time Fourier transform(IFE-STFT)and the IF-embedded continuous wavelet transform(IFE-CWT)were developed.More recently,a direct time-frequency method called the signal separation operation(SSO)was introduced for multicomponent signal separation.SSO bypasses the second step of the two-step SST method for component recovery and is based on variants of the STFT or CWT.In this paper,we propose a direct signal separation method by combining the SSO method with IFE-CWT and IFE-STFT,creating the IFE-CWT-based SSO(IWSSO)and the IFE-STFT-based SSO(IFSSO).Both IWSSO and IFSSO directly separate multicomponent signals without the squeezing operation inherent in SST.Our algorithms and techniques yield more accurate instantaneous frequency estimates and signal separation than conventional SSO or SST methods. 展开更多
关键词 Instantaneous frequency(IF)estimation mode retrieval signal separation operator based on IF-embedded continuous wavelet transform
RetinexWT: Retinex-Based Low-Light Enhancement Method Combining Wavelet Transform 认领 引用
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作者 Hongji Chen Jianxun Zhang +2 位作者 Tianze Yu Yingzhu Zeng Huan Zeng 《Computers, Materials & Continua》 SCIE EI 2026年第2期2113-2132,共20页
Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional ... Low-light image enhancement aims to improve the visibility of severely degraded images captured under insufficient illumination,alleviating the adverse effects of illumination degradation on image quality.Traditional Retinex-based approaches,inspired by human visual perception of brightness and color,decompose an image into illumination and reflectance components to restore fine details.However,their limited capacity for handling noise and complex lighting conditions often leads to distortions and artifacts in the enhanced results,particularly under extreme low-light scenarios.Although deep learning methods built upon Retinex theory have recently advanced the field,most still suffer frominsufficient interpretability and sub-optimal enhancement performance.This paper presents RetinexWT,a novel framework that tightly integrates classical Retinex theory with modern deep learning.Following Retinex principles,RetinexWT employs wavelet transforms to estimate illumination maps for brightness adjustment.A detail-recovery module that synergistically combines Vision Transformer(ViT)and wavelet transforms is then introduced to guide the restoration of lost details,thereby improving overall image quality.Within the framework,wavelet decomposition splits input features into high-frequency and low-frequency components,enabling scale-specific processing of global illumination/color cues and fine textures.Furthermore,a gating mechanism selectively fuses down-sampled and up-sampled features,while an attention-based fusion strategy enhances model interpretability.Extensive experiments on the LOL dataset demonstrate that RetinexWT surpasses existing Retinex-oriented deeplearning methods,achieving an average Peak Signal-to-Noise Ratio(PSNR)improvement of 0.22 dB over the current StateOfTheArt(SOTA),thereby confirming its superiority in low-light image enhancement.Code is available at http://gffzz188fe103f8f1460asvukuuo0n6cov6kqb.ffgz.tsg.suse.edu.cn/CHEN-hJ516/RetinexWT(accessed on 14 October 2025). 展开更多
关键词 Low-light image enhancement retinex algorithm wavelet transform vision transformer
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Explainable electroencephalography-based attentiondeficit/hyperactivity disorder detection model with a combination of ternary pattern and twin wavelet transform 认领 引用
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作者 Yavuz Atas Serkan Kırık +9 位作者 Kübra Yıldırım Burak Tasci Prabal Datta Barua Ferhat Balgetir Sengul Dogan Turker Tuncer Ru-San Tan Elizabeth Palmer Aruna Devi U Rajendra Acharya 《World Journal of Psychiatry》 SCIE 2026年第3期141-158,共18页
BACKGROUND Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental condition characterized by inattention,impulsivity,and hyperactivity.Traditional diagnosis relies on clinical evaluation,which is... BACKGROUND Attention-deficit/hyperactivity disorder(ADHD)is a common neurodevelopmental condition characterized by inattention,impulsivity,and hyperactivity.Traditional diagnosis relies on clinical evaluation,which is timeconsuming and subjective.Electroencephalography(EEG)signals provide an objective alternative,and machine learning methods can improve their diagnostic utility.AIM To develop an explainable EEG-based model for ADHD detection by integrating a novel combination ternary pattern(CTP)feature extractor with twin wavelet transform(TWT)for multilevel signal analysis,and to evaluate its effectiveness in providing accurate,channel-wise,and fusion-based classification results for objective and rapid ADHD diagnosis.METHODS A new EEG dataset containing more than 7000 segments from 137 ADHD patients and 150 controls was studied.A novel feature engineering framework was developed,combining a new CTP extractor with statistical features.A multilevel feature extraction structure was designed using a newly proposed TWT for signal decomposition.Extracted features were reduced to the most informative 263 using neighborhood component analysis.Channelwise classification was performed with k-nearest neighbors,followed by iterative majority voting across 20 EEG channels.RESULTS Single-channel analysis achieved up to 99.12%accuracy.By applying majority voting,overall classification accuracy increased to 99.97%,with similarly high sensitivity and specificity.CONCLUSION Our study introduces a large ADHD EEG dataset and a novel model integrating TWT and CTP.The model provides highly accurate,channel-wise,and fusion-based results,offering a promising objective tool for rapid ADHD diagnosis. 展开更多
关键词 Attention-deficit/hyperactivity disorder detection Combination ternary pattern Electroencephalography signal classification Explainable feature engineering Twin wavelet transform
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Block-Wise Sliding Recursive Wavelet Transform and Its Application in Real-Time Vehicle-Induced Signal Separation 认领 引用
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作者 Jie Li Nan An Youliang Ding 《Structural Durability & Health Monitoring》 EI 2026年第1期1-22,共22页
Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ... Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring. 展开更多
关键词 Wavelet transform vehicle-induced signal separation real-time structure monitoring
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Image Watermarking Method Using Integer-to-Integer Wavelet Transforms 认领 引用
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作者 陈韬 王京春 《Tsinghua Science and Technology》 CAS 2002年第5期508-512,共5页
Digital watermarking is an efficient method for copyright protection for text, image, audio, and video data. This paper presents a new image watermarking method based on integer-to-integer wavelet transforms. The wate... Digital watermarking is an efficient method for copyright protection for text, image, audio, and video data. This paper presents a new image watermarking method based on integer-to-integer wavelet transforms. The watermark is embedded in the significant wavelet coefficients by a simple exclusive OR operation. The method avoids complicated computations and high computer memory requirements that are the main drawbacks of common frequency domain based watermarking algorithms. Simulation results show that the embedded watermark is perceptually invisible and robust to various operations, such as low quality joint picture expert group (JPEG) compression, random and Gaussian noises, and smoothing (mean filtering). 展开更多
关键词 digital watermark data hiding steganography integer-to-integer wavelet transform
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Prediction of wastewater treatment plant influent quality based on discrete wavelet transform and convolutional enhanced transformer 认领 引用
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作者 Lili Ma Danxia Li +2 位作者 Jinrong He Zhirui Niu Zhihua Feng 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第11期405-417,共13页
Accurate prediction of wastewater treatment plants(WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management.However,since existing methods overlook the data noise ge... Accurate prediction of wastewater treatment plants(WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management.However,since existing methods overlook the data noise generated from harsh operations and instruments,while the local feature pattern and long-term dependency in the wastewater quality time series,the prediction performance can be degraded.In this paper,a discrete wavelet transform and convolutional enhanced Transformer(DWT-Ce Transformer) method is developed to predict the influent quality in WWTPs.Specifically,we perform multi-scale analysis on time series of wastewater quality using discrete wavelet transform,effectively removing noise while preserving key data characteristics.Further,a tightly coupled convolutional-enhanced Transformer model is devised where convolutional neural network is used to extract local features,and then these local features are combined with Transformer's self-attention mechanism,so that the model can not only capture long-term dependencies,but also retain the sensitivity to local context.In this study,we conduct comprehensive experiments based on the actual data from a WWTP in Shaanxi Province and the simulated data generated by BSM2.The experimental results show that,compared to baseline models,DWT-Ce Transformer can significantly improve the prediction performance of influent COD and NH3-N.Specifically,MSE,MAE,and RMSE improve by 78.7%,79.5%,and 53.8% for COD,and 79.4%,70.2%,and 54.5% for NH3-N.On simulated data,our method shows strong improvements under various weather conditions,especially in dry weather,with MSE,MAE,and RMSE for COD improving by 68.9%,48.0%,and 44.3%,and for NH3-N by 78.4%,54.8%,and 53.2%. 展开更多
关键词 Wastewater treatment plant Influent quality prediction Discrete wavelet transform Transformer Local feature Long-term dependencies
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Wavelet Transform Convolution and Transformer-Based Learning Approach for Wind Power Prediction in Extreme Scenarios 认领 引用
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作者 Jifeng Liang Qiang Wang +4 位作者 Leibao Wang Ziwei Zhang Yonghui Sun Hongzhu Tao Xiaofei Li 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期945-965,共21页
Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power gr... Wind power generation is subjected to complex and variable meteorological conditions,resulting in intermittent and volatile power generation.Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations.This enhances the efficiency of wind power integration into the grid.It allows grid operators to anticipate and mitigate the impact of wind power fluctuations,significantly improving the resilience of wind farms and the overall power grid.Furthermore,it assists wind farm operators in optimizing the management of power generation facilities and reducing maintenance costs.Despite these benefits,accurate wind power prediction especially in extreme scenarios remains a significant challenge.To address this issue,a novel wind power prediction model based on learning approach is proposed by integrating wavelet transform and Transformer.First,a conditional generative adversarial network(CGAN)generates dynamic extreme scenarios guided by physical constraints and expert rules to ensure realism and capture critical features of wind power fluctuations under extremeconditions.Next,thewavelet transformconvolutional layer is applied to enhance sensitivity to frequency domain characteristics,enabling effective feature extraction fromextreme scenarios for a deeper understanding of input data.The model then leverages the Transformer’s self-attention mechanism to capture global dependencies between features,strengthening its sequence modelling capabilities.Case analyses verify themodel’s superior performance in extreme scenario prediction by effectively capturing local fluctuation featureswhile maintaining a grasp of global trends.Compared to other models,it achieves R-squared(R2)as high as 0.95,and the mean absolute error(MAE)and rootmean square error(RMSE)are also significantly lower than those of othermodels,proving its high accuracy and effectiveness in managing complex wind power generation conditions. 展开更多
关键词 Extreme scenarios conditional generative adversarial network wavelet transform Transformer wind power prediction
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Fluorescence microscopy image denoising via a wavelet-enhanced transformer based on DnCNN network 认领 引用
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作者 Shuhao Shen Mingxuan Cao +2 位作者 Weikai Tan E Du Xueli Chen 《Advanced Photonics Nexus》 CSCD 2025年第6期1-11,共11页
Fluorescence microscopy is indispensable in life science research,yet denoising remains challenging due to varied biological samples and imaging conditions.We introduce a wavelet-enhanced transformer based on DnCNN th... Fluorescence microscopy is indispensable in life science research,yet denoising remains challenging due to varied biological samples and imaging conditions.We introduce a wavelet-enhanced transformer based on DnCNN that fuses wavelet preprocessing with a dual-branch transformer-convolutional neural network(CNN)architecture.Wavelet decomposition separates highand low-frequency components for targeted noise reduction;the CNN branch restores local details,whereas the transformer branch captures global context;and an adaptive loss balances quantitative fidelity with perceptual quality.On the fluorescence microscopy denoising benchmark,our method surpasses leading CNNand transformer-based approaches,improving peak signal-to-noise ratio by 2.34%and 0.88%and structural similarity index measure by 0.53%and 1.07%,respectively.This framework offers enhanced generalization and practical gains for fluorescence image denoising. 展开更多
关键词 fluorescence microscopy denoising deep learning wavelet transform vision transformer convolutional neural network.
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Image Watermarking Algorithm Base on the Second Order Derivative and Discrete Wavelet Transform 认领 引用
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作者 Maazen Alsabaan Zaid Bin Faheem +1 位作者 Yuanyuan Zhu Jehad Ali 《Computers, Materials & Continua》 SCIE EI 2025年第7期491-512,共22页
Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms.Image watermarking can be used to protect the copyright of digital media by embe... Image watermarking is a powerful tool for media protection and can provide promising results when combined with other defense mechanisms.Image watermarking can be used to protect the copyright of digital media by embedding a unique identifier that identifies the owner of the content.Image watermarking can also be used to verify the authenticity of digital media,such as images or videos,by ascertaining the watermark information.In this paper,a mathematical chaos-based image watermarking technique is proposed using discrete wavelet transform(DWT),chaotic map,and Laplacian operator.The DWT can be used to decompose the image into its frequency components,chaos is used to provide extra security defense by encrypting the watermark signal,and the Laplacian operator with optimization is applied to the mid-frequency bands to find the sharp areas in the image.These mid-frequency bands are used to embed the watermarks by modifying the coefficients in these bands.The mid-sub-band maintains the invisible property of the watermark,and chaos combined with the second-order derivative Laplacian is vulnerable to attacks.Comprehensive experiments demonstrate that this approach is effective for common signal processing attacks,i.e.,compression,noise addition,and filtering.Moreover,this approach also maintains image quality through peak signal-to-noise ratio(PSNR)and structural similarity index metrics(SSIM).The highest achieved PSNR and SSIM values are 55.4 dB and 1.In the same way,normalized correlation(NC)values are almost 10%–20%higher than comparative research.These results support assistance in copyright protection in multimedia content. 展开更多
关键词 Discrete wavelet transform laplacian image watermarking chaos multimedia security
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Wavelet-based analysis of aeolian sand dynamics and adaptive mitigation strategies for desert highways 认领 引用
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作者 YANG Lingxiang CHENG Jianjun +3 位作者 YAO Bin WANG Yaqiang GAO Li WU Xiao 《Journal of Mountain Science》 SCIE CSCD 2026年第3期1182-1200,共19页
Scientific analysis of aeolian sand environments is fundamental for sustainable disaster mitigation along desert highways.However,significant regional variability in wind energy conditions complicates accurate charact... Scientific analysis of aeolian sand environments is fundamental for sustainable disaster mitigation along desert highways.However,significant regional variability in wind energy conditions complicates accurate characterization of wind regimes and introduces uncertainty in determining optimal monitoring timescales.Moreover,prevailing sand control measures often rely on standardized designs rather than site-specific adaptive strategies.To address these issues,this study proposes an integrated framework for aeolian environment analysis and develops targeted disaster mitigation strategies tailored for desert highways.The proposed framework employs wavelet transform to unravel the periodic characteristics of wind speed time series and integrates multi-source data(including ERA5 wind datasets,sand samples,ASTER GDEM,and multi-temporal remote sensing imagery)to enable a comprehensive aeolian environmental assessment.Concurrently,a suite of adaptive strategies is formulated to mitigate disaster risks along desert highways.Validated through a case study of the Tumushuk-Kunyu Desert Highway in Xinjiang,China,the framework exhibits high accuracy:predictions of annual aeolian sand transport activity show relative errors mostly below 7%against long-term reference sequences,and the calculated resultant drift direction exhibits a strong correlation with observed dune migration,yielding an R-squared value of 0.96.These findings confirm the framework’s reliability and provide a robust basis for designing adaptive,location-specific mitigation strategies,thereby enhancing the sustainability of desert highway infrastructure. 展开更多
关键词 Desert highway Aeolian sand environment Wavelet transform Drift potential Adaptive mitigation measures
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MewCDNet: A Wavelet-Based Multi-Scale Interaction Network for Efficient Remote Sensing Building Change Detection 认领 引用
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作者 Jia Liu Hao Chen +5 位作者 Hang Gu Yushan Pan Haoran Chen Erlin Tian Min Huang Zuhe Li 《Computers, Materials & Continua》 SCIE EI 2026年第1期687-710,共24页
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra... Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability. 展开更多
关键词 Remote sensing change detection deep learning wavelet transform multi-scale
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A Deep Learning Approach for Fault Diagnosis in Centrifugal Pumps through Wavelet Coherent Analysis and S-Transform Scalograms with CNN-KAN 认领 引用 被引量：2
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作者 Muhammad Farooq Siddique Saif Ullah Jong-Myon Kim 《Computers, Materials & Continua》 SCIE EI 2025年第8期3577-3603,共27页
Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces ... Centrifugal Pumps(CPs)are critical machine components in many industries,and their efficient operation and reliable Fault Diagnosis(FD)are essential for minimizing downtime and maintenance costs.This paper introduces a novel FD method to improve both the accuracy and reliability of detecting potential faults in such pumps.Theproposed method combinesWaveletCoherent Analysis(WCA)and Stockwell Transform(S-transform)scalograms with Sobel and non-local means filters,effectively capturing complex fault signatures from vibration signals.Using Convolutional Neural Network(CNN)for feature extraction,the method transforms these scalograms into image inputs,enabling the recognition of patterns that span both time and frequency domains.The CNN extracts essential discriminative features,which are then merged and passed into a Kolmogorov-Arnold Network(KAN)classifier,ensuring precise fault identification.The proposed approach was experimentally validated on diverse datasets collected under varying conditions,demonstrating its robustness and generalizability.Achieving classification accuracy of 100%,99.86%,and 99.92%across the datasets,this method significantly outperforms traditional fault detection approaches.These results underscore the potential to enhance CP FD,providing an effective solution for predictive maintenance and improving overall system reliability. 展开更多
关键词 Fault diagnosis centrifugal pump wavelet coherent analysis stockwell transform convolutional neural network Kolmogorov-Arnold network
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多尺度非对称注意力遥感去雾Transformer 认领 引用 被引量：1
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作者 王旭阳 梁宇航 《广西师范大学学报（自然科学版）》 CAS 北大核心 2026年第2期77-89,共13页
雾霾干扰会导致遥感图像结构模糊、细节丢失,严重影响下游视觉任务的准确性。为此,本文提出一种异构增强的遥感图像去雾网络,从空间结构建模与频率信息整合2个层面提升特征恢复能力。具体而言,设计多尺度非对称注意力Transformer模块,... 雾霾干扰会导致遥感图像结构模糊、细节丢失,严重影响下游视觉任务的准确性。为此,本文提出一种异构增强的遥感图像去雾网络,从空间结构建模与频率信息整合2个层面提升特征恢复能力。具体而言,设计多尺度非对称注意力Transformer模块,引入方向感知机制以增强模糊边缘与纹理细节的建模;同时构建基于小波变换高低频自适应增强模块,使用Haar小波分解分离频域信息,分别通过高频与低频子模块强化边缘轮廓与结构表达。2个模块分别嵌入特征提取与融合阶段,协同缓解传统方法方向性建模不足与高频特征易丢失等问题。在保持低计算开销的前提下,本文方法在HAZE1K与RICE数据集上的平均PSNR/SSIM性能分别达到24.9936/0.9099与33.1802/0.8942,在细节恢复方面表现出显著优势。 展开更多
关键词 遥感图像去雾 Transformer 非对称注意力 高低频特征增强 小波变换 方向感知建模 深度学习
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基于Transformer和小波变换卷积的SAR影像无监督变化检测方法 认领 引用
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作者 张懂庆 张祥 +2 位作者 王银伟 张宇 庄会富 《自然资源遥感》 CSCD 北大核心 2026年第4期11-20,共10页
无监督变化检测是合成孔径雷达(synthetic aperture radar,SAR)影像信息提取中的研究热点,近年来受到广泛关注。尽管已有研究在该领域取得一定进展,但现有方法大多仅关注空间域特征的利用,在空间-频率双域特征融合利用方面的探索研究较... 无监督变化检测是合成孔径雷达(synthetic aperture radar,SAR)影像信息提取中的研究热点,近年来受到广泛关注。尽管已有研究在该领域取得一定进展,但现有方法大多仅关注空间域特征的利用,在空间-频率双域特征融合利用方面的探索研究较少。因此,文章设计了一种基于Transformer和小波变换卷积的UNet变化检测模型,通过联合对数比值法和模糊C均值法获取伪标签,实现SAR影像变化信息的无监督提取。该模型的编码部分包含2个分支,空间域分支利用Transformer提取空间域全局特征,并与卷积网络提取的空间域局部特征相融合;频率域分支利用小波变换卷积提取多尺度频率域特征,增强模型对承载影像主体信息的低频分量和承载边界细节的高频分量的响应能力。在解码器部分实现多尺度跨域互补信息在UNet架构中的有机融合,并在2个数据集上进行实验来验证所提方法的有效性。结果表明:与对比方法中最好的变化检测结果相比,该文方法在2个数据集上的F1分数分别提高了0.030和0.017,Kappa系数分别提高了0.032和0.018,有效提高了变化检测结果的可靠性。 展开更多
关键词 无监督变化检测 SAR影像 Transformer 小波变换卷积 特征融合
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Wavelet Transform-Based Bayesian Inference Learning with Conditional Variational Autoencoder for Mitigating Injection Attack in 6G Edge Network 认领 引用 被引量：1
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作者 Binu Sudhakaran Pillai Raghavendra Kulkarni +1 位作者 Venkata Satya Suresh kumar Kondeti Surendran Rajendran 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期1141-1166,共26页
Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies... Future 6G communications will open up opportunities for innovative applications,including Cyber-Physical Systems,edge computing,supporting Industry 5.0,and digital agriculture.While automation is creating efficiencies,it can also create new cyber threats,such as vulnerabilities in trust and malicious node injection.Denialof-Service(DoS)attacks can stop many forms of operations by overwhelming networks and systems with data noise.Current anomaly detection methods require extensive software changes and only detect static threats.Data collection is important for being accurate,but it is often a slow,tedious,and sometimes inefficient process.This paper proposes a new wavelet transformassisted Bayesian deep learning based probabilistic(WT-BDLP)approach tomitigate malicious data injection attacks in 6G edge networks.The proposed approach combines outlier detection based on a Bayesian learning conditional variational autoencoder(Bay-LCVariAE)and traffic pattern analysis based on continuous wavelet transform(CWT).The Bay-LCVariAE framework allows for probabilistic modelling of generative features to facilitate capturing how features of interest change over time,spatially,and for recognition of anomalies.Similarly,CWT allows emphasizing the multi-resolution spectral analysis and permits temporally relevant frequency pattern recognition.Experimental testing showed that the flexibility of the Bayesian probabilistic framework offers a vast improvement in anomaly detection accuracy over existing methods,with a maximum accuracy of 98.21%recognizing anomalies. 展开更多
关键词 Bayesian inference learning automaton convolutional wavelet transform conditional variational autoencoder malicious data injection attack edge environment 6G communication
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基于混合小波-Transformer框架的VSP数据耦合噪声压制方法 认领 引用
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作者 王腾宇 陈豪 +4 位作者 张振 易浩然 魏巍 麦尔旦·马合木提 蔡涵鹏 《科学技术与工程》 EI 北大核心 2026年第12期4948-4958,共11页
垂直地震剖面(vertical seismic profile,VSP)数据处理中,耦合噪声是主要的干扰信号,其复杂的传播特性和频率重叠使得传统去噪方法难以实现精准压制。为此,提出一种混合小波-Transformer(hybrid wavelet Transformer,HWT)框架,用于耦合... 垂直地震剖面(vertical seismic profile,VSP)数据处理中,耦合噪声是主要的干扰信号,其复杂的传播特性和频率重叠使得传统去噪方法难以实现精准压制。为此,提出一种混合小波-Transformer(hybrid wavelet Transformer,HWT)框架,用于耦合噪声的高效压制。该方法首先通过离散小波变换(discrete wavelet Transform,DWT)对信号进行多尺度频率分解,将原始信号分解到不同频段以减少频率混叠;随后利用双分支Transformer架构分别捕捉局部与全局特征,其中局部分支提取耦合噪声的高频干扰特性,全局分支建模目标信号的时间-频率全局依赖。此外,设计多级特征聚合模块(multi-level feature aggregation module,MFAM),通过整合DWT分解后的频域特征与双分支输出的局部和全局特性,实现了特征的深度融合。基于时频域掩码生成策略对时频表示进行约束,进一步提升了分离质量。在合成数据和真实VSP数据上的实验表明,该方法相比传统方法能够显著压制耦合噪声的同时保持目标信号完整性,为VSP数据的预处理提供了一种高效且鲁棒的解决方案。 展开更多
关键词 耦合噪声 垂直地震剖面(VSP)数据 混合小波-Transformer 深度学习
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Undecimated Dual-Tree Complex Wavelet Transform and Fuzzy Clustering-Based Sonar Image Denoising Technique 认领 引用 被引量：1
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作者 LIU Biao LIU Guangyu +3 位作者 FENG Wei WANG Shuai ZHOU Bao ZHAO Enming 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第5期998-1008,共11页
Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do n... Imaging sonar devices generate sonar images by receiving echoes from objects,which are often accompanied by severe speckle noise,resulting in image distortion and information loss.Common optical denoising methods do not work well in removing speckle noise from sonar images and may even reduce their visual quality.To address this issue,a sonar image denoising method based on fuzzy clustering and the undecimated dual-tree complex wavelet transform is proposed.This method provides a perfect translation invariance and an improved directional selectivity during image decomposition,leading to richer representation of noise and edges in high frequency coefficients.Fuzzy clustering can separate noise from useful information according to the amplitude characteristics of speckle noise,preserving the latter and achieving the goal of noise removal.Additionally,the low frequency coefficients are smoothed using bilateral filtering to improve the visual quality of the image.To verify the effectiveness of the algorithm,multiple groups of ablation experiments were conducted,and speckle sonar images with different variances were evaluated and compared with existing speckle removal methods in the transform domain.The experimental results show that the proposed method can effectively improve image quality,especially in cases of severe noise,where it still achieves a good denoising performance. 展开更多
关键词 fuzzy clustering bilateral filtering undecimated dual-tree complex wavelet transform image denoising
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Electrocardiogram Signal Denoising Using Optimized Adaptive Hybrid Filter with Empirical Wavelet Transform 认领 引用 被引量：1
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作者 BALASUBRAMANIAN S NARUKA Mahaveer Singh TEWARI Gaurav 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第1期66-80,共15页
Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive met... Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac health.Various health practitioners use the ECG signal to ascertain critical information about the human heart.In this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the EWT.The ECG signals are denoised by the proposed adaptive hybrid filter.The honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters.The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter.The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising. 展开更多
关键词 electrocardiogram(ECG)signal denoising empirical wavelet transform(EWT) honey badge optimization(HBO) adaptive hybrid filter window function
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基于可学习小波变换和Transformer融合的调制识别方法 认领 引用
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作者 田明浩 杨盼云 姚沐汐 《通信技术》 2026年第1期31-37,共7页
针对复杂电磁环境下无线电信号调制识别精度低的问题,提出了一种基于可学习小波变换和Transformer融合的调制识别方法。首先,通过可学习小波变换模块将信号进行奇偶分解,利用强化的预测、更新算子和注意力机制自适应提取多分辨率特征,... 针对复杂电磁环境下无线电信号调制识别精度低的问题,提出了一种基于可学习小波变换和Transformer融合的调制识别方法。首先,通过可学习小波变换模块将信号进行奇偶分解,利用强化的预测、更新算子和注意力机制自适应提取多分辨率特征,同时引入正则化约束确保小波分解的稳定性;其次,构建双分支特征增强架构,通过挤压和激励(SE)注意力对小波特征进行自适应加权,利用Transformer捕获全局依赖关系;最后,将两个分支输出的特征在特征维度拼接后输入到全连接分类器中,以进行调制类型识别。实验结果表明,所提出的模型具有优异的调制识别精度。相较于其他深度学习方法,所提方法的整体识别精度提升了3%~10%,在不同信噪比的条件下均具有更强的特征学习能力和更好的鲁棒性。 展开更多
关键词 调制识别 深度学习 小波变换 Transformer
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An intelligent log-seismic integrated stratigraphic correlation method based on wavelet frequency-division transform and dynamic time warping:A case study from the Lasaxing oilfield 认领 引用
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作者 Mian Lu Dongmei Cai +4 位作者 Xiandi Fu Shunguo Cheng Yu Sun Pengkun Liu Yanli Jiao 《Energy Geoscience》 EI CAS CSCD 2025年第3期26-36,共11页
Stratigraphic correlations are essential for the fine-scale characterization of reservoirs.However,conventional data-driven methods that rely solely on log data struggle to construct isochronous stratigraphic framewor... Stratigraphic correlations are essential for the fine-scale characterization of reservoirs.However,conventional data-driven methods that rely solely on log data struggle to construct isochronous stratigraphic frameworks for complex sedimentary environments and multi-source geological settings.In response,this study proposed an intelligent,automatic,log-seismic integrated stratigraphic correlation method that incorporates wavelet frequency-division transform(WFT)and dynamic time warping(DTW)(also referred to as the WFT-DTW method).This approach integrates seismic data as constraints into stratigraphic correlations,enabling accurate tracking of the seismic marker horizons through WFT.Under the constraints of framework construction,a DTW algorithm was introduced to correlate sublayer boundaries automatically.The effectiveness of the proposed method was verified through a stratigraphic correlation experiment on the SA0 Formation of the Xingshugang block in the Lasaxing oilfield,the Songliao Basin,China.In this block,the target layer exhibits sublayer thicknesses ranging from 5 m to 8 m,an average sandstone thickness of 2.1 m,and pronounced heterogeneity.The verification using 1760 layers in 160 post-test wells indicates that the WFT-DTW method intelligently compared sublayers in zones with underdeveloped faults and distinct marker horizons.As a result,the posterior correlation of 1682 layers was performed,with a coincidence rate of up to 95.6%.The proposed method can complement manual correlation efforts while also providing valuable technical support for the lithologic and sand body characterization of reservoirs. 展开更多
关键词 Log-seismic integration Stratigraphic correlation Wavelet frequency transform Dynamic time warping Lasaxing oilfield
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