Combining wavelet transforms with conventional log differential curves is used to identify fractured sections is a new idea.In this paper,we first compute the mother wavelet transform of conventional logs and the wave...Combining wavelet transforms with conventional log differential curves is used to identify fractured sections is a new idea.In this paper,we first compute the mother wavelet transform of conventional logs and the wavelet decomposed signals are compared with fractures identified from image logs to determine the fracture-matched mother wavelet.Then the mother wavelet-based decomposed signal combined with the differential curves of conventional well logs create a fracture indicator curve,identifying the fractured zone.Finally the fracture density can be precisely evaluated by the linear relationship of the indicator curve and image log fracture density.This method has been successfully used to evaluate igneous reservoir fractures in the southern Songnan basin and the calculated density from the indicator curve and density from image logs are both basically consistent.展开更多
Quantum watermarking is a technique to embed specific information, usually the owner's identification,into quantum cover data such for copyright protection purposes. In this paper, a new scheme for quantum waterma...Quantum watermarking is a technique to embed specific information, usually the owner's identification,into quantum cover data such for copyright protection purposes. In this paper, a new scheme for quantum watermarking based on quantum wavelet transforms is proposed which includes scrambling, embedding and extracting procedures. The invisibility and robustness performances of the proposed watermarking method is confirmed by simulation technique.The invisibility of the scheme is examined by the peak-signal-to-noise ratio(PSNR) and the histogram calculation.Furthermore the robustness of the scheme is analyzed by the Bit Error Rate(BER) and the Correlation Two-Dimensional(Corr 2-D) calculation. The simulation results indicate that the proposed watermarking scheme indicate not only acceptable visual quality but also a good resistance against different types of attack.展开更多
In a preceding letter （2007 Opt. Lett. 32 554） we propose complex continuous wavelet transforms and found Laguerre-Gaussian mother wavelets family. In this work we present the inversion formula and Parseval theorem ...In a preceding letter （2007 Opt. Lett. 32 554） we propose complex continuous wavelet transforms and found Laguerre-Gaussian mother wavelets family. In this work we present the inversion formula and Parseval theorem for complex continuous wavelet transform by virtue of the entangled state representation, which makes the complex continuous wavelet transform theory complete. A new orthogonal property of mother wavelet in parameter space is revealed.展开更多
To establish the algorithm of SAT-TMD system with the wavelet transform(WT),the modal mass participation ratio is proposed to distinguish if the high-rising structure has the characteristic of closely distributed freq...To establish the algorithm of SAT-TMD system with the wavelet transform(WT),the modal mass participation ratio is proposed to distinguish if the high-rising structure has the characteristic of closely distributed frequencies.A time varying analytical model of high-rising structure such as TV-tower with the SAT-TMD is developed.The proposed new idea is to use WT to identify the dominant frequency of structural response in a segment time,and track its variation as a function of time to retune the SAT-TMD.The effectiveness of SAT-TMD is investigated and it is more robust to change in building stiffness and damping than that of the TMD with a fixed frequency corresponding to a fixed mode frequency of the building.It is proved that SAT-TMD is particularly effective in reducing the response even when the building stiffness is changed by ±15%;whereas the TMD loses its effectiveness under such building stiffness variations.展开更多
Traditional watermark embedding schemes inevitably modify the data in a host audio signal and lead to the degradation of the host signal.In this paper,a novel audio zero-watermarking algorithm based on discrete wavele...Traditional watermark embedding schemes inevitably modify the data in a host audio signal and lead to the degradation of the host signal.In this paper,a novel audio zero-watermarking algorithm based on discrete wavelet transform(DWT),discrete cosine transform(DCT),and singular value decomposition(SVD) is presented.The watermark is registered by performing SVD on the coefficients generated through DWT and DCT to avoid data modification and host signal degradation.Simulation results show that the proposed zero-watermarking algorithm is strongly robust to common signal processing methods such as requantization,MP3 compression,resampling,addition of white Gaussian noise,and low-pass filtering.展开更多
On the basis of fractional wavelet transform, we propose a new method called cascaded fractional wavelet transform to encrypt images. It has the virtues of fractional Fourier transform and wavelet transform. Fractiona...On the basis of fractional wavelet transform, we propose a new method called cascaded fractional wavelet transform to encrypt images. It has the virtues of fractional Fourier transform and wavelet transform. Fractional orders, standard focal lengths and scaling factors are its keys. Multistage fractional Fourier transforms can add the keys easily and strengthen information se-curity. This method can also realize partial encryption just as wavelet transform and fractional wavelet transform. Optical reali-zation of encryption and decryption is proposed. Computer simulations confirmed its possibility.展开更多
We introduce the bipartite entangled states to present a quantum mechanical version of complex wavelet transform. Using the technique of integral within an ordered product of operators we show that the complex wavelet...We introduce the bipartite entangled states to present a quantum mechanical version of complex wavelet transform. Using the technique of integral within an ordered product of operators we show that the complex wavelet transform can be studied in terms of various quantum state vectors in two-mode Fock space. In this way the creterion for mother wavelet can be examined quantum-mechanically and therefore more deeply.展开更多
Based on the characteristics of gradual change style seismic signal onset which has more high frequency signal components but less magnitude, this paper selects Gauss linear frequency modulation wavelet as base functi...Based on the characteristics of gradual change style seismic signal onset which has more high frequency signal components but less magnitude, this paper selects Gauss linear frequency modulation wavelet as base function to study the change characteristics of Gauss linear frequency modulation wavelet transform with difference wavelet and signal parameters, analyzes the error origin of seismic phases identification on the basis of Gauss linear frequency modulation wavelet transform, puts forward a kind of new method identifying gradual change style seismic phases with background noise which is called fixed scale wavelet transform ratio, and presents application examples about simulation digital signal and actual seismic phases recording onsets identification.展开更多
In contrast to Fourier transform, wavelet transform is especially suitable for transient analysis because of its time frequency characteristics with automatically adjusted window lengths. Research shows that wavelet...In contrast to Fourier transform, wavelet transform is especially suitable for transient analysis because of its time frequency characteristics with automatically adjusted window lengths. Research shows that wavelet transform is one of the most powerful tools for power system transient analysis. The basic ideas of wavelet transform are presented in the paper together with several power system applications. It is clear that wavelet transform has some clear advantages over other transforms in detecting, analyzing, and identifying various types of power system transients.展开更多
This work aims to study the effect of unwanted peaks and enhance the performance of wireless systems on the basis of tackling such peaks. A new proposition has been made based on wavelet transform method and its entro...This work aims to study the effect of unwanted peaks and enhance the performance of wireless systems on the basis of tackling such peaks. A new proposition has been made based on wavelet transform method and its entropy. Signals with large peak-to-average power ratio (PAPR) will be examined such as the ones that are considered as the major Orthogonal Frequency Division Multiplexing (OFDM) systems drawbacks. Furthermore, aspatial diversity Multiple-Input Multiple- Out-put (MIMO) technology is used to overcome the complexity addition that could arise in our proposition. To draw the best performance of this work, a MATLAB simulation has been used;it is divided into three main stages, namely, MIMO-OFDM symbols’ reconstruction based on wavelet transform, a predetermined thresholding formula, and finally, moving filter. This algorithm is called Peaks’ detection based Entropy Wavelet Transform;PD-EWT. Based on the simulation, and under some constrains such as the bandwidth occupancy and the complexity structure of the transceivers, a peak detection ratio has been achieved and reaches around 0.85. Comparing with our previously published works, the PD-EWT enhances detection ratio for 0.25 more peaks.展开更多
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
We study the approximation of the inverse wavelet transform using Riemannian sums.We show that when the Fourier transforms of wavelet functions satisfy some moderate decay condition,the Riemannian sums converge to the...We study the approximation of the inverse wavelet transform using Riemannian sums.We show that when the Fourier transforms of wavelet functions satisfy some moderate decay condition,the Riemannian sums converge to the function to be reconstructed as the sampling density tends to infinity.We also study the convergence of the operators introduced by the Riemannian sums.Our result improves some known ones.展开更多
With an objective to improve wind power estimation accuracy and reliability,this paper presents Linear Neural Networks with Tapped Delay(LNNTD)in combination with wavelet transform(WT)for probabilistic wind power fore...With an objective to improve wind power estimation accuracy and reliability,this paper presents Linear Neural Networks with Tapped Delay(LNNTD)in combination with wavelet transform(WT)for probabilistic wind power forecasting in a time series framework.For comparison purposes,results of the proposed model are compared with the benchmark model,different neural networks and WT based models considering performance indices such as accuracy,execution time and R^(2) statistic.For the reliability and proper validation of the proposed model,this paper highlights the probabilistic forecast attributes at different skill tests.The historical data of the Ontario Electricity Market(OEM)for the period 2011–2014 were used and tested for two years from November 2012 to October 2014 with one month moving window considering all seasonal aspects.The experimental results clearly show that the results of the proposed model have been found to be better than others.展开更多
Wavelet transform is being widely used in the field of information processing.One-dimension and two-dimension quantum wavelet transforms have been investigated as important tool algorithms.However,three-dimensional qu...Wavelet transform is being widely used in the field of information processing.One-dimension and two-dimension quantum wavelet transforms have been investigated as important tool algorithms.However,three-dimensional quantum wavelet transforms have not been reported.This paper proposes a multi-level three-dimensional quantum wavelet transform theory to implement the wavelet transform for quantum videos.Then,we construct the iterative formulas for the multi-level three-dimensional Haar and Daubechies D4 quantum wavelet transforms,respectively.Next,we design quantum circuits of the two wavelet transforms using iterative methods.Complexity analysis shows that the proposed wavelet transforms offer exponential speed-up over their classical counterparts.Finally,the proposed quantum wavelet transforms are selected to realize quantum video compression as a primary application.Simulation results reveal that the proposed wavelet transforms have better compression performance for quantum videos than two-dimension quantum wavelet transforms.展开更多
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 wat...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
Structural integrity is essential for safety in infrastructure,as it can help prevent catastrophic failures and financial losses.The significance of vibration-based damage detection has grown substantially in fields s...Structural integrity is essential for safety in infrastructure,as it can help prevent catastrophic failures and financial losses.The significance of vibration-based damage detection has grown substantially in fields such as civil and mechanical engineering.Concurrently,the advancements in computational capacities have facilitated the integration of machine learning into damage detection processes through post-processing algorithms.Nevertheless,these require extensive data from structure-affixed sensors,raising computational requirements.In an effort to address this challenge,we propose a novel approach utilizing a pre-trained convolutional neural network(CNN)based on images to identify and assess structural damage.This method involves employing wavelet transform and scalograms to convert numerical acceleration data into image data,preserving spatial and temporal information more effectively compared to conventional Fourier transform frequency analysis.Six acceleration data channels are collected from carefully chosen nodes on a mini bridge model and a corresponding finite element bridge model,to train the CNN.The efficiency of training is further enhanced by applying transfer machine learning through two pre-trained CNNs,namely Alexnet and Resnet.We evaluate our method using different damage scenarios,and both Alexnet and Resnet show prediction accuracies over 90%.展开更多
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
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
This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two ke...This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two key modules:Constrained Deformable Convolution(CDC),which stabilizes geometric alignment by applying a tanh limiter and learnable scaling factor to the predicted offsets,and the Wavelet Frequency Enhancement Module(WFEM),which decomposes features using Haar wavelets to preserve low-frequency structures while enhancing high-frequency boundaries and textures.Evaluations on the CrackSeg9k benchmark demonstrate CW-HRNet’s superior performance,achieving 82.39%mIoU with only 7.49M parameters and 10.34 GFLOPs,outperforming HrSegNet-B48 by 1.83% in segmentation accuracy with minimal complexity overhead.The model also shows strong cross-dataset generalization,achieving 60.01%mIoU and 66.22%F1 on Asphalt3k without fine-tuning.These results highlight CW-HRNet’s favorable accuracyefficiency trade-off for real-world crack segmentation tasks.展开更多
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
基金sponsored by National Science and Technology Major Project of China (No. 2008 ZX 05009-001)
文摘Combining wavelet transforms with conventional log differential curves is used to identify fractured sections is a new idea.In this paper,we first compute the mother wavelet transform of conventional logs and the wavelet decomposed signals are compared with fractures identified from image logs to determine the fracture-matched mother wavelet.Then the mother wavelet-based decomposed signal combined with the differential curves of conventional well logs create a fracture indicator curve,identifying the fractured zone.Finally the fracture density can be precisely evaluated by the linear relationship of the indicator curve and image log fracture density.This method has been successfully used to evaluate igneous reservoir fractures in the southern Songnan basin and the calculated density from the indicator curve and density from image logs are both basically consistent.
基金Supported by Kermanshah Branch,Islamic Azad University,Kermanshah,Iran
文摘Quantum watermarking is a technique to embed specific information, usually the owner's identification,into quantum cover data such for copyright protection purposes. In this paper, a new scheme for quantum watermarking based on quantum wavelet transforms is proposed which includes scrambling, embedding and extracting procedures. The invisibility and robustness performances of the proposed watermarking method is confirmed by simulation technique.The invisibility of the scheme is examined by the peak-signal-to-noise ratio(PSNR) and the histogram calculation.Furthermore the robustness of the scheme is analyzed by the Bit Error Rate(BER) and the Correlation Two-Dimensional(Corr 2-D) calculation. The simulation results indicate that the proposed watermarking scheme indicate not only acceptable visual quality but also a good resistance against different types of attack.
基金supported by the National Natural Science Foundation of China (Grant No. 10775097)the Research Foundation of the Education Department of Jiangxi Province of China (Grant No. GJJ10097)
文摘In a preceding letter （2007 Opt. Lett. 32 554） we propose complex continuous wavelet transforms and found Laguerre-Gaussian mother wavelets family. In this work we present the inversion formula and Parseval theorem for complex continuous wavelet transform by virtue of the entangled state representation, which makes the complex continuous wavelet transform theory complete. A new orthogonal property of mother wavelet in parameter space is revealed.
基金Sponsored by the National Natural Science Foundation of China(Grant No.50478031)China Postdoctoral Science Foundation(Grant No.2006040240)
文摘To establish the algorithm of SAT-TMD system with the wavelet transform(WT),the modal mass participation ratio is proposed to distinguish if the high-rising structure has the characteristic of closely distributed frequencies.A time varying analytical model of high-rising structure such as TV-tower with the SAT-TMD is developed.The proposed new idea is to use WT to identify the dominant frequency of structural response in a segment time,and track its variation as a function of time to retune the SAT-TMD.The effectiveness of SAT-TMD is investigated and it is more robust to change in building stiffness and damping than that of the TMD with a fixed frequency corresponding to a fixed mode frequency of the building.It is proved that SAT-TMD is particularly effective in reducing the response even when the building stiffness is changed by ±15%;whereas the TMD loses its effectiveness under such building stiffness variations.
基金supported by the Open Foundation of Jiangsu Engineering Center of Network Monitoring(Nanjing University of Information Science&Technology)(Grant No.KJR1509)the PAPD fundthe CICAEET fund
文摘Traditional watermark embedding schemes inevitably modify the data in a host audio signal and lead to the degradation of the host signal.In this paper,a novel audio zero-watermarking algorithm based on discrete wavelet transform(DWT),discrete cosine transform(DCT),and singular value decomposition(SVD) is presented.The watermark is registered by performing SVD on the coefficients generated through DWT and DCT to avoid data modification and host signal degradation.Simulation results show that the proposed zero-watermarking algorithm is strongly robust to common signal processing methods such as requantization,MP3 compression,resampling,addition of white Gaussian noise,and low-pass filtering.
基金Project (No. 10276034) supported by the National Natural ScienceFoundation of China
文摘On the basis of fractional wavelet transform, we propose a new method called cascaded fractional wavelet transform to encrypt images. It has the virtues of fractional Fourier transform and wavelet transform. Fractional orders, standard focal lengths and scaling factors are its keys. Multistage fractional Fourier transforms can add the keys easily and strengthen information se-curity. This method can also realize partial encryption just as wavelet transform and fractional wavelet transform. Optical reali-zation of encryption and decryption is proposed. Computer simulations confirmed its possibility.
基金The project supported by National Natural Science Foundation of China under Grant No. 10475056 and the Ph. D Tutoring Foundation of the Ministry of Education
文摘We introduce the bipartite entangled states to present a quantum mechanical version of complex wavelet transform. Using the technique of integral within an ordered product of operators we show that the complex wavelet transform can be studied in terms of various quantum state vectors in two-mode Fock space. In this way the creterion for mother wavelet can be examined quantum-mechanically and therefore more deeply.
基金State Natural Science Foundation of China (40074007) Science and Technology Key Project during the Ten-Year Plan(2001BA601B02-03-06) and the Natural Science Foundation of Shandong Province (Y2000E08).
文摘Based on the characteristics of gradual change style seismic signal onset which has more high frequency signal components but less magnitude, this paper selects Gauss linear frequency modulation wavelet as base function to study the change characteristics of Gauss linear frequency modulation wavelet transform with difference wavelet and signal parameters, analyzes the error origin of seismic phases identification on the basis of Gauss linear frequency modulation wavelet transform, puts forward a kind of new method identifying gradual change style seismic phases with background noise which is called fixed scale wavelet transform ratio, and presents application examples about simulation digital signal and actual seismic phases recording onsets identification.
文摘In contrast to Fourier transform, wavelet transform is especially suitable for transient analysis because of its time frequency characteristics with automatically adjusted window lengths. Research shows that wavelet transform is one of the most powerful tools for power system transient analysis. The basic ideas of wavelet transform are presented in the paper together with several power system applications. It is clear that wavelet transform has some clear advantages over other transforms in detecting, analyzing, and identifying various types of power system transients.
文摘This work aims to study the effect of unwanted peaks and enhance the performance of wireless systems on the basis of tackling such peaks. A new proposition has been made based on wavelet transform method and its entropy. Signals with large peak-to-average power ratio (PAPR) will be examined such as the ones that are considered as the major Orthogonal Frequency Division Multiplexing (OFDM) systems drawbacks. Furthermore, aspatial diversity Multiple-Input Multiple- Out-put (MIMO) technology is used to overcome the complexity addition that could arise in our proposition. To draw the best performance of this work, a MATLAB simulation has been used;it is divided into three main stages, namely, MIMO-OFDM symbols’ reconstruction based on wavelet transform, a predetermined thresholding formula, and finally, moving filter. This algorithm is called Peaks’ detection based Entropy Wavelet Transform;PD-EWT. Based on the simulation, and under some constrains such as the bandwidth occupancy and the complexity structure of the transceivers, a peak detection ratio has been achieved and reaches around 0.85. Comparing with our previously published works, the PD-EWT enhances detection ratio for 0.25 more peaks.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘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.
基金supported partially by National Natural Science Foundation of China(Grant Nos.10971105,10990012)Natural Science Foundation of Tianjin (Grant No.09JCYBJC01000)
文摘We study the approximation of the inverse wavelet transform using Riemannian sums.We show that when the Fourier transforms of wavelet functions satisfy some moderate decay condition,the Riemannian sums converge to the function to be reconstructed as the sampling density tends to infinity.We also study the convergence of the operators introduced by the Riemannian sums.Our result improves some known ones.
文摘With an objective to improve wind power estimation accuracy and reliability,this paper presents Linear Neural Networks with Tapped Delay(LNNTD)in combination with wavelet transform(WT)for probabilistic wind power forecasting in a time series framework.For comparison purposes,results of the proposed model are compared with the benchmark model,different neural networks and WT based models considering performance indices such as accuracy,execution time and R^(2) statistic.For the reliability and proper validation of the proposed model,this paper highlights the probabilistic forecast attributes at different skill tests.The historical data of the Ontario Electricity Market(OEM)for the period 2011–2014 were used and tested for two years from November 2012 to October 2014 with one month moving window considering all seasonal aspects.The experimental results clearly show that the results of the proposed model have been found to be better than others.
基金supported by the Science and Technology Project of Guangxi(2020GXNSFDA238023)the National Natural Science Foundation of China(Grant No.61762012).
文摘Wavelet transform is being widely used in the field of information processing.One-dimension and two-dimension quantum wavelet transforms have been investigated as important tool algorithms.However,three-dimensional quantum wavelet transforms have not been reported.This paper proposes a multi-level three-dimensional quantum wavelet transform theory to implement the wavelet transform for quantum videos.Then,we construct the iterative formulas for the multi-level three-dimensional Haar and Daubechies D4 quantum wavelet transforms,respectively.Next,we design quantum circuits of the two wavelet transforms using iterative methods.Complexity analysis shows that the proposed wavelet transforms offer exponential speed-up over their classical counterparts.Finally,the proposed quantum wavelet transforms are selected to realize quantum video compression as a primary application.Simulation results reveal that the proposed wavelet transforms have better compression performance for quantum videos than two-dimension quantum wavelet transforms.
文摘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).
基金supported by the ATC+Program(20014127,Development of a smart monitoring system integrating 3D printed battery-free antenna sensor technology with AI optimization)funded by the Ministry of Trade,Industry&Energy(MOTIE,Korea).
文摘Structural integrity is essential for safety in infrastructure,as it can help prevent catastrophic failures and financial losses.The significance of vibration-based damage detection has grown substantially in fields such as civil and mechanical engineering.Concurrently,the advancements in computational capacities have facilitated the integration of machine learning into damage detection processes through post-processing algorithms.Nevertheless,these require extensive data from structure-affixed sensors,raising computational requirements.In an effort to address this challenge,we propose a novel approach utilizing a pre-trained convolutional neural network(CNN)based on images to identify and assess structural damage.This method involves employing wavelet transform and scalograms to convert numerical acceleration data into image data,preserving spatial and temporal information more effectively compared to conventional Fourier transform frequency analysis.Six acceleration data channels are collected from carefully chosen nodes on a mini bridge model and a corresponding finite element bridge model,to train the CNN.The efficiency of training is further enhanced by applying transfer machine learning through two pre-trained CNNs,namely Alexnet and Resnet.We evaluate our method using different damage scenarios,and both Alexnet and Resnet show prediction accuracies over 90%.
基金supported by the researcher supporting Project number(RSPD2025R636),King Saud University,Riyadh,Saudi Arabia.
文摘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.
文摘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.
文摘This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two key modules:Constrained Deformable Convolution(CDC),which stabilizes geometric alignment by applying a tanh limiter and learnable scaling factor to the predicted offsets,and the Wavelet Frequency Enhancement Module(WFEM),which decomposes features using Haar wavelets to preserve low-frequency structures while enhancing high-frequency boundaries and textures.Evaluations on the CrackSeg9k benchmark demonstrate CW-HRNet’s superior performance,achieving 82.39%mIoU with only 7.49M parameters and 10.34 GFLOPs,outperforming HrSegNet-B48 by 1.83% in segmentation accuracy with minimal complexity overhead.The model also shows strong cross-dataset generalization,achieving 60.01%mIoU and 66.22%F1 on Asphalt3k without fine-tuning.These results highlight CW-HRNet’s favorable accuracyefficiency trade-off for real-world crack segmentation tasks.
基金the National Natural Science Foundation of China(No.62065001)the Yunnan Young and Middle-aged Academic and Technical Leaders Reserve Talent Project(No.202205AC160001)+1 种基金the Science and Technology Programs of Yunnan Provincial Science and Technology Department(No.202101BA070001-054)the Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities Association(No.2019FH001(-066))。
文摘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.
