This paper discusses the principle and procedures of the second-generation wavelet transform and its application to the denoising of seismic data. Based on lifting steps, it is a flexible wavelet construction method u...This paper discusses the principle and procedures of the second-generation wavelet transform and its application to the denoising of seismic data. Based on lifting steps, it is a flexible wavelet construction method using linear and nonlinear spatial prediction and operators to implement the wavelet transform and to make it reversible. The lifting scheme transform -includes three steps： split, predict, and update. Deslauriers-Dubuc （4, 2） wavelet transforms are used to process both synthetic and real data in our second-generation wavelet transform. The processing results show that random noise is effectively suppressed and the signal to noise ratio improves remarkably. The lifting wavelet transform is an efficient algorithm.展开更多
Ground-penetrating radar(GPR)is a highly efficient,fast and non-destructive exploration method for shallow surfaces.High-precision numerical simulation method is employed to improve the interpretation precision of det...Ground-penetrating radar(GPR)is a highly efficient,fast and non-destructive exploration method for shallow surfaces.High-precision numerical simulation method is employed to improve the interpretation precision of detection.Second-generation wavelet finite element is introduced into the forward modeling of the GPR.As the finite element basis function,the second-generation wavelet scaling function constructed by the scheme is characterized as having multiple scales and resolutions.The function can change the analytical scale arbitrarily according to actual needs.We can adopt a small analysis scale at a large gradient to improve the precision of analysis while adopting a large analytical scale at a small gradient to improve the efficiency of analysis.This approach is beneficial to capture the local mutation characteristics of the solution and improve the resolution without changing mesh subdivision to realize the efficient solution of the forward GPR problem.The algorithm is applied to the numerical simulation of line current radiation source and tunnel non-dense lining model with analytical solutions.Result show that the solution results of the secondgeneration wavelet finite element are in agreement with the analytical solutions and the conventional finite element solutions,thereby verifying the accuracy of the second-generation wavelet finite element algorithm.Furthermore,the second-generation wavelet finite element algorithm can change the analysis scale arbitrarily according to the actual problem without subdividing grids again.The adaptive algorithm is superior to traditional scheme in grid refinement and basis function order increase,which makes this algorithm suitable for solving complex GPR forward-modeling problems with large gradient and singularity.展开更多
An effective de-noising method for fiber optic gyroscopes(FOGs)is proposed.This method is based on second-generation Daubechies D4(DB4)wavelet transform(WT)and level-dependent threshold estimator called Stein's un...An effective de-noising method for fiber optic gyroscopes(FOGs)is proposed.This method is based on second-generation Daubechies D4(DB4)wavelet transform(WT)and level-dependent threshold estimator called Stein's unbiased risk estimator(SURE).The whole approach consists of three critical parts:wavelet decomposition module,parameters estimation module and SURE de-noising module.First,DB4 wavelet is selected as lifting base of the second-generation wavelet in the decomposition module.Second,in the parameters estimation module,maximum likelihood estimation(MLE)is used for stochastic noise parameters estimation.Third,combined with soft threshold de-noising technique,the SURE de-noising module is designed.For comparison,both the traditional universal threshold wavelet and the second-generation Harr wavelet method are also investigated.The experiment results show that the computation cost is 40%less than that of the traditional wavelet method.The standard deviation of de-noised FOG signal is 0.012 and the three noise terms such as angle random walk,bias instability and quantization noise are reduced to 0.0072°/√h,0.0041°/h,and 0.0081°,respectively.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
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
In wave-equation migration and demigration,the cross-correlation imaging/forwarding step implicitly injects an additional copy of the source wavelet,so that the amplitude spectrum of the wavelet is applied redundantly...In wave-equation migration and demigration,the cross-correlation imaging/forwarding step implicitly injects an additional copy of the source wavelet,so that the amplitude spectrum of the wavelet is applied redundantly(effectively imposing a wavelet-spectrum weighting,often akin to an amplitude-squared bias).This redundancy degrades structural fidelity and amplitude balance yet is frequently overlooked.We(i)formalize the mechanism by which cross-correlation duplicates the source-wavelet amplitude effect in both migration and demigration,and(ii)introduce a source-equalized operator that removes the redundancy by deconvolving(or dividing by)the wavelet amplitude spectrum in the imaging condition and its demigration counterpart,while leaving phase/kinematics intact.Using a band-limited Ricker wavelet on a two-layer model and on Marmousi,we show that,if unmanaged,the redundant wavelet spectrum broadens main lobes,introduces ringing,and suppresses vertical resolution in migrated images,and inflates spectrum mismatches between demigrated and observed data even when peak times agree.With our correction,images recover observed-data-consistent bandwidth and sharpened interfaces,and demigrated data also exhibit improved spectrum conformity and reduced amplitude misfit.The results clarify when source amplitudes matter,why cross-correlation makes them redundantly matter,and how a lightweight spectral correction restores physically meaningful amplitude behavior in wave-equation migration/demigration.展开更多
With the emphasis on environmental issues,the recycling of waste concrete,even recycled concrete,has become a hot spot in the field of architecture.But the repeated recycling of waste concrete used in harsh environmen...With the emphasis on environmental issues,the recycling of waste concrete,even recycled concrete,has become a hot spot in the field of architecture.But the repeated recycling of waste concrete used in harsh environments is still a complex problem.This paper discusses the durability and recyclability of recycled aggregate concrete(RAC)as a prefabricated material in the harsh environment,the effect of high-temperature curing(60℃,80℃,and 100℃)on the frost resistance of RAC and physical properties of the second generation recycled coarse aggregate(RCA_(2))of RAC after 300 freeze-thaw cycles were studied.The frost resistance of RAC was characterized by compressive strength,relative dynamic elastic modulus,and mass loss.As the physical properties of RCA_(2),the apparent density,water absorption,and crushing value were measured.And the SEM images of RAC after 300 freeze-thaw cycles were shown.The results indicated that the frost resistance of RAC cured at 80℃ for 7 days was comparable to that cured in the standard condition(cured for 28 days at 20℃±2℃ and 95%humidity),and the RAC cured at 100℃ was slightly worse.However,the frost resistance of RAC cured at 60℃ deteriorated seriously.The RAC cured at 80℃ for 7 days is the best.Whether after the freeze-thaw cycle or not,the RCA that curd at 60℃,80℃,and 100℃ for 7 days can also meet the requirements of Grade III RCA and be used as the aggregate of non-bearing part of prefabricated concrete components.RCA_(2) which is cured at 80℃ for 7 days had the best physical properties.展开更多
Alternating-current losses in a two-layer superconducting cable, each layer being composed of 15 closely-spaced rectangular wires made up of second-generation superconductors when the ends of wires are coated by eithe...Alternating-current losses in a two-layer superconducting cable, each layer being composed of 15 closely-spaced rectangular wires made up of second-generation superconductors when the ends of wires are coated by either a non-magnetic or strong ferromagnetic material having a U profile is numerically investigated. Computations are carried out through the finite-element method. The alternating-current losses do not increase significantly if the relative permeability of the coating is increased three orders of magnitude, provided that the current amplitude is less than half of the critical current in a superconducting wire. However, the losses are much higher for ferromagnetic coating if the amplitude of the applied current oscillating at 50 Hz is close to the critical current. The ferromagnetic coating is seen to accumulate the magnetic field lines normally on its surfaces, while the field lines are parallel to the long axes of the wires, leading to more significant flux penetration in the coated regions. This facilitates a uniform low-loss current flow in the uncoated regions of the wires. In contrast, coating with a non-magnetic material gives rise to a considerably smaller current flow in the uncoated regions, whereas the low-loss flow is maintained in the coated regions. Moreover, the current flows in opposite directions in the coated and uncoated regions, where the direction in each region is converse for the two materials.展开更多
[ Objective] To explore the effects of spaceflight on the second-generation seeds of alfalfa and provide a theoretical basis for mutation breeding. [Method] The seeds of Medicago stavia L. lines no. 1, no. 2 and no. 4...[ Objective] To explore the effects of spaceflight on the second-generation seeds of alfalfa and provide a theoretical basis for mutation breeding. [Method] The seeds of Medicago stavia L. lines no. 1, no. 2 and no. 4 were carried into space by the Shijian-8 seed breeding satellite for a 15-d spaceflight treatment. After returning to the ground, seedlings were transplanted to field. Traits of the second-generation seeds of alfalfa were evaluated. [Result] The 1 000-grain weight of the second-generation seeds were 5% -9% significantly higher than that the control （P 〈 0.05）. The germination rate, seedling weight, shoot length and root length were significantly increased （P 〈 0.05）. The hard seed rate and the rate of moldy seeds were significantly decreased （ P 〈 0.05）. However, the rate of dead seeds was increased. [ Conclusion] Spaceflight treatment has positive mutagenic effects on the second-generation seeds of alfalfa.展开更多
Background:The safety and efficacy of coronary artery bypass grafting(CABG)and second-generation drug-eluting stents(DESs)in patients with coronary artery disease(CAD)remain controversial.Therefore we aimed to compare...Background:The safety and efficacy of coronary artery bypass grafting(CABG)and second-generation drug-eluting stents(DESs)in patients with coronary artery disease(CAD)remain controversial.Therefore we aimed to compare the outcomes of CAD patients treated with CABG and second-generation DESs.Methods:We systematically searched the PubMed,Cochrane Library,Ovid,and Elsevier databases.Studies comparing second-generation DESs with CABG in CAD patients were included.RevMan 5.3 was used to extract and pool the data from the applicable studies.Results:Six trials(N=6604 participants)were included in this meta-analysis.Among all of the CAD patients,second-generation DESs were associated with no differences in the risks of all-cause death[risk ratio(RR)1.18,95% confi dence interval(CI)0.98–1.43,P=0.09],cardiovascular death(RR 1.14,95% CI 0.81–1.59,P=0.45),myocardial infarction(RR 1.22,95% CI 0.98–1.54,P=0.08),and stroke(RR 0.83,95% CI 0.59–1.17,P=0.29),but increased the risks of revascularization(RR 1.95,95% CI 1.66–2.30,P<0.001)and major adverse cardiac and cerebrovascular events(RR 1.72,95% CI:1.31–2.26,P<0.001)when compared with CABG.Conclusions:In the treatment of CAD patients,second-generation DESs was not associated with increased risks of all-cause death,cardiovascular death,myocardial infarction,and stroke,but increased the risks of revascularization and major adverse cardiac and cerebrovascular events when compared with CABG.展开更多
The reuse of waste recycled concrete from harsh environments has become a research hotspot in the field of construction.This study investigated the repair effect of carbonation treatment on second-generation recycled ...The reuse of waste recycled concrete from harsh environments has become a research hotspot in the field of construction.This study investigated the repair effect of carbonation treatment on second-generation recycled fine aggregate(SRFA)obtained from recycled fine aggregate concrete(RFAC)subjected to freeze-thaw(FT)cycles.Before and after carbonation,the properties of SRFA were evaluated.Carbonated second-generation recycled fine aggregate(CSRFA)at five substitution rates(0%,25%,50%,75%,100%)to replace SRFA was used to prepare carbonated second-generation recycled fine aggregate concrete(CSRFAC).The water absorption,porosity and mechanical properties of CSRFAC were tested,and its frost-resisting durability was evaluated.The results showed after carbonation treatment,the physical properties of SRFA was improved and met the requirements of II aggregate.The micro-hardness of the interfacial transition zone and attached mortar in CSRFA was 50.5%and 31.2%higher than that in SRFA,respectively.With the increase of CSRFA replacement rate,the water absorption and porosity of CSRFAC gradually decreased,and the mechanical properties and frost resistance of CSRFAC were gradually improved.Carbonation treatment effectively repairs the damage of SRFA caused by FT cycles and improves its application potential.展开更多
Aims:Research on second-generation antipsychotic drugs (SGAs) has experienced great development in last decades.We did a bibliometric study on the scientific publications on SGAs in Japan.Methods: With theEMBASEandMED...Aims:Research on second-generation antipsychotic drugs (SGAs) has experienced great development in last decades.We did a bibliometric study on the scientific publications on SGAs in Japan.Methods: With theEMBASEandMEDLINEdatabases, we chose papers published from Japan with SGA descriptors. Price’s law and Bradford’s law has been used as bibliometric indicators for quantitating production and dispersion, respectively, of published papers on SGAs. We also calculated the participation index of different countries, and correlated those bibliometric data with some social and health data from Japan (such as totalper capitaexpenditure on health and gross domestic expenditure on research and development). Results: A sum of 669 original documents were published from Japan from 1982 to 2011. Those results fulfilled Price’s law, with scientific production on SGAs showing exponential growth (correlation coefficientr= 0.9261, as against anr= 0.8709 after linear adjustment). The most studied SGAs in Japan wererisperidone (n= 192), aripiprazole (n= 109), and olanzapine (n= 106). Division of documents into Bradford zones yielded a nucleus occupied exclusively by theProgress in Neuro-Psychopharmacology and Biological Psychiatry(49 articles). Those publications were in 157 different journals. Seven of the first 10 frequently used journals had an impact factor of being greater than 3. Conclusions: The SGA publications in Japan have been through exponential growth over the studied period, without evidence of reaching a saturation point.展开更多
To overcome the challenges of poor real-time performance,limited scalability,and low intelligence in conventional jamming pattern recognition methods,this paper proposes a method based on Wavelet Packet Decomposition(...To overcome the challenges of poor real-time performance,limited scalability,and low intelligence in conventional jamming pattern recognition methods,this paper proposes a method based on Wavelet Packet Decomposition(WPD)and enhanced deep learning techniques.In the proposed method,an agent at the receiver processes the received signal using WPD to generate an initial Spectrogram Waterfall(SW),which is subsequently segmented using a sliding window to serve as the input for the jamming recognition network.The network employs a bilateral filter to preprocess the input SW,thereby enhancing the edge features of the jamming signals.To extract abstract features,depthwise separable convolution is utilized instead of traditional convolution,thereby reducing the network’s parameter count and enhancing real-time performance.A pyramid pooling layer is integrated before the fully connected layer to enable the network to process input SW of varying sizes,thus enhancing scalability.During network training,adaptive moment estimation is employed as the optimizer,allowing the network to dynamically adjust the learning rate and accelerate convergence.A comprehensive comparison between the proposed jamming recognition network and six other models is conducted,along with Ablation Experiments(AE)based on numerical simulations.Simulation results demonstrate that the proposed method based on WPD and enhanced deep learning achieves high-precision recognition of various jamming patterns while maintaining a favorable balance among prediction accuracy,network complexity,and prediction time.展开更多
Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-ban...Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-band spectra,hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing.However,existing continuous wavelet analysis(CWA)methods suffer from feature redundancy issues,while the continuous wavelet projection algorithm(CWPA),an optimization approach for feature selection,has not been fully validated to monitor plant diseases.This study utilized rice bacterial leaf blight(BLB)as an example by evaluating the performance of four wavelet basis functions-Gaussian2,Mexican hat,Meyer,andMorlet-within theCWAandCWPAframeworks.Additionally,the classification models were constructed using the k-nearest neighbors(KNN),randomforest(RF),and Naïve Bayes(NB)algorithms.The results showed the following:(1)Compared to traditional CWA,CWPA significantly reduced the number of required features.Under the CWPA framework,almost all the model combinations achieved maximum classification accuracy with only one feature.In contrast,the CWA framework required three to seven features.(2)Thechoice of wavelet basis functions markedly affected the performance of themodel.Of the four functions tested,the Meyer wavelet demonstrated the best overall performance in both the CWPA and CWA frameworks.(3)Under theCWPAframework,theMeyer-KNNandMeyer-NBcombinations achieved the highest overall accuracy of 93.75%using just one feature.In contrast,under the CWA framework,the CWA-RF combination achieved comparable accuracy(93.75%)but required six features.This study verified the technical advantages of CWPA for monitoring crop diseases,identified an optimal wavelet basis function selection scheme,and provided reliable technical support to precisely monitor BLB in rice(Oryza sativa).Moreover,the proposed methodological framework offers a scalable approach for the early diagnosis and assessment of plant stress,which can contribute to improved accuracy and timeliness when plant stress is monitored.展开更多
In the vision transformer(ViT)architecture,image data are transformed into sequential data for processing,which may result in the loss of spatial positional information.While the self-attention mechanism enhances the ...In the vision transformer(ViT)architecture,image data are transformed into sequential data for processing,which may result in the loss of spatial positional information.While the self-attention mechanism enhances the capacity of ViT to capture global features,it compromises the preservation of fine-grained local feature information.To address these challenges,we propose a spatial positional enhancement module and a wavelet transform enhancement module tailored for ViT models.These modules aim to reduce spatial positional information loss during the patch embedding process and enhance the model’s feature extraction capabilities.The spatial positional enhancement module reinforces spatial information in sequential data through convolutional operations and multi-scale feature extraction.Meanwhile,the wavelet transform enhancement module utilizes the multi-scale analysis and frequency decomposition to improve the ViT’s understanding of global and local image structures.This enhancement also improves the ViT’s ability to process complex structures and intricate image details.Experiments on CIFAR-10,CIFAR-100 and ImageNet-1k datasets are done to compare the proposed method with advanced classification methods.The results show that the proposed model achieves a higher classification accuracy,confirming its effectiveness and competitive advantage.展开更多
Atmospheric aerosols are the primary contributors to environmental pollution.As such aerosols are micro-to nanosized particles invisible to the naked eye,it is necessary to utilize LiDAR technology for their detection...Atmospheric aerosols are the primary contributors to environmental pollution.As such aerosols are micro-to nanosized particles invisible to the naked eye,it is necessary to utilize LiDAR technology for their detection.The laser radar echo signal is vulnerable to background light and electronic thermal noise.While single-photon LiDAR can effectively reduce background light interference,electronic thermal noise remains a significant challenge,especially at long distances and in environments with a low signal-to-noise ratio(SNR).However,conventional denoising methods cannot achieve satisfactory results in this case.In this paper,a novel adaptive continuous threshold wavelet denoising algorithm is proposed to filter out the noise.The algorithm features an adaptive threshold and a continuous threshold function.The adaptive threshold is dynamically adjusted according to the wavelet decomposition level,and the continuous threshold function ensures continuity with lower constant error,thus optimizing the denoising process.Simulation results show that the proposed algorithm has excellent performance in improving SNR and reducing root mean square error(RMSE)compared with other algorithms.Experimental results show that denoising of an actual LiDAR echo signal results in a 4.37 dB improvement in SNR and a 39.5%reduction in RMSE.The proposed method significantly enhances the ability of single-photon LiDAR to detect weak signals.展开更多
A distinguished category of operational fluids,known as hybrid nanofluids,occupies a prominent role among various fluid types owing to its superior heat transfer properties.By employing a dovetail fin profile,this wor...A distinguished category of operational fluids,known as hybrid nanofluids,occupies a prominent role among various fluid types owing to its superior heat transfer properties.By employing a dovetail fin profile,this work investigates the thermal reaction of a dynamic fin system to a hybrid nanofluid with shape-based properties,flowing uniformly at a velocity U.The analysis focuses on four distinct types of nanoparticles,i.e.,Al2O3,Ag,carbon nanotube(CNT),and graphene.Specifically,two of these particles exhibit a spherical shape,one possesses a cylindrical form,and the final type adopts a platelet morphology.The investigation delves into the pairing of these nanoparticles.The examination employs a combined approach to assess the constructional and thermal exchange characteristics of the hybrid nanofluid.The fin design,under the specified circumstances,gives rise to the derivation of a differential equation.The given equation is then transformed into a dimensionless form.Notably,the Hermite wavelet method is introduced for the first time to address the challenge posed by a moving fin submerged in a hybrid nanofluid with shape-dependent features.To validate the credibility of this research,the results obtained in this study are systematically compared with the numerical simulations.The examination discloses that the highest heat flux is achieved when combining nanoparticles with spherical and platelet shapes.展开更多
Nonlinear science is a fundamental area of physics research that investigates complex dynamical systems which are often characterized by high sensitivity and nonlinear behaviors.Numerical simulations play a pivotal ro...Nonlinear science is a fundamental area of physics research that investigates complex dynamical systems which are often characterized by high sensitivity and nonlinear behaviors.Numerical simulations play a pivotal role in nonlinear science,serving as a critical tool for revealing the underlying principles governing these systems.In addition,they play a crucial role in accelerating progress across various fields,such as climate modeling,weather forecasting,and fluid dynamics.However,their high computational cost limits their application in high-precision or long-duration simulations.In this study,we propose a novel data-driven approach for simulating complex physical systems,particularly turbulent phenomena.Specifically,we develop an efficient surrogate model based on the wavelet neural operator(WNO).Experimental results demonstrate that the enhanced WNO model can accurately simulate small-scale turbulent flows while using lower computational costs.In simulations of complex physical fields,the improved WNO model outperforms established deep learning models,such as U-Net,Res Net,and the Fourier neural operator(FNO),in terms of accuracy.Notably,the improved WNO model exhibits exceptional generalization capabilities,maintaining stable performance across a wide range of initial conditions and high-resolution scenarios without retraining.This study highlights the significant potential of the enhanced WNO model for simulating complex physical systems,providing strong evidence to support the development of more efficient,scalable,and high-precision simulation techniques.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
文摘This paper discusses the principle and procedures of the second-generation wavelet transform and its application to the denoising of seismic data. Based on lifting steps, it is a flexible wavelet construction method using linear and nonlinear spatial prediction and operators to implement the wavelet transform and to make it reversible. The lifting scheme transform -includes three steps： split, predict, and update. Deslauriers-Dubuc （4, 2） wavelet transforms are used to process both synthetic and real data in our second-generation wavelet transform. The processing results show that random noise is effectively suppressed and the signal to noise ratio improves remarkably. The lifting wavelet transform is an efficient algorithm.
基金supported by the National Natural Science Foundation of China(Nos.41574116 and 41774132)Hunan Provincial Innovation Foundation for Postgraduate(Grant Nos.CX2017B052)the Fundamental Research Funds for the Central Universities of Central South University(Nos.2018zzts693)。
文摘Ground-penetrating radar(GPR)is a highly efficient,fast and non-destructive exploration method for shallow surfaces.High-precision numerical simulation method is employed to improve the interpretation precision of detection.Second-generation wavelet finite element is introduced into the forward modeling of the GPR.As the finite element basis function,the second-generation wavelet scaling function constructed by the scheme is characterized as having multiple scales and resolutions.The function can change the analytical scale arbitrarily according to actual needs.We can adopt a small analysis scale at a large gradient to improve the precision of analysis while adopting a large analytical scale at a small gradient to improve the efficiency of analysis.This approach is beneficial to capture the local mutation characteristics of the solution and improve the resolution without changing mesh subdivision to realize the efficient solution of the forward GPR problem.The algorithm is applied to the numerical simulation of line current radiation source and tunnel non-dense lining model with analytical solutions.Result show that the solution results of the secondgeneration wavelet finite element are in agreement with the analytical solutions and the conventional finite element solutions,thereby verifying the accuracy of the second-generation wavelet finite element algorithm.Furthermore,the second-generation wavelet finite element algorithm can change the analysis scale arbitrarily according to the actual problem without subdividing grids again.The adaptive algorithm is superior to traditional scheme in grid refinement and basis function order increase,which makes this algorithm suitable for solving complex GPR forward-modeling problems with large gradient and singularity.
基金Supported by the Aerospace Science and Technology Innovation Foundation of China(2006)
文摘An effective de-noising method for fiber optic gyroscopes(FOGs)is proposed.This method is based on second-generation Daubechies D4(DB4)wavelet transform(WT)and level-dependent threshold estimator called Stein's unbiased risk estimator(SURE).The whole approach consists of three critical parts:wavelet decomposition module,parameters estimation module and SURE de-noising module.First,DB4 wavelet is selected as lifting base of the second-generation wavelet in the decomposition module.Second,in the parameters estimation module,maximum likelihood estimation(MLE)is used for stochastic noise parameters estimation.Third,combined with soft threshold de-noising technique,the SURE de-noising module is designed.For comparison,both the traditional universal threshold wavelet and the second-generation Harr wavelet method are also investigated.The experiment results show that the computation cost is 40%less than that of the traditional wavelet method.The standard deviation of de-noised FOG signal is 0.012 and the three noise terms such as angle random walk,bias instability and quantization noise are reduced to 0.0072°/√h,0.0041°/h,and 0.0081°,respectively.
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the support of the Major Science and Technology Project of Yunnan Province,China(Grant No.202502AD080007)the National Natural Science Foundation of China(Grant No.52378288)。
文摘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.
基金supported by the National Natural Science Foundation of China(42430303)Strategy Priority Research Program(Category B)of the Chinese Academy of Sciences(XDB0710000)+2 种基金National Natural Science Foundation of China(42288201)the National Key R&D Program of China(2023YFF0803203)the IGGCAS start-up funding(Grant No.E251510101).
文摘In wave-equation migration and demigration,the cross-correlation imaging/forwarding step implicitly injects an additional copy of the source wavelet,so that the amplitude spectrum of the wavelet is applied redundantly(effectively imposing a wavelet-spectrum weighting,often akin to an amplitude-squared bias).This redundancy degrades structural fidelity and amplitude balance yet is frequently overlooked.We(i)formalize the mechanism by which cross-correlation duplicates the source-wavelet amplitude effect in both migration and demigration,and(ii)introduce a source-equalized operator that removes the redundancy by deconvolving(or dividing by)the wavelet amplitude spectrum in the imaging condition and its demigration counterpart,while leaving phase/kinematics intact.Using a band-limited Ricker wavelet on a two-layer model and on Marmousi,we show that,if unmanaged,the redundant wavelet spectrum broadens main lobes,introduces ringing,and suppresses vertical resolution in migrated images,and inflates spectrum mismatches between demigrated and observed data even when peak times agree.With our correction,images recover observed-data-consistent bandwidth and sharpened interfaces,and demigrated data also exhibit improved spectrum conformity and reduced amplitude misfit.The results clarify when source amplitudes matter,why cross-correlation makes them redundantly matter,and how a lightweight spectral correction restores physically meaningful amplitude behavior in wave-equation migration/demigration.
基金This research was funded by the National Natural Science Foundation of China(52078068)Practice Innovation Program of Jiangsu Province(KYCX22_3082).
文摘With the emphasis on environmental issues,the recycling of waste concrete,even recycled concrete,has become a hot spot in the field of architecture.But the repeated recycling of waste concrete used in harsh environments is still a complex problem.This paper discusses the durability and recyclability of recycled aggregate concrete(RAC)as a prefabricated material in the harsh environment,the effect of high-temperature curing(60℃,80℃,and 100℃)on the frost resistance of RAC and physical properties of the second generation recycled coarse aggregate(RCA_(2))of RAC after 300 freeze-thaw cycles were studied.The frost resistance of RAC was characterized by compressive strength,relative dynamic elastic modulus,and mass loss.As the physical properties of RCA_(2),the apparent density,water absorption,and crushing value were measured.And the SEM images of RAC after 300 freeze-thaw cycles were shown.The results indicated that the frost resistance of RAC cured at 80℃ for 7 days was comparable to that cured in the standard condition(cured for 28 days at 20℃±2℃ and 95%humidity),and the RAC cured at 100℃ was slightly worse.However,the frost resistance of RAC cured at 60℃ deteriorated seriously.The RAC cured at 80℃ for 7 days is the best.Whether after the freeze-thaw cycle or not,the RCA that curd at 60℃,80℃,and 100℃ for 7 days can also meet the requirements of Grade III RCA and be used as the aggregate of non-bearing part of prefabricated concrete components.RCA_(2) which is cured at 80℃ for 7 days had the best physical properties.
基金Project supported by the Fund from the Scientific and Technological Research Council of Turkey(TüB˙ITAK)(Grant No.110T876)
文摘Alternating-current losses in a two-layer superconducting cable, each layer being composed of 15 closely-spaced rectangular wires made up of second-generation superconductors when the ends of wires are coated by either a non-magnetic or strong ferromagnetic material having a U profile is numerically investigated. Computations are carried out through the finite-element method. The alternating-current losses do not increase significantly if the relative permeability of the coating is increased three orders of magnitude, provided that the current amplitude is less than half of the critical current in a superconducting wire. However, the losses are much higher for ferromagnetic coating if the amplitude of the applied current oscillating at 50 Hz is close to the critical current. The ferromagnetic coating is seen to accumulate the magnetic field lines normally on its surfaces, while the field lines are parallel to the long axes of the wires, leading to more significant flux penetration in the coated regions. This facilitates a uniform low-loss current flow in the uncoated regions of the wires. In contrast, coating with a non-magnetic material gives rise to a considerably smaller current flow in the uncoated regions, whereas the low-loss flow is maintained in the coated regions. Moreover, the current flows in opposite directions in the coated and uncoated regions, where the direction in each region is converse for the two materials.
基金supported by the grants of the National Key Technology R&D Program (2008BADB3B04 )Basic Science and Research Special Fund for the State Level and Public Scientific Research Institute (Grassland Research Institute,Chinese Academy of Agricultural Sciences) (2007-1-02)
文摘[ Objective] To explore the effects of spaceflight on the second-generation seeds of alfalfa and provide a theoretical basis for mutation breeding. [Method] The seeds of Medicago stavia L. lines no. 1, no. 2 and no. 4 were carried into space by the Shijian-8 seed breeding satellite for a 15-d spaceflight treatment. After returning to the ground, seedlings were transplanted to field. Traits of the second-generation seeds of alfalfa were evaluated. [Result] The 1 000-grain weight of the second-generation seeds were 5% -9% significantly higher than that the control （P 〈 0.05）. The germination rate, seedling weight, shoot length and root length were significantly increased （P 〈 0.05）. The hard seed rate and the rate of moldy seeds were significantly decreased （ P 〈 0.05）. However, the rate of dead seeds was increased. [ Conclusion] Spaceflight treatment has positive mutagenic effects on the second-generation seeds of alfalfa.
基金the National Natural Science Foundation of China (8153000545).
文摘Background:The safety and efficacy of coronary artery bypass grafting(CABG)and second-generation drug-eluting stents(DESs)in patients with coronary artery disease(CAD)remain controversial.Therefore we aimed to compare the outcomes of CAD patients treated with CABG and second-generation DESs.Methods:We systematically searched the PubMed,Cochrane Library,Ovid,and Elsevier databases.Studies comparing second-generation DESs with CABG in CAD patients were included.RevMan 5.3 was used to extract and pool the data from the applicable studies.Results:Six trials(N=6604 participants)were included in this meta-analysis.Among all of the CAD patients,second-generation DESs were associated with no differences in the risks of all-cause death[risk ratio(RR)1.18,95% confi dence interval(CI)0.98–1.43,P=0.09],cardiovascular death(RR 1.14,95% CI 0.81–1.59,P=0.45),myocardial infarction(RR 1.22,95% CI 0.98–1.54,P=0.08),and stroke(RR 0.83,95% CI 0.59–1.17,P=0.29),but increased the risks of revascularization(RR 1.95,95% CI 1.66–2.30,P<0.001)and major adverse cardiac and cerebrovascular events(RR 1.72,95% CI:1.31–2.26,P<0.001)when compared with CABG.Conclusions:In the treatment of CAD patients,second-generation DESs was not associated with increased risks of all-cause death,cardiovascular death,myocardial infarction,and stroke,but increased the risks of revascularization and major adverse cardiac and cerebrovascular events when compared with CABG.
基金financially sponsored by Qing Lan Project in Jiangsu Province of China(2023)Scientific Research Project of Taizhou Polytechnic College(TZYKY-22-4).
文摘The reuse of waste recycled concrete from harsh environments has become a research hotspot in the field of construction.This study investigated the repair effect of carbonation treatment on second-generation recycled fine aggregate(SRFA)obtained from recycled fine aggregate concrete(RFAC)subjected to freeze-thaw(FT)cycles.Before and after carbonation,the properties of SRFA were evaluated.Carbonated second-generation recycled fine aggregate(CSRFA)at five substitution rates(0%,25%,50%,75%,100%)to replace SRFA was used to prepare carbonated second-generation recycled fine aggregate concrete(CSRFAC).The water absorption,porosity and mechanical properties of CSRFAC were tested,and its frost-resisting durability was evaluated.The results showed after carbonation treatment,the physical properties of SRFA was improved and met the requirements of II aggregate.The micro-hardness of the interfacial transition zone and attached mortar in CSRFA was 50.5%and 31.2%higher than that in SRFA,respectively.With the increase of CSRFA replacement rate,the water absorption and porosity of CSRFAC gradually decreased,and the mechanical properties and frost resistance of CSRFAC were gradually improved.Carbonation treatment effectively repairs the damage of SRFA caused by FT cycles and improves its application potential.
文摘Aims:Research on second-generation antipsychotic drugs (SGAs) has experienced great development in last decades.We did a bibliometric study on the scientific publications on SGAs in Japan.Methods: With theEMBASEandMEDLINEdatabases, we chose papers published from Japan with SGA descriptors. Price’s law and Bradford’s law has been used as bibliometric indicators for quantitating production and dispersion, respectively, of published papers on SGAs. We also calculated the participation index of different countries, and correlated those bibliometric data with some social and health data from Japan (such as totalper capitaexpenditure on health and gross domestic expenditure on research and development). Results: A sum of 669 original documents were published from Japan from 1982 to 2011. Those results fulfilled Price’s law, with scientific production on SGAs showing exponential growth (correlation coefficientr= 0.9261, as against anr= 0.8709 after linear adjustment). The most studied SGAs in Japan wererisperidone (n= 192), aripiprazole (n= 109), and olanzapine (n= 106). Division of documents into Bradford zones yielded a nucleus occupied exclusively by theProgress in Neuro-Psychopharmacology and Biological Psychiatry(49 articles). Those publications were in 157 different journals. Seven of the first 10 frequently used journals had an impact factor of being greater than 3. Conclusions: The SGA publications in Japan have been through exponential growth over the studied period, without evidence of reaching a saturation point.
基金supported by National Natural Science Foundation of China under Grant U23A20279China Electronics Tian’ao Innovation Theory and Technology Group Fund under Grand 20221193-04-04.
文摘To overcome the challenges of poor real-time performance,limited scalability,and low intelligence in conventional jamming pattern recognition methods,this paper proposes a method based on Wavelet Packet Decomposition(WPD)and enhanced deep learning techniques.In the proposed method,an agent at the receiver processes the received signal using WPD to generate an initial Spectrogram Waterfall(SW),which is subsequently segmented using a sliding window to serve as the input for the jamming recognition network.The network employs a bilateral filter to preprocess the input SW,thereby enhancing the edge features of the jamming signals.To extract abstract features,depthwise separable convolution is utilized instead of traditional convolution,thereby reducing the network’s parameter count and enhancing real-time performance.A pyramid pooling layer is integrated before the fully connected layer to enable the network to process input SW of varying sizes,thus enhancing scalability.During network training,adaptive moment estimation is employed as the optimizer,allowing the network to dynamically adjust the learning rate and accelerate convergence.A comprehensive comparison between the proposed jamming recognition network and six other models is conducted,along with Ablation Experiments(AE)based on numerical simulations.Simulation results demonstrate that the proposed method based on WPD and enhanced deep learning achieves high-precision recognition of various jamming patterns while maintaining a favorable balance among prediction accuracy,network complexity,and prediction time.
基金supported by the‘Pioneer’and‘Leading Goose’R&D Program of Zhejiang(Grant No.2023C02018)Zhejiang Provincial Natural Science Foundation of China(Grant No.LTGN23D010002)+2 种基金National Natural Science Foundation of China(Grant No.42371385)Funds of the Natural Science Foundation of Hangzhou(Grant No.2024SZRYBD010001)Nanxun Scholars Program of ZJWEU(Grant No.RC2022010755).
文摘Plant diseases are a major threat that can severely impact the production of agriculture and forestry.This can lead to the disruption of ecosystem functions and health.With its ability to capture continuous narrow-band spectra,hyperspectral technology has become a crucial tool to monitor crop diseases using remote sensing.However,existing continuous wavelet analysis(CWA)methods suffer from feature redundancy issues,while the continuous wavelet projection algorithm(CWPA),an optimization approach for feature selection,has not been fully validated to monitor plant diseases.This study utilized rice bacterial leaf blight(BLB)as an example by evaluating the performance of four wavelet basis functions-Gaussian2,Mexican hat,Meyer,andMorlet-within theCWAandCWPAframeworks.Additionally,the classification models were constructed using the k-nearest neighbors(KNN),randomforest(RF),and Naïve Bayes(NB)algorithms.The results showed the following:(1)Compared to traditional CWA,CWPA significantly reduced the number of required features.Under the CWPA framework,almost all the model combinations achieved maximum classification accuracy with only one feature.In contrast,the CWA framework required three to seven features.(2)Thechoice of wavelet basis functions markedly affected the performance of themodel.Of the four functions tested,the Meyer wavelet demonstrated the best overall performance in both the CWPA and CWA frameworks.(3)Under theCWPAframework,theMeyer-KNNandMeyer-NBcombinations achieved the highest overall accuracy of 93.75%using just one feature.In contrast,under the CWA framework,the CWA-RF combination achieved comparable accuracy(93.75%)but required six features.This study verified the technical advantages of CWPA for monitoring crop diseases,identified an optimal wavelet basis function selection scheme,and provided reliable technical support to precisely monitor BLB in rice(Oryza sativa).Moreover,the proposed methodological framework offers a scalable approach for the early diagnosis and assessment of plant stress,which can contribute to improved accuracy and timeliness when plant stress is monitored.
基金National Natural Science Foundation of China(No.62176052)。
文摘In the vision transformer(ViT)architecture,image data are transformed into sequential data for processing,which may result in the loss of spatial positional information.While the self-attention mechanism enhances the capacity of ViT to capture global features,it compromises the preservation of fine-grained local feature information.To address these challenges,we propose a spatial positional enhancement module and a wavelet transform enhancement module tailored for ViT models.These modules aim to reduce spatial positional information loss during the patch embedding process and enhance the model’s feature extraction capabilities.The spatial positional enhancement module reinforces spatial information in sequential data through convolutional operations and multi-scale feature extraction.Meanwhile,the wavelet transform enhancement module utilizes the multi-scale analysis and frequency decomposition to improve the ViT’s understanding of global and local image structures.This enhancement also improves the ViT’s ability to process complex structures and intricate image details.Experiments on CIFAR-10,CIFAR-100 and ImageNet-1k datasets are done to compare the proposed method with advanced classification methods.The results show that the proposed model achieves a higher classification accuracy,confirming its effectiveness and competitive advantage.
基金funded by the National Key R&D Program of China(Grant No.2022YFC3300705)the National Natural Science Foundation of China(Grant Nos.62203056,12202048,and 62201056).
文摘Atmospheric aerosols are the primary contributors to environmental pollution.As such aerosols are micro-to nanosized particles invisible to the naked eye,it is necessary to utilize LiDAR technology for their detection.The laser radar echo signal is vulnerable to background light and electronic thermal noise.While single-photon LiDAR can effectively reduce background light interference,electronic thermal noise remains a significant challenge,especially at long distances and in environments with a low signal-to-noise ratio(SNR).However,conventional denoising methods cannot achieve satisfactory results in this case.In this paper,a novel adaptive continuous threshold wavelet denoising algorithm is proposed to filter out the noise.The algorithm features an adaptive threshold and a continuous threshold function.The adaptive threshold is dynamically adjusted according to the wavelet decomposition level,and the continuous threshold function ensures continuity with lower constant error,thus optimizing the denoising process.Simulation results show that the proposed algorithm has excellent performance in improving SNR and reducing root mean square error(RMSE)compared with other algorithms.Experimental results show that denoising of an actual LiDAR echo signal results in a 4.37 dB improvement in SNR and a 39.5%reduction in RMSE.The proposed method significantly enhances the ability of single-photon LiDAR to detect weak signals.
文摘A distinguished category of operational fluids,known as hybrid nanofluids,occupies a prominent role among various fluid types owing to its superior heat transfer properties.By employing a dovetail fin profile,this work investigates the thermal reaction of a dynamic fin system to a hybrid nanofluid with shape-based properties,flowing uniformly at a velocity U.The analysis focuses on four distinct types of nanoparticles,i.e.,Al2O3,Ag,carbon nanotube(CNT),and graphene.Specifically,two of these particles exhibit a spherical shape,one possesses a cylindrical form,and the final type adopts a platelet morphology.The investigation delves into the pairing of these nanoparticles.The examination employs a combined approach to assess the constructional and thermal exchange characteristics of the hybrid nanofluid.The fin design,under the specified circumstances,gives rise to the derivation of a differential equation.The given equation is then transformed into a dimensionless form.Notably,the Hermite wavelet method is introduced for the first time to address the challenge posed by a moving fin submerged in a hybrid nanofluid with shape-dependent features.To validate the credibility of this research,the results obtained in this study are systematically compared with the numerical simulations.The examination discloses that the highest heat flux is achieved when combining nanoparticles with spherical and platelet shapes.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.42005003 and 41475094)。
文摘Nonlinear science is a fundamental area of physics research that investigates complex dynamical systems which are often characterized by high sensitivity and nonlinear behaviors.Numerical simulations play a pivotal role in nonlinear science,serving as a critical tool for revealing the underlying principles governing these systems.In addition,they play a crucial role in accelerating progress across various fields,such as climate modeling,weather forecasting,and fluid dynamics.However,their high computational cost limits their application in high-precision or long-duration simulations.In this study,we propose a novel data-driven approach for simulating complex physical systems,particularly turbulent phenomena.Specifically,we develop an efficient surrogate model based on the wavelet neural operator(WNO).Experimental results demonstrate that the enhanced WNO model can accurately simulate small-scale turbulent flows while using lower computational costs.In simulations of complex physical fields,the improved WNO model outperforms established deep learning models,such as U-Net,Res Net,and the Fourier neural operator(FNO),in terms of accuracy.Notably,the improved WNO model exhibits exceptional generalization capabilities,maintaining stable performance across a wide range of initial conditions and high-resolution scenarios without retraining.This study highlights the significant potential of the enhanced WNO model for simulating complex physical systems,providing strong evidence to support the development of more efficient,scalable,and high-precision simulation techniques.
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.
