wavelet transform

摘要： Convolutional neural networks(CNNs) have shown great potential for image super-resolution(SR).However,most existing CNNs only reconstruct images in the spatial domain,resulting in insufficient high-frequency details of reconstructed images.To address this issue,a channel attention based wavelet cascaded network for image super-resolution(CWSR) is proposed.Specifically,a second-order channel attention(SOCA) mechanism is incorporated into the network,and the covariance matrix normalization is utilized to explore interdependencies between channel-wise features.Then,to boost the quality of residual features,the non-local module is adopted to further improve the global information integration ability of the network.Finally,taking the image loss in the spatial and wavelet domains into account,a dual-constrained loss function is proposed to optimize the network.Experimental results illustrate that CWSR outperforms several state-of-the-art methods in terms of both visual quality and quantitative metrics.

摘要： In Single-Photon Emission Computed Tomography(SPECT),the reconstructed image has insufficient contrast,poor resolution and inaccurate volume of the tumor size due to physical degradation factors.Generally,nonstationary filtering of the projection or the slice is one of the strategies for correcting the resolution and therefore improving the quality of the reconstructed SPECT images.This paper presents a new 3D algorithm that enhances the quality of reconstructed thoracic SPECT images and reduces the noise level with the best degree of accuracy.The suggested algorithm is composed of three steps.The first one consists of denoising the acquired projections using the benefits of the complementary properties of both the Curvelet transformand theWavelet transforms to provide the best noise reduction.The second step is a simultaneous reconstruction of the axial slices using the 3D Ordered Subset Expectation Maximization(OSEM)algorithm.The last step is post-processing of the reconstructed axial slices using one of the newest anisotropic diffusion models named Partial Differential Equation(PDE).The method is tested on two digital phantoms and clinical bone SPECT images.A comparative study with four algorithms reviewed on state of the art proves the significance of the proposed method.In simulated data,experimental results show that the plot profile of the proposed model keeps close to the original one compared to the other algorithms.Furthermore,it presents a notable gain in terms of contrast to noise ratio(CNR)and execution time.The proposed model shows better results in the computation of contrast metric with a value of 0.68±7.2 and the highest signal to noise ratio(SNR)with a value of 78.56±6.4 in real data.The experimental results prove that the proposed algorithm is more accurate and robust in reconstructing SPECT images than the other algorithms.It could be considered a valuable candidate to correct the resolution of bone in the SPECT images.

摘要： Purpose-The main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during acquisition.Initially,pre-processing is done on EEG signal for quality improvement.Then,by using wavelet transform(WT)feature extraction is done.The artefacts present in the EEG are removed using deep convLSTM.This deep convLSTM is trained by proposed fractional calculus based flower pollination optimisation algorithm.Design/methodology/approach-Nowadays’EEG signals play vital role in the field of neurophysiologic research.Brain activities of human can be analysed by using EEG signals.These signals are frequently affected by noise during acquisition and other external disturbances,which lead to degrade the signal quality.Denoising of EEG signals is necessary for the effective usage of signals in any application.This paper proposes a new technique named as flower pollination fractional calculus optimisation(FPFCO)algorithm for the removal of artefacts fromEEGsignal through deep learning scheme.FPFCOalgorithmis the integration of flower pollination optimisation and fractional calculus which takes the advantages of both the flower pollination optimisation and fractional calculus which is used to train the deep convLSTM.The existed FPO algorithm is used for solution update through global and local pollinations.In this case,the fractional calculus(FC)method attempts to include the past solution by including the second order derivative.As a result,the suggested FPFCO algorithm approaches the best solution faster than the existing flower pollination optimization(FPO)method.Initially,5 EEGsignals are contaminated by artefacts such asEMG,EOG,EEGand randomnoise.These contaminatedEEG signals are pre-processed to remove baseline and power line noises.Further,feature extraction is done by using WTand extracted features are applied to deep convLSTM,which is trained by proposed fractional calculus based flower pollination optimisation algorithm.FPFCO is used for the effective removal of artefacts from EEG signal.The proposed technique is compared with existing techniques in terms of SNR and MSE.Findings-The proposed technique is compared with existing techniques in terms of SNR,RMSE and MSE.Originality/value-100%.

摘要： For scanning electronmicroscopes with high resolution and a strong electric field,biomass materials under observation are prone to radiation damage from the electron beam.This results in blurred or non-viable images,which affect further observation of material microscopic morphology and characterization.Restoring blurred images to their original sharpness is still a challenging problem in image processing.Traditionalmethods can’t effectively separate image context dependency and texture information,affect the effect of image enhancement and deblurring,and are prone to gradient disappearance during model training,resulting in great difficulty in model training.In this paper,we propose the use of an improvedU-Net(U-shapedConvolutional Neural Network)to achieve image enhancement for biomass material characterization and restore blurred images to their original sharpness.The main work is as follows:use of depthwise separable convolution instead of standard convolution in U-Net to reduce model computation effort and parameters;embedding wavelet transform into the U-Net structure to separate image context and texture information,thereby improving image reconstruction quality;using dense multi-receptive field channel modules to extract image detail information,thereby better transmitting the image features and network gradients,and reduce the difficulty of training.The experiments show that the improved U-Net model proposed in this paper is suitable and effective for enhanced deblurring of biomass material characterization images.The PSNR(Peak Signal-to-noise Ratio)and SSIM(Structural Similarity)are enhanced as well.

摘要： :Machine Learning(ML)algorithms have been widely used for financial time series prediction and trading through bots.In this work,we propose a Predictive Error Compensated Wavelet Neural Network(PEC-WNN)ML model that improves the prediction of next day closing prices.In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs.An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence.The performance of the proposed model is evaluated using six different stock data samples in the New York stock exchange.The results have demonstrated significant improvement in forecasting accuracy in all cases when the second network is used in accordance with the first one by adding the outputs.The RMSE error is 33%improved when the proposed PEC-WNN model is used compared to the Long ShortTerm Memory(LSTM)model.Furthermore,through the analysis of training mechanisms,we found that using the updated training the performance of the proposed model is improved.The contribution of this study is the applicability of simultaneously different time frames as inputs.Cascading the predictive error compensation not only reduces the error rate but also helps in avoiding overfitting problems.

摘要： Non-orthogonal multiple access(NOMA)is gaining considerable attention due to its features,such as low out-of-band radiation,signal detection capability,high spectrum gain,fast data rate,and massive D2D connectivity.It may be considered for 5G networks.However,the high peak-to-average power ratio(PAPR)is viewed as a significant disadvantage of a NOMA waveform,and it weakens the quality of signals and the throughput of the scheme.In this article,we introduce a modified NOMA system by employing a block of wavelet transform,an alternative to FFT(Fast Fourier transform).The modified system combines the details of fractional frequency and time analysis of NOMA signals.In this correspondence,we utilize an advanced partial transmission scheme(PTS),and selective mapping(SLM),and present a genetic algorithm(GA)for SLM to investigate the peak power performance of a WT-based NOMA system.The performance of WT-SLM,WT-PTS,and WT-SLM-GA methods is compared with that of the traditional NOMAbased SLM and PTS methods.The simulation results demonstrate that the proposed system effectively reduces PAPR in comparison with the traditional schemes.

摘要： Agriculture plays an important role in the economy of all countries.However,plant diseases may badly affect the quality of food,production,and ultimately the economy.For plant disease detection and management,agriculturalists spend a huge amount of money.However,the manual detection method of plant diseases is complicated and time-consuming.Consequently,automated systems for plant disease detection using machine learning(ML)approaches are proposed.However,most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input data.To address the issue,this article proposes a fully automated method for plant disease detection and recognition using deep neural networks.In the proposed method,AlexNet and VGG19 CNNs are considered as pre-trained architectures.It is capable to obtain the feature extraction of the given data with fine-tuning details.After convolutional neural network feature extraction,it selects the best subset of features through the correlation coefficient and feeds them to the number of classifiers including K-Nearest Neighbor,Support Vector Machine,Probabilistic Neural Network,Fuzzy logic,and Artificial Neural Network.The validation of the proposed method is carried out on a self-collected dataset generated through the augmentation step.The achieved average accuracy of our method is more than 96%and outperforms the recent techniques.

摘要： In the present study, three wavelet basis functions, i.e., Mexican-hat, Morlet, and Wave, were used to analyze the atmospheric turbulence data obtained from an eddy covariance system in order to determine the effect of six meteorological elements including three-dimensional wind speed, temperature, and CO2and H2O concentrations on the time scale of coherent structures. First, we used the degree of correlation between original and reconstructed waveforms to test the three wavelets’performance when determining the time scale of coherent structures. The Wave wavelet’s reconstructed coherent structure signal best matched the original signal;thus, it was used to further analyze the time scale, number, and time cover of the meteorological elements. We found similar results for all elements, though there was some internal variation, suggesting that coherent structures are not inherently dependent on these elements. Our results provide a basis for proper coherent structure detection in atmospheric turbulence and improve the understanding of similarities and differences between coherent structure characteristics of different meteorological elements, which is helpful for further research into atmospheric turbulence and boundary layers.

摘要： Vehicles generate dissimilar sound patterns under different working environments.These generated sound patterns signify the condition of the engines,which in turn is used for diagnosing various faults.In this paper,the sound signals produced by motorcycles are analyzed to locate various faults.The important attributes are extracted from the generated sound signals based on time,frequency and wavelet domains which clearly describe the statistical behavior of the signals.Further,various types of faults are classified using the Extreme Learning Machine(ELM)classifier from the extracted features.Moreover,the improved classification performance is obtained by the combination of feature sets in different domains.The simulation results clearly demonstrate that the proposed hybrid feature set together with the ELM classifier gives more promising results with higher classification accuracy when compared with the other conventional methods.

摘要： This paper presents a low intricate,profoundly energy effective MRI Images combination intended for remote visual sensor frameworks which leads to improved understanding and implementation of treatment;especially for radiology.This is done by combining the original picture which leads to a significant reduction in the computation time and frequency.The proposed technique conquers the calculation and energy impediment of low power tools and is examined as far as picture quality and energy is concerned.Reenactments are performed utilizing MATLAB 2018a,to quantify the resultant vitality investment funds and the reproduction results show that the proposed calculation is very quick and devours just around 1%of vitality decomposition by the hybrid combination plans.Likewise,the effortlessness of our proposed strategy makes it increasingly suitable for continuous applications.

摘要： This paper proposes the scheme of ultra-high-speed transient-based protection(TbP)for EHV/UHV transmission lines，which includes the main protection and the auxiliary protection. The paper firstly introduces basic principles of main components and each other’s logic in this scheme. Then their applicable algorithms and flow charts are depicted in detail with the wavelet technology that fits for transient signal analysis. Theory analysis shows that the main protection—a fast directional blocking pilot protection，discriminates the internal and external faults at every point of the protected line based on two-end transient information and is able to reliably protect the whole line；The auxiliary protection—an ultra-high-speed boundary protection，can distinguishes a certain range fault of the protected line only relying on single-end transient information. Analysis shows that the whole set of TbP meets the demand of ultra-high-speed protective relay.

摘要： This paper proposes the scheme of ultra-high-speed transient-based protection(TbP)for EHV/UHV transmission lines，which includes the main protection and the auxiliary protection. The paper firstly introduces basic principles of main components and each other’s logic in this scheme. Then their applicable algorithms and flow charts are depicted in detail with the wavelet technology that fits for transient signal analysis. Theory analysis shows that the main protection—a fast directional blocking pilot protection，discriminates the internal and external faults at every point of the protected line based on two-end transient information and is able to reliably protect the whole line；The auxiliary protection—an ultra-high-speed boundary protection，can distinguishes a certain range fault of the protected line only relying on single-end transient information. Analysis shows that the whole set of TbP meets the demand of ultra-high-speed protective relay.

摘要： This paper proposes the scheme of ultra-high-speed transient-based protection(TbP)for EHV/UHV transmission lines，which includes the main protection and the auxiliary protection. The paper firstly introduces basic principles of main components and each other’s logic in this scheme. Then their applicable algorithms and flow charts are depicted in detail with the wavelet technology that fits for transient signal analysis. Theory analysis shows that the main protection—a fast directional blocking pilot protection，discriminates the internal and external faults at every point of the protected line based on two-end transient information and is able to reliably protect the whole line；The auxiliary protection—an ultra-high-speed boundary protection，can distinguishes a certain range fault of the protected line only relying on single-end transient information. Analysis shows that the whole set of TbP meets the demand of ultra-high-speed protective relay.

摘要： This paper proposes the scheme of ultra-high-speed transient-based protection(TbP)for EHV/UHV transmission lines，which includes the main protection and the auxiliary protection. The paper firstly introduces basic principles of main components and each other’s logic in this scheme. Then their applicable algorithms and flow charts are depicted in detail with the wavelet technology that fits for transient signal analysis. Theory analysis shows that the main protection—a fast directional blocking pilot protection，discriminates the internal and external faults at every point of the protected line based on two-end transient information and is able to reliably protect the whole line；The auxiliary protection—an ultra-high-speed boundary protection，can distinguishes a certain range fault of the protected line only relying on single-end transient information. Analysis shows that the whole set of TbP meets the demand of ultra-high-speed protective relay.

摘要： This paper proposes the scheme of ultra-high-speed transient-based protection(TbP)for EHV/UHV transmission lines，which includes the main protection and the auxiliary protection. The paper firstly introduces basic principles of main components and each other’s logic in this scheme. Then their applicable algorithms and flow charts are depicted in detail with the wavelet technology that fits for transient signal analysis. Theory analysis shows that the main protection—a fast directional blocking pilot protection，discriminates the internal and external faults at every point of the protected line based on two-end transient information and is able to reliably protect the whole line；The auxiliary protection—an ultra-high-speed boundary protection，can distinguishes a certain range fault of the protected line only relying on single-end transient information. Analysis shows that the whole set of TbP meets the demand of ultra-high-speed protective relay.