This study deduces a general inversion of continuous wavelet transform (CWT) with timescale being real rather than positive. In conventional CWT inversion, wavelet’s dual is assumed to be a reconstruction wavelet or ...This study deduces a general inversion of continuous wavelet transform (CWT) with timescale being real rather than positive. In conventional CWT inversion, wavelet’s dual is assumed to be a reconstruction wavelet or a localized function. This study finds that wavelet’s dual can be a harmonic which is not local. This finding leads to new CWT inversion formulas. It also justifies the concept of normal wavelet transform which is useful in time-frequency analysis and time-frequency filtering. This study also proves a law for CWT inversion: either wavelet or its dual must integrate to zero.展开更多
The research purpose of this paper is to show the limitations of the existing radiometric normalization approaches and their disadvantages in change detection of artificial objects by comparing the existing approaches...The research purpose of this paper is to show the limitations of the existing radiometric normalization approaches and their disadvantages in change detection of artificial objects by comparing the existing approaches,on the basis of which a preprocessing approach to radiometric consistency,based on wavelet transform and a spatial low-pass filter,has been devised.This approach first separates the high frequency information and low frequency information by wavelet transform.Then,the processing of relative radiometric consistency based on a low-pass filter is conducted on the low frequency parts.After processing,an inverse wavelet transform is conducted to obtain the results image.The experimental results show that this approach can substantially reduce the influence on change detection of linear or nonlinear radiometric differences in multi-temporal images.展开更多
Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system.Despite deep learning has proved to be superior to previous approaches that depend on handcrafted...Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system.Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases.The Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems.In recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image processing.For medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial intelligence.These newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death rates.Based on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this article.In context,primarily image quality enhancement procedures like noise removal and contrast normalization are employed.Then the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are extracted.After extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant features.The selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar classes.The proposed model is evaluated in terms of accuracy,precision,recall,and f-measure.The investigation evident the performance of incorporating optimization techniques for medical image classification is better than conventional techniques.展开更多
针对在传统电力谐波检测手段中存在的精度低和速度慢的问题,提出了一种基于提升小波变换和变步长LMS(Least Mean Square)相结合的自适应谐波检测算法。通过对谐波电流进行正交变换,有效减少了输入数据的互相关,并加快了LMS的收敛速度。...针对在传统电力谐波检测手段中存在的精度低和速度慢的问题,提出了一种基于提升小波变换和变步长LMS(Least Mean Square)相结合的自适应谐波检测算法。通过对谐波电流进行正交变换,有效减少了输入数据的互相关,并加快了LMS的收敛速度。采用变步长的收敛因子不仅加快了算法的收敛速度,还实现了较小的稳态误差。同时,采用DSP处理器TMS320F2812设计了电力谐波检测电路系统。实验结果表明,设计的电力谐波检测电路能够准确、实时地检测出电网上的谐波参数,提出的改进算法能在1/4个周期内跟上负载电流的变化,并且稳态误差小于0.5%,比传统的LMS自适应算法具有更高的检测精度和更快的瞬时响应速度,适合于对有源电力滤波器实时性要求较高的场合。展开更多
文摘This study deduces a general inversion of continuous wavelet transform (CWT) with timescale being real rather than positive. In conventional CWT inversion, wavelet’s dual is assumed to be a reconstruction wavelet or a localized function. This study finds that wavelet’s dual can be a harmonic which is not local. This finding leads to new CWT inversion formulas. It also justifies the concept of normal wavelet transform which is useful in time-frequency analysis and time-frequency filtering. This study also proves a law for CWT inversion: either wavelet or its dual must integrate to zero.
基金supported by the National Natural Science Foundation of China (Grant Nos.40901211,60602013)National Basic Research Program of China ("973" Program)(Grant No.2006CB701304)+1 种基金National High Technology Research and Development Program of China ("863" Program) (Grant No.2007AA120204)LIESMARS Special Research Funding & Research Grant of State Key Laboratory of Subtropical Building Science in South China University of Technology (Grant No.2008KB12)
文摘The research purpose of this paper is to show the limitations of the existing radiometric normalization approaches and their disadvantages in change detection of artificial objects by comparing the existing approaches,on the basis of which a preprocessing approach to radiometric consistency,based on wavelet transform and a spatial low-pass filter,has been devised.This approach first separates the high frequency information and low frequency information by wavelet transform.Then,the processing of relative radiometric consistency based on a low-pass filter is conducted on the low frequency parts.After processing,an inverse wavelet transform is conducted to obtain the results image.The experimental results show that this approach can substantially reduce the influence on change detection of linear or nonlinear radiometric differences in multi-temporal images.
基金This research is financially supported by the Deanship of Scientific Research at King Khalid University under research grant number(RGP.2/202/43).
文摘Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system.Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases.The Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems.In recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image processing.For medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial intelligence.These newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death rates.Based on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this article.In context,primarily image quality enhancement procedures like noise removal and contrast normalization are employed.Then the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are extracted.After extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant features.The selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar classes.The proposed model is evaluated in terms of accuracy,precision,recall,and f-measure.The investigation evident the performance of incorporating optimization techniques for medical image classification is better than conventional techniques.
文摘针对在传统电力谐波检测手段中存在的精度低和速度慢的问题,提出了一种基于提升小波变换和变步长LMS(Least Mean Square)相结合的自适应谐波检测算法。通过对谐波电流进行正交变换,有效减少了输入数据的互相关,并加快了LMS的收敛速度。采用变步长的收敛因子不仅加快了算法的收敛速度,还实现了较小的稳态误差。同时,采用DSP处理器TMS320F2812设计了电力谐波检测电路系统。实验结果表明,设计的电力谐波检测电路能够准确、实时地检测出电网上的谐波参数,提出的改进算法能在1/4个周期内跟上负载电流的变化,并且稳态误差小于0.5%,比传统的LMS自适应算法具有更高的检测精度和更快的瞬时响应速度,适合于对有源电力滤波器实时性要求较高的场合。
