Pub Date : 2022-12-01DOI: 10.1142/s0219691322500618
Kyong-il Kim, Wi-Ung Kwak, Kyong-Hyok Choe
{"title":"Closed-form shrinkage function based on mixture of Gauss-Laplace distributions for dropping ambient noise","authors":"Kyong-il Kim, Wi-Ung Kwak, Kyong-Hyok Choe","doi":"10.1142/s0219691322500618","DOIUrl":"https://doi.org/10.1142/s0219691322500618","url":null,"abstract":"","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"7 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120856435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1142/s021969132250062x
Jay Singh Maurya, S. Upadhyay
{"title":"The Bessel wavelet transform of distributions in DL2′-type space","authors":"Jay Singh Maurya, S. Upadhyay","doi":"10.1142/s021969132250062x","DOIUrl":"https://doi.org/10.1142/s021969132250062x","url":null,"abstract":"","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122874257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-22DOI: 10.1142/s021969132250059x
Xue Jiang, Hong Sun
{"title":"Learning performance of uncentered kernel-based principal component analysis","authors":"Xue Jiang, Hong Sun","doi":"10.1142/s021969132250059x","DOIUrl":"https://doi.org/10.1142/s021969132250059x","url":null,"abstract":"","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123860377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-22DOI: 10.1142/s0219691322500606
P. Poornima, Murugesan Kuppusamy
{"title":"Standard pairs and construction of multiwavelets using refinement masks satisfying sum rules of order one","authors":"P. Poornima, Murugesan Kuppusamy","doi":"10.1142/s0219691322500606","DOIUrl":"https://doi.org/10.1142/s0219691322500606","url":null,"abstract":"","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126391228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1142/s0219691322500461
Ling Lei, Binqian Huang, Minchao Ye, Futian Yao, Y. Qian
Hyperspectral image (HSI) classification has long been a hot research topic. Most previous researches concentrate on the classification task of a single HSI scene, called single-scene classification. This research focuses on two closely related HSI scenes (called source and target scenes, respectively), and the problem is named cross-scene classification. This paper aims to explore the shared feature sub-space between two HSI scenes. A transfer learning algorithm called cross-domain residual deep nonnegative matrix factorization (CDRDNMF) is proposed. CDRDNMF is a multi-layer architecture consisting of dual-dictionary nonnegative matrix factorization (DDNMF) layers. In each layer, DDNMF is performed on source and target features for domain-invariant feature extraction. Then a data recovery process is completed, and the residual components from the recovery are passed to the next layer after activation. With such a multi-layer architecture, CDRDNMF delivers knowledge transfer and multi-scale feature extraction tasks. The experimental results prove the excellent performance of CDRDNMF on cross-scene classification.
{"title":"Cross-domain residual deep NMF for transfer learning between different hyperspectral image scenes","authors":"Ling Lei, Binqian Huang, Minchao Ye, Futian Yao, Y. Qian","doi":"10.1142/s0219691322500461","DOIUrl":"https://doi.org/10.1142/s0219691322500461","url":null,"abstract":"Hyperspectral image (HSI) classification has long been a hot research topic. Most previous researches concentrate on the classification task of a single HSI scene, called single-scene classification. This research focuses on two closely related HSI scenes (called source and target scenes, respectively), and the problem is named cross-scene classification. This paper aims to explore the shared feature sub-space between two HSI scenes. A transfer learning algorithm called cross-domain residual deep nonnegative matrix factorization (CDRDNMF) is proposed. CDRDNMF is a multi-layer architecture consisting of dual-dictionary nonnegative matrix factorization (DDNMF) layers. In each layer, DDNMF is performed on source and target features for domain-invariant feature extraction. Then a data recovery process is completed, and the residual components from the recovery are passed to the next layer after activation. With such a multi-layer architecture, CDRDNMF delivers knowledge transfer and multi-scale feature extraction tasks. The experimental results prove the excellent performance of CDRDNMF on cross-scene classification.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134155523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-18DOI: 10.1142/s0219691322500588
Yuhao He, Xianwei Zheng, Qing Miao
The outbreak of the global COVID-19 pandemic has become a public crisis and is threatening human life in every country. Recently, researchers have developed testing methods via patients cough recordings. In order to improve the testing accuracy, in this paper, we establish a novel COVID-19 sound-based diagnosis framework, i.e. TFA-CLSTMNN, which integrates time-frequency domain features of the recorded cough with the Attention-Convolution Long Short-Term Memory Neural Network. Specifically, we calculate the Mel-frequency cepstrum coefficient (MFCC) of the cough data to extract the time-frequency domain features. We then apply the convolutional neural network and the attentional mechanism on the time-frequency features, which is followed by the long short-term memory neural network to analyze the MFCC features of the data. The recognition and classification can be then carried out to evaluate the positiveness or negativeness of the tested samples. Experimental results show that the proposed TFA-CLSTMNN framework outperforms the baseline neural networks in sound-based COVID-19 diagnosis and derives an accuracy over 0.95 on the public real-world datasets.
{"title":"TFA-CLSTMNN: Novel convolutional network for sound-based diagnosis of COVID-19","authors":"Yuhao He, Xianwei Zheng, Qing Miao","doi":"10.1142/s0219691322500588","DOIUrl":"https://doi.org/10.1142/s0219691322500588","url":null,"abstract":"The outbreak of the global COVID-19 pandemic has become a public crisis and is threatening human life in every country. Recently, researchers have developed testing methods via patients cough recordings. In order to improve the testing accuracy, in this paper, we establish a novel COVID-19 sound-based diagnosis framework, i.e. TFA-CLSTMNN, which integrates time-frequency domain features of the recorded cough with the Attention-Convolution Long Short-Term Memory Neural Network. Specifically, we calculate the Mel-frequency cepstrum coefficient (MFCC) of the cough data to extract the time-frequency domain features. We then apply the convolutional neural network and the attentional mechanism on the time-frequency features, which is followed by the long short-term memory neural network to analyze the MFCC features of the data. The recognition and classification can be then carried out to evaluate the positiveness or negativeness of the tested samples. Experimental results show that the proposed TFA-CLSTMNN framework outperforms the baseline neural networks in sound-based COVID-19 diagnosis and derives an accuracy over 0.95 on the public real-world datasets.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130701904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-14DOI: 10.1142/s0219691322500576
N. Athira, M. C. Lineesh
{"title":"Approximation properties of wavelets on p-adic fields","authors":"N. Athira, M. C. Lineesh","doi":"10.1142/s0219691322500576","DOIUrl":"https://doi.org/10.1142/s0219691322500576","url":null,"abstract":"","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"235 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133036973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-14DOI: 10.1142/s0219691322500564
Mateus Gonzalez de Freitas Pinto, Guilherme de Oliveira Lima C. Marques, Chang Chiann
{"title":"Jump detection in high-frequency financial data using wavelets","authors":"Mateus Gonzalez de Freitas Pinto, Guilherme de Oliveira Lima C. Marques, Chang Chiann","doi":"10.1142/s0219691322500564","DOIUrl":"https://doi.org/10.1142/s0219691322500564","url":null,"abstract":"","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132467703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-10DOI: 10.1142/s0219691322500436
Haibo Yang, Yulong Ji, Yanfeng Pan, Bin Zou, Yingxiong Fu
Distributed learning is a very effective divide-and-conquer strategy for dealing with big data. As distributed learning algorithms become more and more mature, network security issues including the risk of privacy disclosure of personal sensitive data, have attracted high attention and vigilance. Differentially private is an important method that maximizes the accuracy of a data query while minimizing the chance of identifying its records when querying from the given data. The known differentially private distributed learning algorithms are based on variable perturbation, but the variable perturbation method may be non-convergence and the experimental results usually have large deviations. Therefore, in this paper, we consider differentially private distributed learning algorithm based on objective function perturbation. We first propose a new distributed logistic regression algorithm based on objective function perturbation (DLR-OFP). We prove that the proposed DLR-OFP satisfies differentially private, and obtain its fast convergence rate by introducing a new acceleration factor for the gradient descent method. The numerical experiments based on benchmark data show that the proposed DLR-OFP algorithm has fast convergence rate and better privacy protection ability.
{"title":"Differentially private distributed logistic regression with the objective function perturbation","authors":"Haibo Yang, Yulong Ji, Yanfeng Pan, Bin Zou, Yingxiong Fu","doi":"10.1142/s0219691322500436","DOIUrl":"https://doi.org/10.1142/s0219691322500436","url":null,"abstract":"Distributed learning is a very effective divide-and-conquer strategy for dealing with big data. As distributed learning algorithms become more and more mature, network security issues including the risk of privacy disclosure of personal sensitive data, have attracted high attention and vigilance. Differentially private is an important method that maximizes the accuracy of a data query while minimizing the chance of identifying its records when querying from the given data. The known differentially private distributed learning algorithms are based on variable perturbation, but the variable perturbation method may be non-convergence and the experimental results usually have large deviations. Therefore, in this paper, we consider differentially private distributed learning algorithm based on objective function perturbation. We first propose a new distributed logistic regression algorithm based on objective function perturbation (DLR-OFP). We prove that the proposed DLR-OFP satisfies differentially private, and obtain its fast convergence rate by introducing a new acceleration factor for the gradient descent method. The numerical experiments based on benchmark data show that the proposed DLR-OFP algorithm has fast convergence rate and better privacy protection ability.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114188118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-04DOI: 10.1142/s0219691322500515
Suja Priyadharsini Subramoniam, K. K. Devi
{"title":"Effective image splicing detection using deep neural network","authors":"Suja Priyadharsini Subramoniam, K. K. Devi","doi":"10.1142/s0219691322500515","DOIUrl":"https://doi.org/10.1142/s0219691322500515","url":null,"abstract":"","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133991214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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