Pub Date : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946466
H. Toda, Zhong Zhang
In this paper, we propose a construction method of a tight wavelet frame using a complex wavelet designed in a free shape on the frequency domain. This method is divided into two parts. First, based on the designed complex wavelet, we construct an approximate tight wavelet frame. Next, based on it, we construct a tight wavelet frame with minor modification. Additionally, for example, we show the construction process of the tight wavelet frame using the approximate Gabor wavelet.
{"title":"Tight Wavelet Frame Using Complex wavelet Designed in Free Shape on Frequency Domain","authors":"H. Toda, Zhong Zhang","doi":"10.1109/ICWAPR48189.2019.8946466","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946466","url":null,"abstract":"In this paper, we propose a construction method of a tight wavelet frame using a complex wavelet designed in a free shape on the frequency domain. This method is divided into two parts. First, based on the designed complex wavelet, we construct an approximate tight wavelet frame. Next, based on it, we construct a tight wavelet frame with minor modification. Additionally, for example, we show the construction process of the tight wavelet frame using the approximate Gabor wavelet.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115788521","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 : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946467
Xinxin Hong, U. KinTak
Based on Non-uniform Rectangular Partition (NURP) and Generative Adversarial Network (GAN), this paper proposes an effective multi-focus image fusion method to generate a full-focus image by combining multi-focus images. Firstly, NURP is applied to left-focus and right-focus images, the size of partitioning grids obtained can be used to judge the fusion pixel to form a rough Fusion Guiding Map (FGM) which will be further optimized by morphological operation and manual adjustment to form an optimized FGM. Then the rough FGM and optimized FGM become the training dataset for the pix2pix GAN. After finishing the training, the trained pix2pix model can be used to optimize any rough FGM from NURP. Finally, the fused pixels are determined according to the FGM to construct the final fused image. The experimental results show that the algorithm improves the visual clarity of the fused image by enhancing the spatial detail of the image and obtains better objective evaluation indicators.
{"title":"Multi-Focus Image Fusion Algorithm Based on Non-Uniform Rectangular Partition and Generative Adversarial Network","authors":"Xinxin Hong, U. KinTak","doi":"10.1109/ICWAPR48189.2019.8946467","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946467","url":null,"abstract":"Based on Non-uniform Rectangular Partition (NURP) and Generative Adversarial Network (GAN), this paper proposes an effective multi-focus image fusion method to generate a full-focus image by combining multi-focus images. Firstly, NURP is applied to left-focus and right-focus images, the size of partitioning grids obtained can be used to judge the fusion pixel to form a rough Fusion Guiding Map (FGM) which will be further optimized by morphological operation and manual adjustment to form an optimized FGM. Then the rough FGM and optimized FGM become the training dataset for the pix2pix GAN. After finishing the training, the trained pix2pix model can be used to optimize any rough FGM from NURP. Finally, the fused pixels are determined according to the FGM to construct the final fused image. The experimental results show that the algorithm improves the visual clarity of the fused image by enhancing the spatial detail of the image and obtains better objective evaluation indicators.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123199288","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 : 2019-07-01DOI: 10.1109/icwapr48189.2019.8946461
{"title":"[Copyright notice]","authors":"","doi":"10.1109/icwapr48189.2019.8946461","DOIUrl":"https://doi.org/10.1109/icwapr48189.2019.8946461","url":null,"abstract":"","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125324450","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}
Production scheduling is not only a necessary part of manufacturing enterprises to ensure normal production work, but also affects the operating costs of enterprises. At present, production scheduling of many manufacturing enterprises only aim at ensuring normal production work, without taking into account the impact of production scheduling on enterprise costs. In order to improve the economic efficiency of the enterprise, this paper research on optimization of the production scheduling. A new optimization algorithm called the Self-Crossover Genetic Algorithm is proposed to support model optimization. A numerical study using actual factory data is implemented in this paper. The result shows that scientific production scheduling can reduce costs indeed without affecting the normal operation of the enterprise. In order to increase the fitness of the optimization, the numerical study adds four sensitivity analyses, which analyzed the optimization effect with different parameters, such as night shift allowance, order required production, self-crossover rate and the shift time. In summary, Self-Crossover Genetic Algorithm can provide a certain degree of reference for enterprises to develop a suitable production schedule.
{"title":"Optimization Of Production Scheduling Using Self-Crossover Genetic Algorithm","authors":"Wanli Wu, Linyu Wang, Fei Zhao, Yiliang Fan, Xin-liang, Ruixin Tang, Yangxu, Yongshen Wen","doi":"10.1109/ICWAPR48189.2019.8946477","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946477","url":null,"abstract":"Production scheduling is not only a necessary part of manufacturing enterprises to ensure normal production work, but also affects the operating costs of enterprises. At present, production scheduling of many manufacturing enterprises only aim at ensuring normal production work, without taking into account the impact of production scheduling on enterprise costs. In order to improve the economic efficiency of the enterprise, this paper research on optimization of the production scheduling. A new optimization algorithm called the Self-Crossover Genetic Algorithm is proposed to support model optimization. A numerical study using actual factory data is implemented in this paper. The result shows that scientific production scheduling can reduce costs indeed without affecting the normal operation of the enterprise. In order to increase the fitness of the optimization, the numerical study adds four sensitivity analyses, which analyzed the optimization effect with different parameters, such as night shift allowance, order required production, self-crossover rate and the shift time. In summary, Self-Crossover Genetic Algorithm can provide a certain degree of reference for enterprises to develop a suitable production schedule.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127614141","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 : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946464
Xiaocui Dang, Lina Yang, Yuanyan Tang, Pu Wei, Hailong Su
In biological information systems, the analysis of biological sequences is a major problem in today’s bioinformatics research. In this paper, a method for protein sequence similarity analysis based on discrete wavelet transform and fractal dimension and single clustering method is proposed. Multivariate decomposition of digital signals containing biological information is performed by discrete wavelets. Using the Higuchi algorithm based on wavelet decomposition, the fractal characteristics of the primary structure of the protein were studied using multiple properties of the protein. The distance matrix between different proteins is obtained by analytical calculation. Phylogenetic tree, and similar analysis of protein sequences. The results show that compared with the traditional methods, wavelet transform and fractal dimension methods and multiple attribute analysis can analyze the similarity of protein sequences more comprehensively, reliably and quickly.
{"title":"Nd5 Protein Sequence Similarity Analysis Based On Discrete Wavelet Transform And Fractal Dimension","authors":"Xiaocui Dang, Lina Yang, Yuanyan Tang, Pu Wei, Hailong Su","doi":"10.1109/ICWAPR48189.2019.8946464","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946464","url":null,"abstract":"In biological information systems, the analysis of biological sequences is a major problem in today’s bioinformatics research. In this paper, a method for protein sequence similarity analysis based on discrete wavelet transform and fractal dimension and single clustering method is proposed. Multivariate decomposition of digital signals containing biological information is performed by discrete wavelets. Using the Higuchi algorithm based on wavelet decomposition, the fractal characteristics of the primary structure of the protein were studied using multiple properties of the protein. The distance matrix between different proteins is obtained by analytical calculation. Phylogenetic tree, and similar analysis of protein sequences. The results show that compared with the traditional methods, wavelet transform and fractal dimension methods and multiple attribute analysis can analyze the similarity of protein sequences more comprehensively, reliably and quickly.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131882529","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 : 2019-07-01DOI: 10.1109/icwapr48189.2019.8946454
{"title":"ICWAPR 2019 Greetings from the General Chairs","authors":"","doi":"10.1109/icwapr48189.2019.8946454","DOIUrl":"https://doi.org/10.1109/icwapr48189.2019.8946454","url":null,"abstract":"","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114397724","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 : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946471
M. Bahri, R. Ashino
The quaternion Fourier transformation based on orthogonal planes split is an extension of the two-sided quaternion Fourier transformations using quaternion split. In the present paper we investigate its basic properties such as linearity, frequency-shift and time-frequency shift. We then study the convolution and correlation definitions for the quaternion Fourier transformation based on orthogonal planes split and obtain their convolution and correlation theorems.
{"title":"Convolution and Correlation Theorems for Quaternion Fourier Transformation Based on the Orthogonal Planes Split","authors":"M. Bahri, R. Ashino","doi":"10.1109/ICWAPR48189.2019.8946471","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946471","url":null,"abstract":"The quaternion Fourier transformation based on orthogonal planes split is an extension of the two-sided quaternion Fourier transformations using quaternion split. In the present paper we investigate its basic properties such as linearity, frequency-shift and time-frequency shift. We then study the convolution and correlation definitions for the quaternion Fourier transformation based on orthogonal planes split and obtain their convolution and correlation theorems.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132640165","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 : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946481
Zhong Zhang, Tatsuya Sugino, T. Akiduki, T. Mashimo
In recent years, deep learning that can learn features from a dataset has been remarkably developing in the field of face recognition and voice recognition and so on. However, it is difficult to pursue cause of misjudgment result because input-output relation of deep learning is a black box. Furthermore, the content has yet to be elucidated what the judgment is based on. Therefore, when introducing deep Learning into multiple fields, it is important to understand the reason. This study aims to pursue cause of misjudgment result by intervening in the preprocessing part of deep learning using 2-dimensional discrete wavelet packet transform.
{"title":"A Study on Development of Wavelet Deep Learning","authors":"Zhong Zhang, Tatsuya Sugino, T. Akiduki, T. Mashimo","doi":"10.1109/ICWAPR48189.2019.8946481","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946481","url":null,"abstract":"In recent years, deep learning that can learn features from a dataset has been remarkably developing in the field of face recognition and voice recognition and so on. However, it is difficult to pursue cause of misjudgment result because input-output relation of deep learning is a black box. Furthermore, the content has yet to be elucidated what the judgment is based on. Therefore, when introducing deep Learning into multiple fields, it is important to understand the reason. This study aims to pursue cause of misjudgment result by intervening in the preprocessing part of deep learning using 2-dimensional discrete wavelet packet transform.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114151546","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 : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946469
Cui Wang, Caixia Deng, Zhibin Hu
In order to filter out image noise better and make it have better clarity, continuity and anti-noise performance in image edge extraction. Firstly, this paper constructs a new threshold function, compared with the traditional soft and hard threshold function and some existing improved methods, the threshold function has better adjustability and it is also continuous and almost smooth everywhere. When dealing with the wavelet coefficients, the real information on them can be retained more, and the noise can be effectively filtered at the same time. The simulation experiment shows that the image processed by the new threshold function has a high PSNR and a small MSE, which can be closer to the original image. Finally, the improved threshold function de-noising algorithm and the dyadic wavelet transform modulus maximum edge detection algorithm are combined to apply to image edge detection. By combining the advantages of the two algorithms, so that we can get clearer and more continuous image edges, and the contour is more complete.
{"title":"An Improved Wavelet Threshold Function And Its Application In Image Edge Detection","authors":"Cui Wang, Caixia Deng, Zhibin Hu","doi":"10.1109/ICWAPR48189.2019.8946469","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946469","url":null,"abstract":"In order to filter out image noise better and make it have better clarity, continuity and anti-noise performance in image edge extraction. Firstly, this paper constructs a new threshold function, compared with the traditional soft and hard threshold function and some existing improved methods, the threshold function has better adjustability and it is also continuous and almost smooth everywhere. When dealing with the wavelet coefficients, the real information on them can be retained more, and the noise can be effectively filtered at the same time. The simulation experiment shows that the image processed by the new threshold function has a high PSNR and a small MSE, which can be closer to the original image. Finally, the improved threshold function de-noising algorithm and the dyadic wavelet transform modulus maximum edge detection algorithm are combined to apply to image edge detection. By combining the advantages of the two algorithms, so that we can get clearer and more continuous image edges, and the contour is more complete.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132435609","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 : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946458
Wanting Liu, G. E. Lau, K. Ngan
Orthogonal wavelets are applied to turbulent flow over a cubical building array. The transfer spectrum, which depends on scale and spatial location, characterises nonlinear energy transfers from one scale to another. Using large-eddy simulation, the interscale energy transfer is decomposed into discrete modes and comparisons made with the usual Fourier spectrum. Spatial variability is quantified with the standard deviations or dual spectra. Wavelet decomposition of the spectral energy transfer shows that energy is cascaded from large to small scales in both the inertial sublayer and outer layer. There is also indication of energy backscatter in the roughness sublayer as shown by the scale-filtered reconstruction error. Based on the urban turbulent flow at various heights, the choice of wavelet basis is also discussed. This work is relevant to the development of multiscale urban canopy characterizations that seek to model the energy transfers between regional and urban scales.
{"title":"Wavelet Analysis Of Spectral Energy Transfers In Urban Turbulence","authors":"Wanting Liu, G. E. Lau, K. Ngan","doi":"10.1109/ICWAPR48189.2019.8946458","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946458","url":null,"abstract":"Orthogonal wavelets are applied to turbulent flow over a cubical building array. The transfer spectrum, which depends on scale and spatial location, characterises nonlinear energy transfers from one scale to another. Using large-eddy simulation, the interscale energy transfer is decomposed into discrete modes and comparisons made with the usual Fourier spectrum. Spatial variability is quantified with the standard deviations or dual spectra. Wavelet decomposition of the spectral energy transfer shows that energy is cascaded from large to small scales in both the inertial sublayer and outer layer. There is also indication of energy backscatter in the roughness sublayer as shown by the scale-filtered reconstruction error. Based on the urban turbulent flow at various heights, the choice of wavelet basis is also discussed. This work is relevant to the development of multiscale urban canopy characterizations that seek to model the energy transfers between regional and urban scales.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"6 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120828045","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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