Int. J. Wavelets Multiresolution Inf. Process.最新文献_第9页

On the construction of the orthonormal wavelet in the Hardy space H2(ℝ) Hardy空间H2(h)中标准正交小波的构造

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-10-06 DOI: 10.1142/s0219691321500442

Hirofumi Hashimoto, T. Kinoshita

引用次数: 0

On the regularity of distributions via the convergence of the continuous shearlet transform in two dimensions 利用二维连续剪切波变换的收敛性研究分布的规律性

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-10-05 DOI: 10.1142/s0219691321500454

Jaime Navarro, David Elizarraraz

引用次数: 0

An improvement for the automatic classification method for ultrasound images used on CNN 对CNN超声图像自动分类方法的改进

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-10-04 DOI: 10.1142/s0219691321500545

Kuldoshbay Avazov, A. Abdusalomov, Mukhriddin Mukhiddinov, Nodirbek Baratov, Fazliddin Makhmudov, Y. Cho

引用次数: 17

A versatile approach based on convolutional neural networks for early identification of diseases in tomato plants 基于卷积神经网络的番茄病害早期识别方法

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-10-02 DOI: 10.1142/s0219691321500430

N. Chandra, K. Reddy, G. Sushanth, S. Sujatha

{"title":"A versatile approach based on convolutional neural networks for early identification of diseases in tomato plants","authors":"N. Chandra, K. Reddy, G. Sushanth, S. Sujatha","doi":"10.1142/s0219691321500430","DOIUrl":"https://doi.org/10.1142/s0219691321500430","url":null,"abstract":"Agriculture is one of the primary occupations in many countries. Tomatoes are grown by many farmers in countries where the water resource is available in abundance. Improper methods of cultivation and failure to identify the diseases when it is in the nascent stage results in the reduction of crop yield thus affecting the outcome of cultivation. This paper proposes a novel method of early identification of diseases in tomato plants by making use of convolutional neural networks (CNN) and image processing. Dataset from an open repository was considered for training and testing and the algorithm was capable of identifying nine different varieties of diseases that affect the tomato plant at its early stages. The images of tomato leaves were fed for identification through processing and classification. An optimum model was developed by analyzing various architectures of CNN including the VGG, ResNet, Inception, Xception, MobileNet and DenseNet. The performance of each of these architectures was compared and various metrics like the accuracy, loss, precision, recall and area under the curve (AUC) were analyzed.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129143888","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}

引用次数: 1

Online regularized pairwise learning with non-i.i.d. observations 非id的在线正则化两两学习。观察

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-09-09 DOI: 10.1142/s0219691321500417

Yimo Qin, Bin Zou, Jingjing Zeng, Zhifei Sheng, Lei Yin

引用次数: 1

Weak nonhomogeneous wavelet dual frames for Walsh reducing subspace of L2(ℝ+) L2(v +) Walsh约简子空间的弱非齐次小波对偶框架

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-09-09 DOI: 10.1142/s0219691321500405

Yan Zhang, Yun‐Zhang Li

引用次数: 1

Visualization of RNA secondary structure with pseudoknots 带假结的RNA二级结构的可视化

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-08-30 DOI: 10.1142/s0219691321500363

Lina Yang, Yang Liu, Huiwu Luo, Xichun Li, Y. Tang

{"title":"Visualization of RNA secondary structure with pseudoknots","authors":"Lina Yang, Yang Liu, Huiwu Luo, Xichun Li, Y. Tang","doi":"10.1142/s0219691321500363","DOIUrl":"https://doi.org/10.1142/s0219691321500363","url":null,"abstract":"The function of pseudoknots cannot be ignored in the RNA secondary structure. Existing methods for analyzing RNA secondary structures with pseudoknots exhibit many shortcomings. This paper presents a novel RNA secondary structure visualization method in the case of a joint analysis of RNA primary structures and secondary structures. The way is based on the page number representation of the RNA secondary structure. It innovatively uses five vectors to represent bases, which are sequentially connected to outline the characteristics of the RNA secondary structure. The method covers almost all the constituent elements of the RNA secondary structure and extracts features completely. Experiments are based on the available techniques for large-scale annotation of RNA secondary structures, using a combination method of discrete wavelet transform and fractal dimension. The classification effect is compared with the previous RNA secondary structure representation methods. Experimental results show that the RNA secondary structure visualization method proposed in this paper has good application prospects in RNA secondary structure classification.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"2008 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127309800","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}

引用次数: 0

Synaptic clef segmentation method based on fractal dimension for ATUM-SEM image of mouse cortex 基于分形维数的小鼠皮层ATUM-SEM图像的突触裂缝分割方法

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-08-11 DOI: 10.1142/s0219691321500387

Chao Ma, Lijun Shen, Hao Deng, Jialin Li

{"title":"Synaptic clef segmentation method based on fractal dimension for ATUM-SEM image of mouse cortex","authors":"Chao Ma, Lijun Shen, Hao Deng, Jialin Li","doi":"10.1142/s0219691321500387","DOIUrl":"https://doi.org/10.1142/s0219691321500387","url":null,"abstract":"It is well known that neurons communicate through synapses in the nervous system, and the size, morphology, and connectivity of synapses determine the functional properties of the neural network. Therefore, synapses have always been one of the key objects of neuroscience. Due to the technical advance in electron microscope (EM), the physical structure of synapses can be observed at high resolution. Nevbarheless, to date, the automatic analysis of the synapse in EM images is still a challenging task. In this paper, we proposed a fractal dimension-based segmentation method for synaptic clef of mouse cortex on EM image stack. Our method does not require a lot of groundtruth to train the model, and shows better adaptive anti-noise performance. That should be ascribed to the stability of segmentation-related key parameters in the data from same tissue. In this way, we only need to give initial values, and then gradually adjust these key parameters. Experiments reveal that our method achieves the desired results, and reduces the time in artificial annotating, so that researchers can focus more on the analysis of segmentation results.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130723743","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}

引用次数: 1

Linear canonical wavelet transform: Properties and inequalities 线性正则小波变换:性质和不等式

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-07-31 DOI: 10.1142/s0219691321500272

M. Bahri, A. K. Amir, R. Ashino

引用次数: 0

Sparse sufficient dimension reduction with heteroscedasticity 具有异方差的稀疏充分降维

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-07-26 DOI: 10.1142/s0219691321500375

Haoyang Cheng, Wenquan Cui

引用次数: 0