Int. J. Wavelets Multiresolution Inf. Process.最新文献_第8页

Multidimensional periodic discrete wavelets 多维周期离散小波

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-11-24 DOI: 10.1142/s0219691321500533

P. Andrianov

引用次数: 1

The wave packet transform in the framework of linear canonical transform 线性正则变换框架下的波包变换

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-11-13 DOI: 10.1142/s0219691321500521

A. Prasad, Z. A. Ansari

引用次数: 0

A method to enhance the noise robustness of correlation velocity measurement using discrete wavelet transform 利用离散小波变换增强相关测速噪声鲁棒性的方法

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-11-06 DOI: 10.1142/s0219691321500491

Pong-Chol Son, Kyong-il Kim, Kyong-Chol Choe, Hyok-Il Kye

{"title":"A method to enhance the noise robustness of correlation velocity measurement using discrete wavelet transform","authors":"Pong-Chol Son, Kyong-il Kim, Kyong-Chol Choe, Hyok-Il Kye","doi":"10.1142/s0219691321500491","DOIUrl":"https://doi.org/10.1142/s0219691321500491","url":null,"abstract":"Navigation at sea requires accurate velocity measurement of a vehicle relative to the seabed. Correlation velocity measurement techniques are efficiently used to measure the velocity of underwater vehicles because they are not affected by sound speed compared to Doppler techniques, have several advantages such as small size and power consumption and tracking deep seabed. We consider the relationship of maximum correlation coefficient and signal-to-noise ratio (SNR), which are important parameters in correlation velocity measurement and present the maximum correlation coefficient equation according to SNR. Next, we propose a method of the noise robustness enhancement using discrete wavelet transform (DWT) in correlation velocity measurement. In addition, we evaluate the noise robustness of the proposed method and various methods of correlation velocity measurement through simulation, and present the maximum correlation coefficient equation according to SNR of our method. Simulation results show that new method of correlation velocity measurement using wavelet thresholding proposed in this paper improves the noise robustness of correlation velocity measurement more than various methods. In addition, in correlation velocity log (CVL) operating under low SNR below 6 dB, the maximum correlation coefficient of new method increases more than 0.1 than the classical method. Simulation results show that the new method of correlation velocity measurement considerably improved the noise robustness of spatial CVL than classical method, and the noise robustness of new method is highest among various methods of correlation velocity measurement.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"20 7-8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125026202","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

Jackson-type theorem on approximation by non-stationary periodic wavelets 非平稳周期小波近似的jackson型定理

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-11-06 DOI: 10.1142/s0219691321500557

Anastassia Gorsanova

引用次数: 0

Learner performance prediction in the e-learning platform using the optimized deep long short-term memory classifier 基于优化深度长短期记忆分类器的在线学习平台学习者表现预测

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-11-06 DOI: 10.1142/s021969132150051x

A. Alzubi

{"title":"Learner performance prediction in the e-learning platform using the optimized deep long short-term memory classifier","authors":"A. Alzubi","doi":"10.1142/s021969132150051x","DOIUrl":"https://doi.org/10.1142/s021969132150051x","url":null,"abstract":"The e-learning platform gains significant attraction in the current scenario due to the outbreak of the epidemic COVID-19 as e-learning ensures the students continue their studies in the safest environment while maintaining the educational standard. The performance prediction is one of the significant tasks to be carried out in the e-learning platform to sort out the students who require immediate attention to enhance their grades before the final assessment. This paper proposes a prediction model that effectively predicts the learners’ performance in the e-khool learning management system (e-khool LMS) based on the proposed wolf-swarm optimization dependent Deep Long Short-term (wolf-swarm optimization-based Deep-LSTM) approach. The optimization algorithm tunes the optimal weights of the Deep-LSTM classifier, which inherits the hybrid characteristics of the traitors and particles. Initially, the learner data from the e-khool database is employed for classification based on the proposed wolf-swarm optimization dependent Deep-LSTM classifier. The effectiveness of the proposed prediction model is analyzed in terms of MSE and RMSE with the value of 5.93 and 2.426, respectively.","PeriodicalId":158567,"journal":{"name":"Int. J. Wavelets Multiresolution Inf. Process.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124446754","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

Optimization in the construction of cardinal and symmetric wavelets on the line 基于基数小波和对称小波的优化构造

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-10-30 DOI: 10.1142/s021969132150048x

Neil D. Dizon, J. Hogan, J. Lakey

引用次数: 1

Multivariate wavelet leaders Rényi dimension and multifractal formalism in mixed Besov spaces 混合Besov空间中的多元小波前导rsamnyi维数与多重分形形式

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-10-27 DOI: 10.1142/s0219691321500478

M. B. Abid, M. B. Slimane, I. Omrane, M. Turkawi

引用次数: 0

Recovery analysis for block ℓp - ℓ1 minimization with prior support information 具有先验支持信息的块p - 1最小化的恢复分析

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-10-18 DOI: 10.1142/s0219691321500570

Jing Zhang, Shuguang Zhang

引用次数: 5

A note on continuous fractional wavelet transform in ℝn 关于连续分数小波变换的一个注记

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-10-06 DOI: 10.1142/s0219691321500508

Amit Verma, B. Gupta

引用次数: 5

Co-evolution-based parameter learning for remote sensing scene classification 基于协同进化的遥感场景分类参数学习

Int. J. Wavelets Multiresolution Inf. Process. Pub Date : 2021-10-06 DOI: 10.1142/s0219691321500466

Di Zhang, Yichen Zhou, Jiaqi Zhao, Yong Zhou

引用次数: 2