From 1db96b38fd62e91ce9cf3b692dfe0a3a686cc3b2 Mon Sep 17 00:00:00 2001 From: xli04 Date: Wed, 19 Jun 2024 16:28:33 +0800 Subject: [PATCH] update 2024 --- 404.html | 2 +- .../static/images/QRcode LI.e56a08f3.png | Bin 0 -> 56932 bytes .../static/images/QRcode Luo.89381ff5.png | Bin 0 -> 145370 bytes ...4-page.54da7dab.js => 404-page.a5f71996.js} | 2 +- .../{client.44a43654.js => client.06689790.js} | 4 ++-- ...25cb.js => page--committees-md.068bc601.js} | 2 +- _saber/js/page--contact-md.050b6c24.js | 1 - _saber/js/page--contact-md.b8bda6e0.js | 1 + ...b6ec8b.js => page--guidance-md.d4a13497.js} | 2 +- ....9df6e42a.js => page--index-md.4d430571.js} | 2 +- ...2d8.js => page--information-md.48c57799.js} | 2 +- ...d.025edf33.js => page--news-md.6cd00fc8.js} | 2 +- ...8a6acd.js => page--speakers-md.a59bdaea.js} | 2 +- ...2b7564.js => page--timeline-md.fa28d13b.js} | 2 +- ...52b22406.js => page--topics-md.6b9eb377.js} | 2 +- committees.html | 2 +- contact.html | 5 ++--- guidance.html | 2 +- index.html | 2 +- information.html | 2 +- news.html | 2 +- speakers.html | 2 +- timeline.html | 2 +- topics.html | 2 +- 24 files changed, 23 insertions(+), 24 deletions(-) create mode 100644 _saber/images/static/images/QRcode LI.e56a08f3.png create mode 100644 _saber/images/static/images/QRcode Luo.89381ff5.png rename _saber/js/{404-page.54da7dab.js => 404-page.a5f71996.js} (87%) rename _saber/js/{client.44a43654.js => client.06689790.js} (99%) rename _saber/js/{page--committees-md.1e1025cb.js => page--committees-md.068bc601.js} (98%) delete mode 100644 _saber/js/page--contact-md.050b6c24.js create mode 100644 _saber/js/page--contact-md.b8bda6e0.js rename _saber/js/{page--guidance-md.eab6ec8b.js => page--guidance-md.d4a13497.js} (98%) rename _saber/js/{page--index-md.9df6e42a.js => page--index-md.4d430571.js} (95%) rename _saber/js/{page--information-md.2ad8e2d8.js => page--information-md.48c57799.js} (99%) rename _saber/js/{page--news-md.025edf33.js => page--news-md.6cd00fc8.js} (97%) rename _saber/js/{page--speakers-md.c88a6acd.js => page--speakers-md.a59bdaea.js} (98%) rename _saber/js/{page--timeline-md.aa2b7564.js => page--timeline-md.fa28d13b.js} (96%) rename _saber/js/{page--topics-md.52b22406.js => page--topics-md.6b9eb377.js} (98%) diff --git a/404.html b/404.html index 233ea82..dd83e15 100644 --- a/404.html +++ b/404.html @@ -1 +1 @@ - Not Found

Not Found

← Back Home
+ Not Found

Not Found

← Back Home
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Clarence De Silve, UBC, Canada
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This event is organized by University of Electronic Science and Technology of China(UESTC), sponsored by The National Natural Science Foundation of China(NSFC), National High Technology Research and Development Program of China and China International Talent Exchange Foundation. ICCWAMTIP 2024 is a not-to-be-missed opportunity that distills the most current knowledge on a rapidly advancing discipline in one conference. Join key researchers and established professionals in the field of information technologies as they assess the current state-of-the-art and roadmap crucial areas for future research.")]),e._v(" "),o("p",[e._v("We aim to building an idea-trading platform for the purpose of encouraging researcher participating in this event. The papers to be presented at ICCWAMTIP past conferences addressed many grand challenges in modern engineering.")]),e._v(" "),o("p",[e._v("The full papers to be presented will be peer-reviewed by expert reviewers including the whole organising committees members. We will be thankful for the contributions to ICCWAMTIP 2024. We look forward to your participation and continued engagement at future ICCWAMTIP conferences.")]),e._v(" "),o("p",[e._v("Affected by the epidemic, this year's conference is a bit different from the past. This year's conference will take the form of an online conference, so international experts and scholars do not need to come to the conference venue, only need to participate in the conference through the Internet. 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ICCWAMTIP 2024, The 21th International Computer Conference on Wavelet Active Media Technology and Information Processing, will take place in Chengdu, China, from December 14th, 2024. This event is organized by University of Electronic Science and Technology of China(UESTC), sponsored by The National Natural Science Foundation of China(NSFC), National High Technology Research and Development Program of China and China International Talent Exchange Foundation. ICCWAMTIP 2024 is a not-to-be-missed opportunity that distills the most current knowledge on a rapidly advancing discipline in one conference. Join key researchers and established professionals in the field of information technologies as they assess the current state-of-the-art and roadmap crucial areas for future research.

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This event is organized by University of Electronic Science and Technology of China(UESTC), sponsored by The National Natural Science Foundation of China(NSFC), National High Technology Research and Development Program of China and China International Talent Exchange Foundation. ICCWAMTIP 2024 is a not-to-be-missed opportunity that distills the most current knowledge on a rapidly advancing discipline in one conference. Join key researchers and established professionals in the field of information technologies as they assess the current state-of-the-art and roadmap crucial areas for future research.")]),e._v(" "),o("p",[e._v("We aim to building an idea-trading platform for the purpose of encouraging researcher participating in this event. 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Dear Authors,\nThe 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (the 21th ICCWAMTIP) will be held in the University of Electronic Science and Technology of China (UESTC) on December 14th-26th , 2024.

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The Conference will focus on recent advances in key information technologies. It will provide a remarkable opportunity for the academic and industrial community to address new challenges and share solutions, and discuss future research directions.\nThe conference proceedings will be published by UESTC, all accepted papers are expected to be included in IEEE Xplore and will be indexed by EI. Some famous experts are going to present at the conference.")]),e._v(" "),n("p",[e._v("The topics of the conference include:")]),e._v(" "),n("ul",[n("li",[e._v("Wavelet algorithm and multi-wavelet")]),e._v(" "),n("li",[e._v("Blockchain technology")]),e._v(" "),n("li",[e._v("Active media technology")]),e._v(" "),n("li",[e._v("Medical Information")]),e._v(" "),n("li",[e._v("Cancer Diagnostic Analysis")]),e._v(" "),n("li",[e._v("Big Data and Cloud Computing")]),e._v(" "),n("li",[e._v("Military-civilian integration")]),e._v(" "),n("li",[e._v("Fractal geometry")]),e._v(" "),n("li",[e._v("Sensor design, integration and fusion")]),e._v(" "),n("li",[e._v("Sensor networks")]),e._v(" "),n("li",[e._v("Sensors based control and automation")]),e._v(" "),n("li",[e._v("Vision and image processing")]),e._v(" "),n("li",[e._v("Sensing and perception")]),e._v(" "),n("li",[e._v("Sensor data analysis and system diagnosis")]),e._v(" "),n("li",[e._v("Neural networks, fuzzy systems and soft computing 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"},38:function(a,e){a.exports="data:image/jpeg;base64,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"},39:function(a,e,t){a.exports=t.p+"images/static/images/tangyuanyan.33678029.jpg"},40:function(a,e,t){a.exports=t.p+"images/static/images/lijianping.ac82bdee.jpg"},47:function(a,e,t){"use 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"),n("div",{staticClass:"media-content"},[n("p",{staticClass:"title is-4"},[a._v("Zhang Jingzhong, Professor")]),a._v(" "),n("p",{staticClass:"subtitle is-6"},[a._v("Academician of Chinese Academy of Sciences, Chengdu Computer Applications Institute, Chinese Academy of Sciences")])])]),a._v(" "),n("div",{staticClass:"content"},[a._v("\n\t\t\tZhang Jingzhong, Professor, Academician of Chinese Academy of Sciences, Computer Scientist, Mathematician, Mathematics Educator. Academician Zhang was Professor of University of Science and Technology of China at some time in the past, and now he is the head of automated reasoning union laboratory of University of Electronic Science and Technology of China and Chengdu Computer Applications Institute, Chinese Academy of Sciences, the honorary director of education software institute of Guangzhou University, the chairman of National Digit Learning Centre of Huazhong Normal University. 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He invented the LianXiang method for augmenting the input of Chinese. From 1984 to 1995 he acted as the CTO of ICTC, i.e. The Company of ICT which was renamed as Legend in 1989 and Lenovo recently. He led the development of Legend Chinese System and Legend Series PC, which were the key products of the company and won the first-class prize of the State Award for Scientific and Technological Achievement in 1988 and 1992 respectively. For years he devoted himself to the development of the homegrown core technology as well as the IT industry of China. In 1994 he was elected as the Academician of Chinese Academy of Engineering. He was rewarded the Lifetime Achievement Award by the Chinese Information Processing Society of China (CIPS) and China Computer Federation (CCF) in 2011 and 2015 respectively.\n\t\t")])])]),a._v(" "),n("div",{staticClass:"card"},[n("div",{staticClass:"card-content"},[n("div",{staticClass:"media"},[n("div",{staticClass:"media-left"},[n("figure",{staticClass:"image"},[n("img",{staticStyle:{width:"250px"},attrs:{src:t(39),alt:"tangyuanyan"}})])]),a._v(" "),n("div",{staticClass:"media-content"},[n("p",{staticClass:"title is-4"},[a._v("Tang Yuanyan, Ph.D., Professor")]),a._v(" "),n("p",{staticClass:"subtitle is-6"},[a._v("Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China")])])]),a._v(" "),n("div",{staticClass:"content"},[a._v("\n\t\t\tTang Yuanyan is a Chair Professor in Faculty of Science and Technology at University of Macau and Professor/Adjunct Professor/Honorary Professor at several institutes in China, USA, Canada, France, and Hong Kong. His current interests include wavelets, pattern recognition, image processing, and artificial intelligence. He has published more than 400 academic papers and is the author/coauthor of over 25 monographs/books/bookchapters. He is the Founder and Editor-in-Chief of International Journal on Wavelets, Multiresolution, and Information Processing (IJWMIP), and Associate Editors of several international journals. He is the Founder and Chair of pattern recognition committee in IEEE SMC. He has serviced as general chair, program chair, and committee member for many international conferences. Dr. Tang is the Founder and General Chair of the series International Conferences on Wavelets Analysis and Pattern Recognition (ICWAPRs). He is the Founder and Chair of the Macau Branch of International Associate of Pattern Recognition (IAPR). Dr. Y. Y. Tang is a Fellow of IEEE, and Fellow of IAPR.\n\t\t")])])]),a._v(" "),n("div",{staticClass:"card"},[n("div",{staticClass:"card-content"},[n("div",{staticClass:"media"},[n("div",{staticClass:"media-left"},[n("figure",{staticClass:"image"},[n("img",{staticStyle:{width:"250px"},attrs:{src:t(40),alt:"lijianping"}})])]),a._v(" "),n("div",{staticClass:"media-content"},[n("p",{staticClass:"title is-4"},[a._v("Li Jianping, Ph.D., Professor")]),a._v(" "),n("p",{staticClass:"subtitle is-6"},[a._v("International Centre for Wavelet Analysis and Its applications,Big Data Research Institute, School of Computer Science,University of Electronic Science and Technology of China（UESTC）")])])]),a._v(" "),n("div",{staticClass:"content"},[a._v("\n\t\t\tProfessor Dr. Li Jianping, male, born on October 11,1964. He received the B.S. degree in applied mathematics from Chongqing University, Chongqing, China, in 1986, the M. Eng. degree in software engineering and M. Sci. degree in computational mathematics from the Graduate School of Xi'an Jiaotong University, Xi'an, Shanxi, China, in 1989, and the Ph.D. degree in computer science from the Graduate School of Chongqing University, Chongqing, China, in 1998. From 1999 to 2000, he was a post doctoral and visiting professor working at Hong Kong Baptist University under the leadership of Professor Dr. Yuan Yan Tang. From 2002 to 2003, 2005 to 2006, and in 2010, 2011, 2013, 2014, 2015,he was a senior visiting professor working respectively at Provence University, France, University of Guelph, Canada, Washington University, USA, University of Zurich, Switzerland. He is presently a Professor in International Centre for Wavelets Analysis and Its Applications (ICWAA) of University of Electronic Science and Technology of China（UESTC）, Chengdu, China, and as a head of ICWAA, he is the dean of Big Data Research Institute of UESTC. Dr. Li has served as a Syndic for Awards of National Science and Technology of China, a founder of the International Journal on Multiresolution, Wavelets, Applications (IJMWA), a founder and Editor in Chief of International Progress on Wavelet Active Media Technology and Information Processing, and also as General Chairman of many international conferences, a reviewer for many international famous Journals. His book Wavelet Analysis & Signal Processing: Theory, Applications & Software Implementations, was awarded the second prize of National Science and Technology of China (1999), and it is very important reference for many scientists. Dr. Li has published more than 300 technical papers and is the author/ co-author of 30 books on subjects ranging from wavelet analysis and its applications to computer science. His current interests include wavelet theory and applications, fractal, image processing, pattern recognition, information security, big data, artificial intelligence.\n\t\t")])])])])},[],!1,null,null,null);"function"==typeof i&&i(r);e.default=r.exports}}]); \ No newline at end of file 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"},40:function(a,e){a.exports="data:image/jpeg;base64,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"},41:function(a,e,t){a.exports=t.p+"images/static/images/tangyuanyan.33678029.jpg"},42:function(a,e,t){a.exports=t.p+"images/static/images/lijianping.ac82bdee.jpg"},49:function(a,e,t){"use 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"),n("div",{staticClass:"media-content"},[n("p",{staticClass:"title is-4"},[a._v("Zhang Jingzhong, Professor")]),a._v(" "),n("p",{staticClass:"subtitle is-6"},[a._v("Academician of Chinese Academy of Sciences, Chengdu Computer Applications Institute, Chinese Academy of Sciences")])])]),a._v(" "),n("div",{staticClass:"content"},[a._v("\n\t\t\tZhang Jingzhong, Professor, Academician of Chinese Academy of Sciences, Computer Scientist, Mathematician, Mathematics Educator. Academician Zhang was Professor of University of Science and Technology of China at some time in the past, and now he is the head of automated reasoning union laboratory of University of Electronic Science and Technology of China and Chengdu Computer Applications Institute, Chinese Academy of Sciences, the honorary director of education software institute of Guangzhou University, the chairman of National Digit Learning Centre of Huazhong Normal University. 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He invented the LianXiang method for augmenting the input of Chinese. From 1984 to 1995 he acted as the CTO of ICTC, i.e. The Company of ICT which was renamed as Legend in 1989 and Lenovo recently. He led the development of Legend Chinese System and Legend Series PC, which were the key products of the company and won the first-class prize of the State Award for Scientific and Technological Achievement in 1988 and 1992 respectively. For years he devoted himself to the development of the homegrown core technology as well as the IT industry of China. In 1994 he was elected as the Academician of Chinese Academy of Engineering. He was rewarded the Lifetime Achievement Award by the Chinese Information Processing Society of China (CIPS) and China Computer Federation (CCF) in 2011 and 2015 respectively.\n\t\t")])])]),a._v(" "),n("div",{staticClass:"card"},[n("div",{staticClass:"card-content"},[n("div",{staticClass:"media"},[n("div",{staticClass:"media-left"},[n("figure",{staticClass:"image"},[n("img",{staticStyle:{width:"250px"},attrs:{src:t(41),alt:"tangyuanyan"}})])]),a._v(" "),n("div",{staticClass:"media-content"},[n("p",{staticClass:"title is-4"},[a._v("Tang Yuanyan, Ph.D., Professor")]),a._v(" "),n("p",{staticClass:"subtitle is-6"},[a._v("Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China")])])]),a._v(" "),n("div",{staticClass:"content"},[a._v("\n\t\t\tTang Yuanyan is a Chair Professor in Faculty of Science and Technology at University of Macau and Professor/Adjunct Professor/Honorary Professor at several institutes in China, USA, Canada, France, and Hong Kong. His current interests include wavelets, pattern recognition, image processing, and artificial intelligence. He has published more than 400 academic papers and is the author/coauthor of over 25 monographs/books/bookchapters. He is the Founder and Editor-in-Chief of International Journal on Wavelets, Multiresolution, and Information Processing (IJWMIP), and Associate Editors of several international journals. He is the Founder and Chair of pattern recognition committee in IEEE SMC. He has serviced as general chair, program chair, and committee member for many international conferences. Dr. Tang is the Founder and General Chair of the series International Conferences on Wavelets Analysis and Pattern Recognition (ICWAPRs). He is the Founder and Chair of the Macau Branch of International Associate of Pattern Recognition (IAPR). Dr. Y. Y. Tang is a Fellow of IEEE, and Fellow of IAPR.\n\t\t")])])]),a._v(" "),n("div",{staticClass:"card"},[n("div",{staticClass:"card-content"},[n("div",{staticClass:"media"},[n("div",{staticClass:"media-left"},[n("figure",{staticClass:"image"},[n("img",{staticStyle:{width:"250px"},attrs:{src:t(42),alt:"lijianping"}})])]),a._v(" "),n("div",{staticClass:"media-content"},[n("p",{staticClass:"title is-4"},[a._v("Li Jianping, Ph.D., Professor")]),a._v(" "),n("p",{staticClass:"subtitle is-6"},[a._v("International Centre for Wavelet Analysis and Its applications,Big Data Research Institute, School of Computer Science,University of Electronic Science and Technology of China（UESTC）")])])]),a._v(" "),n("div",{staticClass:"content"},[a._v("\n\t\t\tProfessor Dr. Li Jianping, male, born on October 11,1964. He received the B.S. degree in applied mathematics from Chongqing University, Chongqing, China, in 1986, the M. Eng. degree in software engineering and M. Sci. degree in computational mathematics from the Graduate School of Xi'an Jiaotong University, Xi'an, Shanxi, China, in 1989, and the Ph.D. degree in computer science from the Graduate School of Chongqing University, Chongqing, China, in 1998. From 1999 to 2000, he was a post doctoral and visiting professor working at Hong Kong Baptist University under the leadership of Professor Dr. Yuan Yan Tang. From 2002 to 2003, 2005 to 2006, and in 2010, 2011, 2013, 2014, 2015,he was a senior visiting professor working respectively at Provence University, France, University of Guelph, Canada, Washington University, USA, University of Zurich, Switzerland. He is presently a Professor in International Centre for Wavelets Analysis and Its Applications (ICWAA) of University of Electronic Science and Technology of China（UESTC）, Chengdu, China, and as a head of ICWAA, he is the dean of Big Data Research Institute of UESTC. Dr. Li has served as a Syndic for Awards of National Science and Technology of China, a founder of the International Journal on Multiresolution, Wavelets, Applications (IJMWA), a founder and Editor in Chief of International Progress on Wavelet Active Media Technology and Information Processing, and also as General Chairman of many international conferences, a reviewer for many international famous Journals. His book Wavelet Analysis & Signal Processing: Theory, Applications & Software Implementations, was awarded the second prize of National Science and Technology of China (1999), and it is very important reference for many scientists. Dr. Li has published more than 300 technical papers and is the author/ co-author of 30 books on subjects ranging from wavelet analysis and its applications to computer science. His current interests include wavelet theory and applications, fractal, image processing, pattern recognition, information security, big data, artificial intelligence.\n\t\t")])])])])},[],!1,null,null,null);"function"==typeof i&&i(r);e.default=r.exports}}]); \ No newline at end of file diff --git a/_saber/js/page--timeline-md.aa2b7564.js b/_saber/js/page--timeline-md.fa28d13b.js similarity index 96% rename from _saber/js/page--timeline-md.aa2b7564.js rename to _saber/js/page--timeline-md.fa28d13b.js index f2eecf1..0cde6e0 100644 --- a/_saber/js/page--timeline-md.aa2b7564.js +++ b/_saber/js/page--timeline-md.fa28d13b.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[9],{48:function(t,e,n){"use strict";n.r(e);var o=n(0),r=function(t){var e,n,o=(n=void 0,{attributes:{slug:"timeline",title:"Conference Timeline",layout:e="page",createdAt:new Date(1718781944e3),updatedAt:new Date(1718781944e3),type:e,permalink:"/timeline.html"},internal:n,contentType:"markdown",content:n}),r=t.options.beforeCreate||[];t.options.beforeCreate=[function(){this.$page=o}].concat(r);["layout","transition"].forEach(function(e){var n=t.options.PageComponent;n&&(t.options[e]=n[e]),void 0===t.options[e]&&(t.options[e]=o.attributes[e])}),t.options.name="page-wrapper-"+o.attributes.slug.replace(/[^0-9a-z\-]/i,"-")},i=Object(o.a)({},function(){var t=this,e=t.$createElement,n=t._self._c||e;return n("layout-manager",[n("h2",{attrs:{id:"conference-timeline"}},[t._v("Conference Timeline")]),t._v(" "),n("table",[n("thead",[n("tr",[n("th",[t._v("Event")]),t._v(" "),n("th",[t._v("Schedule (China Standard Time, UTC+08:00)")])])]),t._v(" "),n("tbody",[n("tr",[n("td",[t._v("Submission")]),t._v(" "),n("td",[t._v("Deadline : "),n("strong",[t._v("Nov. 22th")])])]),t._v(" "),n("tr",[n("td",[t._v("Paper Review")]),t._v(" "),n("td",[t._v("From "),n("strong",[t._v("Oct. 30th")]),t._v(" to "),n("strong",[t._v("Nov. 30th")])])]),t._v(" "),n("tr",[n("td",[t._v("Author Notification")]),t._v(" "),n("td",[t._v("From "),n("strong",[t._v("Nov. 20th")]),t._v(" to "),n("strong",[t._v("Nov. 30th")])])]),t._v(" "),n("tr",[n("td",[t._v("Camera Ready Submission & Payment")]),t._v(" "),n("td",[t._v("Deadline : "),n("strong",[t._v("Dec. 3th")])])]),t._v(" "),n("tr",[n("td",[t._v("Conference")]),t._v(" "),n("td",[t._v("From "),n("strong",[t._v("Dec. 14th")]),t._v(" to "),n("strong",[t._v("Dec. 16th")])])])])])])},[],!1,null,null,null);"function"==typeof r&&r(i);e.default=i.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[9],{50:function(t,e,n){"use strict";n.r(e);var o=n(0),r=function(t){var e,n,o=(n=void 0,{attributes:{slug:"timeline",title:"Conference Timeline",layout:e="page",createdAt:new Date(1718781944e3),updatedAt:new Date(1718781944e3),type:e,permalink:"/timeline.html"},internal:n,contentType:"markdown",content:n}),r=t.options.beforeCreate||[];t.options.beforeCreate=[function(){this.$page=o}].concat(r);["layout","transition"].forEach(function(e){var n=t.options.PageComponent;n&&(t.options[e]=n[e]),void 0===t.options[e]&&(t.options[e]=o.attributes[e])}),t.options.name="page-wrapper-"+o.attributes.slug.replace(/[^0-9a-z\-]/i,"-")},i=Object(o.a)({},function(){var t=this,e=t.$createElement,n=t._self._c||e;return n("layout-manager",[n("h2",{attrs:{id:"conference-timeline"}},[t._v("Conference Timeline")]),t._v(" "),n("table",[n("thead",[n("tr",[n("th",[t._v("Event")]),t._v(" "),n("th",[t._v("Schedule (China Standard Time, UTC+08:00)")])])]),t._v(" "),n("tbody",[n("tr",[n("td",[t._v("Submission")]),t._v(" "),n("td",[t._v("Deadline : "),n("strong",[t._v("Nov. 22th")])])]),t._v(" "),n("tr",[n("td",[t._v("Paper Review")]),t._v(" "),n("td",[t._v("From "),n("strong",[t._v("Oct. 30th")]),t._v(" to "),n("strong",[t._v("Nov. 30th")])])]),t._v(" "),n("tr",[n("td",[t._v("Author Notification")]),t._v(" "),n("td",[t._v("From "),n("strong",[t._v("Nov. 20th")]),t._v(" to "),n("strong",[t._v("Nov. 30th")])])]),t._v(" "),n("tr",[n("td",[t._v("Camera Ready Submission & Payment")]),t._v(" "),n("td",[t._v("Deadline : "),n("strong",[t._v("Dec. 3th")])])]),t._v(" "),n("tr",[n("td",[t._v("Conference")]),t._v(" "),n("td",[t._v("From "),n("strong",[t._v("Dec. 14th")]),t._v(" to "),n("strong",[t._v("Dec. 16th")])])])])])])},[],!1,null,null,null);"function"==typeof r&&r(i);e.default=i.exports}}]); \ No newline at end of file diff --git a/_saber/js/page--topics-md.52b22406.js b/_saber/js/page--topics-md.6b9eb377.js similarity index 98% rename from _saber/js/page--topics-md.52b22406.js rename to _saber/js/page--topics-md.6b9eb377.js index 52f869a..89b82ec 100644 --- a/_saber/js/page--topics-md.52b22406.js +++ b/_saber/js/page--topics-md.6b9eb377.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[10],{49:function(n,i,e){"use strict";e.r(i);var t=e(0),o=function(n){var i,e,t=(e=void 0,{attributes:{slug:"topics",title:"Topics",layout:i="page",excerpt:"

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Gao Yuan, UESTC, China

Accepted abstract & Full paper will be invited to give the presentation at ICCWAMTIP 2020

+ Email: icacia@uestc.edu.cn

Our Links

diff --git a/contact.html b/contact.html index 50be78a..9324a5c 100644 --- a/contact.html +++ b/contact.html @@ -5,8 +5,7 @@ footer[data-v-7968ac62]{background-color:#19181d;color:#b2b1b8}.title[data-v-796 ICCWAMTIP Conference Flyer!

Everything you need to know here. -

Call for paper

Paper format

2024 21th ICCWAMTIP Secretariat

For more information about ICCWAMTIP, feel free to contact.

  • Tel. +86 (0)28-8683-1549

  • E-mail: icacia@uestc.edu.cn

  • WeChat QRCode (We will add you into our conference group):

    < img style="width: 200px" src="../static/images/QRcode Luo.png" /> -< img style="width: 200px" src="../static/images/QRcode LI.png" />

Accepted abstract & Full paper will be invited to give the presentation at ICCWAMTIP 2020

diff --git a/guidance.html b/guidance.html index 24d8c23..b092584 100644 --- a/guidance.html +++ b/guidance.html @@ -8,4 +8,4 @@ footer[data-v-7968ac62]{background-color:#19181d;color:#b2b1b8}.title[data-v-796

Call for paper

Paper format

How to know the paper format of the conference?

Please pay an attention to the left side of this website, you can find PAPER FORMAT.

How to submit my paper?

We use CMT system to manager our conference this time.

  1. First you need to register an account in cmt system.
  2. Log in your account, then go to the submission page.
  3. Submit your paper.

How to know whether my paper is accepted by our conference?

Once your paper is accepted, we will send an email to you. And you can check your paper status all the time in the system.

About the paper fee.

Number of pages Fee
Less than 4 pages (include 4 pages) 2700￥
More than 4 pages,for each additional page 400￥

Example: 6 pages will be charged for 3500￥(2700+400×2).

More questions?

If you have any question about our conference, please send email to us or join our WeChat group in contact page.

Our email: icacia@uestc.edu.cn

Accepted abstract & Full paper will be invited to give the presentation at ICCWAMTIP 2020

+ Email: icacia@uestc.edu.cn

Our Links

diff --git a/index.html b/index.html index 3b61d70..b247f03 100644 --- a/index.html +++ b/index.html @@ -24,4 +24,4 @@ footer[data-v-7968ac62]{background-color:#19181d;color:#b2b1b8}.title[data-v-796 12:25 Question Time

12:30 Three Entrepreneur and Experts's Presentations

12:45 Closing Speech

Important Dates

Event Date (China Standard Time, UTC+08:00)
Submission Deadline November 22th, 2024
Notification Date November 30th, 2024
Conference Ceremony December 15th, 2024

CATALOG NUMBERS

Media Type Request Part Number ISBN Print ISSN Oline ISSN Media Qty Request
XPLORE COMPLIANT CFP20WAM-ART 978-0-7381-4259-3 / 2576-8964 3
USB CFP20WAM-USB 978-0-7381-4258-6 / / 3
PRINT CFP20WAM-PRT 978-0-7381-4257-9 / / 2

Any question, please contact us by Conferene E-mail and Phone Number.

Accepted abstract & Full paper will be invited to give the presentation at ICCWAMTIP 2020

+ Email: icacia@uestc.edu.cn

Our Links

diff --git a/information.html b/information.html index 898fa9d..ab85318 100644 --- a/information.html +++ b/information.html @@ -11,4 +11,4 @@ The conference proceedings will be published by UESTC, all accepted papers are e Please log in the cmt system to submit your paper(strongly recommend), and you can also e-mail your paper directly to icacia@uestc.edu.cn.

Looking forward to seeing you in Chengdu!

And please notice the following date:

  • Submission closed: November 22th,2024
  • Acceptance notification: November 30th, 2024
  • Conference Ceremony: December 15th, 2024

CATALOG NUMBERS

Media Type Request Part Number ISBN Print ISSN Oline ISSN Media Qty Request
XPLORE COMPLIANT CFP20WAM-ART 978-0-7381-4259-3 / 2576-8964 3
USB CFP20WAM-USB 978-0-7381-4258-6 / / 3
PRINT CFP20WAM-PRT 978-0-7381-4257-9 / / 2

Organization of the 21th ICCWAMTIP

International Centre for Wavelet Analysis and Its Applications

Chengdu China, UESTC

August 8th , 2024

Accepted abstract & Full paper will be invited to give the presentation at ICCWAMTIP 2020

+ Email: icacia@uestc.edu.cn

Our Links

diff --git a/news.html b/news.html index 57923a1..76f0417 100644 --- a/news.html +++ b/news.html @@ -8,4 +8,4 @@ footer[data-v-7968ac62]{background-color:#19181d;color:#b2b1b8}.title[data-v-796

Call for paper

Paper format

  • 2020-12-29: Add the final booklet of all papers, please download from this link
  • 2020-12-17: Updated the conference routine.
  • 2020-12-14: The conference will be held at 09:00 AM Dec. 19th, for the conference routine see bellow.
  • 2020-11-26: Advance the deadline for Camera Ready submission (including payment proof submission) from Dec. 8th to Dec. 3th
  • 2020-11-11: Extanded submission deadline from Nov. 15th to Nov. 22th, and accepted notification from Nov. 20th to Nov. 30th
  • 2020-10-22: Updated time schedule about submission deadline, paper review time and author notification time in page timeline
  • 2020-10-19: Updated submission deadline from Oct. 21th to Nov. 15th, and notification date from Nov. 15th to Nov. 20th
  • 2020-09-27: Updated topics and added some new topics, as well as updated CALL FOR PAPER attachment
  • 2020-09-25: Added WeChat QRCode contact method in contact page
  • 2020-09-20: Updated submission deadline from Oct. 15th to Oct. 21th

Accepted abstract & Full paper will be invited to give the presentation at ICCWAMTIP 2020

+ Email: icacia@uestc.edu.cn

Our Links

diff --git a/speakers.html b/speakers.html index 549ca12..2d1949c 100644 --- a/speakers.html +++ b/speakers.html @@ -16,4 +16,4 @@ footer[data-v-7968ac62]{background-color:#19181d;color:#b2b1b8}.title[data-v-796

Accepted abstract & Full paper will be invited to give the presentation at ICCWAMTIP 2020

+ Email: icacia@uestc.edu.cn

Our Links

diff --git a/timeline.html b/timeline.html index 517a6ce..8f68b55 100644 --- a/timeline.html +++ b/timeline.html @@ -8,4 +8,4 @@ footer[data-v-7968ac62]{background-color:#19181d;color:#b2b1b8}.title[data-v-796

Call for paper

Paper format

Conference Timeline

Event Schedule (China Standard Time, UTC+08:00)
Submission Deadline : Nov. 22th
Paper Review From Oct. 30th to Nov. 30th
Author Notification From Nov. 20th to Nov. 30th
Camera Ready Submission & Payment Deadline : Dec. 3th
Conference From Dec. 14th to Dec. 16th

Accepted abstract & Full paper will be invited to give the presentation at ICCWAMTIP 2020

+ Email: icacia@uestc.edu.cn

Our Links

diff --git a/topics.html b/topics.html index 897276d..ef31bdc 100644 --- a/topics.html +++ b/topics.html @@ -8,4 +8,4 @@ footer[data-v-7968ac62]{background-color:#19181d;color:#b2b1b8}.title[data-v-796

Call for paper

Paper format

Topic

The topics of the conference include and not only include:

  1. Theory and Experiment

    • Fractal geometry

    • Wavelet algorithm and multi-wavelet

    • Signal processing

    • Microwave

    • Data mining and knowledge discovery

    • Blockchain technology

    • Machine learning & Deep learning

    • Intelligent communication systems

    • Intelligent algorithms and applications

    • Intelligent mechatronics

    • Neural networks, fuzzy systems and soft computing algorithms

    • Information and cyberspace security

    • Data privacy

    • Biomimetics and bio-inspired systems

    • Cancer Diagnostic Analysis

    • Computer network and internet modeling

    • Parallel, distributed computing

    • Big Data and Cloud Computing

    • Computational dynamical systems

  2. Multimedia Technology

    • Active media technology

    • Medical information

    • Vision and image processing

  3. Embedded System and Others

    • Traffic modeling and congestion control

    • System modeling, identification and simulation

    • Intelligent and biological control systems

    • System control and optimal control

    • Power system analysis and control

    • Adaptive and learning control

    • Process control and industrial automation

    • Feedback and decentralized control

    • Collaborative control systems

    • Robotics and intelligent vehicles

    • Hybrid control and hybrid dynamical systems

    • Tele-robotics, haptics, and tele-operated systems

    • Each layer Design for cognitive radio networks

    • Spectrum sharing and cognitive networking

    • Spectrum sensing

    • Sensing and perception

    • Applications of game theory in cognitive radio networks

    • Anti-interference Techniques for cognitive radio networks

    • Tracking, Telemetering and Command

    • Sensor networks

    • Sensors based control and automation

    • Sensor design, integration and fusion

    • Sensor data analysis and system diagnosis

    • Multi-agent systems and applications

    • Service science, management and engineering

    • Military-civilian integration

Accepted abstract & Full paper will be invited to give the presentation at ICCWAMTIP 2020

+ Email: icacia@uestc.edu.cn

Our Links