diff --git a/404.html b/404.html index ed30e3e..95be48f 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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ICCWAMTIP 2020, The 17th International Computer Conference on Wavelet Active Media Technology and Information Processing, will take place in Chengdu, China, from December 18th, 2020. 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 2020 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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Dear Authors,\nThe 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (the 17th ICCWAMTIP) will be held in the University of Electronic Science and Technology of China (UESTC) on December 18th-20th , 2020.

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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(" "),t("p",[e._v("The topics of the conference include:")]),e._v(" "),t("ul",[t("li",[e._v("Wavelet algorithm and multi-wavelet")]),e._v(" "),t("li",[e._v("Blockchain technology")]),e._v(" "),t("li",[e._v("Active media technology")]),e._v(" "),t("li",[e._v("Medical Information")]),e._v(" "),t("li",[e._v("Cancer Diagnostic Analysis")]),e._v(" "),t("li",[e._v("Big Data and Cloud Computing")]),e._v(" "),t("li",[e._v("Military-civilian integration")]),e._v(" "),t("li",[e._v("Fractal geometry")]),e._v(" "),t("li",[e._v("Sensor design, integration and fusion")]),e._v(" "),t("li",[e._v("Sensor networks")]),e._v(" "),t("li",[e._v("Sensors based control and automation")]),e._v(" "),t("li",[e._v("Vision and image processing")]),e._v(" "),t("li",[e._v("Sensing and perception")]),e._v(" "),t("li",[e._v("Sensor data analysis and system diagnosis")]),e._v(" "),t("li",[e._v("Neural networks, fuzzy systems and soft computing algorithms")]),e._v(" "),t("li",[e._v("Multi-agent systems and applications")]),e._v(" "),t("li",[e._v("Intelligent algorithms and applications")]),e._v(" "),t("li",[e._v("Computer network and internet modeling")]),e._v(" "),t("li",[e._v("Intelligent communication systems")]),e._v(" "),t("li",[e._v("Computational dynamical systems")]),e._v(" "),t("li",[e._v("Parallel, distributed computing")]),e._v(" "),t("li",[e._v("Traffic modeling and congestion control")]),e._v(" "),t("li",[e._v("System modeling, identification and simulation")]),e._v(" "),t("li",[e._v("Intelligent and biological control systems")]),e._v(" "),t("li",[e._v("System control and optimal control")]),e._v(" "),t("li",[e._v("Feedback and decentralized control")]),e._v(" "),t("li",[e._v("Collaborative control systems")]),e._v(" "),t("li",[e._v("Hybrid control and hybrid dynamical systems")]),e._v(" "),t("li",[e._v("Power system analysis and control")]),e._v(" "),t("li",[e._v("Adaptive and learning control")]),e._v(" "),t("li",[e._v("Process control and industrial automation")]),e._v(" "),t("li",[e._v("Robotics and intelligent vehicles")]),e._v(" "),t("li",[e._v("Intelligent mechatronics")]),e._v(" "),t("li",[e._v("Biomimetics and bio-inspired systems")]),e._v(" "),t("li",[e._v("Service science, management and engineering")]),e._v(" "),t("li",[e._v("Data mining and knowledge discovery")])]),e._v(" "),t("p",[e._v("We will recommend 10%-20% good papers to SCI journals, all the papers accepted by our conference in the past years are indexed by EI.\nPlease log in the "),t("a",{attrs:{href:"https://cmt3.research.microsoft.com/ICCWAMTIP2020",target:"_blank",rel:"noopener noreferrer"}},[e._v("cmt system")]),e._v(" to submit your paper("),t("strong",[e._v("strongly recommend")]),e._v("), and you can also e-mail your paper directly to "),t("saber-link",{attrs:{to:"mailto:icacia@uestc.edu.cn"}},[e._v("icacia@uestc.edu.cn")]),e._v(".")],1),e._v(" "),t("p",[e._v("Looking forward to seeing you in Chengdu!")]),e._v(" "),t("p",[e._v("And please notice the following date:")]),e._v(" "),t("ul",[t("li",[e._v("Submission closed: "),t("strong",[e._v("October 21th,2020")])]),e._v(" "),t("li",[e._v("Acceptance notification: "),t("strong",[e._v("November 20th, 2020")])]),e._v(" "),t("li",[e._v("Conference Ceremony: "),t("strong",[e._v("December 18th, 2020")])])]),e._v(" "),t("p",[e._v("Organization of the 17th ICCWAMTIP")]),e._v(" "),t("p",[e._v("International Centre for Wavelet Analysis and Its Applications")]),e._v(" "),t("p",[e._v("Chengdu China, UESTC")]),e._v(" "),t("p",[e._v("August 8th , 2020")])])},[],!1,null,null,null);"function"==typeof o&&o(a);n.default=a.exports}}]); \ No newline at end of file +(window.webpackJsonp=window.webpackJsonp||[]).push([[6],{46:function(e,n,t){"use strict";t.r(n);var i=t(0),o=function(e){var n,t,i=(t=void 0,{attributes:{slug:"information",title:"Information",layout:n="page",excerpt:"

Dear Authors,\nThe 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (the 17th ICCWAMTIP) will be held in the University of Electronic Science and Technology of China (UESTC) on December 18th-20th , 2020.

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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(" "),t("p",[e._v("The topics of the conference include:")]),e._v(" "),t("ul",[t("li",[e._v("Wavelet algorithm and multi-wavelet")]),e._v(" "),t("li",[e._v("Blockchain technology")]),e._v(" "),t("li",[e._v("Active media technology")]),e._v(" "),t("li",[e._v("Medical Information")]),e._v(" "),t("li",[e._v("Cancer Diagnostic Analysis")]),e._v(" "),t("li",[e._v("Big Data and Cloud Computing")]),e._v(" "),t("li",[e._v("Military-civilian integration")]),e._v(" "),t("li",[e._v("Fractal geometry")]),e._v(" "),t("li",[e._v("Sensor design, integration and fusion")]),e._v(" "),t("li",[e._v("Sensor networks")]),e._v(" "),t("li",[e._v("Sensors based control and automation")]),e._v(" "),t("li",[e._v("Vision and image processing")]),e._v(" "),t("li",[e._v("Sensing and perception")]),e._v(" "),t("li",[e._v("Sensor data analysis and system diagnosis")]),e._v(" "),t("li",[e._v("Neural networks, fuzzy systems and soft computing algorithms")]),e._v(" "),t("li",[e._v("Multi-agent systems and applications")]),e._v(" "),t("li",[e._v("Intelligent algorithms and applications")]),e._v(" "),t("li",[e._v("Computer network and internet modeling")]),e._v(" "),t("li",[e._v("Intelligent communication systems")]),e._v(" "),t("li",[e._v("Computational dynamical systems")]),e._v(" "),t("li",[e._v("Parallel, distributed computing")]),e._v(" "),t("li",[e._v("Traffic modeling and congestion control")]),e._v(" "),t("li",[e._v("System modeling, identification and simulation")]),e._v(" "),t("li",[e._v("Intelligent and biological control systems")]),e._v(" "),t("li",[e._v("System control and optimal control")]),e._v(" "),t("li",[e._v("Feedback and decentralized control")]),e._v(" "),t("li",[e._v("Collaborative control systems")]),e._v(" "),t("li",[e._v("Hybrid control and hybrid dynamical systems")]),e._v(" "),t("li",[e._v("Power system analysis and control")]),e._v(" "),t("li",[e._v("Adaptive and learning control")]),e._v(" "),t("li",[e._v("Process control and industrial automation")]),e._v(" "),t("li",[e._v("Robotics and intelligent vehicles")]),e._v(" "),t("li",[e._v("Intelligent mechatronics")]),e._v(" "),t("li",[e._v("Biomimetics and bio-inspired systems")]),e._v(" "),t("li",[e._v("Service science, management and engineering")]),e._v(" "),t("li",[e._v("Data mining and knowledge discovery")])]),e._v(" "),t("p",[e._v("We will recommend 10%-20% good papers to SCI journals, all the papers accepted by our conference in the past years are indexed by EI.\nPlease log in the "),t("a",{attrs:{href:"https://cmt3.research.microsoft.com/ICCWAMTIP2020",target:"_blank",rel:"noopener noreferrer"}},[e._v("cmt system")]),e._v(" to submit your paper("),t("strong",[e._v("strongly recommend")]),e._v("), and you can also e-mail your paper directly to "),t("saber-link",{attrs:{to:"mailto:icacia@uestc.edu.cn"}},[e._v("icacia@uestc.edu.cn")]),e._v(".")],1),e._v(" "),t("p",[e._v("Looking forward to seeing you in Chengdu!")]),e._v(" "),t("p",[e._v("And please notice the following date:")]),e._v(" "),t("ul",[t("li",[e._v("Submission closed: "),t("strong",[e._v("October 21th,2020")])]),e._v(" "),t("li",[e._v("Acceptance notification: "),t("strong",[e._v("November 20th, 2020")])]),e._v(" "),t("li",[e._v("Conference Ceremony: "),t("strong",[e._v("December 18th, 2020")])])]),e._v(" "),t("p",[e._v("Organization of the 17th ICCWAMTIP")]),e._v(" "),t("p",[e._v("International Centre for Wavelet Analysis and Its Applications")]),e._v(" "),t("p",[e._v("Chengdu China, UESTC")]),e._v(" "),t("p",[e._v("August 8th , 2020")])])},[],!1,null,null,null);"function"==typeof o&&o(a);n.default=a.exports}}]); \ No newline at end of file diff --git a/_saber/js/page--news-md.7eadbca8.js b/_saber/js/page--news-md.057c22c6.js similarity index 92% rename from _saber/js/page--news-md.7eadbca8.js rename to _saber/js/page--news-md.057c22c6.js index ff1cc4e..ff03549 100644 --- a/_saber/js/page--news-md.7eadbca8.js +++ b/_saber/js/page--news-md.057c22c6.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[7],{46:function(t,e,n){"use strict";n.r(e);var o=n(0),a=function(t){var e,n,o=(n=void 0,{attributes:{slug:"news",title:"News",layout:e="page",excerpt:"

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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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"},39:function(a,e){a.exports="data:image/jpeg;base64,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"},40:function(a,e,t){a.exports=t.p+"images/static/images/tangyuanyan.33678029.jpg"},41:function(a,e,t){a.exports=t.p+"images/static/images/lijianping.ac82bdee.jpg"},48: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(40),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(41),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.f7e645ab.js b/_saber/js/page--timeline-md.0748aa36.js similarity index 96% rename from _saber/js/page--timeline-md.f7e645ab.js rename to _saber/js/page--timeline-md.0748aa36.js index dc751bc..d47dcb2 100644 --- a/_saber/js/page--timeline-md.f7e645ab.js +++ b/_saber/js/page--timeline-md.0748aa36.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(1600676602488),updatedAt:new Date(1600676602488),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")])])]),t._v(" "),n("tbody",[n("tr",[n("td",[t._v("Submission")]),t._v(" "),n("td",[t._v("Deadline : "),n("strong",[t._v("Oct. 21th")])])]),t._v(" "),n("tr",[n("td",[t._v("Paper Review")]),t._v(" "),n("td",[t._v("From "),n("strong",[t._v("Oct. 16th")]),t._v(" to "),n("strong",[t._v("Nov. 14th")])])]),t._v(" "),n("tr",[n("td",[t._v("Author Notification")]),t._v(" "),n("td",[t._v("From "),n("strong",[t._v("Nov. 15th")]),t._v(" to "),n("strong",[t._v("Nov. 16th")])])]),t._v(" "),n("tr",[n("td",[t._v("Camera Ready Submission & Payment")]),t._v(" "),n("td",[t._v("Deadline : "),n("strong",[t._v("Dec. 8th")])])]),t._v(" "),n("tr",[n("td",[t._v("Conference")]),t._v(" "),n("td",[t._v("From "),n("strong",[t._v("Dec. 18th")]),t._v(" to "),n("strong",[t._v("Dec. 20th")])])])])])])},[],!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],{49: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(1600676602488),updatedAt:new Date(1600676602488),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")])])]),t._v(" "),n("tbody",[n("tr",[n("td",[t._v("Submission")]),t._v(" "),n("td",[t._v("Deadline : "),n("strong",[t._v("Oct. 21th")])])]),t._v(" "),n("tr",[n("td",[t._v("Paper Review")]),t._v(" "),n("td",[t._v("From "),n("strong",[t._v("Oct. 16th")]),t._v(" to "),n("strong",[t._v("Nov. 14th")])])]),t._v(" "),n("tr",[n("td",[t._v("Author Notification")]),t._v(" "),n("td",[t._v("From "),n("strong",[t._v("Nov. 15th")]),t._v(" to "),n("strong",[t._v("Nov. 16th")])])]),t._v(" "),n("tr",[n("td",[t._v("Camera Ready Submission & Payment")]),t._v(" "),n("td",[t._v("Deadline : "),n("strong",[t._v("Dec. 8th")])])]),t._v(" "),n("tr",[n("td",[t._v("Conference")]),t._v(" "),n("td",[t._v("From "),n("strong",[t._v("Dec. 18th")]),t._v(" to "),n("strong",[t._v("Dec. 20th")])])])])])])},[],!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.7156cf1a.js b/_saber/js/page--topics-md.b277f43c.js similarity index 98% rename from _saber/js/page--topics-md.7156cf1a.js rename to _saber/js/page--topics-md.b277f43c.js index 8b1253d..a5e7840 100644 --- a/_saber/js/page--topics-md.7156cf1a.js +++ b/_saber/js/page--topics-md.b277f43c.js @@ -1 +1 @@ -(window.webpackJsonp=window.webpackJsonp||[]).push([[10],{49:function(i,n,t){"use strict";t.r(n);var e=t(0),o=function(i){var n,t,e=(t=void 0,{attributes:{slug:"topics",title:"Topics",layout:n="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 cb7be1e..ccc9116 100644 --- a/contact.html +++ b/contact.html @@ -5,7 +5,7 @@ footer[data-v-5d7efa0c]{background-color:#19181d;color:#b2b1b8}.title[data-v-5d7 ICCWAMTIP Conference Flyer!

Everything you need to know here. -

Call for paper

Paper format

2020 17th ICCWAMTIP Secretariat

For more information about ICCWAMTIP, feel free to contact.

Tel. +86 (0)28-8683-1549

E-mail: icacia@uestc.edu.cn

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

diff --git a/guidance.html b/guidance.html index 4e8e98c..d4e3bdd 100644 --- a/guidance.html +++ b/guidance.html @@ -8,4 +8,4 @@ footer[data-v-5d7efa0c]{background-color:#19181d;color:#b2b1b8}.title[data-v-5d7

Call for paper

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.

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/images/wechat-qr.png b/images/wechat-qr.png new file mode 100755 index 0000000..6a26f43 Binary files /dev/null and b/images/wechat-qr.png differ diff --git a/index.html b/index.html index 84c56ac..8973fd8 100644 --- a/index.html +++ b/index.html @@ -1,12 +1,12 @@ - 2020 17th ICCWAMTIP

+footer[data-v-5d7efa0c]{background-color:#19181d;color:#b2b1b8}.title[data-v-5d7efa0c],footer a[data-v-5d7efa0c]{color:#fff}.information[data-v-5d7efa0c]{margin-top:1rem}i[data-v-5d7efa0c]{vertical-align:middle}

ICCWAMTIP Conference Flyer!

Everything you need to know here. -

Call for paper

Paper format

ICCWAMTIP 2020 | Chengdu, China | December 18th, 2020

ICCWAMTIP 2020, The 17th International Computer Conference on Wavelet Active Media Technology and Information Processing, will take place in Chengdu, China, from December 18th, 2020. 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 2020 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.

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.

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 2020. We look forward to your participation and continued engagement at future ICCWAMTIP conferences.

Latest News

  • Updated submission time from Oct. 15th to Oct. 21th

Important Dates

Event Date
Submission Deadline October 21th, 2020
Notification Date November 15th, 2020
Conference Ceremony December 18th, 2020

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

ICCWAMTIP 2020

The 17th International Computer Conference on Wavelet Active Media Technology and Information Processing

ICCWAMTIP 2020 | Chengdu, China | December 18th, 2020

ICCWAMTIP 2020, The 17th International Computer Conference on Wavelet Active Media Technology and Information Processing, will take place in Chengdu, China, from December 18th, 2020. 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 2020 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.

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.

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 2020. We look forward to your participation and continued engagement at future ICCWAMTIP conferences.

Latest News

  • Updated submission time from Oct. 15th to Oct. 21th
  • Add WeChat QRCode contact method in contact page

Important Dates

Event Date
Submission Deadline October 21th, 2020
Notification Date November 15th, 2020
Conference Ceremony December 18th, 2020

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 abc3f91..d082173 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: October 21th,2020
  • Acceptance notification: November 20th, 2020
  • Conference Ceremony: December 18th, 2020

Organization of the 17th ICCWAMTIP

International Centre for Wavelet Analysis and Its Applications

Chengdu China, UESTC

August 8th , 2020

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 61ebc9a..966c2ad 100644 --- a/news.html +++ b/news.html @@ -8,4 +8,4 @@ footer[data-v-5d7efa0c]{background-color:#19181d;color:#b2b1b8}.title[data-v-5d7

Call for paper

Paper format

comming soon....

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 43262b9..d94ba45 100644 --- a/speakers.html +++ b/speakers.html @@ -16,4 +16,4 @@ footer[data-v-5d7efa0c]{background-color:#19181d;color:#b2b1b8}.title[data-v-5d7

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

+ Email: icacia@uestc.edu.cn

Our Links

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Call for paper

Paper format

Conference Timeline

Event Schedule
Submission Deadline : Oct. 21th
Paper Review From Oct. 16th to Nov. 14th
Author Notification From Nov. 15th to Nov. 16th
Camera Ready Submission & Payment Deadline : Dec. 8th
Conference From Dec. 18th to Dec. 20th

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 118e13e..d75cc1a 100644 --- a/topics.html +++ b/topics.html @@ -8,4 +8,4 @@ footer[data-v-5d7efa0c]{background-color:#19181d;color:#b2b1b8}.title[data-v-5d7

Call for paper

Paper format

Topic

The topics of the conference include and not only include:

  • Wavelet algorithm and multi-wavelet
  • Active media technology
  • Medical information
  • Blockchain technology
  • Cancer Diagnostic Analysis
  • BlockChain technology
  • Big Data and Cloud Computing
  • Military-civilian integration
  • Fractal geometry
  • Sensor design, integration and fusion
  • Sensor networks
  • Sensors based control and automation
  • Vision and image processing
  • Sensing and perception
  • Sensor data analysis and system diagnosis
  • Neural networks, fuzzy systems and soft computing algorithms
  • Multi-agent systems and applications
  • Intelligent algorithms and applications
  • Tele-robotics, haptics, and tele-operated systems
  • Computer network and internet modeling
  • Intelligent communication systems
  • Computational dynamical systems
  • Parallel, distributed computing
  • Traffic modeling and congestion control
  • System modeling, identification and simulation
  • Intelligent and biological control systems
  • System control and optimal control
  • Feedback and decentralized control
  • Collaborative control systems
  • Hybrid control and hybrid dynamical systems
  • Power system analysis and control
  • Adaptive and learning control
  • Process control and industrial automation
  • Robotics and intelligent vehicles
  • Intelligent mechatronics
  • Biomimetics and bio-inspired systems
  • Service science, management and engineering
  • Data mining and knowledge discovery
  • Information and cyberspace security
  • Data privacy

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

+ Email: icacia@uestc.edu.cn

Our Links