{"id":893,"date":"2022-09-29T22:31:40","date_gmt":"2022-09-29T15:31:40","guid":{"rendered":"https:\/\/cpe02.romshopsi.com\/?page_id=893"},"modified":"2022-11-20T20:47:33","modified_gmt":"2022-11-20T13:47:33","slug":"pichid","status":"publish","type":"page","link":"https:\/\/en.rmutr.ac.th\/ece\/?page_id=893","title":{"rendered":"pichid"},"content":{"rendered":"\t<div id=\"gap-580746757\" class=\"gap-element clearfix\" style=\"display:block; height:auto;\">\n\t\t\n<style>\n#gap-580746757 {\n  padding-top: 0px;\n}\n@media (min-width:550px) {\n  #gap-580746757 {\n    padding-top: 30px;\n  }\n}\n<\/style>\n\t<\/div>\n\t\n<div class=\"row\"  id=\"row-2036019557\">\n\n\t<div id=\"col-837431565\" class=\"col medium-4 small-12 large-4\"  >\n\t\t<div class=\"col-inner text-center\"  >\n\t\t\t\n\t\t\t\n\t<div class=\"img has-hover x md-x lg-x y md-y lg-y\" id=\"image_1458785324\">\n\t\t\t\t\t\t\t\t<div class=\"img-inner dark\" >\n\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"512\" height=\"717\" src=\"https:\/\/en.rmutr.ac.th\/ece\/wp-content\/uploads\/2022\/11\/LINE_ALBUM_2022.11.15_221115_7-1.jpg\" class=\"attachment-original size-original\" alt=\"\" srcset=\"https:\/\/en.rmutr.ac.th\/ece\/wp-content\/uploads\/2022\/11\/LINE_ALBUM_2022.11.15_221115_7-1.jpg 512w, https:\/\/en.rmutr.ac.th\/ece\/wp-content\/uploads\/2022\/11\/LINE_ALBUM_2022.11.15_221115_7-1-214x300.jpg 214w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\n<style>\n#image_1458785324 {\n  width: 74%;\n}\n@media (min-width:550px) {\n  #image_1458785324 {\n    width: 82%;\n  }\n}\n<\/style>\n\t<\/div>\n\t\n\t\t<\/div>\n\t\t\t<\/div>\n\n\t\n\n\t<div id=\"col-16661116\" class=\"col medium-8 small-12 large-8\"  >\n\t\t<div class=\"col-inner\"  >\n\t\t\t\n\t\t\t\n<h2>\u0e23\u0e28.\u0e14\u0e23. \u0e1e\u0e34\u0e0a\u0e34\u0e15 \u0e01\u0e34\u0e15\u0e15\u0e34\u0e2a\u0e38\u0e27\u0e23\u0e23\u0e13\u0e4c<\/h2>\n<p>\u0e04\u0e27\u0e32\u0e21\u0e40\u0e0a\u0e35\u0e48\u0e22\u0e27\u0e0a\u0e32\u0e0d: Non-Gaussian Models, Estimation Theory and Wavelet-Based Signal Processing<br \/>\n\u0e27\u0e38\u0e12\u0e34\u0e01\u0e32\u0e23\u0e28\u0e36\u0e01\u0e29\u0e32:<br \/>\n\u0e1b\u0e23\u0e34\u0e0d\u0e0d\u0e32\u0e40\u0e2d\u0e01 : \u0e27\u0e34\u0e28\u0e27\u0e01\u0e23\u0e23\u0e21\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e14\u0e38\u0e29\u0e0e\u0e35\u0e1a\u0e31\u0e13\u0e11\u0e34\u0e15 (\u0e27\u0e34\u0e28\u0e27\u0e01\u0e23\u0e23\u0e21\u0e44\u0e1f\u0e1f\u0e49\u0e32) \u0e08\u0e38\u0e2c\u0e32\u0e25\u0e07\u0e01\u0e23\u0e13\u0e4c\u0e21\u0e2b\u0e32\u0e27\u0e34\u0e17\u0e22\u0e32\u0e25\u0e31\u0e22<\/p>\n<p>\u0e1b\u0e23\u0e34\u0e0d\u0e0d\u0e32\u0e42\u0e17 : \u0e27\u0e34\u0e28\u0e27\u0e01\u0e23\u0e23\u0e21\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e21\u0e2b\u0e32\u0e1a\u0e31\u0e13\u0e11\u0e34\u0e15 (\u0e27\u0e34\u0e28\u0e27\u0e01\u0e23\u0e23\u0e21\u0e44\u0e1f\u0e1f\u0e49\u0e32) \u0e08\u0e38\u0e2c\u0e32\u0e25\u0e07\u0e01\u0e23\u0e13\u0e4c\u0e21\u0e2b\u0e32\u0e27\u0e34\u0e17\u0e22\u0e32\u0e25\u0e31\u0e22<\/p>\n<p>\u0e1b\u0e23\u0e34\u0e0d\u0e0d\u0e32\u0e15\u0e23\u0e35 : \u0e27\u0e34\u0e28\u0e27\u0e01\u0e23\u0e23\u0e21\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e1a\u0e31\u0e13\u0e11\u0e34\u0e15 (\u0e27\u0e34\u0e28\u0e27\u0e01\u0e23\u0e23\u0e21\u0e44\u0e1f\u0e1f\u0e49\u0e32) \u0e21\u0e2b\u0e32\u0e27\u0e34\u0e17\u0e22\u0e32\u0e25\u0e31\u0e22\u0e40\u0e01\u0e29\u0e15\u0e23\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c<br \/>\n\u0e40\u0e1a\u0e2d\u0e23\u0e4c\u0e15\u0e34\u0e14\u0e15\u0e48\u0e2d: 02-4416000 \u0e15\u0e48\u0e2d 2631 \u2013 2632<br \/>\n\u0e2d\u0e35\u0e40\u0e21\u0e25: pichid.kit@rmutr.ac.th<\/p>\n\t\t<\/div>\n\t\t\n<style>\n#col-16661116 > .col-inner {\n  padding: 0px 0px 0px 0px;\n  margin: 0px 0px -4px 0px;\n}\n<\/style>\n\t<\/div>\n\n\t\n<\/div>\n\t<div id=\"gap-2004151294\" class=\"gap-element clearfix\" style=\"display:block; height:auto;\">\n\t\t\n<style>\n#gap-2004151294 {\n  padding-top: 4px;\n}\n@media (min-width:550px) {\n  #gap-2004151294 {\n    padding-top: 30px;\n  }\n}\n<\/style>\n\t<\/div>\n\t\n<div class=\"row\"  id=\"row-175618976\">\n\n\t<div id=\"col-1890367710\" class=\"col small-12 large-12\"  >\n\t\t<div class=\"col-inner\"  >\n\t\t\t\n\t\t\t\n<div class=\"row\"  id=\"row-1829132409\">\n\n\t<div id=\"col-390860639\" class=\"col small-12 large-12\"  >\n\t\t<div class=\"col-inner\" style=\"background-color:rgb(255,255,255);\" >\n\t\t\t\n\t\t\t\n\t<section class=\"section dark\" id=\"section_1294691744\">\n\t\t<div class=\"bg section-bg fill bg-fill  bg-loaded\" >\n\n\t\t\t\n\t\t\t\n\t\t\t\n\t<div class=\"is-border\"\n\t\tstyle=\"border-width:0px 0px 0px 0px;margin:0px 0px 0px 0px;\">\n\t<\/div>\n\n\t\t<\/div>\n\n\t\t<div class=\"section-content relative\">\n\t\t\t\n<h3 style=\"text-align: center;\">\u0e1c\u0e25\u0e07\u0e32\u0e19\u0e27\u0e34\u0e08\u0e31\u0e22\u0e41\u0e25\u0e30\u0e1a\u0e23\u0e34\u0e2b\u0e32\u0e23\u0e42\u0e04\u0e23\u0e07\u0e01\u0e32\u0e23\u0e27\u0e34\u0e08\u0e31\u0e22<\/h3>\n\t\t<\/div>\n\n\t\t\n<style>\n#section_1294691744 {\n  padding-top: 30px;\n  padding-bottom: 30px;\n  background-color: #446084;\n}\n<\/style>\n\t<\/section>\n\t\n\t\t<\/div>\n\t\t\t<\/div>\n\n\t\n<\/div>\n\t\t<\/div>\n\t\t\n<style>\n#col-1890367710 > .col-inner {\n  padding: 0px 0px 0px 0px;\n  margin: 0px 0px -31px 0px;\n}\n<\/style>\n\t<\/div>\n\n\t\n<\/div>\n<div class=\"accordion\" rel=\"\">\n<div class=\"accordion-item\"><a href=\"#\" class=\"accordion-title plain\"><button class=\"toggle\"><i class=\"icon-angle-down\"><\/i><\/button><span>INTERNATIONAL JOURNALS<\/span><\/a><div class=\"accordion-inner\">\n<ol>\n<li>Kittisuwan, P. (2015). Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model. Fluct. Noise Lett., 14 (2), 1550017 (12 pages).<\/li>\n<li>Kittisuwan, P. (2015). Simple Form of MMSE Estimator for Super-Gaussian Prior Densities. Fluct. Noise Lett., 14 (3), 1550025 (7 pages).<\/li>\n<li>Kittisuwan, P. (2015). Image Denoising via Analytical Form of Adaptive Generalized Gaussian Random Vectors in AWGN with MMSE Estimator for Local Adaptive Parameter. Int. J. Wavelets, Multiresolution Inf. Process., 13 (4), 1550026 (12 pages).<\/li>\n<li>Kittisuwan, P. (2016). Medical Image Denoising Using Simple Form of MMSE Estimation in Poisson-Gaussian Noise Model. Int. J. Biomathematics, 9 (2), 1650020 (9 pages).<\/li>\n<li>Kittisuwan, P. (2015). Image Denosing via Bayesian Estimation of Local Variance with Maxwell Density Prior. J. Multiscale Modeling, 6 (2), 1650002 (10 pages).<\/li>\n<li>Kittisuwan, P. (2016). Image Enhancement via MMSE Estimation of Gaussian Scale Mixture with Maxwell Density in AWGN. J. Innovative Optic. Health Sci., 9 (2), 1650021 (8 pages).<\/li>\n<li>Kittisuwan, P. and Chinrungrueng, C. (2017). Differential Form of Bivariate MMSE Estimator Based on Gaussian Noise. J. Circuits Systems Computers, 26 (1), 1750008 (12 pages).<\/li>\n<li>Kittisuwan, P. (2018). Novel Form of MMSE Estimation via Exact Moment Value of Noise and Higher Order Taylor Series in AWGN. IEEE Geosci. Remote Sens. Lett., 15 (3), 394\u2014398.<\/li>\n<li>Kittisuwan, P. (2018). Speckle Noise Redundant of Medical Imaging via Logistic Density in Redundant Wavelet Domain. Inter. J. Artificial Intelligence Tools, 27 (2), 1850006 (15 pages).<\/li>\n<li>Kittisuwan, P. (2018). Textural Region Denoising: Application in Agriculture. Inter. J. Image Graphics, 18 (4), 1850024 (11 pages).<\/li>\n<li>Kittisuwan, P. (2018). Low-Complexity Image Denoising Based on Mixture Model and Simple Form of MMSE Estimation. Inter. J. Wavelets Multiresolution Inf. Process., 16 (5), 1850052 (12 pages).<\/li>\n<li>Kittisuwan, P. (2020). Analytical and Simple Form of Shrinkage Functions for Non-Convex Penalty Functions in Fused Lasso Algorithm. Inter. J. Artificial Intelligence Tools, 29 (6), 2050020 (15 pages).<\/li>\n<li>Kittisuwan, P. and Thaiwirot, W. (2022). Novel Non-Convex Regularization for Generating Double Threshold Value in Penalized Least Squares Regression. Fluct. Noise Lett., 21 (6), 2250056 (15 pages).<\/li>\n<li>Kittisuwan, P. (2022). Relation between Penalized Least Squares Regression and Bayesian Estimation in AWGN Based on Novel Penalty Function of Pareto Density. ICT Express, Article in Press, doi: 10.1016\/j.icte.2022.01.012.<\/li>\n<\/ol>\n<\/div><\/div>\n<div class=\"accordion-item\"><a href=\"#\" class=\"accordion-title plain\"><button class=\"toggle\"><i class=\"icon-angle-down\"><\/i><\/button><span>INTERNATIONAL CONFERENCES<\/span><\/a><div class=\"accordion-inner\">\n<\/div><\/div>\n<div class=\"accordion-item\"><a href=\"#\" class=\"accordion-title plain\"><button class=\"toggle\"><i class=\"icon-angle-down\"><\/i><\/button><span>NATIONAL JOURNALS<\/span><\/a><div class=\"accordion-inner\">\n<\/div><\/div>\n<\/div>\n<div class=\"accordion\" rel=\"\">\n<div class=\"accordion-item\"><a href=\"#\" class=\"accordion-title plain\"><button class=\"toggle\"><i class=\"icon-angle-down\"><\/i><\/button><span>NATIONAL CONFERENCES<\/span><\/a><div class=\"accordion-inner\">\n<\/div><\/div>\n<div class=\"accordion-item\"><a href=\"#\" class=\"accordion-title plain\"><button class=\"toggle\"><i class=\"icon-angle-down\"><\/i><\/button><span>\u0e1a\u0e23\u0e34\u0e2b\u0e32\u0e23\u0e42\u0e04\u0e23\u0e07\u0e01\u0e32\u0e23\u0e27\u0e34\u0e08\u0e31\u0e22<\/span><\/a><div class=\"accordion-inner\">\n<\/div><\/div>\n<\/div>\n\t<div id=\"gap-43783750\" class=\"gap-element clearfix\" style=\"display:block; height:auto;\">\n\t\t\n<style>\n#gap-43783750 {\n  padding-top: 30px;\n}\n<\/style>\n\t<\/div>\n\t\n\t<section class=\"section dark\" id=\"section_1662215958\">\n\t\t<div class=\"bg section-bg fill bg-fill  bg-loaded\" >\n\n\t\t\t\n\t\t\t\n\t\t\t\n\t<div class=\"is-border\"\n\t\tstyle=\"border-width:0px 0px 0px 0px;margin:0px 0px 0px 0px;\">\n\t<\/div>\n\n\t\t<\/div>\n\n\t\t<div class=\"section-content relative\">\n\t\t\t\n<h3 style=\"text-align: center;\">\u0e1c\u0e25\u0e07\u0e32\u0e19\u0e2b\u0e19\u0e31\u0e07\u0e2a\u0e37\u0e2d \u0e15\u0e33\u0e23\u0e32 \u0e40\u0e2d\u0e01\u0e2a\u0e32\u0e23\u0e1b\u0e23\u0e30\u0e01\u0e32\u0e23\u0e2a\u0e2d\u0e19 \u0e41\u0e25\u0e30\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e1a\u0e31\u0e15\u0e23<\/h3>\n\t\t<\/div>\n\n\t\t\n<style>\n#section_1662215958 {\n  padding-top: 15px;\n  padding-bottom: 15px;\n  background-color: #7a9c59;\n}\n@media (min-width:550px) {\n  #section_1662215958 {\n    padding-top: 29px;\n    padding-bottom: 29px;\n  }\n}\n<\/style>\n\t<\/section>\n\t\n\t<div id=\"gap-1197514361\" class=\"gap-element clearfix\" style=\"display:block; height:auto;\">\n\t\t\n<style>\n#gap-1197514361 {\n  padding-top: 30px;\n}\n<\/style>\n\t<\/div>\n\t\n<div class=\"accordion\" rel=\"\">\n<div class=\"accordion-item\"><a href=\"#\" class=\"accordion-title plain\"><button class=\"toggle\"><i class=\"icon-angle-down\"><\/i><\/button><span>\u0e2b\u0e19\u0e31\u0e07\u0e2a\u0e37\u0e2d<\/span><\/a><div class=\"accordion-inner\">\n<ol>\n<li>\u0e1e\u0e34\u0e0a\u0e34\u0e15 \u0e01\u0e34\u0e15\u0e15\u0e34\u0e2a\u0e38\u0e27\u0e23\u0e23\u0e13\u0e4c .(2561). \u0e40\u0e27\u0e1f\u0e40\u0e25\u0e47\u0e15 \u0e01\u0e32\u0e23\u0e27\u0e34\u0e40\u0e04\u0e23\u0e32\u0e30\u0e2b\u0e4c\u0e41\u0e25\u0e30\u0e01\u0e32\u0e23\u0e2d\u0e2d\u0e01\u0e41\u0e1a\u0e1a. \u0e1e\u0e34\u0e21\u0e1e\u0e4c\u0e04\u0e23\u0e31\u0e49\u0e07\u0e17\u0e35\u0e48 2.<br \/>\u0e01\u0e23\u0e38\u0e07\u0e40\u0e17\u0e1e\u0e2f: \u0e17\u0e23\u0e34\u0e1b\u0e40\u0e1e\u0e34\u0e49\u0e25\u0e01\u0e23\u0e38\u0e4a\u0e1b. 148 \u0e2b\u0e19\u0e49\u0e32.\u00a0<\/li>\n<li>\u0e1e\u0e34\u0e0a\u0e34\u0e15 \u0e01\u0e34\u0e15\u0e15\u0e34\u0e2a\u0e38\u0e27\u0e23\u0e23\u0e13\u0e4c .(2561). \u0e04\u0e27\u0e32\u0e21\u0e19\u0e48\u0e32\u0e08\u0e30\u0e40\u0e1b\u0e47\u0e19 \u0e01\u0e32\u0e23\u0e1b\u0e23\u0e30\u0e21\u0e32\u0e13 \u0e41\u0e25\u0e30\u0e01\u0e32\u0e23\u0e25\u0e14\u0e2a\u0e31\u0e0d\u0e0d\u0e32\u0e13\u0e23\u0e1a\u0e01\u0e27\u0e19. \u0e01\u0e23\u0e38\u0e07\u0e40\u0e17\u0e1e\u0e2f: \u0e17\u0e23\u0e34\u0e1b\u0e40\u0e1e\u0e34\u0e49\u0e25\u0e01\u0e23\u0e38\u0e4a\u0e1b. 165 \u0e2b\u0e19\u0e49\u0e32.\u00a0<\/li>\n<\/ol>\n<p>\u00a0<\/p>\n<\/div><\/div>\n<div class=\"accordion-item\"><a href=\"#\" class=\"accordion-title plain\"><button class=\"toggle\"><i class=\"icon-angle-down\"><\/i><\/button><span>\u0e15\u0e33\u0e23\u0e32<\/span><\/a><div class=\"accordion-inner\">\n<\/div><\/div>\n<div class=\"accordion-item\"><a href=\"#\" class=\"accordion-title plain\"><button class=\"toggle\"><i class=\"icon-angle-down\"><\/i><\/button><span>\u0e40\u0e2d\u0e01\u0e2a\u0e32\u0e23\u0e1b\u0e23\u0e30\u0e01\u0e2d\u0e1a\u0e01\u0e32\u0e23\u0e2a\u0e2d\u0e19<\/span><\/a><div class=\"accordion-inner\">\n<\/div><\/div>\n<\/div>\n<div class=\"accordion\" rel=\"\">\n<div class=\"accordion-item\"><a href=\"#\" class=\"accordion-title plain\"><button class=\"toggle\"><i class=\"icon-angle-down\"><\/i><\/button><span>\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e1a\u0e31\u0e15\u0e23<\/span><\/a><div class=\"accordion-inner\">\n<\/div><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-893","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/en.rmutr.ac.th\/ece\/index.php?rest_route=\/wp\/v2\/pages\/893","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/en.rmutr.ac.th\/ece\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/en.rmutr.ac.th\/ece\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/en.rmutr.ac.th\/ece\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/en.rmutr.ac.th\/ece\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=893"}],"version-history":[{"count":18,"href":"https:\/\/en.rmutr.ac.th\/ece\/index.php?rest_route=\/wp\/v2\/pages\/893\/revisions"}],"predecessor-version":[{"id":1158,"href":"https:\/\/en.rmutr.ac.th\/ece\/index.php?rest_route=\/wp\/v2\/pages\/893\/revisions\/1158"}],"wp:attachment":[{"href":"https:\/\/en.rmutr.ac.th\/ece\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=893"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}