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Colorado - ASTR - 5770
The Sunyaev-Zeldovich EffectJason Glenn APSHistorical Perspective Physics of the SZ Effect -Previous Observations & Results Bolocam Imminent Experiments Future Work ReferencesCMB discovered in 1964 by Penzias and Wilson COBE 1989: perfect blackbo
Wisconsin - CS - 0601
Proximal Plane ClassificationKDD 2001 San Francisco August 26-29, 2001Glenn Fung & Olvi MangasarianData Mining InstituteUniversity of Wisconsin - Madison Second Annual Review June 1, 2001Key ContributionsFast new support vector machine class
Wisconsin - REV - 0601
Proximal Plane ClassificationKDD 2001 San Francisco August 26-29, 2001Glenn Fung & Olvi MangasarianData Mining InstituteUniversity of Wisconsin - Madison Second Annual Review June 1, 2001Key ContributionsFast new support vector machine class
St. Johns River Community College - CS - 0601
Proximal Plane ClassificationKDD 2001 San Francisco August 26-29, 2001Glenn Fung & Olvi MangasarianData Mining InstituteUniversity of Wisconsin - Madison Second Annual Review June 1, 2001Key ContributionsFast new support vector machine class
Wisconsin - CS - 0600
Concave Minimization for Support Vector Machine ClassifiersUnlabeled Data Classification & Data SelectionGlenn Fung O. L. MangasarianPart 1: Unlabeled Data Classifications ssssGiven a large unlabeled dataset Use a k-Median clustering al
Wisconsin - REV - 0600
Concave Minimization for Support Vector Machine ClassifiersUnlabeled Data Classification & Data SelectionGlenn Fung O. L. MangasarianPart 1: Unlabeled Data Classifications ssssGiven a large unlabeled dataset Use a k-Median clustering al
St. Johns River Community College - CS - 0600
Concave Minimization for Support Vector Machine ClassifiersUnlabeled Data Classification & Data SelectionGlenn Fung O. L. MangasarianPart 1: Unlabeled Data Classifications ssssGiven a large unlabeled dataset Use a k-Median clustering al
Wisconsin - ECE - 756
Linear Programming and CPLEXTing-Yuan Wang Advisor: Charlie C. ChenDepartment of Electrical and Computer Engineering University of Wisconsin-MadisonFeb. 22 2000CPLEX Optimization Options: Primal, Dual Simplex Methods Network Flow Problems MI
St. Johns River Community College - ECE - 756
Linear Programming and CPLEXTing-Yuan Wang Advisor: Charlie C. ChenDepartment of Electrical and Computer Engineering University of Wisconsin-MadisonFeb. 22 2000CPLEX Optimization Options: Primal, Dual Simplex Methods Network Flow Problems MI
Wisconsin - ECE - 03
Artifact and Textured region Detection- Vishal BangardOutline Need for artifact and textured region detection Aim of the project Techniques used in the imaging world Approaches used Results ConclusionWhy do artifact detection ? A lot of
Wisconsin - ECE - 738
Artifact and Textured region Detection- Vishal BangardOutline Need for artifact and textured region detection Aim of the project Techniques used in the imaging world Approaches used Results ConclusionWhy do artifact detection ? A lot of
St. Johns River Community College - ECE - 03
Artifact and Textured region Detection- Vishal BangardOutline Need for artifact and textured region detection Aim of the project Techniques used in the imaging world Approaches used Results ConclusionWhy do artifact detection ? A lot of
St. Johns River Community College - ECE - 738
Artifact and Textured region Detection- Vishal BangardOutline Need for artifact and textured region detection Aim of the project Techniques used in the imaging world Approaches used Results ConclusionWhy do artifact detection ? A lot of
Wisconsin - ECE - 03
Unequal Error Protection for Video Transmission over Wireless ChannelsECE738 Project PresentationChang, Hong Hong 05/09/20031OutlineUnequal Error Protection/ Unequal Loss Protection Problem Formulation Channel Model RS code Theoretical Res
Wisconsin - ECE - 738
Unequal Error Protection for Video Transmission over Wireless ChannelsECE738 Project PresentationChang, Hong Hong 05/09/20031OutlineUnequal Error Protection/ Unequal Loss Protection Problem Formulation Channel Model RS code Theoretical Res
St. Johns River Community College - ECE - 03
Unequal Error Protection for Video Transmission over Wireless ChannelsECE738 Project PresentationChang, Hong Hong 05/09/20031OutlineUnequal Error Protection/ Unequal Loss Protection Problem Formulation Channel Model RS code Theoretical Res
St. Johns River Community College - ECE - 738
Unequal Error Protection for Video Transmission over Wireless ChannelsECE738 Project PresentationChang, Hong Hong 05/09/20031OutlineUnequal Error Protection/ Unequal Loss Protection Problem Formulation Channel Model RS code Theoretical Res
Wisconsin - ECE - 03
Portraiture MorphingPresented by Fung, Chau-ha JeniceOutline Problem Statement Prior Art: Portraiture Morphing Approaches Results Conclusion Future WorksProblem Statement Image morphing = Image metamorhposis Creating a smooth transfor
Wisconsin - ECE - 738
Portraiture MorphingPresented by Fung, Chau-ha JeniceOutline Problem Statement Prior Art: Portraiture Morphing Approaches Results Conclusion Future WorksProblem Statement Image morphing = Image metamorhposis Creating a smooth transfor
St. Johns River Community College - ECE - 03
Portraiture MorphingPresented by Fung, Chau-ha JeniceOutline Problem Statement Prior Art: Portraiture Morphing Approaches Results Conclusion Future WorksProblem Statement Image morphing = Image metamorhposis Creating a smooth transfor
St. Johns River Community College - ECE - 738
Portraiture MorphingPresented by Fung, Chau-ha JeniceOutline Problem Statement Prior Art: Portraiture Morphing Approaches Results Conclusion Future WorksProblem Statement Image morphing = Image metamorhposis Creating a smooth transfor
Wisconsin - ECE - 03
ECE 738 ProjectBrain segmentation and Phase unwrapping in MRI dataJongHoon LeeOutline Nature of fast MRI: EPI & Field Inhomogeneity Background problem Image Distortion Specific problems a) Brain Segmentation b) Phase Unwrapping Goal Appro
Wisconsin - ECE - 738
ECE 738 ProjectBrain segmentation and Phase unwrapping in MRI dataJongHoon LeeOutline Nature of fast MRI: EPI & Field Inhomogeneity Background problem Image Distortion Specific problems a) Brain Segmentation b) Phase Unwrapping Goal Appro
St. Johns River Community College - ECE - 03
ECE 738 ProjectBrain segmentation and Phase unwrapping in MRI dataJongHoon LeeOutline Nature of fast MRI: EPI & Field Inhomogeneity Background problem Image Distortion Specific problems a) Brain Segmentation b) Phase Unwrapping Goal Appro
St. Johns River Community College - ECE - 738
ECE 738 ProjectBrain segmentation and Phase unwrapping in MRI dataJongHoon LeeOutline Nature of fast MRI: EPI & Field Inhomogeneity Background problem Image Distortion Specific problems a) Brain Segmentation b) Phase Unwrapping Goal Appro
Wisconsin - ECE - 03
ECE738 Presentation of Project SurveyA survey of image-based biometric identification methods: Face, finger print, iris, and othersPresented by: David LinOutline Problems and motivations Different identification methods Face Recognition Fing
Wisconsin - ECE - 738
ECE738 Presentation of Project SurveyA survey of image-based biometric identification methods: Face, finger print, iris, and othersPresented by: David LinOutline Problems and motivations Different identification methods Face Recognition Fing
St. Johns River Community College - ECE - 03
ECE738 Presentation of Project SurveyA survey of image-based biometric identification methods: Face, finger print, iris, and othersPresented by: David LinOutline Problems and motivations Different identification methods Face Recognition Fing
St. Johns River Community College - ECE - 738
ECE738 Presentation of Project SurveyA survey of image-based biometric identification methods: Face, finger print, iris, and othersPresented by: David LinOutline Problems and motivations Different identification methods Face Recognition Fing
Wisconsin - ECE - 03
A survey of Face Recognition TechnologyWei-Yang Lin May 07, 2003Road Map Introduction Challenge in Face Recognition variation in pose Variation in illumination Some recently works in FRT DiscussionIntroduction FRT is a research area span
Wisconsin - ECE - 738
A survey of Face Recognition TechnologyWei-Yang Lin May 07, 2003Road Map Introduction Challenge in Face Recognition variation in pose Variation in illumination Some recently works in FRT DiscussionIntroduction FRT is a research area span
St. Johns River Community College - ECE - 03
A survey of Face Recognition TechnologyWei-Yang Lin May 07, 2003Road Map Introduction Challenge in Face Recognition variation in pose Variation in illumination Some recently works in FRT DiscussionIntroduction FRT is a research area span
St. Johns River Community College - ECE - 738
A survey of Face Recognition TechnologyWei-Yang Lin May 07, 2003Road Map Introduction Challenge in Face Recognition variation in pose Variation in illumination Some recently works in FRT DiscussionIntroduction FRT is a research area span
Wisconsin - ECE - 03
Medical Image Registration: A SurveyAiming LuOutline Introduction Transformation Algorithms Visualization Validation ConclusionIntroduction Image registration matching two images so that corresponding coordinate points in the two images
Wisconsin - ECE - 738
Medical Image Registration: A SurveyAiming LuOutline Introduction Transformation Algorithms Visualization Validation ConclusionIntroduction Image registration matching two images so that corresponding coordinate points in the two images
St. Johns River Community College - ECE - 03
Medical Image Registration: A SurveyAiming LuOutline Introduction Transformation Algorithms Visualization Validation ConclusionIntroduction Image registration matching two images so that corresponding coordinate points in the two images
St. Johns River Community College - ECE - 738
Medical Image Registration: A SurveyAiming LuOutline Introduction Transformation Algorithms Visualization Validation ConclusionIntroduction Image registration matching two images so that corresponding coordinate points in the two images
Wisconsin - ECE - 03
Detecting Artifacts and Textures in Wavelet Coded ImagesRajas A. Sambhare ECE 738, Spring 2003 Final ProjectJanuary 12, 2009Motivation Wavelet based image coders like JPEG 2000 lead to new types of artifacts when used at small bit-rates Bloc
Wisconsin - ECE - 738
Detecting Artifacts and Textures in Wavelet Coded ImagesRajas A. Sambhare ECE 738, Spring 2003 Final ProjectJanuary 12, 2009Motivation Wavelet based image coders like JPEG 2000 lead to new types of artifacts when used at small bit-rates Bloc
St. Johns River Community College - ECE - 03
Detecting Artifacts and Textures in Wavelet Coded ImagesRajas A. Sambhare ECE 738, Spring 2003 Final ProjectJanuary 12, 2009Motivation Wavelet based image coders like JPEG 2000 lead to new types of artifacts when used at small bit-rates Bloc
St. Johns River Community College - ECE - 738
Detecting Artifacts and Textures in Wavelet Coded ImagesRajas A. Sambhare ECE 738, Spring 2003 Final ProjectJanuary 12, 2009Motivation Wavelet based image coders like JPEG 2000 lead to new types of artifacts when used at small bit-rates Bloc
Wisconsin - ECE - 03
MPEG2 FGS ImplementationECE 738 Advanced Digital Image ProcessingAuthor: Deshan Yang05/01/2003Introduction of FGSFGS = fine granularity scalability For MPEG2 / MPEG4 and others Comparing to SNR, temporal, spatial scalability, FGS enhances vi
Wisconsin - ECE - 738
MPEG2 FGS ImplementationECE 738 Advanced Digital Image ProcessingAuthor: Deshan Yang05/01/2003Introduction of FGSFGS = fine granularity scalability For MPEG2 / MPEG4 and others Comparing to SNR, temporal, spatial scalability, FGS enhances vi
St. Johns River Community College - ECE - 03
MPEG2 FGS ImplementationECE 738 Advanced Digital Image ProcessingAuthor: Deshan Yang05/01/2003Introduction of FGSFGS = fine granularity scalability For MPEG2 / MPEG4 and others Comparing to SNR, temporal, spatial scalability, FGS enhances vi
St. Johns River Community College - ECE - 738
MPEG2 FGS ImplementationECE 738 Advanced Digital Image ProcessingAuthor: Deshan Yang05/01/2003Introduction of FGSFGS = fine granularity scalability For MPEG2 / MPEG4 and others Comparing to SNR, temporal, spatial scalability, FGS enhances vi
Wisconsin - ECE - 539
Using Clustering to Make Prediction Intervals For Neural NetworksClaus Benjaminsen ECE539 - final project fall 2005What is a prediction interval? Aninterval within which the true target value is predicted to be Prediction intervals are often
St. Johns River Community College - ECE - 539
Using Clustering to Make Prediction Intervals For Neural NetworksClaus Benjaminsen ECE539 - final project fall 2005What is a prediction interval? Aninterval within which the true target value is predicted to be Prediction intervals are often
Wisconsin - ECE - 539
Music ClassificationUsing Neural Networks Craig Dennis ECE 539Problem and Motivation Peoplehave hundreds of MP3s and other digital music files unclassified on their computer iTunes and other large digital music stores must classify thousands o
St. Johns River Community College - ECE - 539
Music ClassificationUsing Neural Networks Craig Dennis ECE 539Problem and Motivation Peoplehave hundreds of MP3s and other digital music files unclassified on their computer iTunes and other large digital music stores must classify thousands o
Wisconsin - ECE - 539
Determining College Football RankingsWith ClusteringWhere do we start? Look for statistics on the web This keeps data up to date smoother updates. Determine good statistic set Dont want too many so that data is redundant Dont want too few
St. Johns River Community College - ECE - 539
Determining College Football RankingsWith ClusteringWhere do we start? Look for statistics on the web This keeps data up to date smoother updates. Determine good statistic set Dont want too many so that data is redundant Dont want too few
Wisconsin - ECE - 539
Predicting the Winner of an NFL Football GameMatt Gray CS/ECE 539Reasons to Predict NFL Football is watched by millions of people every weekend during the season. Vast amounts of money invested in NFL Football Prediction Polls such as Weekly Fo
St. Johns River Community College - ECE - 539
Predicting the Winner of an NFL Football GameMatt Gray CS/ECE 539Reasons to Predict NFL Football is watched by millions of people every weekend during the season. Vast amounts of money invested in NFL Football Prediction Polls such as Weekly Fo
Wisconsin - ECE - 539
Who Cares About the Arts?Predicting Formal Arts Participation from Survey DataAngela Han ECE 539 December 2005Project Objective Applypattern classifier neural network to arts marketing survey data Use neural network as a predictive model to i
St. Johns River Community College - ECE - 539
Who Cares About the Arts?Predicting Formal Arts Participation from Survey DataAngela Han ECE 539 December 2005Project Objective Applypattern classifier neural network to arts marketing survey data Use neural network as a predictive model to i
Wisconsin - ECE - 539
Prediction of Voting Patterns Based on Census and Demographic DataAnalysis Performed by: Mike He ECE 539, Fall 2005Abstract Prediction of Voting Patterns in 2004Presidential Election Multi-Layer Perceptron, Back-Propagation Based on Demographi
St. Johns River Community College - ECE - 539
Prediction of Voting Patterns Based on Census and Demographic DataAnalysis Performed by: Mike He ECE 539, Fall 2005Abstract Prediction of Voting Patterns in 2004Presidential Election Multi-Layer Perceptron, Back-Propagation Based on Demographi
Wisconsin - ECE - 539
Playing Tic Tac Toe with Neural NetworksJustin Herbrand CS/ECE/ME 539Reason Entertainment Learn the basic idea behind other video games AI Learn what is going on behind the scenes of video gamesGoalBuild a game of tic tac toe that could p
St. Johns River Community College - ECE - 539
Playing Tic Tac Toe with Neural NetworksJustin Herbrand CS/ECE/ME 539Reason Entertainment Learn the basic idea behind other video games AI Learn what is going on behind the scenes of video gamesGoalBuild a game of tic tac toe that could p
Wisconsin - ECE - 539
Knowledge Mining and Soil Mapping using Maximum Likelihood Classifier with Gaussian Mixture ModelsECE539 final project Instructor: Yu Hen Hu Fall 2005Jian Liu 12/13/2005OverviewThis study deals with data mining from soil survey maps and soil m
St. Johns River Community College - ECE - 539
Knowledge Mining and Soil Mapping using Maximum Likelihood Classifier with Gaussian Mixture ModelsECE539 final project Instructor: Yu Hen Hu Fall 2005Jian Liu 12/13/2005OverviewThis study deals with data mining from soil survey maps and soil m