'Algorithm overview' diaporamas de présentation

Partial Shape Matching

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Computing 2D Constrained Delaunay Triangulation Using the GPU

Computing 2D Constrained Delaunay Triangulation Using the GPU. Speaker: Meng Qi co-authors : Thanh-Tung Cao, Tiow-Seng Tan. March 9, 2012. Outline. Background Motivation & Algorithm Overview GPU-CDT Proof & Analysis Experiment Results. Background.

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Creating Competitive Products

Creating Competitive Products. Qian Wan [1] , Raymond Chi-Wing Wong [1] , Ihab F. Ilyas [2] , M. Tamer Ozsu [2] , Yu Peng [1] [1] Hong Kong University of Science and Technology [2] University of Waterloo Presented by Qian Wan Prepared by Qian Wan. Outline. Background

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Scale-Space Representations and their Applications to 3D Matching of Solid Models

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Steven Goodman GOES-R Program Senior Scientist NOAA/NESDIS/GOES-R Program Office

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1 Steven Goodman, 2 R. Blakeslee, 2 W. Koshak, 2 W. A. Petersen, 3 L. Carey, and 3 D. Mach

The Geostationary Lightning Mapper (GLM) on the GOES-R Series: A new operational capability to improve storm forecasts and warnings. 1 Steven Goodman, 2 R. Blakeslee, 2 W. Koshak, 2 W. A. Petersen, 3 L. Carey, and 3 D. Mach

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Multi-Guarded Safe Zone: An Effective Technique to Monitor Moving Circular Range Queries

Multi-Guarded Safe Zone: An Effective Technique to Monitor Moving Circular Range Queries. Presented By: Muhammad Aamir Cheema 1 Joint work with Ljiljana Brankovic 2 , Xuemin Lin 1 , Wenjie Zhang 1 , Wei Wang 1. 1 University of New South Wales, Australia

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AES Microcode Implementation In IXP2400 And A study of Reconfigurable Crypto Unit

AES Microcode Implementation In IXP2400 And A study of Reconfigurable Crypto Unit. Piyush Ranjan Satapathy CS203B Class Project Presentation. Road Map. AES Algorithm Overview IXP2400 Platform: A Quick Look Microcode: Overview Implementation of AES Experimental Results

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Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade

Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade. Paul viola and Michael Jones. Outline. Motivation and objective Definition of feature vectors Asymmetric Adaboosting Experiments and Results Conclusions. Motivation.

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Solving Jigsaw Puzzles

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Tracking Face Orientation

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Planning Among Movable Obstacles with Artificial Constraints

Planning Among Movable Obstacles with Artificial Constraints. by: Mike Stilman and James Kuffner. Presented by: Deborah Meduna and Michael Vitus. Motivation. http://www.cs.cmu.edu/~mstilman/mov/WAFR06-Planning.avi. Outline. Problem Definition Main Topics TRANSIT TRANSFER

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Functional Top Down Approach to Bracing

Functional Top Down Approach to Bracing. RGOs (Reciprocal Gait Orthoses ) HKAFOs (Hip Knee Ankle Foot Orthoses ) KAFOs (Knee Ankle Foot Orthoses ) AFOs (Ankle Foot Orthoses ) FOs (Foot Orthoses ). DISCLAIMER.

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Network operators vs. user's perspective Algorithm overview Experience Tradition/tutorialoutletdotcom

FOR MORE CLASSES VISIT www.tutorialoutlet.com Traffic routing: Network operators vs. user's perspective 1. Algorithm overview The objective of this project is to find optimal control variables allocating traffic to various links connecting source S with destination D. We will consider two cases. In the first one the goal is to find an

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