'Absolute error' presentation slideshows

On Video

On Video. 4c8. Video. A Background to Film, Video and Analogue & Digital TV/Video Formats Exploiting Temporal Redundancy is key to digital video processing A process called motion estimation or optical flow Video processing applications Focus on compression MPEG2/MPEG4 . Film.

737 views • 57 slides

CONTOURING

CONTOURING. By Ashish V. Deshpande Electrical Engineering Department The University of Texas at Arlington June 21 st 2004. OVERVIEW. Geometry of Curves Contour Properties Curve Fitting. CONTOUR. Edges are linked into a representation for a region boundary called contour

806 views • 50 slides

Matthew J. Bunkers WFO Rapid City, SD Last Updated 2/4/2002

Predicting Supercell Motion Using Hodograph Techniques. Matthew J. Bunkers WFO Rapid City, SD Last Updated 2/4/2002. Based on: Predicting Supercell Motion Using A New Hodograph Technique. Matthew J. Bunkers, UNR Brian A. Klimowski, UNR Jon W. Zeitler, HGX Richard L. Thompson, SPC

778 views • 48 slides

Mid-Decadal Global Land Survey

Mid-Decadal Global Land Survey. Jeff Masek Biospheric Sciences Branch, NASA GSFC Steve Covington Aerospace Corporation / USGS January 10, 2007. Mid-Decadal Global Land Survey (MDGLS).

568 views • 38 slides

Operational Numerical Forecasting on Tropical Cyclones

Operational Numerical Forecasting on Tropical Cyclones. Yuqing Wang International Pacific Research Center and Department of Meteorology University of Hawaii at Manoa, Honolulu 96822 Hawaii, USA. Outline. Introduction Background Numerical Weather Prediction Data Assimilation

526 views • 33 slides

CS 391L: Machine Learning Introduction

552 views • 32 slides

400 views • 30 slides

Radiative Forcing and Global Warming Potentials due to CH 4 and N 2 O

Workshop on common metrics to calculate the CO2 equivalence of anthropogenic greenhouse gas emissions by sources and removals by sinks. Radiative Forcing and Global Warming Potentials due to CH 4 and N 2 O. Hua Zhang Ruoyu Zhang. National Climate Center. China Meteorological Administration.

398 views • 28 slides

Hygrometry

Hygrometry. Methods for Measuring Humidity. Removal of Water Vapor from Moist Air. Addition of Water Vapor to Air. Electric Hygrometers. Mechanical Hygrometers. Spectroscopic Hygrometers. Removal of Water Vapor from Moist Air.

521 views • 25 slides

Predicting volatility: a comparative analysis between GARCH Models and Neural Network Models

Predicting volatility: a comparative analysis between GARCH Models and Neural Network Models. MCs Student: Miruna State Supervisor: Professor Moisa Altar - Bucharest, June 2002 -. Contents. Introduction Models for return series GARCH models Mixture Density Networks Aplication and results

598 views • 24 slides

Image Compression

Image Compression. Reference. [1] Gonzalez and Woods, Digital Image Processing. Objective. Reduce the number of bytes required to represent a digital image Redundant data reduction Remove patterns Uncorrelated data confirms redundant data elimination Auto correlation?.

650 views • 21 slides

NTP Performance Analysis

NTP Performance Analysis. David L. Mills University of Delaware http://www.eecis.udel.edu/~mills mailto:mills@udel.edu. Typical local clock phase offsets (1997 survey). Histogram of local clock absolute phase offsets 19,873 Internet peers surveyed running NTP Version 2 and 3

200 views • 19 slides

Lecture 18: The Modeling Environment

Lecture 18: The Modeling Environment. CE 498/698 and ERS 485 Principles of Water Quality Modeling. The modeling environment. Models are an idealized formulation that represents the response of a physical system to external stimuli (p. 10)

371 views • 18 slides

Tropical cyclone wind radii estimation using an empirical inland decay model

Tropical cyclone wind radii estimation using an empirical inland decay model. John Kaplan NOAA/Hurricane Research Division Miami, FL Mark DeMaria NOAA/NESDIS Fort Collins, CO Nicholas Carrasco CIMAS/HRD Miami,FL Jason Dunion NOAA/Hurricane Research Division Miami,FL

419 views • 17 slides

Slide Image Retrieval: A Preliminary Study

276 views • 16 slides

Project MLExAI Machine Learning Experiences in AI http://uhaweb.hartford.edu/compsci/ccli

Project MLExAI Machine Learning Experiences in AI http://uhaweb.hartford.edu/compsci/ccli. Ingrid Russell, University of Hartford Zdravko Markov, Central Connecticut State University Todd Neller, Gettysburg College. Project Goal.

351 views • 15 slides

Absolute Error

Absolute Error. The difference between the measured value and the true value is referred to as the absolute error. Assume that analysis of an iron ore by some method gave 11.1% while the true value was 12.1%, the absolute error is: 11.1% - 12.1% = -1.0% . Relative Error.

2.83k views • 15 slides

Chapter 5 Modeling & Analyzing Inputs

438 views • 14 slides

O&D Passenger Demand Forecasting

O&D Passenger Demand Forecasting. John Blankenbaker, Wassim Chaar DT Operations Research AGIFORS Reservations and YM 2003 Conference June 2 - 5, 2003 Honolulu, Hawaii. Forecasting Methods. Bottom-Up (Direct) Methods Independent time-series Correlated time-series models Top-Down Methods

286 views • 11 slides

Contact information : Polini Osipenko str, 20-1-91, 123007 Moscow, Russia kristuha@gmail.com ; phone: (7-915)423-3291

# 1570. EXTERNAL VALIDTION of the POPULATION MODELS for CARBAMAZEPINE PHARMACOKINETICS and the INDIVIDUALIZING CBZ DOSAGE REGIMEN PROCEDURE BONDAREVA K. student, Moscow State University, Department of Computational Mathematics and Cybernetics, Russia.

135 views • 1 slides