'Scaling factor' presentation slideshows

Outline

Preliminary measurement of the total cross section in pp collisions at ?s=7 TeV with the ALFA subdetector of ATLAS Hasko Stenzel, JLU Giessen on behalf of the ATLAS Collaboration ConfNote : http://cds.cern.ch/record/1740971. Outline. Introduction Experimental setup: ALFA

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CSCE 441 Computer Graphics: 2D Transformations

CSCE 441 Computer Graphics: 2D Transformations. Jinxiang Chai. 2D Transformations. y. y. x. x. y. x. 2D Transformations. y. y. x. Applications: Animation Image/object manipulation Viewing transformation etc. x. y. x. 3. Applications of 2D Transformations.

809 views • 60 slides

Spectral Clustering

Spectral Clustering. Eyal David Image Processing seminar May 2008. Lecture Outline. Motivation Graph overview and construction Demo Spectral Clustering Demo Cool implementations. A Tutorial on Spectral ClusteringArik Azran. Dataset exhibits complex cluster shapes

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Spectral Clustering

Spectral Clustering. Course: Cluster Analysis and Other Unsupervised Learning Methods (Stat 593 E) Speakers: Rebecca Nugent 1, Larissa Stanberry 2 Department of 1 Statistics, 2 Radiology, University of Washington. Outline. What is spectral clustering?

1.33k views • 39 slides

Is the time right to add lightning-NO to operational air quality forecast models?

Is the time right to add lightning-NO to operational air quality forecast models?. Dale Allen Dept. of Atmos and Oceanic Sci, UMD-College Park Kenneth Pickering Atmos Chem and Dyn Branch, NASA-GSFC Robert Pinder and Thomas Pierce Atmos Modeling and Analysis Div, U.S. EPA William Koshak

497 views • 38 slides

Dr. Henry Hexmoor Department of Computer Science Southern Illinois University Carbondale

Network Theory: Computational Phenomena and Processes Social Network Analysis . Dr. Henry Hexmoor Department of Computer Science Southern Illinois University Carbondale. Degree, Indegree , Outdegree Centrality. Degree Centrality: Indegree Centrality: Outdegree Centrality:.

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474 views • 33 slides

Effort Estimation

Effort Estimation. In WBS we said that one can estimate effort ( micro-level ) but needed to know: Size of the deliverable Productivity of resource in producing that deliverable. Effort = Size / Productivity e.g. Effort = 500 loc / (100 loc/person-day) = 5 person-days.

348 views • 24 slides

Data Assimilation for Very Short-Range Forecasting in COSMO

Data Assimilation for Very Short-Range Forecasting in COSMO Christoph Schraff Deutscher Wetterdienst, Offenbach, Germany. operational : radar-derived precipitation rates by latent heat nudging in development : LETKF NWP for nowcasting : 2 examples. COSMO-DE : ?x = 2.8 km

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Example

6. 2. 5. 1. Example. What are the real world coord. of the object in Fig.1a (centered in the window)? Calculate display coord. for the object in the viewport centered in the screen below (Fig.1b). 1000. (0,0). Real world window. 225. 750. 400. screen. viewport. Fig.1a. Fig.1b. 6.

369 views • 24 slides

Valley Splitting Theory for Quantum Wells and Shallow Donors in Silicon

Valley Splitting Theory for Quantum Wells and Shallow Donors in Silicon Mark Friesen, University of Wisconsin-Madison. International Workshop on ESR and Related Phenomena in Low-D Structures Sanremo, March 6-8, 2006. Valley Splitting: An Old Problem. (Fowler, et al., 1966).

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A Time Series-based Approach for Power Management in Mobile Processors and Disks

A Time Series-based Approach for Power Management in Mobile Processors and Disks. X. Liu, P. Shenoy and W. Gong Presented by Dai Lu. Contents. Introduction Time Series based Power Management Utilization Measurement Prediction Model Speed Setting Strategy Implementation Evaluation

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Gradient Edge Detection Predictor for Image Lossless Compression

Gradient Edge Detection Predictor for Image Lossless Compression. Author ? Aleksej Avramovi? , Branimir Reljin ELMAR, 2010 PROCEEDINGS Speaker ? Liu, Yi- Hsien. Outline. Introduction Predictive Coding Compression Efficiency Median Edge Detector Gradient Adjusted Predictor

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CCITT G.726 (ADPCM)

CCITT G.726 (ADPCM). ???? : ??? ???? : ??? ???? :2002/01/10. Outline. Introduction to the recommendation G.726 Description of the ADPCM algorithm Principles and functional descriptions of the ADPCM encoding and decoding. Encoder. Simplified block diagrams. Decoder.

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Converting Measurements

Converting Measurements. CA-ICA-8c: Demonstrate the concept of recipe conversions by identifying and converting recipe formulas to increase or decrease yields. What is Converting?. When the yield of a recipe is not the same as the amount of product needed

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A Cash Flow Based-Approach To Estimate Default Probabilities

A Cash Flow Based-Approach To Estimate Default Probabilities. Arturo Cifuentes (*) Academic Director, CREM arturo.cifuentes@fen.uchile.cl (*) Joint Project with Francisco Hawas , Math Department, University of Chile. Santiago, Chile--January 2014.

254 views • 15 slides

On The BSS Max Idle Period

On The BSS Max Idle Period. Date: 2012-03-13. Authors:. Motivation. BSS Max Idle Period indicates duration during which AP maintains association with a STA active even though it does not receive frames A STA can sleep for Max Idle Period without being disassociated

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Card 1a

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P12215 Wandering Ambassador

P12215 Wandering Ambassador. Week 11 Demo / Test Results Verification. Demo Agenda. Review of Test Results Demo of all devices functioning Demo of functioning diagnostics UI Boot up with the customer Demo of Quick Disconnect. Customer needs. Voltage Regulator Test Results.

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Mathematical Functions when Plotting in PSpice

Mathematical Functions when Plotting in PSpice. Example Circuit. Voltages Plotted in PSpice. Magnitude of the voltage across the 10 W resistor is too small to allow the signal to be resolved, given the scaling factor used. To obtain a clean plot area. To add new traces to the plot.

199 views • 11 slides