Ppt on 2d and 3d figures 2nd

Artifact Evaluation on Noisy Contents Gwanggil Jeon and Young-Sup Lee Department of Embedded Systems Engineering, Incheon National University, 12-1 Songdo-dong,

pp. 41 - 44, 2013 41 © SERSC 2013 http://www.mercubuana.ac.id Proceedings, The 2nd International Conference on Software Technology 3 2D Discrete Wavelet Transform Figure 1 shows the outline of 2D wavelet transform. x 1L [i,j] ↓2↓2 x 1, H1 [i,j] ↓2/Fang, J. Wu, and B. Huang, 2D sparse signal recovery via 2D orthogonal matching pursuit. Science China: Inf. Sci., 55: 889-897, (2012). 8.J. Wu, T. Li, T.-J. Hsieh, Y.-L. Chang, and B. Huang, Digital Signal Processor-based 3D Wavelet Error-resilient Lossless /


CAFCASS and the Judiciary -Unhealthy alliances-

of the animosity to the friends and/or extended family of the alienated parent. LEGAL CITATIONS Coursey v. Superior (Coursey), 194 Cal.App.3d 147,239 Cal.Rptr. 365 /2nd 407 Fla. 4th DCA 1985)1989 Parental fitness Krebsbach v. Gallagher, Supreme Court, App. Div., 181 A.D.2d 363; 587 N.Y.S. 2d 346, (1992). “Interference with the relationship between a child and/ but we do not have figures as they do not suffer! They are forbidden to collect figures for men! Child abuse and empire building The questions here are/


Copyright Infringement and Anti-Piracy Protecting Your Client’s Intellectual Property FALA Winter 2009 – New Orleans Clyde DeWitt and Gill Sperlein.

Law A&M Records v. Napster, Inc., 239 F.3d 1004 (9th Cir. 2001) Napster operated a system /2d Cir. N.Y. 2008). This is not an Internet case, thus the provisions of the DMCA did not apply. CSC Holdings made a remote DVR device that allowed users to record television shows on a remote device and play them back later. In overturning the trial court’s order granting plaintiff’s summary judgment motion, the 2nd/steps to identify them and bring suit against them. In order to work the settlement figure must be low /


1 Data Mining: Concepts and Techniques — Chapter 2 —

of icons Typical visualization methods Chernoff Faces Stick Figures General techniques Shape coding: Use shape to /and so on 50 Three-D Cone Trees 3D cone tree visualization technique works well for up to a thousand nodes or so First build a 2D/and R. Jain,” Similarity measures”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(9), 1999 E. R. Tufte. The Visual Display of Quantitative Information, 2nd ed., Graphics Press, 2001 C. Yu, et al., Visual data mining of multimedia data for social and/


M EDICAL I MAGING By Anuja Kulkarni 1000722132. I NTRODUCTION Medical imaging as the name suggests is the technique and process used to create images.

imaging i.e. JPEG2000 and JPIP. It proposes to demonstrate creation of 3D images of CT/MRI scan from a normal 2D one. It also shows some circumstances of neuroimaging i.e non-diagnostic medical imaging as in Figure 6. R EFERENCES 1./FMRI.jpg 3. M. Xu and L.H. Wang ; "Photoacoustic imaging in biomedicine". Review of Scientific Instruments 77 (4): 041101. doi:10.1063/1.2195024; 2006 4. Herman, G. T., Fundamentals of computerized tomography: Image reconstruction from projection, 2nd edition, Springer, 2009 5./


October 15, 2015Data Mining: Concepts and Techniques1 Data Mining: Concepts and Techniques — Chapter 2 — Jiawei Han, Micheline Kamber, and Jian Pei University.

, 2015Data Mining: Concepts and Techniques37 census data showing age, income, sex, education, etc. used by permission of G. Grinstein, University of Massachusettes at Lowell Stick Figures October 15, 2015Data Mining: Concepts and Techniques38 Hierarchical Techniques Visualization / and Techniques43 Tree-Map of a File System (Schneiderman) October 15, 2015Data Mining: Concepts and Techniques44 Three-D Cone Trees 3D cone tree visualization technique works well for up to a thousand nodes or so First build a 2D/


2016-6-1 1 Big Data Analysis and Mining Lecture 2: Getting to Know Your Data Weixiong Rao 饶卫雄 Tongji University 同济大学软件学院 2015 Fall

icons Typical visualization methods  Chernoff Faces  Stick Figures General techniques  Shape coding: Use shape to /and so on 46 Three-D Cone Trees 3D cone tree visualization technique works well for up to a thousand nodes or so First build a 2D/and R. Jain,” Similarity measures”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(9), 1999 E. R. Tufte. The Visual Display of Quantitative Information, 2nd ed., Graphics Press, 2001 C. Yu, et al., Visual data mining of multimedia data for social and/


HARMONIE a common effort of HIRLAM, ALADIN and LACE on high resolution modelling Jeanette Onvlee COSMO General Meeting Cracow, 20080916.

convection parametrization (either within EDMF or separate) 3D-turbulence scheme Tuning of microphysics Representation of /interfacing at low level in source code difficult. 2nd AROME training course, Lisbon, March 2008 16 /: Clear improvement of extended LAEF system Impact of bias correction and 2d moment calibration GLAMEPS Joint multi-model EPS system for HIRLAM &/frequent analysis) Preliminary results: - improvement for all fields (see figures on the left where red shades indicate that 3h cycling is /


1 Data Mining: Concepts and Techniques — Chapter 2 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign Simon Fraser.

of icons Typical visualization methods Chernoff Faces Stick Figures General techniques Shape coding: Use shape to /and so on 46 Three-D Cone Trees 3D cone tree visualization technique works well for up to a thousand nodes or so First build a 2D/and R. Jain,” Similarity measures”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(9), 1999 E. R. Tufte. The Visual Display of Quantitative Information, 2nd ed., Graphics Press, 2001 C. Yu, et al., Visual data mining of multimedia data for social and/


1 Data Mining: Concepts and Techniques — Chapter 2 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign Simon Fraser.

of icons Typical visualization methods Chernoff Faces Stick Figures General techniques Shape coding: Use shape to /and so on 46 Three-D Cone Trees 3D cone tree visualization technique works well for up to a thousand nodes or so First build a 2D/and R. Jain,” Similarity measures”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(9), 1999 E. R. Tufte. The Visual Display of Quantitative Information, 2nd ed., Graphics Press, 2001 C. Yu, et al., Visual data mining of multimedia data for social and/


1 Data Mining: Concepts and Techniques — Chapter 2 — Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign Simon Fraser.

of icons Typical visualization methods Chernoff Faces Stick Figures General techniques Shape coding: Use shape to /and so on 46 Three-D Cone Trees 3D cone tree visualization technique works well for up to a thousand nodes or so First build a 2D/and R. Jain,” Similarity measures”, IEEE Trans. on Pattern Analysis and Machine Intelligence, 21(9), 1999 E. R. Tufte. The Visual Display of Quantitative Information, 2nd ed., Graphics Press, 2001 C. Yu, et al., Visual data mining of multimedia data for social and/


Data, Information and Knowledge. What is Data? Data consists of raw facts and figures CONDONE Raw facts or raw figures but do not accept has no meaning.

Data consists of raw facts and figures CONDONE Raw facts or raw figures but do not accept has/. 2D/3D, Layering, Library of symbols, wireframe, simulation, dimensioning, prototyping 2x2 5.(b) For two marks must include design on computer and making object using computer driven machine (design and make 1) (object and process/to cover 4 developments to get full marks. (One mark for naming the item, 2nd mark for further amplification and the third mark for the benefit) MP3 player – allowing people to listen to /


Synthesis of Coastal Ocean Circulation Models and Satellite Altimetry: Opportunities & Challenges Coastal Altimetry Workshop, Data Assimilation & Modeling.

present) ALT tracks, 2003-2005 (figure courtesy P.T. Strub) Representer-based variational DA (w/ Egbert, Allen, Miller –/) Assumed data variance level =(0.05 m) 2 In this “2D” case (no alongshore variability): assimilation of surface currents may be more/ high expectation for SSH assimilation to improve location of eddies and fronts, in the fully 3D case) DA (twin experiment, or OSSE): assimilate alongshore/ 1st sampling: 6-13 July 2007 2nd sampling: 14-17 September 2007 3rd sampling: 23-27 November 2007/


Sparse Control of Robot Grasping from 2D Subspaces Aggeliki Tsoli Committee: Michael Black David Laidlaw Odest Chadwicke Jenkins.

3D swiss roll; 3 noisy neighborhood edges added Noisy Swiss Roll PCA 1.2580 x 10 12 FastMVU 2.7928 x 10 12 Isomap 8.7271 x 10 11 BP - FastMVU 2.1027 x 10 12 BP - Isomap 1.2099 x 10 8 Method 2D Embedding Error Table 1: Squared error between Euclidean distances in the noisy swissroll embedding and/ from motion capture data  Widely used in human figure animation, but even transitions with low probability are modeled  Identify and break low probability transitions in motion graphs Acknowledgements Committee:/


CG Summary: Lighting and Shading “From Vertices to Fragments” Discrete Techniques Angel, Chapters 5, 6, 7; “Red Book” slides from AW, red book, etc. CSCI.

example –most common 2D primitive - done 100s or 1000s or 10s of 1000s of times each frame –even 3D wireframes are eventually 2D lines –optimized /2nd frame buffer to which to draw an image (which takes a while) then, when drawn, switch to this 2nd frame/refresh buffer and/and extrapolate to opposite spectrum locus –Adding a color to its complement produces white Gamuts for Color Cube and CIE Again, monitor gamut lies within gamut of human perception –In figure below CIE and color cube within –In figure/


MIT EECS 6.837, Cutler and Durand 1 Rasterization MIT EECS 6.837 Frédo Durand and Barb Cutler.

.837, Cutler and Durand 20 Questions? MIT EECS 6.837, Cutler and Durand 21 Circle Rasterization Generate pixels for 2nd octant only Slope/and Durand 27 2D Scan Conversion Geometric primitive –2D: point, line, polygon, circle... –3D: point, line, polyhedron, sphere... Primitives are continuous; screen is discrete MIT EECS 6.837, Cutler and Durand 28 2D/ New York, NY, 1988. Figure 7: Image from the spinning teapot performance test. Triangle Scan Conversion using 2D Homogeneous Coordinates, Marc Olano Trey Greer/


2D Plots 1 ENGR 1181 MATLAB 12.

2D (x-y) plot polar plot 3D plot Plotting in the Real World Similar to graphing in Excel, we can generate plots in MATLAB to graphically display information. MATLAB has a variety of plot types available, though we will focus on 2D/Plot Example 1st set of data has red dashed lines with asterisk markers 2nd set of data has green dotted lines …still no labels or title /and legend if applicable. Preview of Next Class 2D Plots 2 Plotting with fplot() command Polar plots in MATLAB Multiple plots in the same figure/


3D Computer Vision and Video Computing Introduction Part I Feature Extraction (2) Edge Detection CSc I6716 Fall 2009 Zhigang Zhu, City College of New York.

2nd Derivative Estimate l Laplacian l Difference of Gaussians n Parametric Edge Models (*) 3D Computer Vision and Video Computing Gradient Methods F(x) x F’(x) x Edge= sharp variation Large first derivative 3D Computer Vision and/imshow(BW1) figure, imshow(BW2)  =1, T2=255, T1=1 ‘Y’ or ‘T’ junction problem with Canny operator 3D Computer Vision and Video Computing /and localized in image space. n One operator which satisfies these two constraints is the Gaussian: 3D Computer Vision and Video Computing 2D/


Computer Vision Group University of California Berkeley Recognizing objects and actions in images and video Jitendra Malik U.C. Berkeley.

using orientation energy and texture gradient as features. Computer Vision Group University of California Berkeley Orientation Energy Gaussian 2nd derivative and its Hilbert pair Can detect combination of bar and edge features; also/and movement of the body) Identity of the object/s Activity context Computer Vision Group University of California Berkeley Image/Video  Stick figure  Action Stick figures can be specified in a variety of ways or at various resolutions (deg of freedom) –2D joint positions –3D/


Computer Vision Group University of California Berkeley Visual Grouping and Object Recognition Jitendra Malik * U.C. Berkeley * with S. Belongie, C. Fowlkes,

using orientation energy and texture gradient as features. Computer Vision Group University of California Berkeley Orientation Energy Gaussian 2nd derivative and its Hilbert pair Can detect combination of bar and edge features [Perona/and movement of the body) Identity of the object/s Activity context Computer Vision Group University of California Berkeley Image/Video  Stick figure  Action Stick figures can be specified in a variety of ways or at various resolutions (deg of freedom) –2D joint positions –3D/


MSc project Janneke Ansems 21-08-2007 Intensity and Feature Based 3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr.

3D Rigid Registration of Pre- and Intra-Operative MR Brain Scans Committee: Prof. dr. ir. B.M. ter Haar Romeny Prof. dr. ir. F.N. van de Vosse Dr. ir. B. Platel Dr. ir. G.J. Strijkers 2 Outline  Introduction  Registration  Materials and Methods  Results  Discussion and Conclusion  Recommendations 3 Introduction Medical Background  Brain tumors  Cancer is 2nd/  2D version gave promising results Registration Features Figure 6: Matches found by SIFT algorithm in Polestar data (left) and high resolution/


Chapter 14 14.1 Sequences and Series Definition Sequence A Sequence is an ordered set of numbers Definition Infinite Sequence An Infinite Sequence is.

terms of the sequence f (n) = (–2) n – 1. S OLUTION f (1) = (–2) 1 – 1 = 1 1st term 2nd term 3rd term 4th term 6th term f (2) = (–2) 2 – 1 = –2 f (3) = (–2) 3 – 1 / diagram, suppose each small square has sides 1 unit long, and suppose that the total height and the total width of the entire figure are each n units. B.Explain how your answer to part/1 + d) + d = a 1 + 2d a 4 = (a 1 + 2d) + d = a 1 + 3d … a n = a 1 + (n - l)d The sequence is arithmetic, a 1 = 50 and common difference d = -6. The sequence is /


© David Kirk/NVIDIA and Wen-mei W. Hwu Urbana, Illinois, August 10-14, 2009 1 VSCSE Summer School 2009 Many-core Processors for Science and Engineering.

storage of matrix (single precision): 2D: dimension of F is (256X256) 2 ~ 34 GB 3D: (256X256X256) 3 ~ 2 PB © David Kirk/NVIDIA and Wen-mei W. Hwu Urbana, /m]*cArg + rMu[m]*sArg; } } (b) after loop interchange Figure 7.9 Loop interchange of the F H D computation © David Kirk/NVIDIA and Wen-mei W. Hwu Urbana, Illinois, August 10-14, 2009 25/A.N. Netravali and B.G. Haskell, Digital Pictures: Representation, Compression, and Standards (2nd Ed), Plenum Press, New York, NY (1995). © David Kirk/NVIDIA and Wen-mei W./


This work by John Galeotti and Damion Shelton, © 2004-2013, was made possible in part by NIH NLM contract# HHSN276201000580P, and is licensed under a Creative.

and is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/ or send a letter to Creative Commons, 171 2nd/pixel (3,0,5) in 3D space  Physical space relates to data space by defining the origin and spacing of the image 18 Length of side i 19 Figure 4.1 from the ITK /to nest 53 N-d access troubles, cont. I.e. the following works on 2D images only loop over rows loop over columns build index (row, column) GetPixel(/


Introduction to Percolation

lattice The number and properties of clusters ? 통계물리학 Percolation - First discussed by Hammersley in 1957 Other fun example Lets consider a 2D network as shown in left figure. The communication network/honeycomb lattice – 2D Simple cubic, body-centered cubic, face-centered cubic, diamond lattice -3D Hypercubic lattice – / Continuum Percolation Dynamics Reference Dietrich Stauffer and Amnon Aharony, Introduction to Percolation Theory 2nd (1994) Hoshen-Kopelman algorithm J. Hoshen and R. Kopelman, PRB 14, 3438/


A Study for Yamanaka Images for Camera Gwanggil Jeon and Young-Sup Lee Department of Embedded Systems Engineering, Incheon National University, 12-1 Songdo-dong,

or 540×720 [30]. Figure 2 and Fig. 3 show an example of CPSNR and SCIELAB results. SoftTech 2013, ASTL Vol. 19, pp. 49 - 52, 2013 49 © SERSC 2013 http://www.mercubuana.ac.id Proceedings, The 2nd International Conference on Software Technology /Fang, J. Wu, and B. Huang, 2D sparse signal recovery via 2D orthogonal matching pursuit. Science China: Inf. Sci., 55: 889-897, (2012). 12.J. Wu, T. Li, T.-J. Hsieh, Y.-L. Chang, and B. Huang, Digital Signal Processor-based 3D Wavelet Error-resilient Lossless /


Law and Econometrics Enrique A. Bour August 2010.

Manual on Scientific Evidence (3d ed. 2009). Joseph L. Gastwirth, Statistics in the Courtroom (2007). Joseph B. Kadane ed., Statistics in the Law (2008). Hans Zeisel & David Kaye, Prove It With Figures: Empirical Methods in Law and Litigation (1997). For example/) v. Sears, Roebuck & Co., 839 F.2d 302, 312 & n.9, 313 (7th Cir. 1988) (EEOCs regression studies showing significant differences did not establish liability because surveys and testimony supported the rival hypothesis that women generally had less/


Structure of an HIV gp120 envelope glycoprotein in complex with the CD4 receptor and a neutralizing human antibody. Kevin Paiz-Ramirez Janelle N. Ruiz.

3d pattern of these heavy-atoms (Electron Density Model) K3IrCl6 modelled as 9 partially occupied sites (2sites of occupancy) Poor data quality, small isomorphous differences K2OsCl6 (4 sites of occupancy), highest site at same as 2nd highest for K3IrCl6 Density Modification to Improve Electron Density Model Correlations in region internal to domain 1 of CD4 between experimental electron density and/yellow) Serves as imprint of CD4 on gp120 surface Figure 3d Left side CD4 surface shown in yellow, gp120 /


Diffraction methods and electron microscopy Outline and Introduction to FYS4340 and FYS9340.

P, Joy DC, Romig Jr D, Lyman CE, et al. Scanning electron microscopy and X-ray microanalysis. 2nd ed. New York: Plenum Press; 1992. 819 p. Multi element phase: Contrast / 2s 2 2p 2 2p 4 3s 2 3p 2 3p 4 3d 4 3d 6 Auger electron or x-ray Electron Ionization of inner shells Auger electrons/ is normal to either [010] or [001] (figure 7.6, page 51). The glide with vector a/2 and a reflection, is the symmetry operation. a/2 a/g /2gθ B s g = L 1 /L 2 2gθ B L 2 =λ L/d λ= 2d θ B ~ Bragg’s law g=1/d s g = L 1 /L 2 2gθ B /


Perceptual and Sensory Augmented Computing Computer Vision WS 11/12 Computer Vision – Lecture 16 Camera Calibration & 3D Reconstruction 12.01.2012 Bastian.

DLT = “Direct Linear Transform”) Perceptual and Sensory Augmented Computing Computer Vision WS 11/12 Camera Calibration: DLT Algorithm Notes  P has 11 degrees of freedom (12 parameters, but scale is arbitrary).  One 2D/3D correspondence gives us two linearly independent / XiXi xixi Perceptual and Sensory Augmented Computing Computer Vision WS 11/12 Camera Calibration Once we’ve recovered the numerical form of the camera matrix, we still have to figure out the intrinsic and extrinsic parameters This /


Pattern Projection Techniques Computer Vision and Robotics University of G iro na.

need at least two cameras. A 3D object point has three unknown co-ordinates. Each 2D image point gives two equations. Only / the location of the edges does not coincide Figure by Jens Gühring RGB channels intensity profile of/ accuracy Multi-slit pattern Minimising the window size and the number of colours preserving a good resolution Stripe/ Peak compensation Colour rectification 3D reconstruction Online processes Radial distortion removal Stripes location: binarised 2nd derivative of the luminance channel/


Radar Clutter Modeling and Analysis Maria S. Greco, Fulvio Gini Dept. of Ingegneria dell’Informazione University of Pisa Via G. Caruso 16, I-56122, Pisa,

(field surfaces) 3D clutter map 2D clutter map The illuminated area was covered by agricultural crops (83%), deciduous trees (11%), lakes (4%), and rural farm buildings / scatterers with respect to the antenna.  The spectral broadening shown in the figure is due to the antenna radial motion.  Knowledge of spectral spreading due /and 2nd range intervals: the Weibull distribution provides the best fitting 3rd and 4th range intervals: the data show a behaviour that is intermediate between Weibull and/


DECISION MAKING, SYSTEMS, MODELING, AND SUPPORT

. Models are classified into: Iconic model A scaled physical replica, such as an airplane model (3D) Analog model An abstract, symbolic model of a system that behaves like the system but looks different, such as charts and figure represents water on mountains. (2D) 2nd semester 2010 Dr. Qusai Abuein 2nd semester 2010 Dr. Qusai Abuein (2.3) Models Mental model The mechanisms or images through which/


Multi-Tasking Models and Algorithms

that tasks are assigned to processes for execution. Illustrated in Figures 3.5 and 3.7 Good maps attempt to Maximize the use of / data Block Distribution Cyclic Distribution Block-Cyclic Distribution Randomized Distribution 1D/2D/3D 1D Block Distributions Partitioning a nm two-dimensional array along/are given in textbook, Barry Wilkinson and Michael Allen, “Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers”, 1st or 2nd Edition,1999 & 2005, Prentice /


Grade 3 Mathematics Assessment Eligible Texas Essential Knowledge and Skills Texas Education Agency Student Assessment Division Fall 2011.

and bills. 4.1(B) use place value to read, write, compare, and order decimals involving tenths and hundredths, including money, using concrete objects and pictorial models. 5.2D use place value to relate decimals to fractions that name tenths, hundredths, and/words if the collections represent the same value. 2.3D, 2.3E; 2.12A, 2.12D; 2./, classify, and describe two- and three- dimensional geometric figures by their attributes. The student compares two- dimensional figures, three- dimensional figures, or both/


NiSIS Malta Nov. 07 PERCEPTIVE SWARMS, DATA AND COMPUTABLE HABITATS: Swarm Intelligence in Clustering and Pattern Recognition Vitorino Ramos LaSEEB-IST,

problems is simple: while we engage ourselves in exploiting the extrema, the extrema moves elsewhere NiSIS Malta Nov. 07 Perceptive Swarms, Data and Computable Habitats: 9. Dynamic Optimization NiSIS Malta Nov. 07 Perceptive Swarms, Data and Computable Habitats: Figure - (LEFT) A 3D toroidal changing landscape describing a Dynamic Optimization (DO) Control Problem (8 frames in total). (RIGTH) A self- organized swarm emerging a characteristic/


ADVANCED SIMULATION TECHNIQUES AND MATHEMATICAL TOOLS – S.Métens 2012-2013 Solitons in the sine-Gordon equation Pororoca tidal bore (mascaret in french.

[14 km/h], preserving its original figure some thirty feet [9 m] long and a foot to a foot and a half [300−450 mm] in height. Its height gradually diminished, and after a chase of one or two / at low nonlinearity and (d) discrete soliton formation at high nonlinearity. Top panels: 3D intensity plots; bottom panels: corresponding 2D transverse intensity patterns solitons in optical fibers Solitons are observed in many different fields of science ADVANCED SIMULATION TECHNIQUES AND MATHEMATICAL TOOLS – /


Paul S. Crozier August 10, 2011 Sandia National Laboratories

Scale of Molecular Dynamics Limited length scale is 2nd most serious drawback of MD  coarse-graining /and at interfaces Self-assembly (2d micelles and 3d lipid bilayers) Rhodopsin protein isomerization Whole /and high CO2 adsorption capacity.” http://en.wikipedia.org/wiki/Zeolitic_imidazolate_frameworks ZIF-8 Synonym: 2-Methylimidazole zinc salt, ZIF 8 CAS Number: 59061-53-9 Empirical Formula (Hill Notation): C8H12N4Zn Molecular Weight: 229.60 Crystal structure of ZIF-8 with void space shown in yellow.                 Figure/


Image Processing & Antialiasing

2D space for images) Signals are complex waveforms but they can be decomposed into combinations of simpler waveforms (sines and cosines) of varying frequency, amplitude and phase: Fourier synthesis 9/19/2013 Sampling and Scaling of Images (images courtesy of George Wolburg, Columbia University, in 2nd/convolution, a finite number of x’s. We’ll have to figure out where those should be in the original image, and that will differ for up-scaling and down-scaling, as a function of scale factor 9/19/2013 /


Immunological feature predictions and databases on the web Ole Lund Center for Biological Sequence Analysis BioCentrum-DTU Technical University of Denmark.

of the number of deaths caused by the pathogen per year 2nd column: DNA Advisory Committee (RAC) classification DNA Advisory Committee/.kyoto-u.ac.jp/~ced/ MHC class II pathway Figure by Eric A.J. Reits Virtual matrices HLA-DR/in discontinuous B cell epitopes using protein 3D structures, Pernille Haste Andersen, Morten Nielsen and Ole Lund, Protein Science, 15:2558/Data bases, HLA binding Morten Nielsen HLA binding Jean Vennestrøm 2D proteomics Thomas Blicher (50%) MHC structure Mette Voldby Larsen Phd student/


Introduction to Quantum mechanics and Molecular Spectra Ka-Lok Ng Asia University.

of determining the 3D structure of proteins References House J.E. Fundamentals of quantum chemistry, 2nd ed. Elsevier 2004 Whitford D. Proteins: structure and function. J. /3D wave equation is where  is the amplitude function and v is the phase velocity of the wave. Schrodinger equation The Wave Equation The Schrodinger wave equation The 2D/Raman effect Figure. See http://www.inphotonics.com/raman.htmhttp://www.inphotonics.com/raman.htm http://www.vigyanprasar.gov.in/dream/feb2002/article1.htm Figure. See/


Numerical Transport Anne Douglass Code 613.3 Atmospheric Chemistry and Dynamics Branch NASA Goddard Space Flight Center.

G. J., W. R. Goodin, and J. H. Seinfeld, Numerical solution of the atmospheric diffusion equation for chemically reacting flows, J. Compu. Phys., 45, 1-42, 1982. + P - L Once we have figured out how to obtain a solution for/waves masked differences among transport schemes. Initial (good) results with 3D models (ours and others) and comparisons of scheme performance in 2D models contribute to the idea that “the transport schemes are good enough” and not the main sources of error in atmospheric models. ‘Better/


New Way Chemistry for Hong Kong A-Level Book 11 Chemical Equations and Stoichiometry 3.1Formulae of Compounds 3.2Derivation of Empirical Formulae 3.3Derivation.

-Level Book 14 How can you describe the composition of compound X? Compound X 2nd way = by percentage by mass Mass of carbon atoms inside = …. g Mass/ Check Point 3-2B Check Point 3-2B Example 3-2D Example 3-2D New Way Chemistry for Hong Kong A-Level Book 110 /p.52) Find composition by mass from formula Example 3-3D Example 3-3D Example 3-3E Example 3-3E Check Point 3-3B /mixture was measured with a pH meter. The results were recorded and shown in the following figure. Calculate the value of n in Na 2 CO 3 ·/


Biomedical Signal and Data Processing Group Artificial Life Lenka Lhotska Gerstner laboratory, Department of Cybernetics CTU FEE Prague

the figures – 0, 7, 34, 69, 120, 126, 127, 137, 151, 451, 901 Biomedical Signal and Data /food search, maintenance – keeping inner structure.  2nd law of thermodynamics – entropy is increasing in the /F=F-F++F-F  Axiom and first four iterations  Linear magnification – 3x, thus 4 = 3D and dimension of Koch flake D = 1./  3 = 2D and D = 1.5849625  Unremoved area converges to 0 and the circumference converges to infinity.  Axiom and first four iterations Biomedical Signal and Data Processing Group /


References: Dexter Perkins, 2002, Mineralogy, 2nd edition. Prentice Hall, New Jersey, 483 p. Bloss, F.D., 1971, Crystallography and Crystal Chemistry:

looking at the symmetry of a crystal Projection of 3D orientation data and symmetry of a crystal into 2D using spherical projection Projection lowers the Euclidian dimension of the object by 1, i.e., planes become lines, and lines become point! The poles (normals) of crystal/ it to the S-pole (no geographic significance!) If this point is in the lower hemisphere, connect it to the N-pole Figure 9.29 cont’d For faces below the equator (when using lower hemisphere), place an open circle symbol ( ◯ ) where the/


A Frequency Domain Scrambling Using Different Sized Empty Block Gwanggil Jeon and Young-Sup Lee Department of Embedded Systems Engineering, Incheon National.

2013, ASTL Vol. 19, pp. 219 - 222, 2013 © SERSC 2013 Proceedings, The 2nd International Conference on Software Technology e j 0 = 0 + 0 cos j sin. (/block as Eq.. (5). [ ] (5) 4 Performance Studies of Scrambled Results Figure 1 shows the PSNR results comparison according to block size, 1≤log 2 (BS)/and B. Huang, 2D sparse signal recovery via 2D orthogonal matching pursuit. Science China: Inf. Sci., 55: 889-897, (2012). 15.J. Wu, T. Li, T.-J. Hsieh, Y.-L. Chang, and B. Huang, Digital Signal Processor- based 3D/


The Heavy Ion Fusion Virtual National Laboratory Comparison of final focus magnetic systems for the Assisted Pinched Transport and the RPD-2002 J. Barnard,

25-50 kA Z Pinched Transport can reduce chamber focus requirements and reduce driver costs. Laser Hybrid Target IPROP simulation starts The /) Required average current density: 430 A/mm 2 2D coil peak field @ short sample:13.7 T 3D peak field (expected):15.0 T Required Nb 3/ (cm)l(m)ldrift*(m) last76.73.824.980.34815 2nd-107.4-5.985.57.6970.30 3rd94.86.396.741/5 cm radial buildup R pipe to R coil - consistent with RPD-02 (#) FOD (figure of difficulty) is defined as G 2 D 3 - Better length to aperture ratio /


How to teach function by using of DERIVE Ingrida Kraslanová Mária Slavíčková Faculty of mathematics, physics and informatics Comenius University, Bratislava.

and to compare the results of two types of groups (experimental and control group, 1st and 2nd phase),  to find out whether the computer aided teaching is more effective than the traditional. Investigating the family of functions Example In the figure/operation Problems with understanding basic terms or sketching basic graphs  Better imagination of 3D  Generalization of the knowledge about the function from 2D to 3D and 4D and so on  Better understanding of the procedure Thank you for your attention! Web/


2.11 Assimilation methods, including use of data in cloudy regions Daryl Kleist 1 Data Assimilation and Observing Systems Working Group Beijing, China.

Figure 5. Root mean square background forecast observation departures for radiosonde zonal wind (left column) and radiosonde temperature (right column) for the northern hemisphere (top row), tropics (middle row) and/ is limited to 3D cloud fraction and the model is / & store 2D displacement field Following cycle: read previous displacement field and apply to /and AMSR-2 in operations (August 2015) - Transfer of 4 * MHS to the all-sky framework in operations (May 2015) - All-sky water-vapour channels are in 2nd/


Microwave Imaging and Visualization Diagnostics Developments for the Study of MHD and Microturbulence N.C. Luhmann, Jr. University of California at Davis.

) Measures the electron temperature in 2D 2 nd harmonic X-mode ECE radiation intensity First 2D ECE Imaging diagnostic on TEXTOR very successful /2.1 T) is shown left, showing 2nd and 3rd harmonic X-mode ECE spanning 94 GHz/. T e perturbation amplitude (in eV) shown to right of each figure. Interference with 3rd harmonic ECE may limit viewing here DIII-D Coverage/Extensive laboratory tests will be conducted with simulation study –1.5D and/or 3D EM simulation (PPPL) will be compared with laboratory test to clarify/


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