Wavefront Reconstructor
|
|
- Elwin Thomas
- 6 years ago
- Views:
Transcription
1 A Dstrbuted Smplex B-Splne Based Wavefront Reconstructor Coen de Vsser and Mchel Verhaegen Delft Unversty of Technology
2 Contents Introducton Wavefront reconstructon usng Smplex B-Splnes Dstrbuted wavefront reconstructon usng Smplex B-Splnes Computatonal Aspects Concluson & Future Work 2
3 Introducton Wavefront reconstructon (WFR): necessary because wavefront phase cannot be measured drectly computatonally expensve and Key operaton n AO Example: for E-ELT XAO system usng standard Matrx-Vector-Multplcaton: 4.8 TFLOPS Current sngle core CPU performance: 18 GFLOPS (Core 7-980) 3
4 Introducton Increase of computatonal performance n the near future only through parallelzaton. Large scale WFR for XAO requres parallelzaton! Smplex B-splne (SABRE * ) method s a WFR method that enables massve parallelzaton l and mplementaton on GPU. * C.C. de Vsser and M. Verhaegen, A Wavefront Reconstructon n Adaptve Optcs Systems usng Nonlnear Multvarate Splnes, JOSA A, accepted for publcaton. 4
5 Splne based Aberraton Reconstructon Recently, a new method called the SABRE (Splne based ABeraton REconstructon) for local wavefront reconstructon was ntroduced *. The SABRE uses nonlnear bvarate splnes to locally approxmate the wavefront. The SABRE uses trangular sub- parttons of the global wavefront sensor grd and estmates local wavefront phase. * C.C. de Vsser and M. Verhaegen, A Wavefront Reconstructon n Adaptve Optcs Systems usng Nonlnear Multvarate Splnes, JOSA A, accepted for publcaton. 5
6 Splne based Aberraton Reconstructon SABRE s compatble wth many dfferent wavefront sensor geometres (occluson, msalgnment, etc.). SABRE can approxmate the wavefront usng nonlnear polynomal bass functons. SABRE was shown to exceed reconstructon accuracy of Fred FD methods for all nose levels (*). SABRE can be mplemented n a dstrbuted manner * Black crosses: SH lenslet locatons Grey lnes: trangular sub-parttons * Ths lecture 6
7 Splne based Aberraton Reconstructon SABRE models the wavefront through local bass functons plus contnuty constrants: Polynomal bass functon of degree d Estmated splne coeffcents SABRE slope sensor model s lnear n the parameters (c): sxy d B x y P u c n x y d 1, 1 (, ) d B d d (, ) P ( ) (, ) Slope measurements De Casteljau matrx (*) of degree d to d-1 as a functon of dervatve drecton u Nose model (*) CC C.C. de Vsser et al., Dfferental Constrants for Bounded Recursve Identfcaton wth Multvarate Splnes, Automatca,
8 Splne based Aberraton Reconstructon Constraned optmzaton problem for the splne coeffcents c s : Wth the sparse matrx A contanng the splne smoothness constrants. Now defne N A as the null-space projector of A: N A ker( A) The constraned optmzaton problem can now be reduced to an unconstraned problem by usng a projector on the null-space of A as follows: 8
9 Comparson Fred FD and SABRE Fred Fnte Dfference SABRE Wavefront model ˆ ˆ FD Gs ( xy, ) B d ( x SABRE, y ) c, d 0 Reconstructon matrx Sensor geometry G ( pseudo nverse of G) 1 T T N ( A D D ) D 9
10 Dstrbuted-SABRE Full doman s parttoned nto any number of parttons. Each partton runs on a separate CPU/GPU core. 10
11 Dstrbuted-SABRE Prncple of Dstrbuted WFR: each partton depends only on ts drect neghbors Problem: Each partton wll have an unknown pston mode, and wll be dscontnuous wth ts neghbors on ts borders a three stage soluton 11
12 Dstrbuted-SABRE D-SABRE s a three stage method: Stage 1: local wavefront reconstructon (local LS problem) for partton : cˆ N ( D D ) D s T 1 T A where c are the coeffcents of the splnes used to model the wavefront over the -th partton Stage 2: dstrbuted (teratve) Pston Mode Equalzaton (PME) for partton wth respect to neghbor partton j: ˆ ( ) ˆ ( ) m mean c I c J cˆ cˆ m j 12
13 Frst 2 stages of D-SABRE llustrated Stage 1 Stage 2 Local WF s estmated usng local WF measurements. Global WF s reconstructed n two extra stages: dstrbuted pston mode equalzaton (PME) and nter-partton smoothng. 13
14 Stage 3 of D-SABRE Stage 3: dstrbuted teratve nter-partton smoothng usng dstrbuted Dual Ascent (DA) method (**) : A j Dual varable y s updated usng partton of constrant matrx A: y ( k 1) y ( k) A cˆ ( k), 0 1 j j Splne coeffcents are updated usng dual varable y(k+1) and local partton of constrant matrx A : cˆ ( k 1) cˆ ( k ) ( A ) T y ( k 1) Dstrbuted Optmzaton made possble by the hghly sparse structure of the constrant matrx A! A (*) S. Boyd al., Dstrbuted Optmzaton and Statstcal Learnng va the Alternatng Drecton Method of Multplers, Foundatons and Trends n Machne Learnng,
15 Dstrbuted-SABRE Move: Stage 2; dstrbuted PME Move: Stage 3; dstrbuted Dual Ascent 15
16 Numercal Experment wth D-SABRE Quarter scale (100x100 sensor grd) numercal experment setup: Smulated EPICS turbulence wavefronts = 0.3+/- 0.1) Dynamc wavefront reconstructon usng smple b-cubc DM model 38 [db] sgnal to nose rato 500 turbulence realzatons 100x100 sensor grd 400 parttons for dstrbuted method 16
17 Numercal Experment wth D-SABRE 17
18 Computatonal Aspects of D-SABRE D-SABRE compute requrements per trangulaton partton per stage Stage 1 (local wavefront reconstructon): Matrx-Vector-Multplcaton: ˆ Requrement: 2 ON ( ) Stage 2 (Dstrbuted Pston Mode Equalzaton) p Vector-Add operatons: Requrement: O ( p N ) Stage 3 (Dstrbuted Dual Ascent Smoothng) k teratve Sparse-MVM operatons: Requrement: Ok ( N/ E) c N Q s = Total number cˆ cˆ m of B-coeffcents per partton ( A ) T y ( k 1), A cˆ ( k) y j j 18
19 Computatonal Aspects of D-SABRE D-SABRE total compute requrements per trangulaton partton Stage 1+2+3: Compute requrement: ON p N k N E 2 ( p / ) Stage 2 teraton count p depends on the total number of smplces n a partton, Stage 3 teraton count k depends on contnuty order and nose levels. Stage 1 (local reconstructon) s domnant f and f In general p N, k E N p N k E N Concluson: Stage 1 reconstructon s determnng factor n compute performance! 19
20 Computatonal Aspects of D-SABRE Compute budget for WFR on an ELT class system: Sensor grd: 240x240, Total trangles: Total parttons: 2*240 2 = trangles, 768, wth 150 trangles per partton (ncludes overlap) FLOP s per partton per cycle: (150*3) 2 FLOPS per partton for 3000Hz update rate = 3000*202e3 = 202 KFLOP = 610 MFLOPS TOTAL FLOPS for 768 parttons = 469 GFLOPS Concluson Hardware Requrement: 2 NVda Tesla C2050 GPU s wth peak DP performance 2 * 448 cores * 1 GFLOPS = 896 GFLOPS runnng 1 partton per core (requres 768 cores total) 8 Intel Core CPU s wth peak DP performance 8 * 6 cores * 18 GFLOPS = 864 GFLOPS runnng 18 parttons per core (requres 43 cores total) 20
21 Concluson The SABRE method can locally reconstruct wavefronts on non-rectangular domans usng non-lnear splne functons. The D-SABRE method s a dstrbuted verson of the SABRE splne WFR method publshed n JOSA-2012; t s specfcally desgned for parallel operatons on mult-core hardware.. D-SABRE has all potental to perform real-tme Wavefront Reconstructon at 3000Hz for the E-ELT challenges usng 8 Intel Core class CPU s, or 2 NVda Tesla C2050 class GPU s. 21
22 Future Work The D-SABRE method wll be mplemented n a C-GPU language lke CUDA or OpenCL. The SABRE method wll be refned to enable non-lnear wavefront reconstructon, and the use of non-shack-hartmann based wavefront sensors. A full scale smulaton based on smulated E-ELT phase screens and operatonal (GPU) hardware wll be created. 22
23 Thank you for your attenton! 23
S1 Note. Basis functions.
S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type
More informationClassification / Regression Support Vector Machines
Classfcaton / Regresson Support Vector Machnes Jeff Howbert Introducton to Machne Learnng Wnter 04 Topcs SVM classfers for lnearly separable classes SVM classfers for non-lnearly separable classes SVM
More informationAlternating Direction Method of Multipliers Implementation Using Apache Spark
Alternatng Drecton Method of Multplers Implementaton Usng Apache Spark Deterch Lawson June 4, 2014 1 Introducton Many applcaton areas n optmzaton have benefted from recent trends towards massve datasets.
More informationSmoothing Spline ANOVA for variable screening
Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory
More informationNAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics
Introducton G10 NAG Fortran Lbrary Chapter Introducton G10 Smoothng n Statstcs Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Smoothng Methods... 2 2.2 Smoothng Splnes and Regresson
More informationA MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS
Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung
More informationReview of approximation techniques
CHAPTER 2 Revew of appromaton technques 2. Introducton Optmzaton problems n engneerng desgn are characterzed by the followng assocated features: the objectve functon and constrants are mplct functons evaluated
More informationAMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain
AMath 483/583 Lecture 21 May 13, 2011 Today: OpenMP and MPI versons of Jacob teraton Gauss-Sedel and SOR teratve methods Next week: More MPI Debuggng and totalvew GPU computng Read: Class notes and references
More informationHigh-Boost Mesh Filtering for 3-D Shape Enhancement
Hgh-Boost Mesh Flterng for 3-D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, Azu-Wakamatsu 965-8580 Japan y Computer Graphcs Group,
More informationFeature Reduction and Selection
Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components
More informationQuality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation
Intellgent Informaton Management, 013, 5, 191-195 Publshed Onlne November 013 (http://www.scrp.org/journal/m) http://dx.do.org/10.36/m.013.5601 Qualty Improvement Algorthm for Tetrahedral Mesh Based on
More informationPositive Semi-definite Programming Localization in Wireless Sensor Networks
Postve Sem-defnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer
More informationAn Optimal Algorithm for Prufer Codes *
J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,
More informationGSLM Operations Research II Fall 13/14
GSLM 58 Operatons Research II Fall /4 6. Separable Programmng Consder a general NLP mn f(x) s.t. g j (x) b j j =. m. Defnton 6.. The NLP s a separable program f ts objectve functon and all constrants are
More informationModeling, Manipulating, and Visualizing Continuous Volumetric Data: A Novel Spline-based Approach
Modelng, Manpulatng, and Vsualzng Contnuous Volumetrc Data: A Novel Splne-based Approach Jng Hua Center for Vsual Computng, Department of Computer Scence SUNY at Stony Brook Talk Outlne Introducton and
More informationLecture 5: Multilayer Perceptrons
Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented
More informationHigh resolution 3D Tau-p transform by matching pursuit Weiping Cao* and Warren S. Ross, Shearwater GeoServices
Hgh resoluton 3D Tau-p transform by matchng pursut Wepng Cao* and Warren S. Ross, Shearwater GeoServces Summary The 3D Tau-p transform s of vtal sgnfcance for processng sesmc data acqured wth modern wde
More informationA Newton-Type Method for Constrained Least-Squares Data-Fitting with Easy-to-Control Rational Curves
A Newton-Type Method for Constraned Least-Squares Data-Fttng wth Easy-to-Control Ratonal Curves G. Cascola a, L. Roman b, a Department of Mathematcs, Unversty of Bologna, P.zza d Porta San Donato 5, 4017
More informationImage Representation & Visualization Basic Imaging Algorithms Shape Representation and Analysis. outline
mage Vsualzaton mage Vsualzaton mage Representaton & Vsualzaton Basc magng Algorthms Shape Representaton and Analyss outlne mage Representaton & Vsualzaton Basc magng Algorthms Shape Representaton and
More informationOutline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like:
Self-Organzng Maps (SOM) Turgay İBRİKÇİ, PhD. Outlne Introducton Structures of SOM SOM Archtecture Neghborhoods SOM Algorthm Examples Summary 1 2 Unsupervsed Hebban Learnng US Hebban Learnng, Cntd 3 A
More informationProblem Definitions and Evaluation Criteria for Computational Expensive Optimization
Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty
More informationDetermining the Optimal Bandwidth Based on Multi-criterion Fusion
Proceedngs of 01 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 5 (01) (01) IACSIT Press, Sngapore Determnng the Optmal Bandwdth Based on Mult-crteron Fuson Ha-L Lang 1+, Xan-Mn
More informationParallelism for Nested Loops with Non-uniform and Flow Dependences
Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr
More informationRadial Basis Functions
Radal Bass Functons Mesh Reconstructon Input: pont cloud Output: water-tght manfold mesh Explct Connectvty estmaton Implct Sgned dstance functon estmaton Image from: Reconstructon and Representaton of
More informationOverview. Basic Setup [9] Motivation and Tasks. Modularization 2008/2/20 IMPROVED COVERAGE CONTROL USING ONLY LOCAL INFORMATION
Overvew 2 IMPROVED COVERAGE CONTROL USING ONLY LOCAL INFORMATION Introducton Mult- Smulator MASIM Theoretcal Work and Smulaton Results Concluson Jay Wagenpfel, Adran Trachte Motvaton and Tasks Basc Setup
More informationKent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming
CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems
More informationStitching of off-axis sub-aperture null measurements of an aspheric surface
Sttchng of off-axs sub-aperture null measurements of an aspherc surface Chunyu Zhao* and James H. Burge College of optcal Scences The Unversty of Arzona 1630 E. Unversty Blvd. Tucson, AZ 85721 ABSTRACT
More informationAngle-Independent 3D Reconstruction. Ji Zhang Mireille Boutin Daniel Aliaga
Angle-Independent 3D Reconstructon J Zhang Mrelle Boutn Danel Alaga Goal: Structure from Moton To reconstruct the 3D geometry of a scene from a set of pctures (e.g. a move of the scene pont reconstructon
More informationLecture 4: Principal components
/3/6 Lecture 4: Prncpal components 3..6 Multvarate lnear regresson MLR s optmal for the estmaton data...but poor for handlng collnear data Covarance matrx s not nvertble (large condton number) Robustness
More informationPreconditioning Parallel Sparse Iterative Solvers for Circuit Simulation
Precondtonng Parallel Sparse Iteratve Solvers for Crcut Smulaton A. Basermann, U. Jaekel, and K. Hachya 1 Introducton One mportant mathematcal problem n smulaton of large electrcal crcuts s the soluton
More informationPolyhedral Compilation Foundations
Polyhedral Complaton Foundatons Lous-Noël Pouchet pouchet@cse.oho-state.edu Dept. of Computer Scence and Engneerng, the Oho State Unversty Feb 8, 200 888., Class # Introducton: Polyhedral Complaton Foundatons
More informationS.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION?
S.P.H. : A SOLUTION TO AVOID USING EROSION CRITERION? Célne GALLET ENSICA 1 place Emle Bloun 31056 TOULOUSE CEDEX e-mal :cgallet@ensca.fr Jean Luc LACOME DYNALIS Immeuble AEROPOLE - Bat 1 5, Avenue Albert
More information2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements
Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.
More informationVery simple computational domains can be discretized using boundary-fitted structured meshes (also called grids)
Structured meshes Very smple computatonal domans can be dscretzed usng boundary-ftted structured meshes (also called grds) The grd lnes of a Cartesan mesh are parallel to one another Structured meshes
More informationDifferential formulation of discontinuous Galerkin and related methods for compressible Euler and Navier-Stokes equations
Graduate Theses and Dssertatons Graduate College 2011 Dfferental formulaton of dscontnuous Galerkn and related methods for compressble Euler and Naver-Stokes equatons Hayang Gao Iowa State Unversty Follow
More informationEdge Detection in Noisy Images Using the Support Vector Machines
Edge Detecton n Nosy Images Usng the Support Vector Machnes Hlaro Gómez-Moreno, Saturnno Maldonado-Bascón, Francsco López-Ferreras Sgnal Theory and Communcatons Department. Unversty of Alcalá Crta. Madrd-Barcelona
More informationSupport Vector Machines
/9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.
More informationProgramming in Fortran 90 : 2017/2018
Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values
More informationAn Influence of the Noise on the Imaging Algorithm in the Electrical Impedance Tomography *
Open Journal of Bophyscs, 3, 3, 7- http://dx.do.org/.436/ojbphy.3.347 Publshed Onlne October 3 (http://www.scrp.org/journal/ojbphy) An Influence of the Nose on the Imagng Algorthm n the Electrcal Impedance
More informationFitting & Matching. Lecture 4 Prof. Bregler. Slides from: S. Lazebnik, S. Seitz, M. Pollefeys, A. Effros.
Fttng & Matchng Lecture 4 Prof. Bregler Sldes from: S. Lazebnk, S. Setz, M. Pollefeys, A. Effros. How do we buld panorama? We need to match (algn) mages Matchng wth Features Detect feature ponts n both
More informationLearning the Kernel Parameters in Kernel Minimum Distance Classifier
Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department
More informationExercises (Part 4) Introduction to R UCLA/CCPR. John Fox, February 2005
Exercses (Part 4) Introducton to R UCLA/CCPR John Fox, February 2005 1. A challengng problem: Iterated weghted least squares (IWLS) s a standard method of fttng generalzed lnear models to data. As descrbed
More informationCluster Analysis of Electrical Behavior
Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School
More informationA Five-Point Subdivision Scheme with Two Parameters and a Four-Point Shape-Preserving Scheme
Mathematcal and Computatonal Applcatons Artcle A Fve-Pont Subdvson Scheme wth Two Parameters and a Four-Pont Shape-Preservng Scheme Jeqng Tan,2, Bo Wang, * and Jun Sh School of Mathematcs, Hefe Unversty
More informationMulti-stable Perception. Necker Cube
Mult-stable Percepton Necker Cube Spnnng dancer lluson, Nobuuk Kaahara Fttng and Algnment Computer Vson Szelsk 6.1 James Has Acknowledgment: Man sldes from Derek Hoem, Lana Lazebnk, and Grauman&Lebe 2008
More informationLecture #15 Lecture Notes
Lecture #15 Lecture Notes The ocean water column s very much a 3-D spatal entt and we need to represent that structure n an economcal way to deal wth t n calculatons. We wll dscuss one way to do so, emprcal
More informationPCA Based Gait Segmentation
Honggu L, Cupng Sh & Xngguo L PCA Based Gat Segmentaton PCA Based Gat Segmentaton Honggu L, Cupng Sh, and Xngguo L 2 Electronc Department, Physcs College, Yangzhou Unversty, 225002 Yangzhou, Chna 2 Department
More informationAssignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.
Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton
More informationCourse Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms
Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques
More informationComputer Animation and Visualisation. Lecture 4. Rigging / Skinning
Computer Anmaton and Vsualsaton Lecture 4. Rggng / Sknnng Taku Komura Overvew Sknnng / Rggng Background knowledge Lnear Blendng How to decde weghts? Example-based Method Anatomcal models Sknnng Assume
More informationNUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS
ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana
More informationLearning a Class-Specific Dictionary for Facial Expression Recognition
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 16, No 4 Sofa 016 Prnt ISSN: 1311-970; Onlne ISSN: 1314-4081 DOI: 10.1515/cat-016-0067 Learnng a Class-Specfc Dctonary for
More informationThe Research of Support Vector Machine in Agricultural Data Classification
The Research of Support Vector Machne n Agrcultural Data Classfcaton Le Sh, Qguo Duan, Xnmng Ma, Me Weng College of Informaton and Management Scence, HeNan Agrcultural Unversty, Zhengzhou 45000 Chna Zhengzhou
More informationDifferential wavefront curvature sensor
Dfferental wavefront curvature sensor Weyao Zou and Jannck Rolland College of Optcs and Photoncs: CREOL& FPCE, Unversty of Central Florda Orlando, Florda 3816-700 ABSTRACT In ths paper, a wavefront curvature
More informationWhy visualisation? IRDS: Visualization. Univariate data. Visualisations that we won t be interested in. Graphics provide little additional information
Why vsualsaton? IRDS: Vsualzaton Charles Sutton Unversty of Ednburgh Goal : Have a data set that I want to understand. Ths s called exploratory data analyss. Today s lecture. Goal II: Want to dsplay data
More informationCost-efficient deployment of distributed software services
1/30 Cost-effcent deployment of dstrbuted software servces csorba@tem.ntnu.no 2/30 Short ntroducton & contents Cost-effcent deployment of dstrbuted software servces Cost functons Bo-nspred decentralzed
More informationCOVERAGE CONTROL ON MULTI- AGENT SYSTEM
Artkel Reguler COVERAGE CONTROL ON MULTI- AGENT SYSTEM Reka Inovan 1, Adha Imam Cahyad 2, Sgt Basuk Wbowo 3 Abstract In ths work, we study the problem of maxmzng the coverage of a Moble Sensor Network
More informationChapter 6 Programmng the fnte element method Inow turn to the man subject of ths book: The mplementaton of the fnte element algorthm n computer programs. In order to make my dscusson as straghtforward
More informationClassifier Selection Based on Data Complexity Measures *
Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.
More informationInvestigations of Topology and Shape of Multi-material Optimum Design of Structures
Advanced Scence and Tecnology Letters Vol.141 (GST 2016), pp.241-245 ttp://dx.do.org/10.14257/astl.2016.141.52 Investgatons of Topology and Sape of Mult-materal Optmum Desgn of Structures Quoc Hoan Doan
More informationMultiple optimum values
1.204 Lecture 22 Unconstraned nonlnear optmzaton: Amoeba BFGS Lnear programmng: Glpk Multple optmum values A B C G E Z X F Y D X 1 X 2 Fgure by MIT OpenCourseWare. Heurstcs to deal wth multple optma: Start
More informationSupport Vector Machines
Support Vector Machnes Decson surface s a hyperplane (lne n 2D) n feature space (smlar to the Perceptron) Arguably, the most mportant recent dscovery n machne learnng In a nutshell: map the data to a predetermned
More informationHermite Splines in Lie Groups as Products of Geodesics
Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the
More informationThe Codesign Challenge
ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.
More informationOutline. Discriminative classifiers for image recognition. Where in the World? A nearest neighbor recognition example 4/14/2011. CS 376 Lecture 22 1
4/14/011 Outlne Dscrmnatve classfers for mage recognton Wednesday, Aprl 13 Krsten Grauman UT-Austn Last tme: wndow-based generc obect detecton basc ppelne face detecton wth boostng as case study Today:
More informationAPPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT
3. - 5. 5., Brno, Czech Republc, EU APPLICATION OF MULTIVARIATE LOSS FUNCTION FOR ASSESSMENT OF THE QUALITY OF TECHNOLOGICAL PROCESS MANAGEMENT Abstract Josef TOŠENOVSKÝ ) Lenka MONSPORTOVÁ ) Flp TOŠENOVSKÝ
More informationRepeater Insertion for Two-Terminal Nets in Three-Dimensional Integrated Circuits
Repeater Inserton for Two-Termnal Nets n Three-Dmensonal Integrated Crcuts Hu Xu, Vasls F. Pavlds, and Govann De Mchel LSI - EPFL, CH-5, Swtzerland, {hu.xu,vasleos.pavlds,govann.demchel}@epfl.ch Abstract.
More informationNAG Library Function Document nag_kalman_sqrt_filt_info_var (g13ecc)
g13 Tme Seres Analyss g13ecc NAG Lbrary Functon Document nag_kalman_sqrt_flt_nfo_var (g13ecc) 1 Purpose nag_kalman_sqrt_flt_nfo_var (g13ecc) performs a combned measurement and tme update of one teraton
More informationA Fast Content-Based Multimedia Retrieval Technique Using Compressed Data
A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,
More informationSolving two-person zero-sum game by Matlab
Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by
More informationStereo Depth Continuity
Stereo Depth Contnuty Steven Damond (stevend@stanford.edu), Jessca Taylor (jacobt@stanford.edu) March 17, 014 1 Abstract We tackle the problem of producng depth maps from stereo vdeo. Some algorthms for
More informationLU Decomposition Method Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America
nbm_sle_sm_ludecomp.nb 1 LU Decomposton Method Jame Trahan, Autar Kaw, Kevn Martn Unverst of South Florda Unted States of Amerca aw@eng.usf.edu nbm_sle_sm_ludecomp.nb 2 Introducton When solvng multple
More informationOutline. Type of Machine Learning. Examples of Application. Unsupervised Learning
Outlne Artfcal Intellgence and ts applcatons Lecture 8 Unsupervsed Learnng Professor Danel Yeung danyeung@eee.org Dr. Patrck Chan patrckchan@eee.org South Chna Unversty of Technology, Chna Introducton
More informationComputer Graphics. - Spline and Subdivision Surfaces - Hendrik Lensch. Computer Graphics WS07/08 Spline & Subdivision Surfaces
Computer Graphcs - Splne and Subdvson Surfaces - Hendrk Lensch Overvew Last Tme Image-Based Renderng Today Parametrc Curves Lagrange Interpolaton Hermte Splnes Bezer Splnes DeCasteljau Algorthm Parameterzaton
More informationExplicit Formulas and Efficient Algorithm for Moment Computation of Coupled RC Trees with Lumped and Distributed Elements
Explct Formulas and Effcent Algorthm for Moment Computaton of Coupled RC Trees wth Lumped and Dstrbuted Elements Qngan Yu and Ernest S.Kuh Electroncs Research Lab. Unv. of Calforna at Berkeley Berkeley
More informationCategories and Subject Descriptors B.7.2 [Integrated Circuits]: Design Aids Verification. General Terms Algorithms
3. Fndng Determnstc Soluton from Underdetermned Equaton: Large-Scale Performance Modelng by Least Angle Regresson Xn L ECE Department, Carnege Mellon Unversty Forbs Avenue, Pttsburgh, PA 3 xnl@ece.cmu.edu
More informationUniversität Stuttgart Direkte numerische Simulation von Strömungslärm in komplexen Geometrien
Unverstät Stuttgart Drekte numersche Smulaton von Strömungslärm n komplexen Geometren Claus-Deter Munz Gregor Gassner, Floran Hndenlang, Andreas Brkefeld, Andrea Beck, Marc Staudenmaer, Thomas Bolemann,
More informationSVM-based Learning for Multiple Model Estimation
SVM-based Learnng for Multple Model Estmaton Vladmr Cherkassky and Yunqan Ma Department of Electrcal and Computer Engneerng Unversty of Mnnesota Mnneapols, MN 55455 {cherkass,myq}@ece.umn.edu Abstract:
More informationToday Using Fourier-Motzkin elimination for code generation Using Fourier-Motzkin elimination for determining schedule constraints
Fourer Motzkn Elmnaton Logstcs HW10 due Frday Aprl 27 th Today Usng Fourer-Motzkn elmnaton for code generaton Usng Fourer-Motzkn elmnaton for determnng schedule constrants Unversty Fourer-Motzkn Elmnaton
More informationLoad Balancing for Hex-Cell Interconnection Network
Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,
More informationA HIGH-ORDER SPECTRAL (FINITE) VOLUME METHOD FOR CONSERVATION LAWS ON UNSTRUCTURED GRIDS
AIAA-00-058 A HIGH-ORDER SPECTRAL (FIITE) VOLUME METHOD FOR COSERVATIO LAWS O USTRUCTURED GRIDS Z.J. Wang Department of Mechancal Engneerng Mchgan State Unversty, East Lansng, MI 88 Yen Lu * MS T7B-, ASA
More informationClassifying Acoustic Transient Signals Using Artificial Intelligence
Classfyng Acoustc Transent Sgnals Usng Artfcal Intellgence Steve Sutton, Unversty of North Carolna At Wlmngton (suttons@charter.net) Greg Huff, Unversty of North Carolna At Wlmngton (jgh7476@uncwl.edu)
More informationCorrespondence-free Synchronization and Reconstruction in a Non-rigid Scene
Correspondence-free Synchronzaton and Reconstructon n a Non-rgd Scene Lor Wolf and Assaf Zomet School of Computer Scence and Engneerng, The Hebrew Unversty, Jerusalem 91904, Israel e-mal: {lwolf,zomet}@cs.huj.ac.l
More informationRelevance Assignment and Fusion of Multiple Learning Methods Applied to Remote Sensing Image Analysis
Assgnment and Fuson of Multple Learnng Methods Appled to Remote Sensng Image Analyss Peter Bajcsy, We-Wen Feng and Praveen Kumar Natonal Center for Supercomputng Applcaton (NCSA), Unversty of Illnos at
More informationReducing Frame Rate for Object Tracking
Reducng Frame Rate for Object Trackng Pavel Korshunov 1 and We Tsang Oo 2 1 Natonal Unversty of Sngapore, Sngapore 11977, pavelkor@comp.nus.edu.sg 2 Natonal Unversty of Sngapore, Sngapore 11977, oowt@comp.nus.edu.sg
More informationA Geometric Approach for Multi-Degree Spline
L X, Huang ZJ, Lu Z. A geometrc approach for mult-degree splne. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 27(4): 84 850 July 202. DOI 0.007/s390-02-268-2 A Geometrc Approach for Mult-Degree Splne Xn L
More informationAn Accurate Evaluation of Integrals in Convex and Non convex Polygonal Domain by Twelve Node Quadrilateral Finite Element Method
Internatonal Journal of Computatonal and Appled Mathematcs. ISSN 89-4966 Volume, Number (07), pp. 33-4 Research Inda Publcatons http://www.rpublcaton.com An Accurate Evaluaton of Integrals n Convex and
More informationControl strategies for network efficiency and resilience with route choice
Control strateges for networ effcency and reslence wth route choce Andy Chow Ru Sha Centre for Transport Studes Unversty College London, UK Centralsed strateges UK 1 Centralsed strateges Some effectve
More informationLobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide
Lobachevsky State Unversty of Nzhn Novgorod Polyhedron Quck Start Gude Nzhn Novgorod 2016 Contents Specfcaton of Polyhedron software... 3 Theoretcal background... 4 1. Interface of Polyhedron... 6 1.1.
More informationWhat are the camera parameters? Where are the light sources? What is the mapping from radiance to pixel color? Want to solve for 3D geometry
Today: Calbraton What are the camera parameters? Where are the lght sources? What s the mappng from radance to pel color? Why Calbrate? Want to solve for D geometry Alternatve approach Solve for D shape
More informationEmpirical Distributions of Parameter Estimates. in Binary Logistic Regression Using Bootstrap
Int. Journal of Math. Analyss, Vol. 8, 4, no. 5, 7-7 HIKARI Ltd, www.m-hkar.com http://dx.do.org/.988/jma.4.494 Emprcal Dstrbutons of Parameter Estmates n Bnary Logstc Regresson Usng Bootstrap Anwar Ftranto*
More informationJ-DSP-CONTROL: A CONTROL SYSTEMS SIMULATION ENVIRONMENT +
J-DSP-CONTROL: A CONTROL SYSTEMS SIMULATION ENVIRONMENT + T. Thrasyvoulou, K. Tsakals and A. Spanas MIDL Department of Electrcal Engneerng Arzona State Unversty, Tempe, AZ 85287-7206 thrassos@asu.edu,
More informationAn inverse problem solution for post-processing of PIV data
An nverse problem soluton for post-processng of PIV data Wt Strycznewcz 1,* 1 Appled Aerodynamcs Laboratory, Insttute of Avaton, Warsaw, Poland *correspondng author: wt.strycznewcz@lot.edu.pl Abstract
More informationFeature-Preserving Mesh Denoising via Bilateral Normal Filtering
Feature-Preservng Mesh Denosng va Blateral Normal Flterng Ka-Wah Lee, Wen-Png Wang Computer Graphcs Group Department of Computer Scence, The Unversty of Hong Kong kwlee@cs.hku.hk, wenpng@cs.hku.hk Abstract
More informationDiscontinuous Galerkin methods for flow and transport problems in porous media
T COMMUNICATIONS IN NUMERICA METHODS IN ENGINEERING Commun. Numer. Meth. Engng 2; :1 6 [Verson: 2/3/22 v1.] Dscontnuous Galerkn methods for flow and transport problems n porous meda Béatrve Rvère and Mary
More informationHybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 16, No 2 Sofa 2016 Prnt ISSN: 1311-9702; Onlne ISSN: 1314-4081 DOI: 10.1515/cat-2016-0017 Hybrdzaton of Expectaton-Maxmzaton
More informationFace Recognition University at Buffalo CSE666 Lecture Slides Resources:
Face Recognton Unversty at Buffalo CSE666 Lecture Sldes Resources: http://www.face-rec.org/algorthms/ Overvew of face recognton algorthms Correlaton - Pxel based correspondence between two face mages Structural
More informationLine Clipping by Convex and Nonconvex Polyhedra in E 3
Lne Clppng by Convex and Nonconvex Polyhedra n E 3 Václav Skala 1 Department of Informatcs and Computer Scence Unversty of West Bohema Unverztní 22, Box 314, 306 14 Plzeò Czech Republc e-mal: skala@kv.zcu.cz
More informationStructural Optimization Using OPTIMIZER Program
SprngerLnk - Book Chapter http://www.sprngerlnk.com/content/m28478j4372qh274/?prnt=true ق.ظ 1 of 2 2009/03/12 11:30 Book Chapter large verson Structural Optmzaton Usng OPTIMIZER Program Book III European
More informationA Binarization Algorithm specialized on Document Images and Photos
A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a
More information