1-D matrix method. U 4 transmitted. incident U 2. reflected U 1 U 5 U 3 L 2 L 3 L 4. EE 439 matrix method 1
|
|
- Mercy Shields
- 5 years ago
- Views:
Transcription
1 -D matrx method We ca expad the smple plae-wave scatterg for -D examples that we ve see to a more versatle matrx approach that ca be used to hadle may terestg -D problems. The basc dea s that we ca break a problem havg a complcated potetal profle to a sequece of costat potetal regos. Sce we already kow the TISE solutos for regos of costat potetal, the problem bols dow to coectg the solutos at each terface. A matrx approach leds tself well to ths type of problem. cdet reflected U 4 trasmtted U U U 3 U 5 L L 3 L EE 439 matrx method
2 Also, we wll see that the method ca be used to fd eergy levels cofg quatum wells. etc E E Fally, t ca be used to obta approxmate solutos to complex potetal profles. EE 439 matrx method
3 r t A C A 3 t r B D B 3 D 3 3 B 4 D C 3 A 4 C 4 I a mult-layer problem, the dffcultes come hadlg the reflectos at all of the terfaces. However, f we ca determe how the plae waves relate from oe sde of a costat potetal rego to the other, cludg the effects of scatterg at the terfaces, the we ca relate the trasmtted ampltude to the cdet ampltude or reflected to cdet) of the overall system. A For each rego, we ll try to wrte a matrx of the form: [M B ] where [M ] s a x matrx descrbg the th rego. EE 439 matrx method 3 C D
4 r t A C A 3 t r B D B 3 D 3 3 B 4 D C 3 A 4 C 4 apple A B [M ] apple C D [M ] apple A3 B 3 [M ][M 3 ] apple C3 D 3 [M ][M 3 ] apple A4 B 4 [M ][M 3 ][M 4 ] apple apple t r [I L][M ][M 3 ][M 4 ][I R ] 0 apple C4 D 4 EE 439 matrx method 4
5 apple r [I L][M ][M 3 ][M 4 ][I R ] apple t 0 apple r apple M M M M apple t 0 trasmsso: reflecto: t M r M M Fdg reflecto ad trasmsso coeffcets s a process of multplyg dvdual layer matrces to obta a overall system matrx. The reflecto ad trasmsso coeffcet ratos are foud from the elemets of the system matrx. EE 439 matrx method 5
6 Ufortuately, our approach wo t be qute as tdy as the prevous slde would mply. Sce the electro waves are scattered at terfaces betwee layers, we eed a matrx to descrbe what happes at each terface. Of course, a terface s ot a property of sgle layer, but depeds o the layer propertes o ether sde of the terface. Also, the waves chage phase as they propagate across regos where E > U, or they grow ad decay expoetally across regos where E < U. We wll eed propagato matrces to descrbe these chages. Itutvely the, each layer leads to two matrces to be cluded the sequece, oe propagato matrx ad oe terface matrx. However, oce we have the form of the propagato ad terface matrces, we ll see that we ca combe them the rght way to obta a smple, oe-matrx descrpto of each layer. However, gettg to ths pot s ot tutve, so we ll take a roud-about approach, but oe that wll hopefully gve a clearer pcture of what we re dog. EE 439 matrx method 6
7 Iterface matrces E Case - E > U o both sde of the terface. U U x We assume the terface occurs at x x. For x < x, the potetal s U, ad for x > x, t s U. Sce E > U ad E > U, we use plaewave solutos o both sdes of the terface. ) ) )] )] )] )] Apply the boudary codtos EE 439 matrx method 7
8 Use the two equatos to wrte A ad B terms of C ad D. I matrx form A I B I I I C D Note that the matrx elemets ths case are all real. EE 439 matrx method 8
9 Case - E > U o the left ad E < U o the rght. As before, we assume the terface occurs at x x. For x < x, the potetal s U, ad for x > x, t s U. For x < x, we eed plae wave solutos ad for x > x, we use growg ad decayg expoetals. ) ) )] )] U E U x )] )] Usg the coecto rules at the terface: EE 439 matrx method 9
10 Solvg for A ad B terms of C ad D: I matrx form A B k k k k C D Note that the matrx elemets ths case are all complex. EE 439 matrx method 0
11 Case 3 - E < U o the left ad E > U o the rght. Same sog, thrd verse. For x < x, the potetal s U, ad for x > x, t s U. For x < x, we eed growg ad decayg expoetals ad for x > x, we use plae wave solutos. ) ) )] )] U E U x )] )] Usg the coecto rules at the terface: EE 439 matrx method
12 Solvg for A ad B terms of C ad D: I matrx form A B k k k k C D The matrx elemets are aga all complex. EE 439 matrx method
13 Case 4 - E < U o the left ad E < U o the rght. U Fal staza. Everythg s growg ad decayg expoetals. U E x ) ) )] )] )] )] Applyg the boudary codtos oe last tme: EE 439 matrx method 3
14 Solvg for A ad B terms of C ad D: I matrx form A B C D The matrx elemets are aga all real. EE 439 matrx method 4
15 Summary: E > U E > U E > U E < U E < U E > U E < U E < U EE 439 I I k k k k I I k k k k k k k k k k k k It s pretty easy to see that k should be replaced wth -α regos where E < U. matrx method 5
16 Propagato matrces As a travelg wave crosses a rego where E > U, t chages phase. For a wave travelg the x drecto, we ca wrte by specto: C A expk L ) E U A k C For a wave travelg the -x drecto B D B exp k L ) x x D Expressed matrx form: apple A B apple P P P P apple C D apple A B apple exp k L ) 0 0 expk L ) apple C D EE 439 matrx method 6
17 For a evaescet wave a rego where E < U, there s o phase chage, but the ampltude must chage expoetally. C A exp L ) D B exp L ) U E A α C B D x x I ths case, the propagato matrx s apple A B apple exp L ) 0 0 exp L ) apple C D EE 439 matrx method 7
18 U 4 cdet trasmtted U U U 3 U 5 reflected L L 3 L 4 Lookg aga at the potetal posed o the frst slde, we see that we eed a whole strg of terface ad propagato matrces. apple r [I ][P ][I 3][P 3 ][I 34][P 4 ][I 45] apple t 0 apple M M M M apple t 0 t M r M M T k 5 k t k 5 k M R k r k M M EE 439 matrx method 8
19 The matrx techque leds tself well to programmg Matlab or some other laguage. However, hadlg the terfaces s a bt uweldy sce the terface matrx volve propertes of two layers. It would be ce f everythg about a gve layer could be cluded oe matrx. Ca ths be doe? Look at the form of a terface matrx. I, 3 k k 4 k k 5 k k k Mathematcally, t ca be splt two: 3 I, p 4 apple k 5 p k ) k ) k The matrx wth k represets the left sde of the terface. The matrx wth k represets the rght sde of the terface. [, ][ ][ ] Alteratvely, the matrx wth k represets the rght ed of the th layer. The matrx wth k represets the left ed of the )st layer. EE 439 matrx method 9 k
20 Look a secto of the sequece of matrces from our orgal problem. [ ] [ ][ ][ ][ ][ ][ ][ ] Splt the terface matrces [ ] [ ][ ][ ][ ][ ][ ][ ][ ][ ][ ][ ] [ ][ ][ ][ ][ ][ ][ ][ ][ ][ ][ ] where [ ][ ][ ][ ][ ] [ ][ ][ ][ ] The layer matrx [M ] cotas all of the formato about a partcular layer. The parameters for layer show up oly that partcular matrx. Ths makes t easer to specfy ad compute the matrces a program. EE 439 matrx method 0
21 [ ][ ][ ][ ] I the layer, f E > U propagatg wave) [M ] k [M ] k exp k L ) 0 cos k L ) k s k L ) 0 exp k L ) k k k s k L ) cos k L ) If E < U evaescet wave) [M ] [M ] EE 439 exp 0 cosh L ) sh L ) L ) 0 exp L ) sh L ) cosh L ) matrx method
22 Example - tuelg through a square barrer redux) We ve doe ths before ad kow the result. Ths may a good test for our matrx approach. cdet reflected U 0 x 0 x L U U o trasmtted 3 There s a electro cdet from the left rego where U 0), so we eed a left half matrx for rego at x 0. We eed a layer matrx of the E < U varety) for the barrer. Fally, we must have a rght-half matrx for rego 3 at x L. Sce k k 3 k ad rego s characterzed by α, we ca dspese wth the subscrpts. [M] k k cosh L) sh L) sh L) cosh L) k k Now comes tedous algebra to get to the aswer. Note that to the fd the trasmsso probablty, we oly eed M. EE 439 matrx method
23 M cosh L) cosh k k ) sh sh ) EE 439 ) ) sh ) sh sh sh k3 T k M M sh L) ) ) ) ) As we saw earler whe we frst looked at tuelg. sh ) 6E Uo Uo E) exp L) matrx method 3
24 Boud states Ca the matrx method be used to lear somethg about boud states? It requres a slghtly dfferet approach, sce a boud state does ot propagate ad so we wll ot calculate trasmsso or reflecto probabltes. A boud state s characterzed by the requremet that ψ x ± ) 0. Ths requremet meas that, the put ad output regos, the wave fucto must be the form of a decayg expoetal. 0 B [M] C 0 M 0 So the matrx procedure would be to fd the total matrx descrpto for the problem, ad the fds the roots of the M matrx elemet. EE 439 matrx method 4
25 Example - fte heght square well redux) U U o U U o 3 L/ U 0 L/ 0 x To gve a quattatve comparso, use U o ev ad L m. Usg the eve / odd approach wth solvg the trascedetal characterstc equato repeatedly gve four solutos: w.3 E ev w E ev w.608 E 0.60 ev w E ev EE 439 matrx method 5
26 U Uo U Uo L/ ] [ ][ M EE 439 ][ L/ U0 0 [ 3 x ] cos kl) k k cos kl) k s kl) k s kl) cos kl) s kl) matrx method 6
27 To fd the boud states, set M 0 ad fd the roots. cos kl) k k s kl) 0 Of course, k ad α deped o eergy, so we wll be fdg partcular eerges for whch the above equato goes to 0. A easy way to see what s gog o s to make a plot. 6! 5! Just from the plot, we see that the approxmate eerges are: M! 4! 3!!! 0! -! -! 0! 0.! 0.! 0.3! 0.4! 0.5! 0.6! 0.7! 0.8! 0.9!! Eergy ev)! E 0.06 ev E 0.5 ev E ev E ev Wth just a bt of effort, the umbers ca be made more precse. EE 439 matrx method 7
Bezier curves. 1. Defining a Bezier curve. A closed Bezier curve can simply be generated by closing its characteristic polygon
Curve represetato Copyrght@, YZU Optmal Desg Laboratory. All rghts reserved. Last updated: Yeh-Lag Hsu (--). Note: Ths s the course materal for ME55 Geometrc modelg ad computer graphcs, Yua Ze Uversty.
More informationPoint Estimation-III: General Methods for Obtaining Estimators
Pot Estmato-III: Geeral Methods for Obtag Estmators RECAP 0.-0.6 Data: Radom Sample from a Populato of terest o Real valued measuremets: o Assumpto (Hopefully Reasoable) o Model: Specfed Probablty Dstrbuto
More informationOffice Hours. COS 341 Discrete Math. Office Hours. Homework 8. Currently, my office hours are on Friday, from 2:30 to 3:30.
Oce Hours Curretly, my oce hours are o Frday, rom :30 to 3:30. COS 31 Dscrete Math 1 Oce Hours Curretly, my oce hours are o Frday, rom :30 to 3:30. Nobody seems to care. Chage oce hours? Tuesday, 8 PM
More informationFace Recognition using Supervised & Unsupervised Techniques
Natoal Uversty of Sgapore EE5907-Patter recogto-2 NAIONAL UNIVERSIY OF SINGAPORE EE5907 Patter Recogto Project Part-2 Face Recogto usg Supervsed & Usupervsed echques SUBMIED BY: SUDEN NAME: harapa Reddy
More informationFor all questions, answer choice E) NOTA" means none of the above answers is correct. A) 50,500 B) 500,000 C) 500,500 D) 1,001,000 E) NOTA
For all questos, aswer choce " meas oe of the above aswers s correct.. What s the sum of the frst 000 postve tegers? A) 50,500 B) 500,000 C) 500,500 D),00,000. What s the sum of the tegers betwee 00 ad
More informationITEM ToolKit Technical Support Notes
ITEM ToolKt Notes Fault Tree Mathematcs Revew, Ic. 2875 Mchelle Drve Sute 300 Irve, CA 92606 Phoe: +1.240.297.4442 Fax: +1.240.297.4429 http://www.itemsoft.com Page 1 of 15 6/1/2016 Copyrght, Ic., All
More informationCapturing light. Source: A. Efros
Capturg lght Source: A. Efros Radometr What determes the brghtess of a mage pel? Sesor characterstcs Lght source propertes Eposure Surface shape ad oretato Optcs Surface reflectace propertes Slde b L.
More informationCOMSC 2613 Summer 2000
Programmg II Fal Exam COMSC 63 Summer Istructos: Name:. Prt your ame the space provded Studet Id:. Prt your studet detfer the space Secto: provded. Date: 3. Prt the secto umber of the secto whch you are
More informationChEn 475 Statistical Analysis of Regression Lesson 1. The Need for Statistical Analysis of Regression
Statstcal-Regresso_hadout.xmcd Statstcal Aalss of Regresso ChE 475 Statstcal Aalss of Regresso Lesso. The Need for Statstcal Aalss of Regresso What do ou do wth dvdual expermetal data pots? How are the
More informationClustering documents with vector space model using n-grams
Clusterg documets wth vector space model usg -grams Klas Skogmar, d97ksk@efd.lth.se Joha Olsso, d97jo@efd.lth.se Lud Isttute of Techology Supervsed by: Perre Nugues, Perre.Nugues@cs.lth.se Abstract Ths
More informationEight Solved and Eight Open Problems in Elementary Geometry
Eght Solved ad Eght Ope Problems Elemetary Geometry Floret Smaradache Math & Scece Departmet Uversty of New Mexco, Gallup, US I ths paper we revew eght prevous proposed ad solved problems of elemetary
More informationMATHEMATICAL PROGRAMMING MODEL OF THE CRITICAL CHAIN METHOD
MATHEMATICAL PROGRAMMING MODEL OF THE CRITICAL CHAIN METHOD TOMÁŠ ŠUBRT, PAVLÍNA LANGROVÁ CUA, SLOVAKIA Abstract Curretly there s creasgly dcated that most of classcal project maagemet methods s ot sutable
More informationNine Solved and Nine Open Problems in Elementary Geometry
Ne Solved ad Ne Ope Problems Elemetary Geometry Floret Smaradache Math & Scece Departmet Uversty of New Mexco, Gallup, US I ths paper we revew e prevous proposed ad solved problems of elemetary D geometry
More informationEight Solved and Eight Open Problems in Elementary Geometry
Eght Solved ad Eght Ope Problems Elemetary Geometry Floret Smaradache Math & Scece Departmet Uversty of New Mexco, Gallup, US I ths paper we revew eght prevous proposed ad solved problems of elemetary
More informationFitting. We ve learned how to detect edges, corners, blobs. Now what? We would like to form a. compact representation of
Fttg Fttg We ve leared how to detect edges, corers, blobs. Now what? We would lke to form a hgher-level, h l more compact represetato of the features the mage b groupg multple features accordg to a smple
More informationDescriptive Statistics: Measures of Center
Secto 2.3 Descrptve Statstcs: Measures of Ceter Frequec dstrbutos are helpful provdg formato about categorcal data, but wth umercal data we ma wat more formato. Statstc: s a umercal measure calculated
More informationMachine Learning: Algorithms and Applications
/03/ Mache Learg: Algorthms ad Applcatos Florao Z Free Uversty of Boze-Bolzao Faculty of Computer Scece Academc Year 0-0 Lecture 3: th March 0 Naïve Bayes classfer ( Problem defto A trag set X, where each
More informationPERSPECTIVES OF THE USE OF GENETIC ALGORITHMS IN CRYPTANALYSIS
PERSPECTIVES OF THE USE OF GENETIC ALGORITHMS IN CRYPTANALYSIS Lal Besela Sokhum State Uversty, Poltkovskaa str., Tbls, Georga Abstract Moder cryptosystems aalyss s a complex task, the soluto of whch s
More informationSimulator for Hydraulic Excavator
Smulator for Hydraulc Excavator Tae-Hyeog Lm*, Hog-Seo Lee ** ad Soo-Yog Yag *** * Departmet of Mechacal ad Automotve Egeerg, Uversty of Ulsa,Ulsa, Korea (Tel : +82-52-259-273; E-mal: bulbaram@mal.ulsa.ac.kr)
More informationCubic fuzzy H-ideals in BF-Algebras
OSR Joural of Mathematcs (OSR-JM) e-ssn: 78-578 p-ssn: 39-765X Volume ssue 5 Ver (Sep - Oct06) PP 9-96 wwwosrjouralsorg Cubc fuzzy H-deals F-lgebras Satyaarayaa Esraa Mohammed Waas ad U du Madhav 3 Departmet
More informationBlind Steganalysis for Digital Images using Support Vector Machine Method
06 Iteratoal Symposum o Electrocs ad Smart Devces (ISESD) November 9-30, 06 Bld Stegaalyss for Dgtal Images usg Support Vector Mache Method Marcelus Hery Meor School of Electrcal Egeerg ad Iformatcs Badug
More informationReflection models. Rendering equation. Taxonomy 2. Taxonomy 1. Digital Image Synthesis Yung-Yu Chuang 11/01/2005
Rederg equato Reflecto models Dgtal Image Sythess Yug-Yu Chuag 11/01/005 wth sldes by Pat Haraha ad Matt Pharr Taxoomy 1 ( xyt,,, θ, φλ, ) ( xyt,,, θφλ,, ) Geeral fucto = 1D Scatterg fucto = 9D out Assume
More informationChapter 3 Descriptive Statistics Numerical Summaries
Secto 3.1 Chapter 3 Descrptve Statstcs umercal Summares Measures of Cetral Tedecy 1. Mea (Also called the Arthmetc Mea) The mea of a data set s the sum of the observatos dvded by the umber of observatos.
More informationLP: example of formulations
LP: eample of formulatos Three classcal decso problems OR: Trasportato problem Product-m problem Producto plag problem Operatos Research Massmo Paolucc DIBRIS Uversty of Geova Trasportato problem The decso
More informationNEURO FUZZY MODELING OF CONTROL SYSTEMS
NEURO FUZZY MODELING OF CONTROL SYSTEMS Efré Gorrosteta, Carlos Pedraza Cetro de Igeería y Desarrollo Idustral CIDESI, Av Pe de La Cuesta 70. Des. Sa Pablo. Querétaro, Qro, Méxco gorrosteta@teso.mx pedraza@cdes.mx
More informationOn a Sufficient and Necessary Condition for Graph Coloring
Ope Joural of Dscrete Matheatcs, 04, 4, -5 Publshed Ole Jauary 04 (http://wwwscrporg/joural/ojd) http://dxdoorg/0436/ojd04400 O a Suffcet ad Necessary Codto for raph Colorg Maodog Ye Departet of Matheatcs,
More informationPEIECWISE CONSTANT LEVEL SET METHOD BASED FINITE ELEMENT ANALYSIS FOR STRUCTURAL TOPOLOGY OPTIMIZATION USING PHASE FIELD METHOD
INTERNATIONAL JOURNAL OF OPTIMIZATION IN CIVIL ENGINEERING It. J. Optm. Cvl Eg., 05; 5(4):389-407 PEIECWISE CONSTANT LEVEL SET METHOD BASED FINITE ELEMENT ANALYSIS FOR STRUCTURAL TOPOLOGY OPTIMIZATION
More informationOptimal Allocation of Complex Equipment System Maintainability
Optmal Allocato of Complex Equpmet System ataablty X Re Graduate School, Natoal Defese Uversty, Bejg, 100091, Cha edcal Protecto Laboratory, Naval edcal Research Isttute, Shagha, 200433, Cha Emal:rexs841013@163.com
More informationAutomated approach for the surface profile measurement of moving objects based on PSP
Uversty of Wollogog Research Ole Faculty of Egeerg ad Iformato Sceces - Papers: Part B Faculty of Egeerg ad Iformato Sceces 207 Automated approach for the surface profle measuremet of movg objects based
More informationAPR 1965 Aggregation Methodology
Sa Joaqu Valley Ar Polluto Cotrol Dstrct APR 1965 Aggregato Methodology Approved By: Sged Date: March 3, 2016 Araud Marjollet, Drector of Permt Servces Backgroud Health rsk modelg ad the collecto of emssos
More informationA Comparison of Heuristics for Scheduling Spatial Clusters to Reduce I/O Cost in Spatial Join Processing
Edth Cowa Uversty Research Ole ECU Publcatos Pre. 20 2006 A Comparso of Heurstcs for Schedulg Spatal Clusters to Reduce I/O Cost Spatal Jo Processg Jta Xao Edth Cowa Uversty 0.09/ICMLC.2006.258779 Ths
More informationInternational Mathematical Forum, 1, 2006, no. 31, ON JONES POLYNOMIALS OF GRAPHS OF TORUS KNOTS K (2, q ) Tamer UGUR, Abdullah KOPUZLU
Iteratoal Mathematcal Forum,, 6, o., 57-54 ON JONES POLYNOMIALS OF RAPHS OF TORUS KNOTS K (, q ) Tamer UUR, Abdullah KOPUZLU Atatürk Uverst Scece Facult Dept. of. Math. 54 Erzurum, Turkey tugur@atau.edu.tr
More informationCS 2710 Foundations of AI Lecture 22. Machine learning. Machine Learning
CS 7 Foudatos of AI Lecture Mache learg Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Mache Learg The feld of mache learg studes the desg of computer programs (agets) capable of learg from past eperece
More informationDEEP (Displacement Estimation Error Back-Propagation) Method for Cascaded ViSPs (Visually Servoed Paired Structured Light Systems)
DEEP (Dsplacemet Estmato Error Back-Propagato) Method for Cascaded VSPs (Vsually Servoed Pared Structured Lght Systems) Haem Jeo 1), Jae-Uk Sh 2), Wachoel Myeog 3), Yougja Km 4), ad *Hyu Myug 5) 1), 3),
More informationPerformance Impact of Load Balancers on Server Farms
erformace Impact of Load Balacers o Server Farms Ypg Dg BMC Software Server Farms have gaed popularty for provdg scalable ad relable computg / Web servces. A load balacer plays a key role ths archtecture,
More informationImage Compression. CS 663, Ajit Rajwade
Image Compresso CS 663, Ajt Rajwade Image Compresso Process of covertg a mage fle to aother mage fle that occupes less storage space, wthout sacrfcg ts vsual cotet Useful for savg storage space, ad trasmsso
More information2 General Regression Neural Network (GRNN)
4 Geeral Regresso Neural Network (GRNN) GRNN, as proposed b oald F. Specht [Specht 9] falls to the categor of probablstc eural etworks as dscussed Chapter oe. Ths eural etwork lke other probablstc eural
More informationArea and Power Efficient Modulo 2^n+1 Multiplier
Iteratoal Joural of Moder Egeerg Research (IJMER) www.jmer.com Vol.3, Issue.3, May-Jue. 013 pp-137-1376 ISSN: 49-6645 Area ad Power Effcet Modulo ^+1 Multpler K. Ptambar Patra, 1 Saket Shrvastava, Sehlata
More informationMode Changes in Priority Pre-emptively Scheduled Systems. K. W. Tindell, A. Burns, A. J. Wellings
ode hages rorty re-emptvely Scheduled Systems. W. dell, A. Burs, A.. Wellgs Departmet of omputer Scece, Uversty of York, Eglad Abstract may hard real tme systems the set of fuctos that a system s requred
More informationA hybrid method using FAHP and TOPSIS for project selection Xuan Lia, Jiang Jiangb and Su Deng c
5th Iteratoal Coferece o Computer Sceces ad Automato Egeerg (ICCSAE 205) A hybrd method usg FAHP ad TOPSIS for project selecto Xua La, Jag Jagb ad Su Deg c College of Iformato System ad Maagemet, Natoal
More informationProf. Feng Liu. Winter /24/2019
Prof. Feg Lu Wter 209 http://www.cs.pd.edu/~flu/courses/cs40/ 0/24/209 Last Tme Feature detecto 2 Toda Feature matchg Fttg The followg sldes are largel from Prof. S. Lazebk 3 Wh etract features? Motvato:
More informationTransistor/Gate Sizing Optimization
Trasstor/Gate Szg Optmzato Gve: Logc etwork wth or wthout cell lbrary Fd: Optmal sze for each trasstor/gate to mmze area or power, both uder delay costrat Statc szg: based o tmg aalyss ad cosder all paths
More informationProbabilistic properties of topologies of finite metric spaces minimal fillings.
arxv:308.656v [math.mg] Aug 03 Probablstc propertes of topologes of fte metrc spaces mmal fllgs. Vsevolod Salkov Abstract I ths work we provde a way to troduce a probablty measure o the space of mmal fllgs
More informationEnumerating XML Data for Dynamic Updating
Eumeratg XML Data for Dyamc Updatg Lau Ho Kt ad Vcet Ng Departmet of Computg, The Hog Kog Polytechc Uversty, Hug Hom, Kowloo, Hog Kog cstyg@comp.polyu.edu.h Abstract I ths paper, a ew mappg model, called
More informationProcess Quality Evaluation based on Maximum Entropy Principle. Yuhong Wang, Chuanliang Zhang, Wei Dai a and Yu Zhao
Appled Mechacs ad Materals Submtted: 204-08-26 ISSN: 662-7482, Vols. 668-669, pp 625-628 Accepted: 204-09-02 do:0.4028/www.scetfc.et/amm.668-669.625 Ole: 204-0-08 204 Tras Tech Publcatos, Swtzerlad Process
More informationSome Interesting SAR Change Detection Studies
Some Iterestg SAR Chage Detecto Studes Lesle M. ovak Scetfc Sstems Compa, Ic. 500 West Cummgs Park, Sute 3000 Wobur, MA 080 USA E-mal lovak@ssc.co ovakl@charter.et ABSTRACT Performace results of coheret
More informationPHY 114 A General Physics II 11 AM-12:15 PM TR Olin 101
PHY 4 A Geeral Physcs II AM-:5 PM TR Ol Pla or Lecture 9 (Chapter 36): Optcal propertes o lght. Mrror relectos. Images lat ad sphercal mrrors 4/4/ PHY 4 A Sprg -- Lecture 9 4/4/ PHY 4 A Sprg -- Lecture
More informationDelay based Duplicate Transmission Avoid (DDA) Coordination Scheme for Opportunistic routing
Delay based Duplcate Trasmsso Avod (DDA) Coordato Scheme for Opportustc routg Ng L, Studet Member IEEE, Jose-Fera Martez-Ortega, Vcete Heradez Daz Abstract-Sce the packet s trasmtted to a set of relayg
More informationA Genetic K-means Clustering Algorithm Applied to Gene Expression Data
A Geetc K-meas Clusterg Algorthm Appled to Gee Expresso Data Fag-Xag Wu, W. J. Zhag, ad Athoy J. Kusal Dvso of Bomedcal Egeerg, Uversty of Sasatchewa, Sasatoo, S S7N 5A9, CANADA faw34@mal.usas.ca, zhagc@egr.usas.ca
More informationKeywords: complete graph, coloursignlesslaplacian matrix, coloursignlesslaplacian energy of a graph.
Amerca Iteratoal Joural of Research Scece, Techology, Egeerg & Mathematcs Avalable ole at http://www.asr.et ISSN (Prt): 38-3491, ISSN (Ole): 38-3580, ISSN (CD-ROM): 38-369 AIJRSTEM s a refereed, dexed,
More informationBiconnected Components
Presetato for use wth the textbook, Algorthm Desg ad Applcatos, by M. T. Goodrch ad R. Tamassa, Wley, 2015 Bcoected Compoets SEA PVD ORD FCO SNA MIA 2015 Goodrch ad Tamassa Bcoectvty 1 Applcato: Networkg
More informationEstimating the effect of semi-transparent low-height road traffic noise barriers with Ultra Weak Variational Formulation
Revsed Mauscrpt Clc here to dowload Mauscrpt: Estmatg the effect of sem-trasparet road traffc ose barrer wth Ultra Wea Varatoal Form Estmatg the effect of sem-trasparet low-heght road traffc ose barrers
More informationA Comparison of Univariate Smoothing Models: Application to Heart Rate Data Marcus Beal, Member, IEEE
A Comparso of Uvarate Smoothg Models: Applcato to Heart Rate Data Marcus Beal, Member, IEEE E-mal: bealm@pdx.edu Abstract There are a umber of uvarate smoothg models that ca be appled to a varety of olear
More informationCOMBINATORIAL METHOD OF POLYNOMIAL EXPANSION OF SYMMETRIC BOOLEAN FUNCTIONS
COMBINATORIAL MTHOD O POLYNOMIAL XPANSION O SYMMTRIC BOOLAN UNCTIONS Dala A. Gorodecky The Uted Isttute of Iformatcs Prolems of Natoal Academy of Sceces of Belarus, Msk,, Belarus, dala.gorodecky@gmal.com.
More informationMarcus Gallagher School of Information Technology and Electrical Engineering The University of Queensland QLD 4072, Australia
O the Importace of Dversty Mateace Estmato of Dstrbuto Algorthms Bo Yua School of Iformato Techology ad Electrcal Egeerg The Uversty of Queeslad QLD 4072, Australa +6-7-3365636 boyua@tee.uq.edu.au Marcus
More informationShort Vector SIMD Code Generation for DSP Algorithms
Short Vector SMD Code Geerato for DSP Algorthms Fraz Frachett Chrstoph Ueberhuber Appled ad Numercal Mathematcs Techcal Uversty of Vea Austra Markus Püschel José Moura Electrcal ad Computer Egeerg Carege
More informationUsing Linear-threshold Algorithms to Combine Multi-class Sub-experts
Usg Lear-threshold Algorthms to Combe Mult-class Sub-experts Chrs Mesterharm MESTERHA@CS.RUTGERS.EDU Rutgers Computer Scece Departmet 110 Frelghuyse Road Pscataway, NJ 08854 USA Abstract We preset a ew
More informationBicubic G 1 interpolation of arbitrary quad meshes using a 4-split
Bcubc G terpolato of arbtrary quad meshes usg a 4-splt Stefae Hahma, Georges-Perre Boeau, Baptste Caramaux Laboratore Jea Kutzma, Greoble Uersty, Frace Abstract We preset a pecewse b-cubc parametrc G sple
More informationEinführung in Visual Computing
Eführug Vsual Computg 868 Global Illumato Werer Purgathofer Surface-Rederg Methods polygo rederg methods ray tracg global llumato evromet mappg teture mappg bump mappg Werer Purgathofer Global Illumato
More informationPolynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0
Polyomial Fuctios ad Models 1 Learig Objectives 1. Idetify polyomial fuctios ad their degree 2. Graph polyomial fuctios usig trasformatios 3. Idetify the real zeros of a polyomial fuctio ad their multiplicity
More informationA PROCEDURE FOR SOLVING INTEGER BILEVEL LINEAR PROGRAMMING PROBLEMS
ISSN: 39-8753 Iteratoal Joural of Iovatve Research Scece, Egeerg ad Techology A ISO 397: 7 Certfed Orgazato) Vol. 3, Issue, Jauary 4 A PROCEDURE FOR SOLVING INTEGER BILEVEL LINEAR PROGRAMMING PROBLEMS
More informationReview Statistics review 1: Presenting and summarising data Elise Whitley* and Jonathan Ball
Crtcal Care February Vol 6 No Whtley ad Ball Revew Statstcs revew : Presetg ad summarsg data Else Whtley* ad Joatha Ball *Lecturer Medcal Statstcs, Uversty of Brstol, Brstol, UK Lecturer Itesve Care Medce,
More informationMINIMIZATION OF THE VALUE OF DAVIES-BOULDIN INDEX
MIIMIZATIO OF THE VALUE OF DAVIES-BOULDI IDEX ISMO ÄRÄIE ad PASI FRÄTI Departmet of Computer Scece, Uversty of Joesuu Box, FI-800 Joesuu, FILAD ABSTRACT We study the clusterg problem whe usg Daves-Bould
More informationMeshfree Analysis Using the Generalized Meshfree (GMF) Approximation
11 th Iteratoal LS-DYNA Users Coferece Smulato (4) Meshfree Aalyss Usg the Geeralzed Meshfree (GMF) Approxmato Chug-Kyu Park *, Cheg-Tag Wu ** ad Cg-Dao (Steve) Ka * * Natoal Crash Aalyss Ceter (NCAC),
More informationECE Digital Image Processing and Introduction to Computer Vision
ECE59064 Dgtal Image Processg ad Itroducto to Computer Vso Depart. of ECE NC State Uverst Istructor: Tafu Matt Wu Sprg 07 Outle Recap Le Segmet Detecto Fttg Least square Total square Robust estmator Hough
More informationA MapReduce-Based Multiple Flow Direction Runoff Simulation
A MapReduce-Based Multple Flow Drecto Ruoff Smulato Ahmed Sdahmed ad Gyozo Gdofalv GeoIformatcs, Urba lag ad Evromet, KTH Drottg Krstas väg 30 100 44 Stockholm Telephoe: +46-8-790 8709 Emal:{sdahmed, gyozo}@
More informationSoftware Clustering Techniques and the Use of Combined Algorithm
Software Clusterg Techques ad the Use of Combed Algorthm M. Saeed, O. Maqbool, H.A. Babr, S.Z. Hassa, S.M. Sarwar Computer Scece Departmet Lahore Uversty of Maagemet Sceces DHA Lahore, Paksta oaza@lums.edu.pk
More informationEDGE- ODD Gracefulness of the Tripartite Graph
EDGE- ODD Graceuless o the Trpartte Graph C. Vmala, A. Saskala, K. Ruba 3, Asso. Pro, Departmet o Mathematcs, Peryar Maamma Uversty, Vallam, Thajavur Post.. Taml Nadu, Ida. 3 M. Phl Scholar, Departmet
More informationSALAM A. ISMAEEL Computer Man College for Computer Studies, Khartoum / Sudan
AAPTIVE HYBRI-WAVELET ETHO FOR GPS/ SYSTE INTEGRATION SALA A. ISAEEL Computer a College for Computer Studes, Khartoum / Suda salam.smaeel@gmal.com ABSTRACT I ths paper, a techque for estmato a global postog
More informationPreventing Information Leakage in C Applications Using RBAC-Based Model
Proceedgs of the 5th WSEAS It. Cof. o Software Egeerg Parallel ad Dstrbuted Systems Madrd Spa February 5-7 2006 (pp69-73) Prevetg Iformato Leakage C Applcatos Usg RBAC-Based Model SHIH-CHIEN CHOU Departmet
More informationData Structures and Algorithms(2)
Mg Zhag Data Structures ad Algorthms Data Structures ad Algorthms(2) Istructor: Mg Zhag Textbook Authors: Mg Zhag, Tegjao Wag ad Haya Zhao Hgher Educato Press, 2008.6 (the "Eleveth Fve-Year" atoal plag
More informationBi-harmonic Surface Based As-Rigid-As-Possible Mesh Deformation
Joural of Computers Vol. 9 No. 4, 018, pp. 161-175 do:10.3966/1991159901808904013 B-harmoc Surface Based As-Rgd-As-Possble Mesh Deformato Qyu Su 1,*, Wagge Wa 1,, Xag Feg 1,, Guolag Che 1,, Muhammad Rzwa
More informationVanishing Point Detection: Representation Analysis and New Approaches
Publshed the Proceedgs of the th Iteratoal Coferece o Image Aalyss ad Processg (ICIAP ). IEEE. Persoal use of ths materal s permtted. However, permsso to reprt/republsh ths materal for advertsg or promotoal
More informationAPPLICATION OF CLUSTERING METHODS IN BANK S PROPENSITY MODEL
APPLICATION OF CLUSTERING METHODS IN BANK S PROPENSITY MODEL Sergej Srota Haa Řezaková Abstract Bak s propesty models are beg developed for busess support. They should help to choose clets wth a hgher
More informationPRIVATE set intersection (PSI) is a cryptographic protocol that
Effcet Delegated Prvate Set Itersecto o Outsourced Prvate Datasets Ayd Abad, Sotros Terzs, Roberto Metere, Chagyu Dog Abstract Prvate set tersecto (PSI) s a essetal cryptographc protocol that has may real
More informationDifferentiated Service of Streaming Media Playback Technology
Iteratoal Coferece o Advaced Iformato ad Commucato Techology for Educato (ICAICTE 2013) Dfferetated Servce of Streamg Meda Playback Techology CHENG Z-ao 1 MENG Bo 1 WANG Da-hua 1 ZHAO Yue 1 1 Iformatzato
More informationReliable Surface Extraction from Point-Clouds using Scanner-Dependent Parameters
1 Relable Surface Extracto from Pot-Clouds usg Scaer-Depedet Parameters Hrosh Masuda 1, Ichro Taaka 2, ad Masakazu Eomoto 3 1 The Uversty of Tokyo, masuda@sys.t.u-tokyo.ac.jp 2 Tokyo Dek Uversty, taaka@cck.deda.ac.jp
More informationSpatial Error Concealment Based on Bezier Curves Ocultamiento de Errores Espacial Mediante Curvas de Bezier
Computacó y Sstemas Vol. 9 Núm. 3, pp. 256-269 2006, CIC-IPN, ISSN 1405-5546, Impreso e Méxco Ocultameto de Errores Espacal Medate Curvas de Bezer Rogelo Hasmoto-Beltrá 1 ad Ashfaq A. Khokhar 2 1 Cetro
More informationSelf-intersection Avoidance for 3-D Triangular Mesh Model
Self-tersecto Avodace for 3-D Tragular Mesh Model Habtamu Masse Aycheh 1) ad M Ho Kyug ) 1) Departmet of Computer Egeerg, Ajou Uversty, Korea, ) Departmet of Dgtal Meda, Ajou Uversty, Korea, 1) hab01@ajou.ac.kr
More informationThis sample is not for commercial use. Springer Science+Business Media New York
Ths sample s ot for commercal use. Sprger Scece+Busess Meda New York Cotets (page umbers are ot fal oes) DEDICATION CONTENTS AUTHOR BIOGRAHY REFACE TO THE SECOND EDITION ACKNOWLEDGMENTS V VII XIII XV XIX
More informationTopology Design for Directional Range Extension Networks with Antenna Blockage
Topology Desg for Drectoal Rage Exteso etworks wth Atea Blockage Thomas Shake MIT Lcol Laboratory shake@ll.mt.edu Abstract Extedg the rage of local area surface etworks by usg small arcraft to relay traffc
More informationFUZZY SET APPROXIMATION BY WEIGHTED LEAST SQUARES REGRESSION
ANNALS OF THE FACULTY OF ENGINEERING HUNEDOARA 006, Tome IV, Fasccole, (ISSN 584 665) FACULTY OF ENGINEERING HUNEDOARA, 5, REVOLUTIEI, 338, HUNEDOARA FUZZY SET APPROXIMATION BY WEIGHTED LEAST SQUARES REGRESSION.
More informationA Framework for Block-Based Timing Sensitivity Analysis
39.3 Framework for Block-Based Tmg Sestvty alyss Sajay V. Kumar Chadramoul V. Kashyap Sach S. Sapatekar Uversty of Mesota Itel Corporato Uversty of Mesota Meapols MN 55455 Hllsboro OR 973 Meapols MN 55455
More informationA Type of Variation of Hamilton Path Problem with Applications
Edth Cowa Uersty Research Ole ECU Publcatos Pre. 20 2008 A Type of Varato of Hamlto Path Problem wth Applcatos Jta Xao Edth Cowa Uersty Ju Wag Wezhou Uersty, Zhejag, Cha 0.09/ICYCS.2008.470 Ths artcle
More informationBeijing University of Technology, Beijing , China; Beijing University of Technology, Beijing , China;
d Iteratoal Coferece o Machery, Materals Egeerg, Chemcal Egeerg ad Botechology (MMECEB 5) Research of error detecto ad compesato of CNC mache tools based o laser terferometer Yuemg Zhag, a, Xuxu Chu, b
More informationStatistical Techniques Employed in Atmospheric Sampling
Appedx A Statstcal Techques Employed Atmospherc Samplg A.1 Itroducto Proper use of statstcs ad statstcal techques s ecessary for assessg the qualty of ambet ar samplg data. For a comprehesve dscusso of
More informationA modified Logic Scoring Preference method for dynamic Web services evaluation and selection
A modfed Logc Scorg Preferece method for dyamc Web servces evaluato ad selecto Hog Qg Yu ad Herá Mola 2 Departmet of Computer Scece, Uversty of Lecester, UK hqy@mcs.le.ac.uk 2 Departmet of Iformatcs, School
More informationABSTRACT Keywords
A Preprocessg Scheme for Hgh-Cardalty Categorcal Attrbutes Classfcato ad Predcto Problems Daele Mcc-Barreca ClearCommerce Corporato 1100 Metrc Blvd. Aust, TX 78732 ABSTRACT Categorcal data felds characterzed
More informationNetwork Security Evaluation Based on Variable Weight Fuzzy Cloud Model
207 2 d Iteratoal Coferece o Computer Scece ad Techology (CST 207) ISBN: 978--60595-46-5 Networ Securty Evaluato Based o Varable Weght Fuzzy Cloud Model Yag JIANG a*, Cheg-ha LI, Zh-peg LI ad Mg-ca SUN
More informationNUMERICAL INTEGRATION BY GENETIC ALGORITHMS. Vladimir Morozenko, Irina Pleshkova
5 Iteratoal Joural Iformato Theores ad Applcatos, Vol., Number 3, 3 NUMERICAL INTEGRATION BY GENETIC ALGORITHMS Vladmr Morozeko, Ira Pleshkova Abstract: It s show that geetc algorthms ca be used successfully
More informationA New Newton s Method with Diagonal Jacobian Approximation for Systems of Nonlinear Equations
Joural of Mathematcs ad Statstcs 6 (3): 46-5, ISSN 549-3644 Scece Publcatos A New Newto s Method wth Dagoal Jacoba Appromato for Systems of Nolear Equatos M.Y. Wazr, W.J. Leog, M.A. Hassa ad M. Mos Departmet
More informationFuzzy Multi-objective Linear Programming Approach for Traveling Salesman Problem
Fuzzy Mult-objectve Lear Programmg Approach for Travelg Salesma Problem Ama Rehmat Pujab Uversty College of Iformato Techology Uversty of the Pujab, Lahore, Pasta ama_mmal@yahoo.com Ha Saeed Pujab Uversty
More informationLaplacian Meshes Deformation Based on the Offset of Sketching
JOURNAL OF SOFTWARE, VOL. 7, NO. 9, SEPTEMBER 202 2083 Laplaca Meshes Deformato Based o the Offset of Sketchg Sha Chemg School of Software, Harb Uversty of Scece ad Techology, Harb, Cha Emal: shachm@63.com
More informationComplex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.
Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal
More informationMath Section 2.2 Polynomial Functions
Math 1330 - Sectio. Polyomial Fuctios Our objectives i workig with polyomial fuctios will be, first, to gather iformatio about the graph of the fuctio ad, secod, to use that iformatio to geerate a reasoably
More informationCLUSTERING ASSISTED FUNDAMENTAL MATRIX ESTIMATION
CLUSERING ASSISED FUNDAMENAL MARIX ESIMAION Hao Wu ad Y Wa School of Iformato Scece ad Egeerg, Lazhou Uversty, Cha wuhao1195@163com, wayjs@163com ABSRAC I computer vso, the estmato of the fudametal matrx
More informationA Feature Based Method of Image Matching for computing stereo models
> REPLCE THIS LINE WITH YOUR PPER IDENTIFICTION NUMER (DOULE-CLICK HERE TO EDIT) < Feature ased Method of Image Matchg for computg stereo models Ch-Kou Shu bstract The purpose of ths paper s to propose
More informationConstructive Semi-Supervised Classification Algorithm and Its Implement in Data Mining
Costructve Sem-Supervsed Classfcato Algorthm ad Its Implemet Data Mg Arvd Sgh Chadel, Arua Twar, ad Naredra S. Chaudhar Departmet of Computer Egg. Shr GS Ist of Tech.& Sc. SGSITS, 3, Par Road, Idore (M.P.)
More informationEstimation of Co-efficient of Variation in PPS sampling.
It. Statstcal Ist.: Proc. 58th World Statstcal Cogress, 0, Dubl (Sesso CPS00) p.409 Estmato of Co-effcet of Varato PPS samplg. Archaa. V ( st Author) Departmet of Statstcs, Magalore Uverst Magalagagotr,
More informationIJIRST International Journal for Innovative Research in Science & Technology Volume 1 Issue 8 January 2015 ISSN (online):
IJIRST Iteratoal Joural for Iovatve Research Scece & Techology Volume Issue 8 Jauary 05 ISSN (ole): 349-600 Sestvty alyss of GR Method For Itutostc Fuzzy Iformato of MDM: The Results of Chage I The Weght
More information