Additional Measurement Algorithms in the Overhauser Magnetometer POS-1
|
|
- Joleen York
- 5 years ago
- Views:
Transcription
1 Additionl Mesurement Algorithms in the Overhuser Mgnetometer POS-1 O.V. Denisov, A.Y. Denisov, V.A. Spunov (QM Lortory of Url Stte Tehnil University, Mir 19, Ekterinurg, , Russi) J.L. Rsson (Royl Meteorologil Institute, B-5670, Doures, Belgium) Introdution The Quntum Mgnetometry Lortory produes the proessor Overhuser mgnetometer POS-1 sine The mgnetometer is preision instrument for mesurements of the geomgneti field modulus with sensitivity up to 0.01 nt t n opertionl yle of 3 s. Besides the field mesurement the POS-1 hs dditionl possiilities for ontinuous monitoring of the ftors ffeting its funtioning. A sttistil nlysis of digitized periods of the nuler preession signl in single mesurement gives with good ury the signl to noise rtio, the level nd hrter of noise nd the dynmis of period (frequeny) hnges. In prtiulr, the knowledge of these prmeters llows one to estimte the devie sensitivity nd the field ehvior during mesurement, in ft to rry out the dditionl ontrol for oth the mgnetometer nd the mient mgneti onditions. Qulity of Mesurement Conditions In modern wek field nuler-preession mgnetometers, lgorithms re used whih trnsform the signl preession frequeny ω to the field modulus B (frequeny-field onversion) y proessing time-series of signl s zero rossing moments. In generl, mesured period is funtion of zero rossing time moments t i : T = F (t,t,...t ), N 0 where N is the numer of reorded zero rossings over mesurement. In presene of noise, t i 0 differs from the proper vlue t i. The stndrd devition (SD) of the field lulted vlue for n undmped signl nd unorrelted Gussin noise is: σ B 1 N σ N t FN = B, T i= 0 t i where σ t is the SD of the zero rossing time moments t i. The SD σ B is n unised prmeter of mesurement qulity under similr onditions. The distriution lw of the lulted field vlue is ssumed norml. In this se, the ove formul llows one to estimte the onfidene intervl under the given reliility index (onfidene proility), i.e. to ssess the dt reliility. The presented lgorithm for the mesurement rndom error estimtion t onfidene proility 0.68 (in ft SD) is relized in the POS-1. This funtion is lled the Qulity of Mesurement Conditions (QMC). At norml onditions nd yle of 3 s the POS-1 stores t lest thousnd of rossings. This ft llows us to relily estimte σ t nd thus to predit the SD σ B for series of the mesurements rried out t similr onditions nd t onstnt geomgneti field. The QMC is determined y the POS-1 t every field mesurement nd is trnsmitted y the RS232-port with the field vlue. This prmeter is intended for the ontinuous ontrol of sensitivity, whih depends on the internl devie noise nd externl ulturl disturnes. The QMC ws experimentlly tested using POS-1 mgnetometer nd POS-2 grdiometer
2 A numer of experiments were rried out t the nturl mgneti field (Arty oservtory), t RMI mgneti field stndrd (Doures oservtory) nd t the QML y the etlon signl trnsmission from the genertor to the sensor hed y mens of urrent loop. The results of the experiments showed tht for the norml noise the QMC prmeter ws in good qulittive greement with the rel mgnetometer sensitivity, unmiguously refleting the noise sitution hnges. Quntittive disgreement ws up to 100% (the QMC predited the est sensitivity). This n proly e explined y not hving inluded the noise orreltion in the QMC formul. However, suh disgreement is eptle for prmeters of this kind. At the Arty oservtory the QMC nd experimentlly otined SD were ompred while the mgneti field vritions were exluded y mens of the POS-2 grdiometer. Fig. 1 shows n exmple of reorded geomgneti field vritions nd field differene for sensors sped 1.8 m prt. The SD over the presented time mounts to nt nd the QMC verges to nt (the disgreement is 44%). Other dt showed the sme greement. Similr results were otined t the QML testing equipment y provoking vritions of genertor etlon frequeny nd signl mplitude orresponding to rel signl/noise vlues nd Gussin noise (for exmple fig. 3). However, when the signl/noise ws pproximtely 5 nd less, the QMC nd SD showed n rupt disgreement proly euse of normlity of zero rossing times t i. Also essentil disgreements were oserved for non- Gussin externl noise (espeilly impulse noise) t ll signl/noise vlues. This is explined y the nture of the QMC, whih is lulted for norml noise only. The striking exmple is POS-1 testing in the Doures stndrd t 3 vlues of stilized mgneti field (20 µt, 50 µt nd 78 µt). The mesurements were mde y the mnul freezing method: the field ws stilized nd then frozen during the polriztion nd frequeny mesurement to prevent mlfuntion of the field stilizer, whih ws pertured y the POS-1 polriztion mgneti field. Aording to testing results t 20 µt SD = nd verge QMC = 0.022, t 50µT SD = 0,075 nd verge QMC = 0.020, t 78µT SD = nd verge QMC = It is the impulse noise disturne tht uses suh essentil disgreement. Fig.1. Nturl geomgneti field B () nd field differene G () for sensors sped 1.8 m. Arty oservtory. One POS sensor: SD = nt, QMC = t, h As result of the QMC funtion disussion it must e noted: 270
3 1. In spite of some quntittive disgreement the QMC funtion reflets dequtely the rel mesurement onditions t norml noise. This prmeter llows one to ontrol the qulity nd dt uthentiity, to estimte the mient noise sitution nd to pre-tune the devie. All stndrd mgnetometers of POS fmily re supplied with this useful QMC funtion. 2. To inrese the QMC funtion ury the development of nother lgorithm is neessry, in prtiulr one, whih tkes into ount the noise orreltion. Time Derivtive Mode The seond feture to e tested is the dditionl funtion Time Derivtive Mode (TDM), whih is in development stge. This funtion is intended for mesurements of mgneti field vritions. Besides the field mesurement, the mgnetometer hs proessing lgorithm for extrting the time derivtive of field modulus (db/dt) in single mesurement. The ide of TDM onsists in using the zero rossing times rry umulted during mesurement time for the lultion of field vritions. This is relized y the lultion of two funtions for verge period determintion y N 1 rossings with dely n (n + N 1 = N): -1-1 ( F (t,t,...t ) F (t,t,...t ) B B D N 0 1 N N n n 1 n N ) t n The N 1 nd n prmeters re hosen suh tht the lgorithm error is minimized. The theoretil estimtions show tht for stndrd POS-1 sensor t mesurement time of 1.5 s (totl yle time is 3 s), the sensitivity (SD) for TDM is up to nt/s. For the lgorithm evlution the stndrd POS ws equipped with the dditionl TDM funtion. The tests were rried out in the nturl geomgneti field of Doures oservtory with n dded swtooth rtifiil field vrition supplied y oil system nd y mens of urrent genertor (see for exmple fig. 2-5). The results showed good greement of the TDM experimentl sensitivity nd theoretil predition. Fig.2. Nturl geomgneti field with dded swtooth rtifiil vrition y the oil system. Doures oservtory. Mesured field modulus B (). Derivtive db/dt determined y two sequentil mesurements (). Derivtive db/dt lulted y TDM (). 271
4 It is to e noted tht for the long-time vrition oservtion the TDM is t disdvntge n reltion to the method using two sequentil mesurements (fig. 2-5, nd ). Theoretilly, for equl yle times, the TDM sensitivity is pproximtely smller y ftor 11 thn the σ[b] = 0.01 QMC = σ[db/dt] = σ TDM [db/dt] = two-point method. If the yle time for TDM is pproximtely 2.6 times s lrge s the yle for the two-point method, the lgorithm sensitivities re lose. In ft, to hieve the sensitivity of the two-point method, speil sensor hed with relxtion time of the working sustne 2.6 times s lrge s for the stndrd POS-1 hed is neessry. However, when ssessing the TDM versus the two-point method, we must tke into ount tht the ltter my e strongly ffeted y lising errors, when one tries to ompute the derivtive for two mesurements widely seprted in time, wheres TDM will orretly estimte the two derivtives. The dvntges of the TDM re the simultneous determintion of the derivtive with the mgneti field mesurement nd orret response of the TDM funtion to rpid vritions with periods of the order of the mesurement time. The estimtion showed tht for the equl time intervl t (on whih derivtive B/ t is lulted) for the oth methods, the TDM funtion hs n dvntge ftor of 1.4. In onlusion it is possile to sum up the TDM nlysis: 1. The TDM funtion showed results in greement with the theoretil preditions. However the sensitivity of the TDM hieved y the stndrd POS-1 sensor is not enough for useful oservtion of stndrd vritions of the geomgneti field. The plnned sensitivity n e hieved y employing of speil sensor hed with long relxtion time of proton signl. 272 Fig.3. Exmple of reords B nd db/dt t simultion of undmped proton signl y the genertor signl. Signl mplitude pproprite to working signl of the POS-1. Mesured field modulus B (). Derivtive db/dt determined y two sequentil mesurements (). Derivtive db/dt lulted y TDM ().
5 2. The TDM funtion ury improvement is possile not only y wy of sensor speiliztion, ut lso t the expense of lgorithm improvement. 3. The TDM is t n dvntge in omprison with the stndrd methods when mesuring rpid vritions (up to 40 %). 4. The TDM funtion n e used s n dditionl ontrol prmeter of externl mgneti onditions for utonomous oservtories with low yle rtes nd for geologil explortions. 5. The development of the TDM nd the use of other estimtions of the rel mgneti sitution re mjor ontriution to the retion of smrt high-preision mgneti equipment 273
6 Fig.4. Simultion of field vrition 0.5 nt/s y the genertor signl, t whih field ws modulted y swtooth lw. Mesured field modulus B (). Derivtive db/dt determined y two sequentil mesurements (). Derivtive db/dt lulted y TDM (). Fig.5. Simultion of field vrition 0.25 nt/s y the genertor signl, t whih field ws modulted y swtooth lw. Mesured field modulus B (). Derivtive db/dt determined y two sequentil mesurements (). Derivtive db/dt lulted y TDM (). 274
CMPUT101 Introduction to Computing - Summer 2002
CMPUT Introdution to Computing - Summer 22 %XLOGLQJ&RPSXWHU&LUFXLWV Chpter 4.4 3XUSRVH We hve looked t so fr how to uild logi gtes from trnsistors. Next we will look t how to uild iruits from logi gtes,
More informationPhotovoltaic Panel Modelling Using a Stochastic Approach in MATLAB &Simulink
hotovolti nel Modelling Using Stohsti Approh in MATLAB &Simulink KAREL ZALATILEK, JAN LEUCHTER eprtment of Eletril Engineering University of efene Kouniov 65, 61 City of Brno CZECH REUBLIC krelzpltilek@unoz,
More informationCS 241 Week 4 Tutorial Solutions
CS 4 Week 4 Tutoril Solutions Writing n Assemler, Prt & Regulr Lnguges Prt Winter 8 Assemling instrutions utomtilly. slt $d, $s, $t. Solution: $d, $s, nd $t ll fit in -it signed integers sine they re 5-it
More informationP(r)dr = probability of generating a random number in the interval dr near r. For this probability idea to make sense we must have
Rndom Numers nd Monte Crlo Methods Rndom Numer Methods The integrtion methods discussed so fr ll re sed upon mking polynomil pproximtions to the integrnd. Another clss of numericl methods relies upon using
More informationA METHOD FOR CHARACTERIZATION OF THREE-PHASE UNBALANCED DIPS FROM RECORDED VOLTAGE WAVESHAPES
A METHOD FOR CHARACTERIZATION OF THREE-PHASE UNBALANCED DIPS FROM RECORDED OLTAGE WAESHAPES M.H.J. Bollen, L.D. Zhng Dept. Eletri Power Engineering Chlmers University of Tehnology, Gothenurg, Sweden Astrt:
More informationParallelization Optimization of System-Level Specification
Prlleliztion Optimiztion of System-Level Speifition Luki i niel. Gjski enter for Emedded omputer Systems University of liforni Irvine, 92697, US {li, gjski} @es.ui.edu strt This pper introdues the prlleliztion
More informationFEEDBACK: The standard error of a regression is not an unbiased estimator for the standard deviation of the error in a multiple regression model.
Introutory Eonometris: A Moern Approh 6th Eition Woolrige Test Bnk Solutions Complete ownlo: https://testbnkre.om/ownlo/introutory-eonometris-moern-pproh-6th-eition-jeffreym-woolrige-test-bnk/ Solutions
More informationGENG2140 Modelling and Computer Analysis for Engineers
GENG4 Moelling n Computer Anlysis or Engineers Letures 9 & : Gussin qurture Crete y Grn Romn Joles, PhD Shool o Mehnil Engineering, UWA GENG4 Content Deinition o Gussin qurture Computtion o weights n points
More informationError Numbers of the Standard Function Block
A.2.2 Numers of the Stndrd Funtion Blok evlution The result of the logi opertion RLO is set if n error ours while the stndrd funtion lok is eing proessed. This llows you to rnh to your own error evlution
More informationOutline. Motivation Background ARCH. Experiment Additional usages for Input-Depth. Regular Expression Matching DPI over Compressed HTTP
ARCH This work ws supported y: The Europen Reserh Counil, The Isreli Centers of Reserh Exellene, The Neptune Consortium, nd Ntionl Siene Foundtion wrd CNS-119748 Outline Motivtion Bkground Regulr Expression
More informationCS 551 Computer Graphics. Hidden Surface Elimination. Z-Buffering. Basic idea: Hidden Surface Removal
CS 55 Computer Grphis Hidden Surfe Removl Hidden Surfe Elimintion Ojet preision lgorithms: determine whih ojets re in front of others Uses the Pinter s lgorithm drw visile surfes from k (frthest) to front
More informationIntroduction to Algebra
INTRODUCTORY ALGEBRA Mini-Leture 1.1 Introdution to Alger Evlute lgeri expressions y sustitution. Trnslte phrses to lgeri expressions. 1. Evlute the expressions when =, =, nd = 6. ) d) 5 10. Trnslte eh
More informationDuality in linear interval equations
Aville online t http://ijim.sriu..ir Int. J. Industril Mthemtis Vol. 1, No. 1 (2009) 41-45 Dulity in liner intervl equtions M. Movhedin, S. Slhshour, S. Hji Ghsemi, S. Khezerloo, M. Khezerloo, S. M. Khorsny
More information10.2 Graph Terminology and Special Types of Graphs
10.2 Grph Terminology n Speil Types of Grphs Definition 1. Two verties u n v in n unirete grph G re lle jent (or neighors) in G iff u n v re enpoints of n ege e of G. Suh n ege e is lle inient with the
More informationThe Fundamental Theorem of Calculus
MATH 6 The Fundmentl Theorem of Clculus The Fundmentl Theorem of Clculus (FTC) gives method of finding the signed re etween the grph of f nd the x-xis on the intervl [, ]. The theorem is: FTC: If f is
More informationRight Angled Trigonometry. Objective: To know and be able to use trigonometric ratios in rightangled
C2 Right Angled Trigonometry Ojetive: To know nd e le to use trigonometri rtios in rightngled tringles opposite C Definition Trigonometry ws developed s method of mesuring ngles without ngulr units suh
More informationChapter 9. Greedy Technique. Copyright 2007 Pearson Addison-Wesley. All rights reserved.
Chpter 9 Greey Tehnique Copyright 2007 Person Aison-Wesley. All rights reserve. Greey Tehnique Construts solution to n optimiztion prolem piee y piee through sequene of hoies tht re: fesile lolly optiml
More informationa < a+ x < a+2 x < < a+n x = b, n A i n f(x i ) x. i=1 i=1
Mth 33 Volume Stewrt 5.2 Geometry of integrls. In this section, we will lern how to compute volumes using integrls defined by slice nlysis. First, we recll from Clculus I how to compute res. Given the
More informationDistance vector protocol
istne vetor protool Irene Finohi finohi@i.unirom.it Routing Routing protool Gol: etermine goo pth (sequene of routers) thru network from soure to Grph strtion for routing lgorithms: grph noes re routers
More informationFinal Exam Review F 06 M 236 Be sure to look over all of your tests, as well as over the activities you did in the activity book
inl xm Review 06 M 236 e sure to loo over ll of your tests, s well s over the tivities you did in the tivity oo 1 1. ind the mesures of the numered ngles nd justify your wor. Line j is prllel to line.
More informationUT1553B BCRT True Dual-port Memory Interface
UTMC APPICATION NOTE UT553B BCRT True Dul-port Memory Interfce INTRODUCTION The UTMC UT553B BCRT is monolithic CMOS integrted circuit tht provides comprehensive MI-STD- 553B Bus Controller nd Remote Terminl
More informationEngineer To Engineer Note
Engineer To Engineer Note EE-169 Technicl Notes on using Anlog Devices' DSP components nd development tools Contct our technicl support by phone: (800) ANALOG-D or e-mil: dsp.support@nlog.com Or visit
More informationOn the Detection of Step Edges in Algorithms Based on Gradient Vector Analysis
On the Detection of Step Edges in Algorithms Bsed on Grdient Vector Anlysis A. Lrr6, E. Montseny Computer Engineering Dept. Universitt Rovir i Virgili Crreter de Slou sin 43006 Trrgon, Spin Emil: lrre@etse.urv.es
More informationA Tautology Checker loosely related to Stålmarck s Algorithm by Martin Richards
A Tutology Checker loosely relted to Stålmrck s Algorithm y Mrtin Richrds mr@cl.cm.c.uk http://www.cl.cm.c.uk/users/mr/ University Computer Lortory New Museum Site Pemroke Street Cmridge, CB2 3QG Mrtin
More informationLecture 12 : Topological Spaces
Leture 12 : Topologil Spes 1 Topologil Spes Topology generlizes notion of distne nd loseness et. Definition 1.1. A topology on set X is olletion T of susets of X hving the following properties. 1. nd X
More informationDistance Computation between Non-convex Polyhedra at Short Range Based on Discrete Voronoi Regions
Distne Computtion etween Non-onvex Polyhedr t Short Rnge Bsed on Disrete Voronoi Regions Ktsuki Kwhi nd Hiroms Suzuki Deprtment of Preision Mhinery Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku,
More informationDoubts about how to use azimuth values from a Coordinate Object. Juan Antonio Breña Moral
Douts out how to use zimuth vlues from Coordinte Ojet Jun Antonio Breñ Morl # Definition An Azimuth is the ngle from referene vetor in referene plne to seond vetor in the sme plne, pointing towrd, (ut
More informationVSxF-2/-3/-4 SMALL LINEAR VALVES PN16 FOR MODULATING AND ON/OFF-CONTROL SPECIFICATIONS
VSxF2/3/4 SMLL LINER VLVES PN16 FOR MODULTING ND ON/OFFCONTROL VSxF2 VSxF3 VSxF4 GENERL These smll liner vlves re used in omintion with smll eletri liner vlve tutors nd thermoeletri tutors for the ontrol
More informationSlides for Data Mining by I. H. Witten and E. Frank
Slides for Dt Mining y I. H. Witten nd E. Frnk Simplicity first Simple lgorithms often work very well! There re mny kinds of simple structure, eg: One ttriute does ll the work All ttriutes contriute eqully
More informationA distributed edit-compile workflow
Time Synhroniztion nd Logil Cloks Tody 1. The need for time synhroniztion 2. Wll lok time synhroniztion 3. Logil Time: Lmport Cloks COS 418: Distriuted Systems Leture 4 Kyle Jmieson 2 A distriuted edit-ompile
More informationLecture 7: Integration Techniques
Lecture 7: Integrtion Techniques Antiderivtives nd Indefinite Integrls. In differentil clculus, we were interested in the derivtive of given rel-vlued function, whether it ws lgeric, eponentil or logrithmic.
More informationLesson 4.4. Euler Circuits and Paths. Explore This
Lesson 4.4 Euler Ciruits nd Pths Now tht you re fmilir with some of the onepts of grphs nd the wy grphs onvey onnetions nd reltionships, it s time to egin exploring how they n e used to model mny different
More informationSMALL SIZE EDGE-FED SIERPINSKI CARPET MICROSTRIP PATCH ANTENNAS
Progress In Eletromgnetis Reserh C, Vol. 3, 195 22, 28 SMALL SIZE EDGE-FED SIERPINSKI CARPET MICROSTRIP PATCH ANTENNAS W.-L. Chen nd G.-M. Wng Rdr Engineering Deprtment Missile Institute of Air Fore Engineering
More informationGreedy Algorithm. Algorithm Fall Semester
Greey Algorithm Algorithm 0 Fll Semester Optimiztion prolems An optimiztion prolem is one in whih you wnt to fin, not just solution, ut the est solution A greey lgorithm sometimes works well for optimiztion
More informationSelecting the Most Highly Correlated Pairs within a Large Vocabulary
Seleting the Most Highl Correlted Pirs within Lrge Voulr Koji Umemur Deprtment of Computer Siene Toohshi Universit of Tehnolog umemur@tutistutjp Astrt Ourene ptterns of words in douments n e epressed s
More informationLecture 10 Evolutionary Computation: Evolution strategies and genetic programming
Lecture 10 Evolutionry Computtion: Evolution strtegies nd genetic progrmming Evolution strtegies Genetic progrmming Summry Negnevitsky, Person Eduction, 2011 1 Evolution Strtegies Another pproch to simulting
More informationUTMC APPLICATION NOTE UT1553B BCRT TO INTERFACE PSEUDO-DUAL-PORT RAM ARCHITECTURE INTRODUCTION ARBITRATION DETAILS DESIGN SELECTIONS
UTMC APPLICATION NOTE UT1553B BCRT TO 80186 INTERFACE INTRODUCTION The UTMC UT1553B BCRT is monolithi CMOS integrte iruit tht provies omprehensive Bus Controller n Remote Terminl funtions for MIL-STD-
More informationLINX MATRIX SWITCHERS FIRMWARE UPDATE INSTRUCTIONS FIRMWARE VERSION
Overview LINX MATRIX SWITCHERS FIRMWARE UPDATE INSTRUCTIONS FIRMWARE VERSION 4.4.1.0 Due to the omplex nture of this updte, plese fmilirize yourself with these instrutions nd then ontt RGB Spetrum Tehnil
More informationthis grammar generates the following language: Because this symbol will also be used in a later step, it receives the
LR() nlysis Drwcks of LR(). Look-hed symols s eplined efore, concerning LR(), it is possile to consult the net set to determine, in the reduction sttes, for which symols it would e possile to perform reductions.
More informationCOMPUTATION AND VISUALIZATION OF REACHABLE DISTRIBUTION NETWORK SUBSTATION VOLTAGE
24 th Interntionl Conferene on Eletriity Distriution Glsgow, 12-15 June 2017 Pper 0615 COMPUTATION AND VISUALIZATION OF REACHABLE DISTRIBUTION NETWORK SUBSTATION VOLTAGE Mihel SANKUR Dniel ARNOLD Lun SCHECTOR
More information[SYLWAN., 158(6)]. ISI
The proposl of Improved Inext Isomorphi Grph Algorithm to Detet Design Ptterns Afnn Slem B-Brhem, M. Rizwn Jmeel Qureshi Fulty of Computing nd Informtion Tehnology, King Adulziz University, Jeddh, SAUDI
More informationMidterm Exam CSC October 2001
Midterm Exm CSC 173 23 Otoer 2001 Diretions This exm hs 8 questions, severl of whih hve suprts. Eh question indites its point vlue. The totl is 100 points. Questions 5() nd 6() re optionl; they re not
More informationSPLIT PLOT AND STRIP PLOT DESIGNS
1. Split Plot Design SPLIT PLOT AND STRIP PLOT DESIGNS D.K. SEHGAL Indin Agriculturl Sttistics Reserch Institute Lirry Avenue, New Delhi-110 01 dksehgl@isri.res.in 1.1 Introduction In conducting experiments,
More informationDistributed Systems Principles and Paradigms
Distriuted Systems Priniples nd Prdigms Christoph Dorn Distriuted Systems Group, Vienn University of Tehnology.dorn@infosys.tuwien..t http://www.infosys.tuwien..t/stff/dorn Slides dpted from Mrten vn Steen,
More informationA dual of the rectangle-segmentation problem for binary matrices
A dul of the rectngle-segmenttion prolem for inry mtrices Thoms Klinowski Astrct We consider the prolem to decompose inry mtrix into smll numer of inry mtrices whose -entries form rectngle. We show tht
More informationIntroduction to Integration
Introduction to Integrtion Definite integrls of piecewise constnt functions A constnt function is function of the form Integrtion is two things t the sme time: A form of summtion. The opposite of differentition.
More informationGeometrical reasoning 1
MODULE 5 Geometril resoning 1 OBJECTIVES This module is for study y n individul teher or group of tehers. It: looks t pprohes to developing pupils visulistion nd geometril resoning skills; onsiders progression
More informationParadigm 5. Data Structure. Suffix trees. What is a suffix tree? Suffix tree. Simple applications. Simple applications. Algorithms
Prdigm. Dt Struture Known exmples: link tble, hep, Our leture: suffix tree Will involve mortize method tht will be stressed shortly in this ourse Suffix trees Wht is suffix tree? Simple pplitions History
More informationCOMMON FRACTIONS. or a / b = a b. , a is called the numerator, and b is called the denominator.
COMMON FRACTIONS BASIC DEFINITIONS * A frtion is n inite ivision. or / * In the frtion is lle the numertor n is lle the enomintor. * The whole is seprte into "" equl prts n we re onsiering "" of those
More informationIn the last lecture, we discussed how valid tokens may be specified by regular expressions.
LECTURE 5 Scnning SYNTAX ANALYSIS We know from our previous lectures tht the process of verifying the syntx of the progrm is performed in two stges: Scnning: Identifying nd verifying tokens in progrm.
More informationPresentation Martin Randers
Presenttion Mrtin Rnders Outline Introduction Algorithms Implementtion nd experiments Memory consumption Summry Introduction Introduction Evolution of species cn e modelled in trees Trees consist of nodes
More informationChapter 4 Fuzzy Graph and Relation
Chpter 4 Fuzzy Grph nd Reltion Grph nd Fuzzy Grph! Grph n G = (V, E) n V : Set of verties(node or element) n E : Set of edges An edge is pir (x, y) of verties in V.! Fuzzy Grph ~ n ( ~ G = V, E) n V :
More information4452 Mathematical Modeling Lecture 4: Lagrange Multipliers
Mth Modeling Lecture 4: Lgrnge Multipliers Pge 4452 Mthemticl Modeling Lecture 4: Lgrnge Multipliers Lgrnge multipliers re high powered mthemticl technique to find the mximum nd minimum of multidimensionl
More informationMTH 146 Conics Supplement
105- Review of Conics MTH 146 Conics Supplement In this section we review conics If ou ne more detils thn re present in the notes, r through section 105 of the ook Definition: A prol is the set of points
More informationUnit 5 Vocabulary. A function is a special relationship where each input has a single output.
MODULE 3 Terms Definition Picture/Exmple/Nottion 1 Function Nottion Function nottion is n efficient nd effective wy to write functions of ll types. This nottion llows you to identify the input vlue with
More information6.045J/18.400J: Automata, Computability and Complexity. Quiz 2: Solutions. Please write your name in the upper corner of each page.
6045J/18400J: Automt, Computbility nd Complexity Mrh 30, 2005 Quiz 2: Solutions Prof Nny Lynh Vinod Vikuntnthn Plese write your nme in the upper orner of eh pge Problem Sore 1 2 3 4 5 6 Totl Q2-1 Problem
More information[Prakash* et al., 5(8): August, 2016] ISSN: IC Value: 3.00 Impact Factor: 4.116
[Prksh* et l 58: ugust 6] ISSN: 77-9655 I Vlue: Impt Ftor: 6 IJESRT INTERNTIONL JOURNL OF ENGINEERING SIENES & RESERH TEHNOLOGY SOME PROPERTIES ND THEOREM ON FUZZY SU-TRIDENT DISTNE Prveen Prksh* M Geeth
More informationProblem Final Exam Set 2 Solutions
CSE 5 5 Algoritms nd nd Progrms Prolem Finl Exm Set Solutions Jontn Turner Exm - //05 0/8/0. (5 points) Suppose you re implementing grp lgoritm tt uses ep s one of its primry dt strutures. Te lgoritm does
More informationM.R. Yeadon and M.A. King
Yedon, M.R. nd King, M.A. 008. Computer simultion modelling in sport. In Biomehnil Anlysis of Movement in Sport & Exerise (Eds C.J. Pyton nd R.M. Brtlett), pp. 176-05. London: Routledge. This hpter desries
More informationV = set of vertices (vertex / node) E = set of edges (v, w) (v, w in V)
Definitions G = (V, E) V = set of verties (vertex / noe) E = set of eges (v, w) (v, w in V) (v, w) orere => irete grph (igrph) (v, w) non-orere => unirete grph igrph: w is jent to v if there is n ege from
More informationEngineer To Engineer Note
Engineer To Engineer Note EE-208 Technicl Notes on using Anlog Devices' DSP components nd development tools Contct our technicl support by phone: (800) ANALOG-D or e-mil: dsp.support@nlog.com Or visit
More informationCalculus Differentiation
//007 Clulus Differentition Jeffrey Seguritn person in rowot miles from the nerest point on strit shoreline wishes to reh house 6 miles frther down the shore. The person n row t rte of mi/hr nd wlk t rte
More informationMixed-Signal Testability Analysis for Data-Converter IPs
Mixed-Signl Testility Anlysis for Dt-Converter IPs Arldo vn de Krts nd Hns G. Kerkhoff Testle Design nd Testing of Nnosystems Group MESA+ Institute for Nnotehnology 7500AE Enshede, the Netherlnds Emil:
More informationSingle-Layer Trunk Routing Using 45-Degree Lines within Critical Areas for PCB Routing
SASIMI 2010 Proeedings (R3-8) Single-Lyer Trunk Routing Using 45-Degree Lines within Critil Ares for PCB Routing Kyosuke SHINODA Yukihide KOHIRA Atsushi TAKAHASHI Tokyo Institute of Tehnology Dept. of
More informationMITSUBISHI ELECTRIC RESEARCH LABORATORIES Cambridge, Massachusetts. Introduction to Matroids and Applications. Srikumar Ramalingam
Cmrige, Msshusetts Introution to Mtrois n Applitions Srikumr Rmlingm MERL mm//yy Liner Alger (,0,0) (0,,0) Liner inepenene in vetors: v, v2,..., For ll non-trivil we hve s v s v n s, s2,..., s n 2v2...
More informationLecture 8: Graph-theoretic problems (again)
COMP36111: Advned Algorithms I Leture 8: Grph-theoreti prolems (gin) In Prtt-Hrtmnn Room KB2.38: emil: iprtt@s.mn..uk 2017 18 Reding for this leture: Sipser: Chpter 7. A grph is pir G = (V, E), where V
More informationPointwise convergence need not behave well with respect to standard properties such as continuity.
Chpter 3 Uniform Convergence Lecture 9 Sequences of functions re of gret importnce in mny res of pure nd pplied mthemtics, nd their properties cn often be studied in the context of metric spces, s in Exmples
More informationThe Network Layer: Routing in the Internet. The Network Layer: Routing & Addressing Outline
CPSC 852 Internetworking The Network Lyer: Routing in the Internet Mihele Weigle Deprtment of Computer Siene Clemson University mweigle@s.lemson.edu http://www.s.lemson.edu/~mweigle/ourses/ps852 1 The
More informationUNIT 5 PLANE TABLE SURVEYING
UNIT 5 PLANE TABLE SURVEYING Plne Tle Surveying Struture 5.1 Introdution Ojetives 5.2 Plne Tle 5.2.1 Bsi Priniple 5.2.2 Equipment 5.2.3 Aessories 5.2.4 Advntges nd Disdvntges 5.3 Setting Up the Plne Tle
More informationCOMP108 Algorithmic Foundations
Grph Theory Prudene Wong http://www.s.liv..uk/~pwong/tehing/omp108/201617 How to Mesure 4L? 3L 5L 3L ontiner & 5L ontiner (without mrk) infinite supply of wter You n pour wter from one ontiner to nother
More informationSOFTWARE-BUG LOCALIZATION WITH GRAPH MINING
Chpter 17 SOFTWARE-BUG LOCALIZATION WITH GRAPH MINING Frnk Eihinger Institute for Progrm Strutures nd Dt Orgniztion (IPD) Universit-t Krlsruhe (TH), Germny eihinger@ipd.uk.de Klemens B-ohm Institute for
More informationComputational geometry
Leture 23 Computtionl geometry Supplementl reding in CLRS: Chpter 33 exept 33.3 There re mny importnt prolems in whih the reltionships we wish to nlyze hve geometri struture. For exmple, omputtionl geometry
More informationExam #1 for Computer Simulation Spring 2005
Exm # for Computer Simultion Spring 005 >>> SOLUTION
More informationFault tree conversion to binary decision diagrams
Loughorough University Institutionl Repository Fult tree onversion to inry deision digrms This item ws sumitted to Loughorough University's Institutionl Repository y the/n uthor. Cittion: ANDREWS, J.D.
More informationChapter 2 Sensitivity Analysis: Differential Calculus of Models
Chpter 2 Sensitivity Anlysis: Differentil Clculus of Models Abstrct Models in remote sensing nd in science nd engineering, in generl re, essentilly, functions of discrete model input prmeters, nd/or functionls
More informationSIGNAL MODELING (2) PART II. RANDOM SIGNAL MODELING THE STOCHATIC MODELS
SIGNAL MODELING PART II. RANDOM SIGNAL MODELING THE STOHATI MODELS Autoegessive Moving Aveging Models The signl model: The eo to be minimied: Modified Yule-Wle eutions MYWE method: Etended Yule-Wle eutions
More informationDistributed Systems Principles and Paradigms. Chapter 11: Distributed File Systems
Distriuted Systems Priniples nd Prdigms Mrten vn Steen VU Amsterdm, Dept. Computer Siene steen@s.vu.nl Chpter 11: Distriuted File Systems Version: Deemer 10, 2012 2 / 14 Distriuted File Systems Distriuted
More information2 Computing all Intersections of a Set of Segments Line Segment Intersection
15-451/651: Design & Anlysis of Algorithms Novemer 14, 2016 Lecture #21 Sweep-Line nd Segment Intersection lst chnged: Novemer 8, 2017 1 Preliminries The sweep-line prdigm is very powerful lgorithmic design
More informationPattern Matching. Pattern Matching. Pattern Matching. Review of Regular Expressions
Pttern Mthing Pttern Mthing Some of these leture slides hve een dpted from: lgorithms in C, Roert Sedgewik. Gol. Generlize string serhing to inompletely speified ptterns. pplitions. Test if string or its
More informationThe Greedy Method. The Greedy Method
Lists nd Itertors /8/26 Presenttion for use with the textook, Algorithm Design nd Applictions, y M. T. Goodrich nd R. Tmssi, Wiley, 25 The Greedy Method The Greedy Method The greedy method is generl lgorithm
More informationMcAfee Web Gateway
Relese Notes Revision C MAfee We Gtewy 7.6.2.11 Contents Aout this relese Enhnement Resolved issues Instlltion instrutions Known issues Additionl informtion Find produt doumenttion Aout this relese This
More informationIMAGE COMPRESSION USING HIRARCHICAL LINEAR POLYNOMIAL CODING
Rsh Al-Tmimi et l, Interntionl Journl of Computer Siene nd Mobile Computing, Vol.4 Issue.1, Jnury- 015, pg. 11-119 Avilble Online t www.ijsm.om Interntionl Journl of Computer Siene nd Mobile Computing
More informationAnalysis of Computed Diffraction Pattern Diagram for Measuring Yarn Twist Angle
Textiles nd Light ndustril Science nd Technology (TLST) Volume 3, 2014 DO: 10.14355/tlist.2014.0301.01 http://www.tlist-journl.org Anlysis of Computed Diffrction Pttern Digrm for Mesuring Yrn Twist Angle
More informationSmall Business Networking
Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd processes. Introducing technology
More informationOn String Matching in Chunked Texts
On String Mtching in Chunked Texts Hnnu Peltol nd Jorm Trhio {hpeltol, trhio}@cs.hut.fi Deprtment of Computer Science nd Engineering Helsinki University of Technology P.O. Box 5400, FI-02015 HUT, Finlnd
More informationMulti-dimensional Selectivity Estimation Using Compressed Histogram Information*
Multi-dimensionl Seletivity Estimtion Using Compressed Histogrm Informtion* Ju-Hong Lee Deo-Hwn Kim Chin-Wn Chung À Deprtment of Informtion nd Communition Engineering À Deprtment of Computer Siene Kore
More informationCSCI 3130: Formal Languages and Automata Theory Lecture 12 The Chinese University of Hong Kong, Fall 2011
CSCI 3130: Forml Lnguges nd utomt Theory Lecture 12 The Chinese University of Hong Kong, Fll 2011 ndrej Bogdnov In progrmming lnguges, uilding prse trees is significnt tsk ecuse prse trees tell us the
More informationLETKF compared to 4DVAR for assimilation of surface pressure observations in IFS
LETKF compred to 4DVAR for ssimiltion of surfce pressure oservtions in IFS Pu Escrià, Mssimo Bonvit, Mts Hmrud, Lrs Isksen nd Pul Poli Interntionl Conference on Ensemle Methods in Geophysicl Sciences Toulouse,
More informationInter-domain Routing
COMP 631: NETWORKED & DISTRIBUTED SYSTEMS Inter-domin Routing Jsleen Kur Fll 2016 1 Internet-sle Routing: Approhes DV nd link-stte protools do not sle to glol Internet How to mke routing slle? Exploit
More informationAlgorithm Design (5) Text Search
Algorithm Design (5) Text Serch Tkshi Chikym School of Engineering The University of Tokyo Text Serch Find sustring tht mtches the given key string in text dt of lrge mount Key string: chr x[m] Text Dt:
More informationInternational Conference on Mechanics, Materials and Structural Engineering (ICMMSE 2016)
\ Interntionl Conference on Mechnics, Mterils nd tructurl Engineering (ICMME 2016) Reserch on the Method to Clibrte tructure Prmeters of Line tructured Light Vision ensor Mingng Niu1,, Kngnin Zho1, b,
More informationComparison between nmos Pass Transistor logic style vs. CMOS Complementary Cells*
Comprison etween nmos Pss Trnsistor logi style vs. CMOS Complementry Cells* Rkesh Mehrotr, Mssoud Pedrm Xunwei Wu Dept. of E.E.-Systems Dept. of Eletroni Eng. University of Southern Cliforni Hngzhou University
More informationClass-XI Mathematics Conic Sections Chapter-11 Chapter Notes Key Concepts
Clss-XI Mthemtics Conic Sections Chpter-11 Chpter Notes Key Concepts 1. Let be fixed verticl line nd m be nother line intersecting it t fixed point V nd inclined to it t nd ngle On rotting the line m round
More informationIZT DAB ContentServer, IZT S1000 Testing DAB Receivers Using ETI
IZT DAB ContentServer, IZT S1000 Testing DAB Receivers Using ETI Appliction Note Rel-time nd offline modultion from ETI files Generting nd nlyzing ETI files Rel-time interfce using EDI/ETI IZT DAB CONTENTSERVER
More informationFood Quality and Preference
Food Qulity nd Preference () Contents lists ville t ScienceDirect Food Qulity nd Preference journl homepge: www.elsevier.com/locte/foodqul Interpreting sensory dt y comining principl component nlysis nd
More informationCOSC 6374 Parallel Computation. Dense Matrix Operations
COSC 6374 Prllel Computtion Dense Mtrix Opertions Edgr Griel Fll Edgr Griel Prllel Computtion Edgr Griel erminology Dense Mtrix: ll elements of the mtrix ontin relevnt vlues ypilly stored s 2-D rry, (e.g.
More informationFig.1. Let a source of monochromatic light be incident on a slit of finite width a, as shown in Fig. 1.
Answer on Question #5692, Physics, Optics Stte slient fetures of single slit Frunhofer diffrction pttern. The slit is verticl nd illuminted by point source. Also, obtin n expression for intensity distribution
More informationComplete Coverage Path Planning of Mobile Robot Based on Dynamic Programming Algorithm Peng Zhou, Zhong-min Wang, Zhen-nan Li, Yang Li
2nd Interntionl Conference on Electronic & Mechnicl Engineering nd Informtion Technology (EMEIT-212) Complete Coverge Pth Plnning of Mobile Robot Bsed on Dynmic Progrmming Algorithm Peng Zhou, Zhong-min
More information1 The Definite Integral
The Definite Integrl Definition. Let f be function defined on the intervl [, b] where
More informationCOSC 6374 Parallel Computation. Non-blocking Collective Operations. Edgar Gabriel Fall Overview
COSC 6374 Prllel Computtion Non-loking Colletive Opertions Edgr Griel Fll 2014 Overview Impt of olletive ommunition opertions Impt of ommunition osts on Speedup Crtesin stenil ommunition All-to-ll ommunition
More information