SGNoise and AGDas - tools for processing of superconducting and absolute gravity data Vojtech Pálinkáš and Miloš Vaľko

Size: px
Start display at page:

Download "SGNoise and AGDas - tools for processing of superconducting and absolute gravity data Vojtech Pálinkáš and Miloš Vaľko"

Transcription

1 SGNose and AGDas - tools for processng of superconductng and absolute gravty data Vojtech Pálnkáš and Mloš Vaľko 1 Research Insttute of Geodesy, Topography and Cartography, Czech Republc

2 SGNose Web tool prmarly orented for SG data qualty control n near real tme Wrtten n PHP5 usng the GD graphcal lbrary Data are processed on daly bass from raw SG data (1 sec samplng rate) Inspred by the prevous works of Rosat et al. (23b, 24, 211, 213), whch establshed a standard feature of the nose spectrum analyss of SGs n the GGP network. The data qualty quantfcaton s represented by the evaluaton of ambent nose level at SG statons by spectral analyss of gravty resduals and ts vsualzaton through spectrograms and probablty densty functons.

3 SGNose - methodology 1 sec data, consstng of gravty and ar pressure sgnal (n ASCII or mseed format) Two basc steps based on Banka and Crossley (1999): computng resdual gravty seres spectral analyss of resduals The ambent nose levels at SG statons are quantfed by Power Spectral Denstes of gravty resduals from.15 mhz (111 mn) up to 61.5 mhz (16 s). Vsualsaton: Spectrograms Probablty densty functons (PDFs usng algorthms gven by McNamara and Buland 24 for sesmc data) Calbraton of gravty data; Subtracton of the tdes usng the observed or synthetc tdal parameters; Reducton for the redstrbuton of atmospherc masses usng local ar pressure data; Subtracton of a best fttng 9 th degree polynomal to elmnate the nstrumental drft and any resdual tdal sgnal. Wndowng wth a Hann wndow; Power Spectral Densty (PSD) estmaton accordng to Cooley and Tukey, (1965); PSD smoothng usng the 11-pont Parzen frequency wndow and ts expresson to decbels relatve to 1 (m/s 2 ) 2 /Hz through 1 log 1 PSD, where PSD s expressed n unts (m/s 2 ) 2 /Hz.

4 SGNose, Vsualzaton

5 SGNose, web page

6 SGNose, Daly outputs

7 Monthly overvew

8 Yearly overvew

9 SGNose, problem detecton

10 SGNose, Comparson

11 AGDAS, objectve Asolute Gravmeter Data Analyss Software wrtten n Matlab Why the AGDAS has been developped? valdaton of the g software accurate defnton of the reference nstrumental heght complex analyss of gravty resduals n spectral and tme doman expermental evaluaton of the questonable correcton from the fnte speed of lght.

12 AGDAS, raw AG data ), cos(2 ) sn (2 ) 24 2 ( ) 6 ( mod mod 4 ' 2 ' 3 ' ' t f b t f a t t g t t v z z c z t t ' t = sec : Tme Dstance pars for at least 7 zero crossngs

13 AGDAS, postonng dt dz v 2 2 g v z z 2 2 g v g g top 2 2 * * * t g t v z z 2 A * A 2 ) ( ) ( g v g g h eff Nebauer et al. (1995) Top of the drop Effectve poston Palnkas et al. (212)

14 AGDAS, effectve heght

15 AGDAS vs. g RefX: Dfference of.3 nm s -2

16 AGDAS, resduals

17 AGDAS, frnge choce Reference soluton Frnges: 3-629

18 AGDAS, spectrum Equally spaced data Non-equally tmed data 1) Interpolaton 2) Least squares spectral analyss

19 AGDAS, experment Correcton due to fnte speed of lght Surprsng theoretcal (Rothletner and Francs 211) and expermental results (Rothletner et al. 214), whch should lead to correct FG5 (and gravty reference too) for 4 Gal. AGDAS s easy to use for detecton f measured g s dependent on the velocty of the test mass

20 Summary and conclusons SGNose s provdng useful nformaton related to the qualty of SG data. Repeated absolute measurements at the staton mght be nmedately compared wth gravty resduals. There s a possblty to extend the nose level analyss to the sub-sesmc and tdal bands. The next work wll be orented for mprovement of automatc data processng. AGDAS s a powerful tool for advanced analyss of AG data as verfcaton of the correcton due to fnte speed of lght. Furher, AGDAS has some advantages wth respect to the g outputs as 1) analyss of resduals for a gven campagn, 2) accurate referencng of the results.

21 Thank you for your attenton!

A correction model for zenith dry delay of GPS signals using regional meteorological sites. GPS-based determination of atmospheric water vapour

A correction model for zenith dry delay of GPS signals using regional meteorological sites. GPS-based determination of atmospheric water vapour Geodetc Week 00 October 05-07, Cologne S4: Appled Geodesy and GNSS A correcton model for zenth dry delay of GPS sgnals usng regonal meteorologcal stes Xaoguang Luo Geodetc Insttute, Department of Cvl Engneerng,

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information

Invariant deformation parameters from GPS permanent networks using stochastic interpolation

Invariant deformation parameters from GPS permanent networks using stochastic interpolation Invarant deformaton parameters from GPS permanent networks usng stochastc nterpolaton Ludovco Bag, Poltecnco d Mlano, DIIAR Athanasos Dermans, Arstotle Unversty of Thessalonk Outlne Startng hypotheses

More information

Curve Fitting with the Least Square Method

Curve Fitting with the Least Square Method WIKI Document Number 5 Interpolaton wth Least Squares Curve Fttng wth the Least Square Method Mattheu Bultelle Department of Bo-Engneerng Imperal College, London Context We wsh to model the postve feedback

More information

A Hybrid Variational Iteration Method for Blasius Equation

A Hybrid Variational Iteration Method for Blasius Equation Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method

More information

Meshless Surfaces. presented by Niloy J. Mitra. An Nguyen

Meshless Surfaces. presented by Niloy J. Mitra. An Nguyen Meshless Surfaces presented by Nloy J. Mtra An Nguyen Outlne Mesh-Independent Surface Interpolaton D. Levn Outlne Mesh-Independent Surface Interpolaton D. Levn Pont Set Surfaces M. Alexa, J. Behr, D. Cohen-Or,

More information

NON-LINEAR CONVOLUTION: A NEW APPROACH FOR THE AURALIZATION OF DISTORTING SYSTEMS

NON-LINEAR CONVOLUTION: A NEW APPROACH FOR THE AURALIZATION OF DISTORTING SYSTEMS NON-LINEAR CONVOLUTION: A NEW APPROAC FOR TE AURALIZATION OF DISTORTING SYSTEMS Angelo Farna, Alberto Belln and Enrco Armellon Industral Engneerng Dept., Unversty of Parma, Va delle Scenze 8/A Parma, 00

More information

29th Monitoring Research Review: Ground-Based Nuclear Explosion Monitoring Technologies

29th Monitoring Research Review: Ground-Based Nuclear Explosion Monitoring Technologies TESTING THE SPECTRAL DECONVOLUTION ALGORITHM TOOL (SDAT) WITH XE SPECTRA Steven R. Begalsk, Kendra M. Foltz Begalsk, and Derek A. Haas The Unversty of Texas at Austn Sponsored by Army Space and Mssle Defense

More information

Suppose that there s a measured wndow of data fff k () ; :::; ff k g of a sze w, measured dscretely wth varable dscretzaton step. It s convenent to pl

Suppose that there s a measured wndow of data fff k () ; :::; ff k g of a sze w, measured dscretely wth varable dscretzaton step. It s convenent to pl RECURSIVE SPLINE INTERPOLATION METHOD FOR REAL TIME ENGINE CONTROL APPLICATIONS A. Stotsky Volvo Car Corporaton Engne Desgn and Development Dept. 97542, HA1N, SE- 405 31 Gothenburg Sweden. Emal: astotsky@volvocars.com

More information

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis Statstcal analyss usng matlab HY 439 Presented by: George Fortetsanaks Roadmap Probablty dstrbutons Statstcal estmaton Fttng data to probablty dstrbutons Contnuous dstrbutons Contnuous random varable X

More information

829. An adaptive method for inertia force identification in cantilever under moving mass

829. An adaptive method for inertia force identification in cantilever under moving mass 89. An adaptve method for nerta force dentfcaton n cantlever under movng mass Qang Chen 1, Mnzhuo Wang, Hao Yan 3, Haonan Ye 4, Guola Yang 5 1,, 3, 4 Department of Control and System Engneerng, Nanng Unversty,

More information

Radial-Basis Function Networks

Radial-Basis Function Networks Radal-Bass uncton Networs v.0 March 00 Mchel Verleysen Radal-Bass uncton Networs - Radal-Bass uncton Networs p Orgn: Cover s theorem p Interpolaton problem p Regularzaton theory p Generalzed RBN p Unversal

More information

Development of a Semi-Automated Approach for Regional Corrector Surface Modeling in GPS-Levelling

Development of a Semi-Automated Approach for Regional Corrector Surface Modeling in GPS-Levelling Development of a Sem-Automated Approach for Regonal Corrector Surface Modelng n GPS-Levellng G. Fotopoulos, C. Kotsaks, M.G. Sders, and N. El-Shemy Presented at the Annual Canadan Geophyscal Unon Meetng

More information

Digital Signal Processing

Digital Signal Processing Dgtal Sgnal Processng Dscrete-tme System Analyss Manar Mohasen Offce: F8 Emal: manar.subh@ut.ac.r School of IT Engneerng Revew of Precedent Class Contnuous Sgnal The value of the sgnal s avalable over

More information

The Fourier Transform

The Fourier Transform e Processng ourer Transform D The ourer Transform Effcent Data epresentaton Dscrete ourer Transform - D Contnuous ourer Transform - D Eamples + + + Jean Baptste Joseph ourer Effcent Data epresentaton Data

More information

Inductance Calculation for Conductors of Arbitrary Shape

Inductance Calculation for Conductors of Arbitrary Shape CRYO/02/028 Aprl 5, 2002 Inductance Calculaton for Conductors of Arbtrary Shape L. Bottura Dstrbuton: Internal Summary In ths note we descrbe a method for the numercal calculaton of nductances among conductors

More information

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient 58:080 Expermental Engneerng 1 OBJECTIVE Lab 2e Thermal System Response and Effectve Heat Transfer Coeffcent Warnng: though the experment has educatonal objectves (to learn about bolng heat transfer, etc.),

More information

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011 Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected

More information

Research Article Green s Theorem for Sign Data

Research Article Green s Theorem for Sign Data Internatonal Scholarly Research Network ISRN Appled Mathematcs Volume 2012, Artcle ID 539359, 10 pages do:10.5402/2012/539359 Research Artcle Green s Theorem for Sgn Data Lous M. Houston The Unversty of

More information

Assignment 4. Adsorption Isotherms

Assignment 4. Adsorption Isotherms Insttute of Process Engneerng Assgnment 4. Adsorpton Isotherms Part A: Compettve adsorpton of methane and ethane In large scale adsorpton processes, more than one compound from a mxture of gases get adsorbed,

More information

Section 8.1 Exercises

Section 8.1 Exercises Secton 8.1 Non-rght Trangles: Law of Snes and Cosnes 519 Secton 8.1 Exercses Solve for the unknown sdes and angles of the trangles shown. 10 70 50 1.. 18 40 110 45 5 6 3. 10 4. 75 15 5 6 90 70 65 5. 6.

More information

Beyond Zudilin s Conjectured q-analog of Schmidt s problem

Beyond Zudilin s Conjectured q-analog of Schmidt s problem Beyond Zudln s Conectured q-analog of Schmdt s problem Thotsaporn Ae Thanatpanonda thotsaporn@gmalcom Mathematcs Subect Classfcaton: 11B65 33B99 Abstract Usng the methodology of (rgorous expermental mathematcs

More information

Parametric fractional imputation for missing data analysis. Jae Kwang Kim Survey Working Group Seminar March 29, 2010

Parametric fractional imputation for missing data analysis. Jae Kwang Kim Survey Working Group Seminar March 29, 2010 Parametrc fractonal mputaton for mssng data analyss Jae Kwang Km Survey Workng Group Semnar March 29, 2010 1 Outlne Introducton Proposed method Fractonal mputaton Approxmaton Varance estmaton Multple mputaton

More information

Supplementary Information

Supplementary Information Supplementary Informaton Procedure for nose cross-correlaton computaton and stackng Fgure S syntheszes the cross-correlaton procedure. To normalze the sesmc nose n tme and spectral domans, we appled a

More information

High Precision Edge Detection Algorithm for Mechanical Parts

High Precision Edge Detection Algorithm for Mechanical Parts MEASUREMENT SCIENCE REVIEW, 18, (018), No., 65-71 Journal homepage: http://www.degruyter.com/vew/j/msr Hgh Precson Edge Detecton Algorthm for Mechancal Parts Zhenyun Duan 1, Nng Wang 1, Jngshun Fu 1, Wenhu

More information

Lecture 10: May 6, 2013

Lecture 10: May 6, 2013 TTIC/CMSC 31150 Mathematcal Toolkt Sprng 013 Madhur Tulsan Lecture 10: May 6, 013 Scrbe: Wenje Luo In today s lecture, we manly talked about random walk on graphs and ntroduce the concept of graph expander,

More information

Topic- 11 The Analysis of Variance

Topic- 11 The Analysis of Variance Topc- 11 The Analyss of Varance Expermental Desgn The samplng plan or expermental desgn determnes the way that a sample s selected. In an observatonal study, the expermenter observes data that already

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

APPLICATION OF EDDY CURRENT PRINCIPLES FOR MEASUREMENT OF TUBE CENTERLINE

APPLICATION OF EDDY CURRENT PRINCIPLES FOR MEASUREMENT OF TUBE CENTERLINE APPLICATION OF EDDY CURRENT PRINCIPLES FOR MEASUREMENT OF TUBE CENTERLINE DEFLECTION E. J. Chern Martn Maretta Laboratores 1450 South Rollng Road Baltmore, MD 21227 INTRODUCTION Tubes are a vtal component

More information

BACKGROUND SUBTRACTION WITH EIGEN BACKGROUND METHODS USING MATLAB

BACKGROUND SUBTRACTION WITH EIGEN BACKGROUND METHODS USING MATLAB BACKGROUND SUBTRACTION WITH EIGEN BACKGROUND METHODS USING MATLAB 1 Ilmyat Sar 2 Nola Marna 1 Pusat Stud Komputas Matematka, Unverstas Gunadarma e-mal: lmyat@staff.gunadarma.ac.d 2 Pusat Stud Komputas

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

SPANC -- SPlitpole ANalysis Code User Manual

SPANC -- SPlitpole ANalysis Code User Manual Functonal Descrpton of Code SPANC -- SPltpole ANalyss Code User Manual Author: Dale Vsser Date: 14 January 00 Spanc s a code created by Dale Vsser for easer calbratons of poston spectra from magnetc spectrometer

More information

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University PHYS 45 Sprng semester 7 Lecture : Dealng wth Expermental Uncertantes Ron Refenberger Brck anotechnology Center Purdue Unversty Lecture Introductory Comments Expermental errors (really expermental uncertantes)

More information

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017

U.C. Berkeley CS294: Beyond Worst-Case Analysis Luca Trevisan September 5, 2017 U.C. Berkeley CS94: Beyond Worst-Case Analyss Handout 4s Luca Trevsan September 5, 07 Summary of Lecture 4 In whch we ntroduce semdefnte programmng and apply t to Max Cut. Semdefnte Programmng Recall that

More information

Chapter 6. Supplemental Text Material

Chapter 6. Supplemental Text Material Chapter 6. Supplemental Text Materal S6-. actor Effect Estmates are Least Squares Estmates We have gven heurstc or ntutve explanatons of how the estmates of the factor effects are obtaned n the textboo.

More information

2 Finite difference basics

2 Finite difference basics Numersche Methoden 1, WS 11/12 B.J.P. Kaus 2 Fnte dfference bascs Consder the one- The bascs of the fnte dfference method are best understood wth an example. dmensonal transent heat conducton equaton T

More information

Comparative Studies of Law of Conservation of Energy. and Law Clusters of Conservation of Generalized Energy

Comparative Studies of Law of Conservation of Energy. and Law Clusters of Conservation of Generalized Energy Comparatve Studes of Law of Conservaton of Energy and Law Clusters of Conservaton of Generalzed Energy No.3 of Comparatve Physcs Seres Papers Fu Yuhua (CNOOC Research Insttute, E-mal:fuyh1945@sna.com)

More information

Math1110 (Spring 2009) Prelim 3 - Solutions

Math1110 (Spring 2009) Prelim 3 - Solutions Math 1110 (Sprng 2009) Solutons to Prelm 3 (04/21/2009) 1 Queston 1. (16 ponts) Short answer. Math1110 (Sprng 2009) Prelm 3 - Solutons x a 1 (a) (4 ponts) Please evaluate lm, where a and b are postve numbers.

More information

A large scale tsunami run-up simulation and numerical evaluation of fluid force during tsunami by using a particle method

A large scale tsunami run-up simulation and numerical evaluation of fluid force during tsunami by using a particle method A large scale tsunam run-up smulaton and numercal evaluaton of flud force durng tsunam by usng a partcle method *Mtsuteru Asa 1), Shoch Tanabe 2) and Masaharu Isshk 3) 1), 2) Department of Cvl Engneerng,

More information

EVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES

EVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES EVALUATION OF THE VISCO-ELASTIC PROPERTIES IN ASPHALT RUBBER AND CONVENTIONAL MIXES Manuel J. C. Mnhoto Polytechnc Insttute of Bragança, Bragança, Portugal E-mal: mnhoto@pb.pt Paulo A. A. Perera and Jorge

More information

RBF Neural Network Model Training by Unscented Kalman Filter and Its Application in Mechanical Fault Diagnosis

RBF Neural Network Model Training by Unscented Kalman Filter and Its Application in Mechanical Fault Diagnosis Appled Mechancs and Materals Submtted: 24-6-2 ISSN: 662-7482, Vols. 62-65, pp 2383-2386 Accepted: 24-6- do:.428/www.scentfc.net/amm.62-65.2383 Onlne: 24-8- 24 rans ech Publcatons, Swtzerland RBF Neural

More information

Basic Statistical Analysis and Yield Calculations

Basic Statistical Analysis and Yield Calculations October 17, 007 Basc Statstcal Analyss and Yeld Calculatons Dr. José Ernesto Rayas Sánchez 1 Outlne Sources of desgn-performance uncertanty Desgn and development processes Desgn for manufacturablty A general

More information

De-noising Method Based on Kernel Adaptive Filtering for Telemetry Vibration Signal of the Vehicle Test Kejun ZENG

De-noising Method Based on Kernel Adaptive Filtering for Telemetry Vibration Signal of the Vehicle Test Kejun ZENG 6th Internatonal Conference on Mechatroncs, Materals, Botechnology and Envronment (ICMMBE 6) De-nosng Method Based on Kernel Adaptve Flterng for elemetry Vbraton Sgnal of the Vehcle est Kejun ZEG PLA 955

More information

Support Vector Machines. Vibhav Gogate The University of Texas at dallas

Support Vector Machines. Vibhav Gogate The University of Texas at dallas Support Vector Machnes Vbhav Gogate he Unversty of exas at dallas What We have Learned So Far? 1. Decson rees. Naïve Bayes 3. Lnear Regresson 4. Logstc Regresson 5. Perceptron 6. Neural networks 7. K-Nearest

More information

SINGLE EVENTS, TIME SERIES ANALYSIS, AND PLANETARY MOTION

SINGLE EVENTS, TIME SERIES ANALYSIS, AND PLANETARY MOTION SINGLE EVENTS, TIME SERIES ANALYSIS, AND PLANETARY MOTION John N. Harrs INTRODUCTION The advent of modern computng devces and ther applcaton to tme-seres analyses permts the nvestgaton of mathematcal and

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Problem Points Score Total 100

Problem Points Score Total 100 Physcs 450 Solutons of Sample Exam I Problem Ponts Score 1 8 15 3 17 4 0 5 0 Total 100 All wor must be shown n order to receve full credt. Wor must be legble and comprehensble wth answers clearly ndcated.

More information

risk and uncertainty assessment

risk and uncertainty assessment Optmal forecastng of atmospherc qualty n ndustral regons: rsk and uncertanty assessment Vladmr Penenko Insttute of Computatonal Mathematcs and Mathematcal Geophyscs SD RAS Goal Development of theoretcal

More information

Average Decision Threshold of CA CFAR and excision CFAR Detectors in the Presence of Strong Pulse Jamming 1

Average Decision Threshold of CA CFAR and excision CFAR Detectors in the Presence of Strong Pulse Jamming 1 Average Decson hreshold of CA CFAR and excson CFAR Detectors n the Presence of Strong Pulse Jammng Ivan G. Garvanov and Chrsto A. Kabachev Insttute of Informaton echnologes Bulgaran Academy of Scences

More information

Generalized Linear Methods

Generalized Linear Methods Generalzed Lnear Methods 1 Introducton In the Ensemble Methods the general dea s that usng a combnaton of several weak learner one could make a better learner. More formally, assume that we have a set

More information

CHAPTER 4 SPEECH ENHANCEMENT USING MULTI-BAND WIENER FILTER. In real environmental conditions the speech signal may be

CHAPTER 4 SPEECH ENHANCEMENT USING MULTI-BAND WIENER FILTER. In real environmental conditions the speech signal may be 55 CHAPTER 4 SPEECH ENHANCEMENT USING MULTI-BAND WIENER FILTER 4.1 Introducton In real envronmental condtons the speech sgnal may be supermposed by the envronmental nterference. In general, the spectrum

More information

Chapter 4. Velocity analysis

Chapter 4. Velocity analysis 1 Chapter 4 Velocty analyss Introducton The objectve of velocty analyss s to determne the sesmc veloctes of layers n the subsurface. Sesmc veloctes are used n many processng and nterpretaton stages such

More information

Annexes. EC.1. Cycle-base move illustration. EC.2. Problem Instances

Annexes. EC.1. Cycle-base move illustration. EC.2. Problem Instances ec Annexes Ths Annex frst llustrates a cycle-based move n the dynamc-block generaton tabu search. It then dsplays the characterstcs of the nstance sets, followed by detaled results of the parametercalbraton

More information

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M CIS56: achne Learnng Lecture 3 (Sept 6, 003) Preparaton help: Xaoyng Huang Lnear Regresson Lnear regresson can be represented by a functonal form: f(; θ) = θ 0 0 +θ + + θ = θ = 0 ote: 0 s a dummy attrbute

More information

ITTC - Recommended Procedures and Guidelines

ITTC - Recommended Procedures and Guidelines Page of 9 able of Contents UCERAIY AALYSIS - EXAMPLE FOR WAERJE PROPULSIO ES... 2. PURPOSE OF PROCEDURE... 2 2. EXAMPLE FOR WAERJE PROPULSIO ES... 2 2. est Desgn... 2 2.2 Measurement Systems and Procedure...

More information

Report on Image warping

Report on Image warping Report on Image warpng Xuan Ne, Dec. 20, 2004 Ths document summarzed the algorthms of our mage warpng soluton for further study, and there s a detaled descrpton about the mplementaton of these algorthms.

More information

Phase I Monitoring of Nonlinear Profiles

Phase I Monitoring of Nonlinear Profiles Phase I Montorng of Nonlnear Profles James D. Wllams Wllam H. Woodall Jeffrey B. Brch May, 003 J.D. Wllams, Bll Woodall, Jeff Brch, Vrgna Tech 003 Qualty & Productvty Research Conference, Yorktown Heghts,

More information

Finding Dense Subgraphs in G(n, 1/2)

Finding Dense Subgraphs in G(n, 1/2) Fndng Dense Subgraphs n Gn, 1/ Atsh Das Sarma 1, Amt Deshpande, and Rav Kannan 1 Georga Insttute of Technology,atsh@cc.gatech.edu Mcrosoft Research-Bangalore,amtdesh,annan@mcrosoft.com Abstract. Fndng

More information

ADAPTIVE IMAGE FILTERING

ADAPTIVE IMAGE FILTERING Why adaptve? ADAPTIVE IMAGE FILTERING average detals and contours are aected Averagng should not be appled n contour / detals regons. Adaptaton Adaptaton = modyng the parameters o a prrocessng block accordng

More information

Laboratory 1c: Method of Least Squares

Laboratory 1c: Method of Least Squares Lab 1c, Least Squares Laboratory 1c: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly

More information

Laboratory 3: Method of Least Squares

Laboratory 3: Method of Least Squares Laboratory 3: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly they are correlated wth

More information

Polynomial Regression Models

Polynomial Regression Models LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance

More information

Pop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing

Pop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing Advanced Scence and Technology Letters, pp.164-168 http://dx.do.org/10.14257/astl.2013 Pop-Clc Nose Detecton Usng Inter-Frame Correlaton for Improved Portable Audtory Sensng Dong Yun Lee, Kwang Myung Jeon,

More information

Title: Radiative transitions and spectral broadening

Title: Radiative transitions and spectral broadening Lecture 6 Ttle: Radatve transtons and spectral broadenng Objectves The spectral lnes emtted by atomc vapors at moderate temperature and pressure show the wavelength spread around the central frequency.

More information

Measurement of Positional Deviation of Numerically Controlled Axes

Measurement of Positional Deviation of Numerically Controlled Axes XXXII. Semnar ASR '7 Instruments and Control, Farana, Smutný, Kočí & Babuch (eds) 7, VŠB-TUO, Ostrava, ISBN 978-8-48-7-4 Measurement of Postonal Devaton of Numercally Controlled Axes KUREKOVÁ, Eva, HAAJ,

More information

Complex Numbers, Signals, and Circuits

Complex Numbers, Signals, and Circuits Complex Numbers, Sgnals, and Crcuts 3 August, 009 Complex Numbers: a Revew Suppose we have a complex number z = x jy. To convert to polar form, we need to know the magntude of z and the phase of z. z =

More information

A Quantum Gauss-Bonnet Theorem

A Quantum Gauss-Bonnet Theorem A Quantum Gauss-Bonnet Theorem Tyler Fresen November 13, 2014 Curvature n the plane Let Γ be a smooth curve wth orentaton n R 2, parametrzed by arc length. The curvature k of Γ s ± Γ, where the sgn s postve

More information

Sequential Condition Diagnosis for Centrifugal Pump System Using Fuzzy Neural Network

Sequential Condition Diagnosis for Centrifugal Pump System Using Fuzzy Neural Network eural Informaton Processng Letters and Revews Vol., o. 3, March 007 LETTER Sequental Condton Dagnoss for Centrfugal Pump System Usng Fuzzy eural etwork Huaqng Wang and Peng Chen Department of Envronmental

More information

IMGS-261 Solutions to Homework #9

IMGS-261 Solutions to Homework #9 IMGS-6 Solutons to Homework #9. For f [] SINC [] sn[π], use the modulaton theorem to evaluate and sketch π the Fourer transform of f [] f [] f [] (f []) Soluton: We know that F{RECT []} SINC [] so we use

More information

Nodal analysis of finite square resistive grids and the teaching effectiveness of students projects

Nodal analysis of finite square resistive grids and the teaching effectiveness of students projects 2 nd World Conference on Technology and Engneerng Educaton 2 WIETE Lublana Slovena 5-8 September 2 Nodal analyss of fnte square resstve grds and the teachng effectveness of students proects P. Zegarmstrz

More information

Week 9 Chapter 10 Section 1-5

Week 9 Chapter 10 Section 1-5 Week 9 Chapter 10 Secton 1-5 Rotaton Rgd Object A rgd object s one that s nondeformable The relatve locatons of all partcles makng up the object reman constant All real objects are deformable to some extent,

More information

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

More information

Dynamics of a Superconducting Qubit Coupled to an LC Resonator

Dynamics of a Superconducting Qubit Coupled to an LC Resonator Dynamcs of a Superconductng Qubt Coupled to an LC Resonator Y Yang Abstract: We nvestgate the dynamcs of a current-based Josephson juncton quantum bt or qubt coupled to an LC resonator. The Hamltonan of

More information

Feb 14: Spatial analysis of data fields

Feb 14: Spatial analysis of data fields Feb 4: Spatal analyss of data felds Mappng rregularly sampled data onto a regular grd Many analyss technques for geophyscal data requre the data be located at regular ntervals n space and/or tme. hs s

More information

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016 U.C. Berkeley CS94: Spectral Methods and Expanders Handout 8 Luca Trevsan February 7, 06 Lecture 8: Spectral Algorthms Wrap-up In whch we talk about even more generalzatons of Cheeger s nequaltes, and

More information

U-Pb Geochronology Practical: Background

U-Pb Geochronology Practical: Background U-Pb Geochronology Practcal: Background Basc Concepts: accuracy: measure of the dfference between an expermental measurement and the true value precson: measure of the reproducblty of the expermental result

More information

GEMINI GEneric Multimedia INdexIng

GEMINI GEneric Multimedia INdexIng GEMINI GEnerc Multmeda INdexIng Last lecture, LSH http://www.mt.edu/~andon/lsh/ Is there another possble soluton? Do we need to perform ANN? 1 GEnerc Multmeda INdexIng dstance measure Sub-pattern Match

More information

GEO-SLOPE International Ltd, Calgary, Alberta, Canada Vibrating Beam

GEO-SLOPE International Ltd, Calgary, Alberta, Canada   Vibrating Beam GEO-SLOPE Internatonal Ltd, Calgary, Alberta, Canada www.geo-slope.com Introducton Vbratng Beam Ths example looks at the dynamc response of a cantlever beam n response to a cyclc force at the free end.

More information

Statistical Circuit Optimization Considering Device and Interconnect Process Variations

Statistical Circuit Optimization Considering Device and Interconnect Process Variations Statstcal Crcut Optmzaton Consderng Devce and Interconnect Process Varatons I-Jye Ln, Tsu-Yee Lng, and Yao-Wen Chang The Electronc Desgn Automaton Laboratory Department of Electrcal Engneerng Natonal Tawan

More information

Homework Assignment 3 Due in class, Thursday October 15

Homework Assignment 3 Due in class, Thursday October 15 Homework Assgnment 3 Due n class, Thursday October 15 SDS 383C Statstcal Modelng I 1 Rdge regresson and Lasso 1. Get the Prostrate cancer data from http://statweb.stanford.edu/~tbs/elemstatlearn/ datasets/prostate.data.

More information

An Application of Fuzzy Hypotheses Testing in Radar Detection

An Application of Fuzzy Hypotheses Testing in Radar Detection Proceedngs of the th WSES Internatonal Conference on FUZZY SYSEMS n pplcaton of Fuy Hypotheses estng n Radar Detecton.K.ELSHERIF, F.M.BBDY, G.M.BDELHMID Department of Mathematcs Mltary echncal Collage

More information

Digital Modems. Lecture 2

Digital Modems. Lecture 2 Dgtal Modems Lecture Revew We have shown that both Bayes and eyman/pearson crtera are based on the Lkelhood Rato Test (LRT) Λ ( r ) < > η Λ r s called observaton transformaton or suffcent statstc The crtera

More information

Uncertainty as the Overlap of Alternate Conditional Distributions

Uncertainty as the Overlap of Alternate Conditional Distributions Uncertanty as the Overlap of Alternate Condtonal Dstrbutons Olena Babak and Clayton V. Deutsch Centre for Computatonal Geostatstcs Department of Cvl & Envronmental Engneerng Unversty of Alberta An mportant

More information

The 3D time dependent transformation model on the NOANET CORS GNSS. A collaboration between Geodesy & Geodynamics

The 3D time dependent transformation model on the NOANET CORS GNSS. A collaboration between Geodesy & Geodynamics The 3D tme dependent transformaton model on the NOANET CORS GNSS. A collaboraton between Geodesy & Geodynamcs N. KALAMAKIS 1, D. AMPATZIDIS 2, K. KATSAMBALOS 1 1 DEPA RTMEN T OF GEODESY & SURVEYING - A

More information

Assessment of Site Amplification Effect from Input Energy Spectra of Strong Ground Motion

Assessment of Site Amplification Effect from Input Energy Spectra of Strong Ground Motion Assessment of Ste Amplfcaton Effect from Input Energy Spectra of Strong Ground Moton M.S. Gong & L.L Xe Key Laboratory of Earthquake Engneerng and Engneerng Vbraton,Insttute of Engneerng Mechancs, CEA,

More information

STUDY ON TWO PHASE FLOW IN MICRO CHANNEL BASED ON EXPERI- MENTS AND NUMERICAL EXAMINATIONS

STUDY ON TWO PHASE FLOW IN MICRO CHANNEL BASED ON EXPERI- MENTS AND NUMERICAL EXAMINATIONS Blucher Mechancal Engneerng Proceedngs May 0, vol., num. www.proceedngs.blucher.com.br/evento/0wccm STUDY ON TWO PHASE FLOW IN MICRO CHANNEL BASED ON EXPERI- MENTS AND NUMERICAL EXAMINATIONS Takahko Kurahash,

More information

Natural Language Processing and Information Retrieval

Natural Language Processing and Information Retrieval Natural Language Processng and Informaton Retreval Support Vector Machnes Alessandro Moschtt Department of nformaton and communcaton technology Unversty of Trento Emal: moschtt@ds.untn.t Summary Support

More information

FFT Based Spectrum Analysis of Three Phase Signals in Park (d-q) Plane

FFT Based Spectrum Analysis of Three Phase Signals in Park (d-q) Plane Proceedngs of the 00 Internatonal Conference on Industral Engneerng and Operatons Management Dhaka, Bangladesh, January 9 0, 00 FFT Based Spectrum Analyss of Three Phase Sgnals n Park (d-q) Plane Anuradha

More information

High resolution entropy stable scheme for shallow water equations

High resolution entropy stable scheme for shallow water equations Internatonal Symposum on Computers & Informatcs (ISCI 05) Hgh resoluton entropy stable scheme for shallow water equatons Xaohan Cheng,a, Yufeng Ne,b, Department of Appled Mathematcs, Northwestern Polytechncal

More information

Section 8.3 Polar Form of Complex Numbers

Section 8.3 Polar Form of Complex Numbers 80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the

More information

Evaluation of Validation Metrics. O. Polach Final Meeting Frankfurt am Main, September 27, 2013

Evaluation of Validation Metrics. O. Polach Final Meeting Frankfurt am Main, September 27, 2013 Evaluaton of Valdaton Metrcs O. Polach Fnal Meetng Frankfurt am Man, September 7, 013 Contents What s Valdaton Metrcs? Valdaton Metrcs evaluated n DynoTRAIN WP5 Drawbacks of Valdaton Metrcs Conclusons

More information

This column is a continuation of our previous column

This column is a continuation of our previous column Comparson of Goodness of Ft Statstcs for Lnear Regresson, Part II The authors contnue ther dscusson of the correlaton coeffcent n developng a calbraton for quanttatve analyss. Jerome Workman Jr. and Howard

More information

A linear imaging system with white additive Gaussian noise on the observed data is modeled as follows:

A linear imaging system with white additive Gaussian noise on the observed data is modeled as follows: Supplementary Note Mathematcal bacground A lnear magng system wth whte addtve Gaussan nose on the observed data s modeled as follows: X = R ϕ V + G, () where X R are the expermental, two-dmensonal proecton

More information

Chapter 9: Statistical Inference and the Relationship between Two Variables

Chapter 9: Statistical Inference and the Relationship between Two Variables Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,

More information

Bezier curves. Michael S. Floater. August 25, These notes provide an introduction to Bezier curves. i=0

Bezier curves. Michael S. Floater. August 25, These notes provide an introduction to Bezier curves. i=0 Bezer curves Mchael S. Floater August 25, 211 These notes provde an ntroducton to Bezer curves. 1 Bernsten polynomals Recall that a real polynomal of a real varable x R, wth degree n, s a functon of the

More information

ERROR RESEARCH ON A HEPA FILTER MEDIA TESTING SYSTEM OF MPPS(MOST PENETRATION PARTICLE SIZE) EFFICIENCY

ERROR RESEARCH ON A HEPA FILTER MEDIA TESTING SYSTEM OF MPPS(MOST PENETRATION PARTICLE SIZE) EFFICIENCY Proceedngs: Indoor Ar 2005 ERROR RESEARCH ON A HEPA FILTER MEDIA TESTING SYSTEM OF MPPS(MOST PENETRATION PARTICLE SIZE) EFFICIENCY S Lu, J Lu *, N Zhu School of Envronmental Scence and Technology, Tanjn

More information

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be

More information

For all questions, answer choice E) NOTA" means none of the above answers is correct.

For all questions, answer choice E) NOTA means none of the above answers is correct. 0 MA Natonal Conventon For all questons, answer choce " means none of the above answers s correct.. In calculus, one learns of functon representatons that are nfnte seres called power 3 4 5 seres. For

More information

SIMPLE REACTION TIME AS A FUNCTION OF TIME UNCERTAINTY 1

SIMPLE REACTION TIME AS A FUNCTION OF TIME UNCERTAINTY 1 Journal of Expermental Vol. 5, No. 3, 1957 Psychology SIMPLE REACTION TIME AS A FUNCTION OF TIME UNCERTAINTY 1 EDMUND T. KLEMMER Operatonal Applcatons Laboratory, Ar Force Cambrdge Research Center An earler

More information