Packet TDEV, MTIE, and MATIE  for Estimating the Frequency and Phase Stability of a Packet Slave Clock. Antti Pietiläinen


 Dina Ford
 3 years ago
 Views:
Transcription
1 Packet TDEV, MTIE, and MATIE  for Estimating the Frequency and Phase Stability of a Packet Slave Clock Antti Pietiläinen Soc Classification level 1 Nokia Siemens Networks
2 Expressing performance of clocks TIE = Time interval error MTIE = Maximum time interval error MTIE/ = Maximum frequency error estimate from MTIE TDEV = Time deviation MDEV = Modified Allan deviation MATIE = Maximum average time interval error (proposal) MAFE = Maximum average frequency error (proposal)
3 TIE [s] Time interval error (TIE) Indicates the phase of a clock compared with the phase of a reference clock. For example if the frequency of a clock is 10 ppb too large, TIE will increase by 10 ns every second. The quantities considered of interest in standardization bodies for characterization of time and frequency stability are calculated from TIE, e.g. MTIE, TDEV, and MDEV TIE of a packet clock synchronized over a DSL connection Time [s]
4 MTIE  maximum time interval error MTIE defines maximum wander within an observation window. The observation window is slid over the TIE data. The sizes of the observation windows are indicated by the xaxis values of the MTIE curve. MTIE is specified in G.810. MTIE masks have been specified, for example, in G.812, G.813, G.823, G.824, and G.8261 T = (N  1) 0 x(t) Time error = n0 xppk (from G.810) k k + n N i T
5 MTIE [s] MTIE of a practical packet clock Each observation window scanned over the TIE curve (see previous slide) will produce one point on the MTIE or MRTIE curve. MTIE of a TCXO based packet clock synchronized over a production network and DSL connection shoots about one order of magnitude over the PDH specifications. Does one need to average this clock for seconds to go below 16 ppb? 1E3 1E4 MTIE of clock MTIE of a packet clock synchronized over a DSL connection. 1E5 G Mbit/s traffic interf. G Mbit/s network interf. 1E6 15 ppb 16 ppb OBSAI RP1 1E tau [s]
6 TIE [s] MTIE is a pessimistic estimator when used to indicate achievable frequency stability MTIE predicts a stability of 127 ppb. However, by passing the signal through a 1 st order lowpass filter with time constant of 1000 s, the maximum frequency error drops to 67 ppb. With a better filter, the frequency error could be further reduced without widening the window. 60.0µs 40.0µs 20.0µs 0.0s 20.0µs 40.0µs 60.0µs MTIE(1000 s) =127 µs s MTIE/ = 127 ppb f max = 500 ppb Time [s] Original TIE MTIE(1000 s) = 12.8 µs. MTIE/ = 12.8 ppb f max = 67 ppb After passing lowpass filter of time constant = 1000 s
7 TDEV [s] Time deviation (TDEV) TDEV indicates phase variation of a clock as a function of averaging time. TDEV masks have been specified in various ITU T recommendations. TDEV averages out extremes. TDEV and MDEV (on following slide) are closely related, see formula. MDEV 3 TDEV 1E4 1E5 1E6 1E7 1E8 1E9 TDEV of a packet clock synchronized over a DSL connection tau [s]
8 Modified Allan deviation Modified Allan deviation (MDEV) (root of modified Allan variance) Is used to indicate frequency variation and frequency uncertainty of clocks. Because frequency variation is built up from various noise phenomena, the fundamental accuracy limits of a clock can be determined by averaging the clock frequency over a period of time. Consequently, Allan variance is indicated in graphs as a function of averaging window size. Allan variance is an averaging function and hides occasional bad performance. 1E8 16 ppb 1E9 MDEV of a packet clock synchronized over a DSL connection. 1E Averaging window, tau [s]
9 MATIE (maximum average time interval error), MAFE (maximum average frequency error) A new metric has to be defined. The target: a metric that Describes the upper bound of the phase or frequency error of a clock. Is presented as a function of averaging window width, similar to TDEV or MDEV. The averaging window size at which the metric drops below required level corresponds to the required local oscillator stability. Can be calculated from packet delay of fastest packets and from the phase (TIE) of a clock. Solution : Calculate average phase (or packet delay) difference between two windows next to each other. Find the maximum value of the difference over the whole data.
10 Calculating Maximum average time interval error Calculate x i = abs[average(blue)average(purple)] Slide the windows over the data and find max(x i ) Change the window size and start over until all window sizes of interest have been covered. MATIE can be calculated from time interval error (TIE) of a clock, as well as from packet delays. In case of packet delays, a percentile selection is done first. In the figure, 10 fastest of each 1000 consecutive samples have been preselected and averaged.
11 TIE [s] Average TIE [s] Determining the maximum average time interval error MATIE and max. average frequency error MAFE at = 3000s. TIE (time interval error) is averaged over observation window and maximum change between two consecutive windows is determined Note, this is a simplification. In reality the adjacent averaging windows slide over the data The example shows how MATIE MATIE at and = MAFE 3000 s are calculated at observation window of 3000 s. = 3000 s The metric is calculated at different window sizes. MATIE and MAFE are plotted as a function MATIE of observation window =53 µs MATIE = 53 µs size. (Max average freq. err. MAFE = MATIE/ =18 ppb) The function estimates achievable worstcase stability over the whole TIE measurement as a function of the averaging time of the clock algorithm Time [s]
12 MDEV, MAFE, max freq error, MTIE/ [relative] TIE [s] TIE Comparison between frequency stability estimations of MDEV, MTIE/, and MAFE (corresponds to TDEV, MTIE, and MATIE in phase domain) MDEV varies depending on fill ratio and is optimistic. MTIE/ is too pessimistic. The different metrics estimate very different frequency stability. It is expected that lowpass filtered max. frequency error will approach MAFE when the filter is more optimal. 1E6 80µs 40µs 1E7 0s 40µs 80µs 1E µs 80.0µs 60.0µs 40.0µs 20.0µs 1E9 0.0s 20.0µs 40.0µs 60.0µs 80.0µs µs Time [s] 1 Time [s] st order lowpass filtered maximum frequency error. Time constant = tau 16 ppb tau [s] MDEV MTIE/ MAFE
13 MATIE follows quite closely the framework set by TDEV and MTIE MATIE nk 1 ik n max x x 0 1k N2n1 1 n in i, n = 1, 2,..., integer part (N/2) TDEV n N 3n1 n j xi2n 2xin xi 6n N 3n 1 j1 i j 2, n = 1, 2,..., integer part N 3 MTIE( n 0 ) max max x x, n, N 1 k N n k i k n i min k i k n i 1, 2,... 1 n 0 is the observation window length, n is the number of samples in the window, 0 is the sample interval, N is the number of samples in the data set. Index variable i is incremented to scan across the window and k or j is incremented for sliding the window.
14 Estimating packet clock performance from packet delay variation Packet delay variation is not a good indicator of expected packet clock properties because the time interval error of a packet clock is a small and unknown fraction of the packet delay variation. Black: Timing packet delay Yellow: TIE of the packet clock
15 Describing packet networks capability to support packet timing: Proposals Measurement Author Published Time Minimum TDEV Symmetricom ITU contrib. accepted Minimum picking TDEV June 2007 Semtech ITU contrib. June 2007 Percentile TDEV Symmetricom No public or standards documents Fixed selection window percentile TDEV & MDEV Fixed selection window percentile MTIE Nokia Siemens Networks Nokia Siemens Networks Not published Taken into discussion in ~December 2007 ITU contrib. May 2008 MATIE, MAFE Nokia Siemens Networks ITU contrib. Sep 2008
16 Modified Allan deviation Comparing modified Allan deviation of packet clock and packet MDEV of delay values At small averaging windows the shortterm stability of the local oscillator dominates the packet clock performance. At certain point the curves almost combine. TDEV (and MDEV) first showed the link between packet delay variation and packet clock performance! 1E4 1E5 1E6 1E7 Packet delay MDEVs 0.1 % 1 % fastest packets selected from various window sizes 1E8 16 ppb 1E9 Packet clock MDEV 1E10 1E Averaging window, tau [s]
17 MTIE [s] Packet MTIE vs. packet clock MTIE Packet MTIE would allow setting a performance limit mask. However, in addition to being pessimistic considering frequency stability, it is difficult to find parameters for packet MTIE that would accurately describe the performance of a packet clock. 1E3 1/100 Minimum picking (6s window) 1E4 1/1000 Minimum picking (60s window) 1% averaging in 60s window 1/10000 Minimum picking (600s window) 1% averaging in 300s window 1% averaging in 600s window 1% averaging in 1200s window SLA requirement: Best at 60 s Best at 300 s Best at 600 s 1E5 Slave clock 16 ppb Observation interval [s]
18 MAFE [relative] Maximum average frequency error (MAFE) Regardless of the exact packet selection method used in calculating packet MAFE the curves remain within a reasonably small range. The packet MAFE and MAFE of the clock coincide at large averaging windows. 1E4 1E5 1E6 fpmafe, 1 %, 6s selection window (minimum picking) pmafe, 1% percentile packet MAFE fpmafe, 1 %, 60s selection window A candidate mask for reaching 15 ppb with 6000s averaging 1E7 MAFE of packet clock 1E8 1E tau [s]
19 Conclusions of packet timing metrics The introduction of packet TDEV in the form of mintdev was the longawaited breakthrough in correlating packet delay variation with packet clock performance. TDEV and MDEV describe average performance. Therefore, not accurately usable as limit values when occasionally protruding packet delay variations determine the boundary values of the performance. MTIE has been used traditionally also for defining frequency accuracy limits. However, in this use gives usually worse estimation than actual performance, incorporating thus unnecessary and variable safety margin. Further, it is difficult to match packet MTIE with packet clock MTIE. MATIE and MAFE seem to avoid these issues and the first analysis considering them as performance estimators and as limiting values have been promising. More analysis with various TIE data and reference slave clocks are still needed.
20 Backup formulas as pictures p. 7. p. 13. MATIE MDEV nk 1 ik n max x x 0 3 TDEV 1k N 2n1 1 n in i, n = 1, 2,..., integer part (N/2) TDEV n N 3n1 n j xi2n 2xin xi 6n N 3n 1 j1 i j MTIE( n 0 ) max max x x, n, N 1 k N n k i k n i min k i k n i 1, 2, , n = 1, 2,..., integer part N 3 n 0 is the observation window length, n is the number of samples in the window, 0 is the sample interval, N is the number of samples in the data set. Index variable i is incremented to scan across the window and k or j is incremented for sliding the window.
List of CTS Oscillators for Telecom Timing and Synchronization
Application Note 1. Foreword This document provides a list of possible CTS oscillators for various Stratum Levels, Wireless Synchronization and ToP (Timing over Packet) applications, which include the
More informationITSF 13 November, 2011 Status of ITU SG15 synchronization standards
ITSF 13 November, 2011 Status of ITU SG15 synchronization standards Presenter: JeanLoup Ferrant ITUT SG15 Q13 rapporteur sponsored by Calnex Solutions Ltd Synchronization activity in ITU SG15 Q2 Q4
More informationEvaluating 1588v2 Performance Rev 2
Evaluating 1588v2 Performance Rev 2 Application Note IEEE 1588v2 is the preferred protocol for transport frequency and phase synchronization over Ethernet, which is required for 3G and 4G mobile networks.
More informationSYNCHRONIZATION IN PACKET NETWORKS: TIMING METRICS AND MONITORING
SYNCHRONIZATION IN PACKET NETWORKS: TIMING METRICS AND MONITORING Charles Barry and Srinivas Bangalore Brilliant Telecommunications 307 Orchard City Drive, San Jose, CA 95008, USA Email: srinivas@brillianttelecom.com
More informationAlternative schemes for arraywide clock generation/distribution
Alternative schemes for arraywide clock generation/distribution FPI/ELEC parallel session CTA general meeting Toulouse, 17.05.11 A. Vollhardt, Universität Zürich Motivation For synchronising events from
More informationPacket Synchronization in Cellular Backhaul Networks By Patrick Diamond, PhD, Semtech Corporation
Packet Synchronization in Cellular Backhaul Networks By Patrick Diamond, PhD, Semtech Corporation (Semtech White Paper October 2008) INTRODUCTION For carriers to leverage costeffective IP networks to
More informationSynchronizace a stabilita. Měření SyncE a 1588v2 PTP s přístroji VeEX nové vlastnosti
Synchronizace a stabilita Měření SyncE a 1588v2 PTP s přístroji VeEX nové vlastnosti VeEX má velký náskok  Po 3 letech zaznamenáváme reálný nárůst zájmu o synchronní aplikace  Jedna z nejžádanějších
More informationIEEE 1588 in Mobile Networks Migration scenario. Antti Pietiläinen, Nokia Siemens Networks
IEEE 1588 in Mobile Networks Migration scenario Antti Pietiläinen, Nokia Siemens Networks 1 Introduction Migration of mobile networks into packet transport requires a new solution for distributing frequency
More informationBest Practices for Testing Ethernet and Network Synchronization at the Cell Site
Best Practices for Testing Ethernet and Network Synchronization at the Cell Site The explosive growth in the number of 4G mobile subscribers and everincreasing mobile data usage is driving mobile operators
More informationAgenda. clock tower in old city of Neuchatel. 2014 ADVA Optical Networking. All rights reserved.
Time and Phase Delivery and Assurance for TDLTE and LTEA Gil Biran General Manager WSTS, June 2014, San Jose Agenda Delivering time and phase in Mobile Backhaul networks Addressing the LTEA challenges
More informationINTERNATIONAL TELECOMMUNICATION UNION
INTERNATIONAL TELECOMMUNICATION UNION ITUT G.825 TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU (03/2000) SERIES G: TRANSMISSION SYSTEMS AND MEDIA, DIGITAL SYSTEMS AND NETWORKS Digital networks Quality
More informationSecuring GNSS with PTP & SyncE Adam Wertheimer Microsemi Adam.Wertheimer@microsemi.com. Power Matters
Securing GNSS with PTP & SyncE Adam Wertheimer Microsemi Adam.Wertheimer@microsemi.com Power Matters Introduction Base stations and other end nodes need reliable synchronization Typically GPS was used
More informationField Measurement Methodologies for Synchronization in Mobile Networks Neil Hobbs Director EMEA Technical Sales Support
Field Measurement Methodologies for Synchronization in Mobile Networks Neil Hobbs Director EMEA Technical Sales Support 2012 EXFO Inc. All rights reserved. 1 The Challenge Traditional frequency (Mbps/MHz/SyncE)
More informationPrecision Time Protocol (PTP/IEEE1588)
White Paper W H I T E P A P E R "Smarter Timing Solutions" Precision Time Protocol (PTP/IEEE1588) The Precision Time Protocol, as defined in the IEEE1588 standard, provides a method to precisely synchronize
More informationJitter Measurements in Serial Data Signals
Jitter Measurements in Serial Data Signals Michael Schnecker, Product Manager LeCroy Corporation Introduction The increasing speed of serial data transmission systems places greater importance on measuring
More informationPortable Time Interval Counter with Picosecond Precision
Portable Time Interval Counter with Picosecond Precision R. SZPLET, Z. JACHNA, K. ROZYC, J. KALISZ Department of Electronic Engineering Military University of Technology Gen. S. Kaliskiego 2, 00908 Warsaw
More informationInformation Technology Services will be updating the mark sense test scoring hardware and software on Monday, May 18, 2015. We will continue to score
Information Technology Services will be updating the mark sense test scoring hardware and software on Monday, May 18, 2015. We will continue to score all Spring term exams utilizing the current hardware
More informationJava Modules for Time Series Analysis
Java Modules for Time Series Analysis Agenda Clustering Nonnormal distributions Multifactor modeling Implied ratings Time series prediction 1. Clustering + Cluster 1 Synthetic Clustering + Time series
More informationElectronic Communications Committee (ECC) within the European Conference of Postal and Telecommunications Administrations (CEPT)
Page 1 Electronic Communications Committee (ECC) within the European Conference of Postal and Telecommunications Administrations (CEPT) ECC RECOMMENDATION (06)01 Bandwidth measurements using FFT techniques
More informationDeploying SyncE and IEEE 1588 in Wireless Backhaul
Power Matters Deploying SyncE and IEEE 1588 in Wireless Backhaul Mondy Lim mondy.lim@microsemi.com March 2012 Outline Why is Synchronization required in mobile networks? Synchronization in legacy mobile
More informationAbstract. Cycle Domain Simulator for PhaseLocked Loops
Abstract Cycle Domain Simulator for PhaseLocked Loops Norman James December 1999 As computers become faster and more complex, clock synthesis becomes critical. Due to the relatively slower bus clocks
More informationPurpose of Time Series Analysis. Autocovariance Function. Autocorrelation Function. Part 3: Time Series I
Part 3: Time Series I Purpose of Time Series Analysis (Figure from Panofsky and Brier 1968) Autocorrelation Function Harmonic Analysis Spectrum Analysis Data Window Significance Tests Some major purposes
More informationPhase coherency of CDMA caller location processing based on TCXO frequency reference with intermittent GPS correction
Phase coherency of CDMA caller location processing based on TCXO frequency reference with intermittent GPS correction Dingchen Lu, Alfredo Lopez, Surendran K. Shanmugam, John Nielsen and Gerard Lachapelle
More informationSilicon Laboratories, Inc. Rev 1.0 1
Clock Division with Jitter and Phase Noise Measurements Introduction As clock speeds and communication channels run at ever higher frequencies, accurate jitter and phase noise measurements become more
More informationMonitoring of Internet traffic and applications
Monitoring of Internet traffic and applications Chadi BARAKAT INRIA Sophia Antipolis, France Planète research group ETH Zurich October 2009 Email: Chadi.Barakat@sophia.inria.fr WEB: http://www.inria.fr/planete/chadi
More informationTime and Clocks. Time and Clocks. Time
Time and Clocks Time: we model the continuum of realtime as a directed timeline consisting of an infinite set {T} of instants with the following properties: {T} is an ordered set, i.e., if p and q are
More informationCharacterizing Digital Cameras with the Photon Transfer Curve
Characterizing Digital Cameras with the Photon Transfer Curve By: David Gardner Summit Imaging (All rights reserved) Introduction Purchasing a camera for high performance imaging applications is frequently
More informationMeasurement Systems Analysis MSA for Suppliers
Measurement Systems Analysis MSA for Suppliers Copyright 20032007 Raytheon Company. All rights reserved. R6σ is a Raytheon trademark registered in the United States and Europe. Raytheon Six Sigma is a
More informationComputer Networks and Internets, 5e Chapter 6 Information Sources and Signals. Introduction
Computer Networks and Internets, 5e Chapter 6 Information Sources and Signals Modified from the lecture slides of Lami Kaya (LKaya@ieee.org) for use CECS 474, Fall 2008. 2009 Pearson Education Inc., Upper
More informationALLAN VARIANCE ANALYSIS ON ERROR CHARACTERS OF LOW COST MEMS ACCELEROMETER MMA8451Q
HENRI COANDA AIR FORCE ACADEMY ROMANIA INTERNATIONAL CONFERENCE of SCIENTIFIC PAPER AFASES 04 Brasov, 4 May 04 GENERAL M.R. STEFANIK ARMED FORCES ACADEMY SLOVAK REPUBLIC ALLAN VARIANCE ANALYSIS ON ERROR
More informationClocks Basics in 10 Minutes or Less. Edgar Pineda Field Applications Engineer Arrow Components Mexico
Clocks Basics in 10 Minutes or Less Edgar Pineda Field Applications Engineer Arrow Components Mexico Presentation Overview Introduction to Clocks Clock Functions Clock Parameters Common Applications Summary
More informationA frequency distribution is a table used to describe a data set. A frequency table lists intervals or ranges of data values called data classes
A frequency distribution is a table used to describe a data set. A frequency table lists intervals or ranges of data values called data classes together with the number of data values from the set that
More informationMeasuring Line Edge Roughness: Fluctuations in Uncertainty
Tutor6.doc: Version 5/6/08 T h e L i t h o g r a p h y E x p e r t (August 008) Measuring Line Edge Roughness: Fluctuations in Uncertainty Line edge roughness () is the deviation of a feature edge (as
More informationLin s Concordance Correlation Coefficient
NSS Statistical Software NSS.com hapter 30 Lin s oncordance orrelation oefficient Introduction This procedure calculates Lin s concordance correlation coefficient ( ) from a set of bivariate data. The
More informationIEEE 1588 Frequency and Time & phase profiles at ITUT
IEEE 1588 Frequency and Time & phase profiles at ITUT Silvana Rodrigues, System Engineering, IDT, silvana.rodrigues@idt.com ITSF 2012 Time & Sync in Telecoms 68 November, 2012, Nice, France 2009 Integrated
More informationEngineering Problem Solving and Excel. EGN 1006 Introduction to Engineering
Engineering Problem Solving and Excel EGN 1006 Introduction to Engineering Mathematical Solution Procedures Commonly Used in Engineering Analysis Data Analysis Techniques (Statistics) Curve Fitting techniques
More informationAPPENDIX N. Data Validation Using Data Descriptors
APPENDIX N Data Validation Using Data Descriptors Data validation is often defined by six data descriptors: 1) reports to decision maker 2) documentation 3) data sources 4) analytical method and detection
More informationMaster s Thesis. A Study on Active Queue Management Mechanisms for. Internet Routers: Design, Performance Analysis, and.
Master s Thesis Title A Study on Active Queue Management Mechanisms for Internet Routers: Design, Performance Analysis, and Parameter Tuning Supervisor Prof. Masayuki Murata Author Tomoya Eguchi February
More informationAPSYN420A/B Specification 1.24. 0.6520.0 GHz Low Phase Noise Synthesizer
APSYN420A/B Specification 1.24 0.6520.0 GHz Low Phase Noise Synthesizer 1 Introduction The APSYN420 is a wideband low phasenoise synthesizer operating from 0.65 to 20 GHz. The nominal output power is
More information5 Transforming Time Series
5 Transforming Time Series In many situations, it is desirable or necessary to transform a time series data set before using the sophisticated methods we study in this course: 1. Almost all methods assume
More informationConfidence Intervals for Cp
Chapter 296 Confidence Intervals for Cp Introduction This routine calculates the sample size needed to obtain a specified width of a Cp confidence interval at a stated confidence level. Cp is a process
More informationITUT G Frequency Profile
ITUT G.8265.1  Frequency Profile Silvana Rodrigues, System Engineering, IDT, silvana.rodrigues@idt.com ITSF 2013 Time & Sync in Telecoms 57 November, 2013, Lisbon, Portugal 2009 Integrated Device Technology,
More informationIn this lesson you will learn to find zeros of polynomial functions that are not factorable.
2.6. Rational zeros of polynomial functions. In this lesson you will learn to find zeros of polynomial functions that are not factorable. REVIEW OF PREREQUISITE CONCEPTS: A polynomial of n th degree has
More informationStatistical Rules of Thumb
Statistical Rules of Thumb Second Edition Gerald van Belle University of Washington Department of Biostatistics and Department of Environmental and Occupational Health Sciences Seattle, WA WILEY AJOHN
More informationEXPERIMENTAL ERROR AND DATA ANALYSIS
EXPERIMENTAL ERROR AND DATA ANALYSIS 1. INTRODUCTION: Laboratory experiments involve taking measurements of physical quantities. No measurement of any physical quantity is ever perfectly accurate, except
More informationMaximizing Receiver Dynamic Range for Spectrum Monitoring
Home Maximizing Receiver Dynamic Range for Spectrum Monitoring Brian Avenell, National Instruments Corp., Austin, TX October 15, 2012 As consumers continue to demand more data wirelessly through mobile
More informationMultiple Optimization Using the JMP Statistical Software Kodak Research Conference May 9, 2005
Multiple Optimization Using the JMP Statistical Software Kodak Research Conference May 9, 2005 Philip J. Ramsey, Ph.D., Mia L. Stephens, MS, Marie Gaudard, Ph.D. North Haven Group, http://www.northhavengroup.com/
More information103 Measures of Central Tendency and Variation
103 Measures of Central Tendency and Variation So far, we have discussed some graphical methods of data description. Now, we will investigate how statements of central tendency and variation can be used.
More informationThe Role of Precise Timing in HighSpeed, LowLatency Trading
The Role of Precise Timing in HighSpeed, LowLatency Trading The race to zero nanoseconds Whether measuring network latency or comparing realtime trading data from different computers on the planet,
More informationAN1963 IEEE 1588 Synchronization Over Standard Networks Using the
Application Report AN963 IEEE 588 Synchronization Over Standard Networks Using the... ABSTRACT This application report describes a method of synchronization that provides much more accurate synchronization
More informationENUMERATION OF UNIQUELY SOLVABLE OPEN SURVO PUZZLES
ENUMERATION OF UNIQUELY SOLVABLE OPEN SURVO PUZZLES SEPPO MUSTONEN 30 OCTOBER 2007 1. Introduction In an m n Survo puzzle an m n table has to be filled by integers 1, 2,..., mn in such a way that the their
More informationNetwork Performance Monitoring at Small Time Scales
Network Performance Monitoring at Small Time Scales Konstantina Papagiannaki, Rene Cruz, Christophe Diot Sprint ATL Burlingame, CA dina@sprintlabs.com Electrical and Computer Engineering Department University
More informationManual for NTP Web Pages
Manual for NTP Web Pages The NTP Web Pages are a set of statically produced HTML documents which extract, visualize and sort the data which is found in the NTP produced log files loopstats and peerstats.
More informationTemperature Scales. The metric system that we are now using includes a unit that is specific for the representation of measured temperatures.
Temperature Scales INTRODUCTION The metric system that we are now using includes a unit that is specific for the representation of measured temperatures. The unit of temperature in the metric system is
More informationDetecting Network Anomalies. Anant Shah
Detecting Network Anomalies using Traffic Modeling Anant Shah Anomaly Detection Anomalies are deviations from established behavior In most cases anomalies are indications of problems The science of extracting
More informationContingent Annuity (CA) Analysis
Contingent Annuity (CA) Analysis Contingent Annuity Work Group (CAWG) of the Life Products Committee of the American Academy of Actuaries January 19, 2012 All Rights Reserved. 1 Background The 10/28/11
More informationANALYZER BASICS WHAT IS AN FFT SPECTRUM ANALYZER? 21
WHAT IS AN FFT SPECTRUM ANALYZER? ANALYZER BASICS The SR760 FFT Spectrum Analyzer takes a time varying input signal, like you would see on an oscilloscope trace, and computes its frequency spectrum. Fourier's
More informationActive Queue Management
Active Queue Management TELCOM2321 CS2520 Wide Area Networks Dr. Walter Cerroni University of Bologna Italy Visiting Assistant Professor at SIS, Telecom Program Slides partly based on Dr. Znati s material
More informationSynchronous Ethernet explained
WHITEPAPER Joan d Austria, 112  Barcelona  SP  08018 Chalfont St Peter  Bucks  UK  SL9 9TR www.albedotelecom.com Synchronous Ethernet explained 1. FROM ASYNCHRONOUS TO SYNCHRONOUS ETHERNET Synchronous
More informationClock Jitter Definitions and Measurement Methods
January 2014 Clock Jitter Definitions and Measurement Methods 1 Introduction Jitter is the timing variations of a set of signal edges from their ideal values. Jitters in clock signals are typically caused
More informationBroadband Networks. Prof. Dr. Abhay Karandikar. Electrical Engineering Department. Indian Institute of Technology, Bombay. Lecture  29.
Broadband Networks Prof. Dr. Abhay Karandikar Electrical Engineering Department Indian Institute of Technology, Bombay Lecture  29 Voice over IP So, today we will discuss about voice over IP and internet
More informationR 60 DoMC01 rev.1 Additional requirements from the United States. Accuracy class III L. Revision number Date of the revision Nature of the revision
R 60 DoMC01 rev.1 Additional requirements from the United States Accuracy class III L Revision number Date of the revision Nature of the revision Rev.0 29/09/2006 Initial document Rev.1 03/03/2014 Update
More informationTHE STEERING OF A REAL TIME CLOCK TO UTC(NBS) AND TO UTC by
THE STEERNG OF A REAL TME CLOCK TO UTC(NBS) AND TO UTC by J. Levine and D.W. Allan Time and Frequency Division National nstitute of Standards and Technology Boulder, Colorado 833 ABSTRACT We describe the
More informationLecture Notes Module 1
Lecture Notes Module 1 Study Populations A study population is a clearly defined collection of people, animals, plants, or objects. In psychological research, a study population usually consists of a specific
More informationVirtual Met Mast verification report:
Virtual Met Mast verification report: June 2013 1 Authors: Alasdair Skea Karen Walter Dr Clive Wilson Leo HumeWright 2 Table of contents Executive summary... 4 1. Introduction... 6 2. Verification process...
More informationSoftware Metrics & Software Metrology. Alain Abran. Chapter 4 Quantification and Measurement are Not the Same!
Software Metrics & Software Metrology Alain Abran Chapter 4 Quantification and Measurement are Not the Same! 1 Agenda This chapter covers: The difference between a number & an analysis model. The Measurement
More informationMultiple Linear Regression in Data Mining
Multiple Linear Regression in Data Mining Contents 2.1. A Review of Multiple Linear Regression 2.2. Illustration of the Regression Process 2.3. Subset Selection in Linear Regression 1 2 Chap. 2 Multiple
More informationInvestigations into Relay Deployments within the LTE Advanced Framework
Investigations into Relay Deployments within the LTE Advanced Framework Abdallah Bou Saleh, Ömer Bulakci PhD candidates of Helsinki University of Technology (TKK) @ Nokia Siemens Networks 18.02.2010 1
More informationOptimizing VCO PLL Evaluations & PLL Synthesizer Designs
Optimizing VCO PLL Evaluations & PLL Synthesizer Designs Today s mobile communications systems demand higher communication quality, higher data rates, higher operation, and more channels per unit bandwidth.
More informationA Concise Guide to Mobile Backhaul Synchronization
The Verification Experts WHITE PAPER A Concise Guide to Mobile Backhaul Synchronization August 2012 VeEX Inc. 2827 Lakeview Court, Fremont, CA 94538 USA Tel: +1.510.651.0500 Fax: +1.510.651.0505 www.veexinc.com
More informationMA107 Precalculus Algebra Exam 2 Review Solutions
MA107 Precalculus Algebra Exam 2 Review Solutions February 24, 2008 1. The following demand equation models the number of units sold, x, of a product as a function of price, p. x = 4p + 200 a. Please write
More informationClock Recovery and Channelized SDH/SONET
Clock Recovery and Channelized SDH/SONET Tao Lang Wintegra Inc. tao@wintegra.com +15123453808 A Simple Agenda Problem Statement Solution Summary Time & Synchronisation in Telecoms Conference 2008 2
More informationA Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data
A Comparative Study of the Pickup Method and its Variations Using a Simulated Hotel Reservation Data Athanasius Zakhary, Neamat El Gayar Faculty of Computers and Information Cairo University, Giza, Egypt
More informationTelecommunications Synchronization Overview
1. Introduction Telecommunications Synchronization Overview The introduction of digital 64 kb/s circuit switches (end office and tandem switching systems) and digital crossconnect systems in the late
More informationMidterm Review Problems
Midterm Review Problems October 19, 2013 1. Consider the following research title: Cooperation among nursery school children under two types of instruction. In this study, what is the independent variable?
More informationInstruction Manual Service Program ULTRAPROGIR
Instruction Manual Service Program ULTRAPROGIR Parameterizing Software for Ultrasonic Sensors with Infrared Interface Contents 1 Installation of the Software ULTRAPROGIR... 4 1.1 System Requirements...
More informationSTANDPOINT FOR QUALITYOFSERVICE MEASUREMENT
STANDPOINT FOR QUALITYOFSERVICE MEASUREMENT 1. TIMING ACCURACY The accurate multipoint measurements require accurate synchronization of clocks of the measurement devices. If for example time stamps
More informationClock Synchronization
Clock Synchronization Henrik Lönn Electronics & Software Volvo Technological Development Contents General Types of Synchronisation Faults and problems to cope with Example algorithms Transmission delays
More information1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number
1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number A. 3(x  x) B. x 3 x C. 3x  x D. x  3x 2) Write the following as an algebraic expression
More informationINTERFERENCE OF SOUND WAVES
1/2016 Sound 1/8 INTERFERENCE OF SOUND WAVES PURPOSE: To measure the wavelength, frequency, and propagation speed of ultrasonic sound waves and to observe interference phenomena with ultrasonic sound waves.
More information22.302 Experiment 5. Strain Gage Measurements
22.302 Experiment 5 Strain Gage Measurements Introduction The design of components for many engineering systems is based on the application of theoretical models. The accuracy of these models can be verified
More information8 Modeling network traffic using game theory
8 Modeling network traffic using game theory Network represented as a weighted graph; each edge has a designated travel time that may depend on the amount of traffic it contains (some edges sensitive to
More informationMEASURES OF DISPERSION
MEASURES OF DISPERSION Measures of Dispersion While measures of central tendency indicate what value of a variable is (in one sense or other) average or central or typical in a set of data, measures of
More informationPowerPC Microprocessor Clock Modes
nc. Freescale Semiconductor AN1269 (Freescale Order Number) 1/96 Application Note PowerPC Microprocessor Clock Modes The PowerPC microprocessors offer customers numerous clocking options. An internal phaselock
More informationTiming over Packet. Technical Brief
Technical Brief 02/08 1. Abstract This paper is designed to help operators understand how to deploy Precision Time Protocol (PTP, or IEEE 1588v2) in mobile networks for the purpose of synchronizing base
More informationConfidence Intervals for the Difference Between Two Means
Chapter 47 Confidence Intervals for the Difference Between Two Means Introduction This procedure calculates the sample size necessary to achieve a specified distance from the difference in sample means
More information5.5. San Diego (8/22/03 10/4/04)
NSF UV SPECTRORADIOMETER NETWORK 2324 OPERATIONS REPORT 5.5. San Diego (8/22/3 1/4/4) The 2324 season at San Diego includes the period 8/22/3 1/4/4. In contrast to other network sites, San Diego serves
More informationExercise 1.12 (Pg. 2223)
Individuals: The objects that are described by a set of data. They may be people, animals, things, etc. (Also referred to as Cases or Records) Variables: The characteristics recorded about each individual.
More informationWe will use the following data sets to illustrate measures of center. DATA SET 1 The following are test scores from a class of 20 students:
MODE The mode of the sample is the value of the variable having the greatest frequency. Example: Obtain the mode for Data Set 1 77 For a grouped frequency distribution, the modal class is the class having
More informationSTATISTICA Formula Guide: Logistic Regression. Table of Contents
: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 SigmaRestricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary
More informationUnderstand the role that hypothesis testing plays in an improvement project. Know how to perform a two sample hypothesis test.
HYPOTHESIS TESTING Learning Objectives Understand the role that hypothesis testing plays in an improvement project. Know how to perform a two sample hypothesis test. Know how to perform a hypothesis test
More informationDelivering NIST Time to Financial Markets Via CommonView GPS Measurements
Delivering NIST Time to Financial Markets Via CommonView GPS Measurements Michael Lombardi NIST Time and Frequency Division lombardi@nist.gov 55 th CGSIC Meeting Timing Subcommittee Tampa, Florida September
More informationTime Calibrator. 2013 Fountain Computer Products
Time Calibrator Time Calibrator All rights reserved. No parts of this work may be reproduced in any form or by any means  graphic, electronic, or mechanical, including photocopying, recording, taping,
More informationCHAPTER 5 FUZZY LOGIC CONTROLLER FOR THE CONICAL TANK PROCESS
99 CHAPTER 5 FUZZY LOGIC CONTROLLER FOR THE CONICAL TANK PROCESS 5.1 INTRODUCTION Fuzzy Logic Controllers are normally preferred for control applications, due to their fault tolerance, knowledge representation,
More informationApplication Note 58 Crystal Considerations with Dallas Real Time Clocks
www.dalsemi.com Application Note 58 Crystal Considerations with Dallas Real Time Clocks Dallas Semiconductor offers a variety of real time clocks (RTCs). The majority of these are available either as integrated
More informationStatistical Confidence Calculations
Statistical Confidence Calculations Statistical Methodology Omniture Test&Target utilizes standard statistics to calculate confidence, confidence intervals, and lift for each campaign. The student s T
More informationReal vs. Synthetic Web Performance Measurements, a Comparative Study
Real vs. Synthetic Web Performance Measurements, a Comparative Study By John Bartlett and Peter Sevcik December 2004 Enterprises use today s Internet to find customers, provide them information, engage
More informationOn Correlating Performance Metrics
On Correlating Performance Metrics Yiping Ding and Chris Thornley BMC Software, Inc. Kenneth Newman BMC Software, Inc. University of Massachusetts, Boston Performance metrics and their measurements are
More informationAn overview on Internet Measurement Methodologies, Techniques and Tools
An overview on Internet Measurement Methodologies, Techniques and Tools AA 2012/2013 emiliano.casalicchio@uniroma2.it (Agenda) Lezione 24/04/2013 Part 1 Intro basic concepts ISP Traffic exchange (peering)
More informationUsing Excel (Microsoft Office 2007 Version) for Graphical Analysis of Data
Using Excel (Microsoft Office 2007 Version) for Graphical Analysis of Data Introduction In several upcoming labs, a primary goal will be to determine the mathematical relationship between two variable
More information