Analysis of M/M/n/K Queue with Multiple Priorities
|
|
- Anna McDonald
- 6 years ago
- Views:
Transcription
1 Analysis of M/M/n/K Queue with Multile Priorities Coyright, Sanjay K. Bose For a P-riority system, class P of highest riority Indeendent, Poisson arrival rocesses for each class with i as average arrival rate for class i Service times for each class are indeendent of each other and of the arrival rocesses and are exonentially distributed with mean / i for class i Both Non-reemtive and Preemtive Priority Service discilines are considered Since the service times are exonentially distributed (i.e. memory less), the results for reemtive resume and reemtive non-resume will be identical Coyright, Sanjay K. Bose
2 Solution Aroach Define System State aroriately Draw the corresonding State Transition Diagram with the aroriate flows between the states Write and solve the balance equations to obtain the system state robabilities Note that we have given here the solution aroach that may be taken to solve a queueing roblem of this kind. This has been illustrated with simle examles. More comlex cases may be similarly formulated and solved with a corresonding increase in the solution comlexity Coyright, Sanjay K. Bose 3 M/M/-/- Queue with Preemtive Priority For a P-riority queue of this tye, define the system state as the following P-tule where (n, n,,n P ) n i = Number of jobs of riority class i in the queue i=,..,p Note that the server will always be engaged by a job of the highest riority class resent in the system, i.e. by a job of class j with service rate j if n j and n j+ =...=n P =. Coyright, Sanjay K. Bose 4
3 We illustrate the aroach first for a -riority M/M// queue,,,,,3,,,, 3, Coyright, Sanjay K. Bose 5 The corresonding balance equations for the -riority M/M// queue will be given by (,,, ( ( ( ) = + + +, + ) = + ) = + ) =,,,,,, These may be solved to obtain the desired state robabilities We illustrate next how this aroach may be generalized to aly to queues with finite caacity and/or multile servers, Coyright, Sanjay K. Bose 6 3
4 -Priority M/M//3 Queue (Preemtive Priority) (An examle of a queue with finite caacity),,,,,3,,, 3, New arrivals will be lost if they come when the system is in any of the circled states State Transition Diagram for the -Priority M/M//3 Queue with Preemtive Priority Coyright, Sanjay K. Bose 7 The corresonding balance equations are (,,,,,,,,3 3, ( ( ( ( ( + ) =,,,,, + + ) =,,,,, + + ) =,,, + + ) = + + ) = = = + + ) = = =,,,3,, 3, Normalization Condition,,3, 3,, =,,,, Coyright, Sanjay K. Bose 8 4
5 We can solve for the state robability distribution by solving any nine of the ten balance equation along with the equation for the normalization condition Job loss robability (or the blocking robability) =, +, + 3, +,3 Other desired robabilities may also be found from these state robabilities. Some examles are - P{server busy serving low riority job} =, +, + 3, P{one high riority job in the system} =, +, +, Coyright, Sanjay K. Bose 9 -Priority M/M//3 Queue (Preemtive Priority) (An examle of a queue with finite caacity and multile servers),,,3,,,,, 3, New arrivals will be lost if they come when the system is in any of the circled states Solve in the usual manner for the system state robabilities Coyright, Sanjay K. Bose 5
6 M/M/-/- Queue with Non-reemtive Priority We can roose two different methods of reresenting the system state for a M/M/c/K queue of this tye with P riority classes. Aroach I : If P < c, then this aroach gives a more comact reresentation using a P-tule than the more general Aroach II given next. State Reresentation (n,,n P, s,.,s P ) where n j = number of jobs of class j in system j=,,p s k = number of servers currently busy serving jobs of riority class k k=,..,p Coyright, Sanjay K. Bose Aroach II : This requires a (P+c)-tule of the following form State Reresentation n,,n P, s,.,s c ) where n j = number of jobs of class j in system j=,,p s k = riority class of the service currently on-going at server k k=,..,c Note that n +...+n P K for a finite caacity system We have used the reresentation of Aroach II in the examle described subsequently Coyright, Sanjay K. Bose 6
7 -Priority M/M//3 Queue (Non-reemtive Priority) (An examle of a single server queue with finite caacity),,,,,,,,,,,,,,,,,,,3,,, 3,, State Transition Diagram Coyright, Sanjay K. Bose 3 The balance equations for this queue are (,,,,,,,,,,,, ( ( ( ( + ) = ( ( ,, + ) = + ) = + ) = + ) =,, + ) = + ) =,,,,,,,,,,,,,,,,,,,,,,,3, 3,,.and. Coyright, Sanjay K. Bose 4 7
8 ,,,,,,,,,3, 3,, = = = = = =,,,,,,,,,,,,,,,, with the following normalization condition,,,,,,,,,,,,,3,,,,,,, 3,,,, = Coyright, Sanjay K. Bose 5 These equations may be solved on the usual way to obtain the individual state robabilities as er the definition of the system state These state robabilities may then be used to comute other erformance arameters and robabilities that may be of interest. For examle, the blocking robability of this system will be given by (,3, 3,,,,,,,,,, ) Other, similar robabilities and erformance measures may also be calculated Coyright, Sanjay K. Bose 6 8
9 Aroach may be extended in the usual fashion to analyze other similar systems as follows - More than two riority classes Other buffer caacity values or even queues with infinite buffer caacities Different caacity limits for the different riority classes Queues with more than one server Coyright, Sanjay K. Bose 7 9
CHAPTER-5 PERFORMANCE ANALYSIS OF AN M/M/1/K QUEUE WITH PREEMPTIVE PRIORITY
CHAPTER-5 PERFORMANCE ANALYSIS OF AN M/M//K QUEUE WITH PREEMPTIVE PRIORITY 5. INTRODUCTION In last chater we discussed the case of non-reemtive riority. Now we tae the case of reemtive riority. Preemtive
More informationTHE ISRAELI QUEUE WITH INFINITE NUMBER OF GROUPS
Probability in the Engineering and Informational Sciences, 8, 04, 9 doi:007/s0699648300096 THE ISRAELI QUEUE WITH INFINITE NUMBER OF GROUPS NIR PEREL AND URI YECHIALI Deartment of Statistics and Oerations
More informationMulti-Operation Multi-Machine Scheduling
Multi-Oeration Multi-Machine Scheduling Weizhen Mao he College of William and Mary, Williamsburg VA 3185, USA Abstract. In the multi-oeration scheduling that arises in industrial engineering, each job
More informationMODELING THE RELIABILITY OF C4ISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL
Technical Sciences and Alied Mathematics MODELING THE RELIABILITY OF CISR SYSTEMS HARDWARE/SOFTWARE COMPONENTS USING AN IMPROVED MARKOV MODEL Cezar VASILESCU Regional Deartment of Defense Resources Management
More informationQueues and Queueing Networks
Queues and Queueing Networks Sanjay K. Bose Dept. of EEE, IITG Copyright 2015, Sanjay K. Bose 1 Introduction to Queueing Models and Queueing Analysis Copyright 2015, Sanjay K. Bose 2 Model of a Queue Arrivals
More informationDiscrete-time Geo/Geo/1 Queue with Negative Customers and Working Breakdowns
Discrete-time GeoGeo1 Queue with Negative Customers and Working Breakdowns Tao Li and Liyuan Zhang Abstract This aer considers a discrete-time GeoGeo1 queue with server breakdowns and reairs. If the server
More information1 Gambler s Ruin Problem
Coyright c 2017 by Karl Sigman 1 Gambler s Ruin Problem Let N 2 be an integer and let 1 i N 1. Consider a gambler who starts with an initial fortune of $i and then on each successive gamble either wins
More informationCombining Logistic Regression with Kriging for Mapping the Risk of Occurrence of Unexploded Ordnance (UXO)
Combining Logistic Regression with Kriging for Maing the Risk of Occurrence of Unexloded Ordnance (UXO) H. Saito (), P. Goovaerts (), S. A. McKenna (2) Environmental and Water Resources Engineering, Deartment
More informationECE 6960: Adv. Random Processes & Applications Lecture Notes, Fall 2010
ECE 6960: Adv. Random Processes & Alications Lecture Notes, Fall 2010 Lecture 16 Today: (1) Markov Processes, (2) Markov Chains, (3) State Classification Intro Please turn in H 6 today. Read Chater 11,
More informationpage 1 This question-paper you hand in, together with your solutions. ================================================================
age EXAMINATION Jan 9 Time :5-7:5 QUEUEING THEORY EP9, HF ( 6H78), Lecturer: Armin Halilovic Instructions:. You ARE allowed to use a calculator.. You are NOT allowed to use your own tables of mathematical
More informationHotelling s Two- Sample T 2
Chater 600 Hotelling s Two- Samle T Introduction This module calculates ower for the Hotelling s two-grou, T-squared (T) test statistic. Hotelling s T is an extension of the univariate two-samle t-test
More informationAMS10 HW1 Grading Rubric
AMS10 HW1 Grading Rubric Problem 1 (16ts- ts/each). Left hand side is shown to equal right hand side using examles with real vectors. A vector sace is a set V on which two oerations, vector addition and
More informationSolved Problems. (a) (b) (c) Figure P4.1 Simple Classification Problems First we draw a line between each set of dark and light data points.
Solved Problems Solved Problems P Solve the three simle classification roblems shown in Figure P by drawing a decision boundary Find weight and bias values that result in single-neuron ercetrons with the
More informationEfficient Approximations for Call Admission Control Performance Evaluations in Multi-Service Networks
Efficient Aroximations for Call Admission Control Performance Evaluations in Multi-Service Networks Emre A. Yavuz, and Victor C. M. Leung Deartment of Electrical and Comuter Engineering The University
More informationSignaled Queueing. Laura Brink, Robert Shorten, Jia Yuan Yu ABSTRACT. Categories and Subject Descriptors. General Terms. Keywords
Signaled Queueing Laura Brink, Robert Shorten, Jia Yuan Yu ABSTRACT Burstiness in queues where customers arrive indeendently leads to rush eriods when wait times are long. We roose a simle signaling scheme
More informationPeriodic scheduling 05/06/
Periodic scheduling T T or eriodic scheduling, the best that we can do is to design an algorithm which will always find a schedule if one exists. A scheduler is defined to be otimal iff it will find a
More informationOutline. Markov Chains and Markov Models. Outline. Markov Chains. Markov Chains Definitions Huizhen Yu
and Markov Models Huizhen Yu janey.yu@cs.helsinki.fi Det. Comuter Science, Univ. of Helsinki Some Proerties of Probabilistic Models, Sring, 200 Huizhen Yu (U.H.) and Markov Models Jan. 2 / 32 Huizhen Yu
More informationMATHEMATICAL MODELLING OF THE WIRELESS COMMUNICATION NETWORK
Comuter Modelling and ew Technologies, 5, Vol.9, o., 3-39 Transort and Telecommunication Institute, Lomonosov, LV-9, Riga, Latvia MATHEMATICAL MODELLIG OF THE WIRELESS COMMUICATIO ETWORK M. KOPEETSK Deartment
More informationCHAPTER 5 STATISTICAL INFERENCE. 1.0 Hypothesis Testing. 2.0 Decision Errors. 3.0 How a Hypothesis is Tested. 4.0 Test for Goodness of Fit
Chater 5 Statistical Inference 69 CHAPTER 5 STATISTICAL INFERENCE.0 Hyothesis Testing.0 Decision Errors 3.0 How a Hyothesis is Tested 4.0 Test for Goodness of Fit 5.0 Inferences about Two Means It ain't
More informationImproved Capacity Bounds for the Binary Energy Harvesting Channel
Imroved Caacity Bounds for the Binary Energy Harvesting Channel Kaya Tutuncuoglu 1, Omur Ozel 2, Aylin Yener 1, and Sennur Ulukus 2 1 Deartment of Electrical Engineering, The Pennsylvania State University,
More informationModeling and Estimation of Full-Chip Leakage Current Considering Within-Die Correlation
6.3 Modeling and Estimation of Full-Chi Leaage Current Considering Within-Die Correlation Khaled R. eloue, Navid Azizi, Farid N. Najm Deartment of ECE, University of Toronto,Toronto, Ontario, Canada {haled,nazizi,najm}@eecg.utoronto.ca
More informationA Quadratic Cumulative Production Model for the Material Balance of Abnormally-Pressured Gas Reservoirs
A Quadratic Cumulative Production Model for the Material Balance of Abnormally-Pressured as Reservoirs F.E. onale, M.S. Thesis Defense 7 October 2003 Deartment of Petroleum Engineering Texas A&M University
More informationAlgorithms for Air Traffic Flow Management under Stochastic Environments
Algorithms for Air Traffic Flow Management under Stochastic Environments Arnab Nilim and Laurent El Ghaoui Abstract A major ortion of the delay in the Air Traffic Management Systems (ATMS) in US arises
More informationApproximating min-max k-clustering
Aroximating min-max k-clustering Asaf Levin July 24, 2007 Abstract We consider the roblems of set artitioning into k clusters with minimum total cost and minimum of the maximum cost of a cluster. The cost
More informationBOUNDS FOR THE COUPLING TIME IN QUEUEING NETWORKS PERFECT SIMULATION
BOUNDS FOR THE COUPLING TIME IN QUEUEING NETWORKS PERFECT SIMULATION JANTIEN G. DOPPER, BRUNO GAUJAL AND JEAN-MARC VINCENT Abstract. In this aer, the duration of erfect simulations for Markovian finite
More informationA PEAK FACTOR FOR PREDICTING NON-GAUSSIAN PEAK RESULTANT RESPONSE OF WIND-EXCITED TALL BUILDINGS
The Seventh Asia-Pacific Conference on Wind Engineering, November 8-1, 009, Taiei, Taiwan A PEAK FACTOR FOR PREDICTING NON-GAUSSIAN PEAK RESULTANT RESPONSE OF WIND-EXCITED TALL BUILDINGS M.F. Huang 1,
More informationDeveloping A Deterioration Probabilistic Model for Rail Wear
International Journal of Traffic and Transortation Engineering 2012, 1(2): 13-18 DOI: 10.5923/j.ijtte.20120102.02 Develoing A Deterioration Probabilistic Model for Rail Wear Jabbar-Ali Zakeri *, Shahrbanoo
More informationUnderstanding and Using Availability
Understanding and Using Availability Jorge Luis Romeu, Ph.D. ASQ CQE/CRE, & Senior Member Email: romeu@cortland.edu htt://myrofile.cos.com/romeu ASQ/RD Webinar Series Noviembre 5, J. L. Romeu - Consultant
More informationDiscrete Signal Reconstruction by Sum of Absolute Values
JOURNAL OF L A TEX CLASS FILES, VOL., NO. 4, DECEMBER 22 Discrete Signal Reconstruction by Sum of Absolute Values Masaaki Nagahara, Senior Member, IEEE, arxiv:53.5299v [cs.it] 8 Mar 25 Abstract In this
More informationOptimal Design of Truss Structures Using a Neutrosophic Number Optimization Model under an Indeterminate Environment
Neutrosohic Sets and Systems Vol 14 016 93 University of New Mexico Otimal Design of Truss Structures Using a Neutrosohic Number Otimization Model under an Indeterminate Environment Wenzhong Jiang & Jun
More informationUniversal Finite Memory Coding of Binary Sequences
Deartment of Electrical Engineering Systems Universal Finite Memory Coding of Binary Sequences Thesis submitted towards the degree of Master of Science in Electrical and Electronic Engineering in Tel-Aviv
More informationScaling Multiple Point Statistics for Non-Stationary Geostatistical Modeling
Scaling Multile Point Statistics or Non-Stationary Geostatistical Modeling Julián M. Ortiz, Steven Lyster and Clayton V. Deutsch Centre or Comutational Geostatistics Deartment o Civil & Environmental Engineering
More informationA MONOTONICITY RESULT FOR A G/GI/c QUEUE WITH BALKING OR RENEGING
J. Al. Prob. 43, 1201 1205 (2006) Printed in Israel Alied Probability Trust 2006 A MONOTONICITY RESULT FOR A G/GI/c QUEUE WITH BALKING OR RENEGING SERHAN ZIYA, University of North Carolina HAYRIYE AYHAN
More informationDistributed Rule-Based Inference in the Presence of Redundant Information
istribution Statement : roved for ublic release; distribution is unlimited. istributed Rule-ased Inference in the Presence of Redundant Information June 8, 004 William J. Farrell III Lockheed Martin dvanced
More informationModel checking, verification of CTL. One must verify or expel... doubts, and convert them into the certainty of YES [Thomas Carlyle]
Chater 5 Model checking, verification of CTL One must verify or exel... doubts, and convert them into the certainty of YES or NO. [Thomas Carlyle] 5. The verification setting Page 66 We introduce linear
More informationPretest (Optional) Use as an additional pacing tool to guide instruction. August 21
Trimester 1 Pretest (Otional) Use as an additional acing tool to guide instruction. August 21 Beyond the Basic Facts In Trimester 1, Grade 8 focus on multilication. Daily Unit 1: Rational vs. Irrational
More information2-D Analysis for Iterative Learning Controller for Discrete-Time Systems With Variable Initial Conditions Yong FANG 1, and Tommy W. S.
-D Analysis for Iterative Learning Controller for Discrete-ime Systems With Variable Initial Conditions Yong FANG, and ommy W. S. Chow Abstract In this aer, an iterative learning controller alying to linear
More information4. Score normalization technical details We now discuss the technical details of the score normalization method.
SMT SCORING SYSTEM This document describes the scoring system for the Stanford Math Tournament We begin by giving an overview of the changes to scoring and a non-technical descrition of the scoring rules
More informationDynamic-Priority Scheduling. CSCE 990: Real-Time Systems. Steve Goddard. Dynamic-priority Scheduling
CSCE 990: Real-Time Systems Dynamic-Priority Scheduling Steve Goddard goddard@cse.unl.edu htt://www.cse.unl.edu/~goddard/courses/realtimesystems Dynamic-riority Scheduling Real-Time Systems Dynamic-Priority
More informationEstimation of Separable Representations in Psychophysical Experiments
Estimation of Searable Reresentations in Psychohysical Exeriments Michele Bernasconi (mbernasconi@eco.uninsubria.it) Christine Choirat (cchoirat@eco.uninsubria.it) Raffaello Seri (rseri@eco.uninsubria.it)
More informationOn the Relationship Between Packet Size and Router Performance for Heavy-Tailed Traffic 1
On the Relationshi Between Packet Size and Router Performance for Heavy-Tailed Traffic 1 Imad Antonios antoniosi1@southernct.edu CS Deartment MO117 Southern Connecticut State University 501 Crescent St.
More informationA Comparison between Biased and Unbiased Estimators in Ordinary Least Squares Regression
Journal of Modern Alied Statistical Methods Volume Issue Article 7 --03 A Comarison between Biased and Unbiased Estimators in Ordinary Least Squares Regression Ghadban Khalaf King Khalid University, Saudi
More informationNew Schedulability Test Conditions for Non-preemptive Scheduling on Multiprocessor Platforms
New Schedulability Test Conditions for Non-reemtive Scheduling on Multirocessor Platforms Technical Reort May 2008 Nan Guan 1, Wang Yi 2, Zonghua Gu 3 and Ge Yu 1 1 Northeastern University, Shenyang, China
More informationUnderstanding and Using Availability
Understanding and Using Availability Jorge Luis Romeu, Ph.D. ASQ CQE/CRE, & Senior Member C. Stat Fellow, Royal Statistical Society Past Director, Region II (NY & PA) Director: Juarez Lincoln Marti Int
More informationLinear diophantine equations for discrete tomography
Journal of X-Ray Science and Technology 10 001 59 66 59 IOS Press Linear diohantine euations for discrete tomograhy Yangbo Ye a,gewang b and Jiehua Zhu a a Deartment of Mathematics, The University of Iowa,
More informationImproving Patent Examination Efficiency and Quality: An Operations Research Analysis of the USPTO, Using Queuing Theory.
Imroving Patent Examination Eiciency and Quality: An Oerations Research Analysis o the USPTO, Using Queuing Theory By Ayal Sharon and Yian Liu Aendices APPENDIX I FOUNDATIONAL FORMULAS. Formula or Mean
More informationAN OPTIMAL CONTROL CHART FOR NON-NORMAL PROCESSES
AN OPTIMAL CONTROL CHART FOR NON-NORMAL PROCESSES Emmanuel Duclos, Maurice Pillet To cite this version: Emmanuel Duclos, Maurice Pillet. AN OPTIMAL CONTROL CHART FOR NON-NORMAL PRO- CESSES. st IFAC Worsho
More informationTraffic Engineering in a Multipoint-to-Point Network
1 Traffic Engineering in a Multioint-to-Point Network Guillaume Urvoy-Keller, Gérard Hébuterne, and Yves Dallery Abstract The need to guarantee Quality of Service (QoS) to multimedia alications leads to
More informationMorten Frydenberg Section for Biostatistics Version :Friday, 05 September 2014
Morten Frydenberg Section for Biostatistics Version :Friday, 05 Setember 204 All models are aroximations! The best model does not exist! Comlicated models needs a lot of data. lower your ambitions or get
More informationApproximate Dynamic Programming for Dynamic Capacity Allocation with Multiple Priority Levels
Aroximate Dynamic Programming for Dynamic Caacity Allocation with Multile Priority Levels Alexander Erdelyi School of Oerations Research and Information Engineering, Cornell University, Ithaca, NY 14853,
More informationA Study of Active Queue Management for Congestion Control
A Study of Active Queue Management for Congestion Control Victor Firoiu vfiroiu@nortelnetworks.com Nortel Networks 3 Federal St. illerica, MA 1821 USA Marty orden mborden@tollbridgetech.com Tollridge Technologies
More informationEstimation of component redundancy in optimal age maintenance
EURO MAINTENANCE 2012, Belgrade 14-16 May 2012 Proceedings of the 21 st International Congress on Maintenance and Asset Management Estimation of comonent redundancy in otimal age maintenance Jorge ioa
More informationA Qualitative Event-based Approach to Multiple Fault Diagnosis in Continuous Systems using Structural Model Decomposition
A Qualitative Event-based Aroach to Multile Fault Diagnosis in Continuous Systems using Structural Model Decomosition Matthew J. Daigle a,,, Anibal Bregon b,, Xenofon Koutsoukos c, Gautam Biswas c, Belarmino
More informationPERFORMANCE BASED DESIGN SYSTEM FOR CONCRETE MIXTURE WITH MULTI-OPTIMIZING GENETIC ALGORITHM
PERFORMANCE BASED DESIGN SYSTEM FOR CONCRETE MIXTURE WITH MULTI-OPTIMIZING GENETIC ALGORITHM Takafumi Noguchi 1, Iei Maruyama 1 and Manabu Kanematsu 1 1 Deartment of Architecture, University of Tokyo,
More informationAdaptive estimation with change detection for streaming data
Adative estimation with change detection for streaming data A thesis resented for the degree of Doctor of Philosohy of the University of London and the Diloma of Imerial College by Dean Adam Bodenham Deartment
More informationDelay Performance of Threshold Policies for Dynamic Spectrum Access
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL., NO. 7, JULY 83 Delay Performance of Threshold Policies for Dynamic Sectrum Access Rong-Rong Chen and Xin Liu Abstract In this aer, we analyze the delay
More informationResearch of PMU Optimal Placement in Power Systems
Proceedings of the 5th WSEAS/IASME Int. Conf. on SYSTEMS THEORY and SCIENTIFIC COMPUTATION, Malta, Setember 15-17, 2005 (38-43) Research of PMU Otimal Placement in Power Systems TIAN-TIAN CAI, QIAN AI
More informationQUEUING MODELS AND MARKOV PROCESSES
QUEUING MODELS AND MARKOV ROCESSES Queues form when customer demand for a service cannot be met immediately. They occur because of fluctuations in demand levels so that models of queuing are intrinsically
More informationNUMERICAL AND THEORETICAL INVESTIGATIONS ON DETONATION- INERT CONFINEMENT INTERACTIONS
NUMERICAL AND THEORETICAL INVESTIGATIONS ON DETONATION- INERT CONFINEMENT INTERACTIONS Tariq D. Aslam and John B. Bdzil Los Alamos National Laboratory Los Alamos, NM 87545 hone: 1-55-667-1367, fax: 1-55-667-6372
More informationAnytime communication over the Gilbert-Eliot channel with noiseless feedback
Anytime communication over the Gilbert-Eliot channel with noiseless feedback Anant Sahai, Salman Avestimehr, Paolo Minero Deartment of Electrical Engineering and Comuter Sciences University of California
More informationOn Code Design for Simultaneous Energy and Information Transfer
On Code Design for Simultaneous Energy and Information Transfer Anshoo Tandon Electrical and Comuter Engineering National University of Singaore Email: anshoo@nus.edu.sg Mehul Motani Electrical and Comuter
More informationA Recursive Block Incomplete Factorization. Preconditioner for Adaptive Filtering Problem
Alied Mathematical Sciences, Vol. 7, 03, no. 63, 3-3 HIKARI Ltd, www.m-hiari.com A Recursive Bloc Incomlete Factorization Preconditioner for Adative Filtering Problem Shazia Javed School of Mathematical
More informationA Model for Randomly Correlated Deposition
A Model for Randomly Correlated Deosition B. Karadjov and A. Proykova Faculty of Physics, University of Sofia, 5 J. Bourchier Blvd. Sofia-116, Bulgaria ana@hys.uni-sofia.bg Abstract: A simle, discrete,
More informationSome Unitary Space Time Codes From Sphere Packing Theory With Optimal Diversity Product of Code Size
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 5, NO., DECEMBER 4 336 Some Unitary Sace Time Codes From Shere Packing Theory With Otimal Diversity Product of Code Size Haiquan Wang, Genyuan Wang, and Xiang-Gen
More informationShadow Computing: An Energy-Aware Fault Tolerant Computing Model
Shadow Comuting: An Energy-Aware Fault Tolerant Comuting Model Bryan Mills, Taieb Znati, Rami Melhem Deartment of Comuter Science University of Pittsburgh (bmills, znati, melhem)@cs.itt.edu Index Terms
More informationPower Aware Wireless File Downloading: A Constrained Restless Bandit Approach
PROC. WIOP 204 Power Aware Wireless File Downloading: A Constrained Restless Bandit Aroach Xiaohan Wei and Michael J. Neely, Senior Member, IEEE Abstract his aer treats ower-aware throughut maximization
More informationECE 534 Information Theory - Midterm 2
ECE 534 Information Theory - Midterm Nov.4, 009. 3:30-4:45 in LH03. You will be given the full class time: 75 minutes. Use it wisely! Many of the roblems have short answers; try to find shortcuts. You
More informationCHAPTER-II Control Charts for Fraction Nonconforming using m-of-m Runs Rules
CHAPTER-II Control Charts for Fraction Nonconforming using m-of-m Runs Rules. Introduction: The is widely used in industry to monitor the number of fraction nonconforming units. A nonconforming unit is
More informationCMSC 425: Lecture 4 Geometry and Geometric Programming
CMSC 425: Lecture 4 Geometry and Geometric Programming Geometry for Game Programming and Grahics: For the next few lectures, we will discuss some of the basic elements of geometry. There are many areas
More informationProf. Daniel A. Menascé Department of Computer Science George Mason University
Pro. Daniel. Menascé Deartment o Comuter Science George Mason University www.cs.gmu.edu/aculty/menasce.html 2004 D.. Menascé. ll Rights Reserved. 1 Most o the igures in this set o slides come rom the book
More informationy p 2 p 1 y p Flexible System p 2 y p2 p1 y p u, y 1 -u, y 2 Component Breakdown z 1 w 1 1 y p 1 P 1 P 2 w 2 z 2
ROBUSTNESS OF FLEXIBLE SYSTEMS WITH COMPONENT-LEVEL UNCERTAINTIES Peiman G. Maghami Λ NASA Langley Research Center, Hamton, VA 368 Robustness of flexible systems in the resence of model uncertainties at
More informationCryptanalysis of Pseudorandom Generators
CSE 206A: Lattice Algorithms and Alications Fall 2017 Crytanalysis of Pseudorandom Generators Instructor: Daniele Micciancio UCSD CSE As a motivating alication for the study of lattice in crytograhy we
More informationMathematical Efficiency Modeling of Static Power Converters
Fabrício Hoff Duont Regional Integrated University of Uer Uruguai and Missions (URI Av. Assis Brasil, 9, 980 000 Frederico Westhalen, RS, Brazil Contact: fhd@ieee.org Mathematical Efficiency Modeling of
More informationA General Damage Initiation and Evolution Model (DIEM) in LS-DYNA
9th Euroean LS-YNA Conference 23 A General amage Initiation and Evolution Model (IEM) in LS-YNA Thomas Borrvall, Thomas Johansson and Mikael Schill, YNAmore Nordic AB Johan Jergéus, Volvo Car Cororation
More informationThe non-stochastic multi-armed bandit problem
Submitted for journal ublication. The non-stochastic multi-armed bandit roblem Peter Auer Institute for Theoretical Comuter Science Graz University of Technology A-8010 Graz (Austria) auer@igi.tu-graz.ac.at
More informationTime Domain Calculation of Vortex Induced Vibration of Long-Span Bridges by Using a Reduced-order Modeling Technique
2017 2nd International Conference on Industrial Aerodynamics (ICIA 2017) ISBN: 978-1-60595-481-3 Time Domain Calculation of Vortex Induced Vibration of Long-San Bridges by Using a Reduced-order Modeling
More informationarxiv: v1 [physics.data-an] 26 Oct 2012
Constraints on Yield Parameters in Extended Maximum Likelihood Fits Till Moritz Karbach a, Maximilian Schlu b a TU Dortmund, Germany, moritz.karbach@cern.ch b TU Dortmund, Germany, maximilian.schlu@cern.ch
More informationProof Nets and Boolean Circuits
Proof Nets and Boolean Circuits Kazushige Terui terui@nii.ac.j National Institute of Informatics, Tokyo 14/07/04, Turku.1/44 Motivation (1) Proofs-as-Programs (Curry-Howard) corresondence: Proofs = Programs
More informationChapter 7 Sampling and Sampling Distributions. Introduction. Selecting a Sample. Introduction. Sampling from a Finite Population
Chater 7 and s Selecting a Samle Point Estimation Introduction to s of Proerties of Point Estimators Other Methods Introduction An element is the entity on which data are collected. A oulation is a collection
More informationAI*IA 2003 Fusion of Multiple Pattern Classifiers PART III
AI*IA 23 Fusion of Multile Pattern Classifiers PART III AI*IA 23 Tutorial on Fusion of Multile Pattern Classifiers by F. Roli 49 Methods for fusing multile classifiers Methods for fusing multile classifiers
More informationOne-way ANOVA Inference for one-way ANOVA
One-way ANOVA Inference for one-way ANOVA IPS Chater 12.1 2009 W.H. Freeman and Comany Objectives (IPS Chater 12.1) Inference for one-way ANOVA Comaring means The two-samle t statistic An overview of ANOVA
More informationPositive decomposition of transfer functions with multiple poles
Positive decomosition of transfer functions with multile oles Béla Nagy 1, Máté Matolcsi 2, and Márta Szilvási 1 Deartment of Analysis, Technical University of Budaest (BME), H-1111, Budaest, Egry J. u.
More informationResearch Note REGRESSION ANALYSIS IN MARKOV CHAIN * A. Y. ALAMUTI AND M. R. MESHKANI **
Iranian Journal of Science & Technology, Transaction A, Vol 3, No A3 Printed in The Islamic Reublic of Iran, 26 Shiraz University Research Note REGRESSION ANALYSIS IN MARKOV HAIN * A Y ALAMUTI AND M R
More informationUncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning
TNN-2009-P-1186.R2 1 Uncorrelated Multilinear Princial Comonent Analysis for Unsuervised Multilinear Subsace Learning Haiing Lu, K. N. Plataniotis and A. N. Venetsanooulos The Edward S. Rogers Sr. Deartment
More informationStatistical Multiplexing Gain of Link Scheduling Algorithms in QoS Networks (Short Version)
1 Statistical Multilexing Gain of Link Scheduling Algorithms in QoS Networks (Short Version) Technical Reort: University of Virginia, CS-99-23, July 1999 Robert Boorstyn Almut Burchard Jörg Liebeherr y
More informationAnswers Investigation 2
Answers Alications 1. a. Plan 1: y = x + 5; Plan 2: y = 1.5x + 2.5 b. Intersection oint (5, 10) is an exact solution to the system of equations. c. x + 5 = 1.5x + 2.5 leads to x = 5; (5) + 5 = 10 or 1.5(5)
More informationA Network-Flow Based Cell Sizing Algorithm
A Networ-Flow Based Cell Sizing Algorithm Huan Ren and Shantanu Dutt Det. of ECE, University of Illinois-Chicago Abstract We roose a networ flow based algorithm for the area-constrained timing-driven discrete
More information1-way quantum finite automata: strengths, weaknesses and generalizations
1-way quantum finite automata: strengths, weaknesses and generalizations arxiv:quant-h/9802062v3 30 Se 1998 Andris Ambainis UC Berkeley Abstract Rūsiņš Freivalds University of Latvia We study 1-way quantum
More informationLecture 6. 2 Recurrence/transience, harmonic functions and martingales
Lecture 6 Classification of states We have shown that all states of an irreducible countable state Markov chain must of the same tye. This gives rise to the following classification. Definition. [Classification
More informationOn the Role of Finite Queues in Cooperative Cognitive Radio Networks with Energy Harvesting
On the Role of Finite Queues in Cooerative Cognitive Radio Networks with Energy Harvesting Mohamed A. Abd-Elmagid, Tamer Elatt, and Karim G. Seddik Wireless Intelligent Networks Center (WINC), Nile University,
More informationSupplementary Materials for Robust Estimation of the False Discovery Rate
Sulementary Materials for Robust Estimation of the False Discovery Rate Stan Pounds and Cheng Cheng This sulemental contains roofs regarding theoretical roerties of the roosed method (Section S1), rovides
More informationLower Confidence Bound for Process-Yield Index S pk with Autocorrelated Process Data
Quality Technology & Quantitative Management Vol. 1, No.,. 51-65, 15 QTQM IAQM 15 Lower onfidence Bound for Process-Yield Index with Autocorrelated Process Data Fu-Kwun Wang * and Yeneneh Tamirat Deartment
More informationSystem Reliability Estimation and Confidence Regions from Subsystem and Full System Tests
009 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June 0-, 009 FrB4. System Reliability Estimation and Confidence Regions from Subsystem and Full System Tests James C. Sall Abstract
More informationEXACTLY PERIODIC SUBSPACE DECOMPOSITION BASED APPROACH FOR IDENTIFYING TANDEM REPEATS IN DNA SEQUENCES
EXACTLY ERIODIC SUBSACE DECOMOSITION BASED AROACH FOR IDENTIFYING TANDEM REEATS IN DNA SEUENCES Ravi Guta, Divya Sarthi, Ankush Mittal, and Kuldi Singh Deartment of Electronics & Comuter Engineering, Indian
More informationSUMS OF TWO SQUARES PAIR CORRELATION & DISTRIBUTION IN SHORT INTERVALS
SUMS OF TWO SQUARES PAIR CORRELATION & DISTRIBUTION IN SHORT INTERVALS YOTAM SMILANSKY Abstract. In this work we show that based on a conjecture for the air correlation of integers reresentable as sums
More informationCryptography Assignment 3
Crytograhy Assignment Michael Orlov orlovm@cs.bgu.ac.il) Yanik Gleyzer yanik@cs.bgu.ac.il) Aril 9, 00 Abstract Solution for Assignment. The terms in this assignment are used as defined in [1]. In some
More informationLINEAR SYSTEMS WITH POLYNOMIAL UNCERTAINTY STRUCTURE: STABILITY MARGINS AND CONTROL
LINEAR SYSTEMS WITH POLYNOMIAL UNCERTAINTY STRUCTURE: STABILITY MARGINS AND CONTROL Mohammad Bozorg Deatment of Mechanical Engineering University of Yazd P. O. Box 89195-741 Yazd Iran Fax: +98-351-750110
More informationInformation collection on a graph
Information collection on a grah Ilya O. Ryzhov Warren Powell February 10, 2010 Abstract We derive a knowledge gradient olicy for an otimal learning roblem on a grah, in which we use sequential measurements
More informationDETC2003/DAC AN EFFICIENT ALGORITHM FOR CONSTRUCTING OPTIMAL DESIGN OF COMPUTER EXPERIMENTS
Proceedings of DETC 03 ASME 003 Design Engineering Technical Conferences and Comuters and Information in Engineering Conference Chicago, Illinois USA, Setember -6, 003 DETC003/DAC-48760 AN EFFICIENT ALGORITHM
More informationPublished: 14 October 2013
Electronic Journal of Alied Statistical Analysis EJASA, Electron. J. A. Stat. Anal. htt://siba-ese.unisalento.it/index.h/ejasa/index e-issn: 27-5948 DOI: 1.1285/i275948v6n213 Estimation of Parameters of
More information