Max-plus-linear systems

Size: px
Start display at page:

Download "Max-plus-linear systems"

Transcription

1 Max-plus-linear systems Ton van den Boom Introduction week 2018 Introductionweek 2018, DCSC slide 1

2 Algebra Conventional(+, )-algebra: (a+b) c= a c+b c a (b+c)= a b+a c (λ a) (µ b)=(λ µ) (a b) Introduce a different algebra: + = max = + Introductionweek 2018, DCSC slide 2

3 Max-Plus Algebra Introduce notation from max-plus algebra: Matrices: x y=max(x,y) x y=x+y [A B] i j = a i j b i j = max(a i j,b i j ) [A C] i j = n k=1 a ik c k j = max k=1,...,n (a ik+ c k j ) Introductionweek 2018, DCSC slide 3

4 Max-plus-linear discrete-event systems System description: x(k) = A x(k 1) B u(k) y(k) = C x(k) k is an event counter x i (k) is time that state event i occurs in kth cycle. u(k) is time that input event occurs in kth cycle. y(k) is time that output event occurs in kth cycle. Introductionweek 2018, DCSC slide 4

5 Modeling of railway networks Railway networks can be modeled using max-plus linear models. D train 2 A B C train 1 A railway junction Introductionweek 2018, DCSC slide 5

6 x 1 = departure time of train 1 from station A x 2 = departure time of train 2 from station D x 3 = departure time of train 1 from station B d 3 = scheduled departure time of train 1 from station B t 1 = traveling time from station A to station B t 2 = traveling time from station D to station B Time table: x 3 d 3 Continuity: x 3 x 1 +t 1 + w B Connection: x 3 x 2 +t 2 + c B Departure time of train 1 from station B: D x 2 train 2 A B C x 1 x 3 x 3 = max( x 1 +t 1 + w B, x 2 +t 2 + c B, d 3 ) train 1 Introductionweek 2018, DCSC slide 6

7 Departure time of train 1 from station B: x 3 = max( x , x , 56) =( x 1 25) (x 2 33) 56 [ ] = x 1 56 x 2 For a network with departure vector x we obtain: x= A x d For a cyclic timetable this will result in: x(k)= A x(k 1) d(k) Introductionweek 2018, DCSC slide 7

8 Legged robots Aerial phase 6 Ground phase t Introductionweek 2018, DCSC slide 8

9 Legged robots Aerial phase 6 Ground phase t Introductionweek 2018, DCSC slide 9

10 Legged robots Aerial phase 6 Ground phase t Introductionweek 2018, DCSC slide 10

11 Legged robots Aerial phase 6 Ground phase t Introductionweek 2018, DCSC slide 11

12 Legged robots Aerial phase 6 Ground phase t Introductionweek 2018, DCSC slide 12

13 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 13

14 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 14

15 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 15

16 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 16

17 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 17

18 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 18

19 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 19

20 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 20

21 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 21

22 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 22

23 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 23

24 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 24

25 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 25

26 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 26

27 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 27

28 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 28

29 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 29

30 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 30

31 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 31

32 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 32

33 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 33

34 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 34

35 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 35

36 Large-scale Printers 22 STEPS: 3 SHEETS INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 36

37 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 37

38 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 38

39 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 39

40 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 40

41 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 41

42 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 42

43 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 43

44 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 44

45 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 45

46 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 46

47 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 47

48 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 48

49 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 49

50 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 50

51 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 51

52 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 52

53 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 53

54 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 54

55 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 55

56 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 56

57 Large-scale Printers INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 57

58 Large-scale Printers 20 STEPS: 6 SHEETS INPUT PRINTING OUTPUT INVERTER Introductionweek 2018, DCSC slide 58

59 Container terminal QUAY CRANES AGV S YARD CRANES Introductionweek 2018, DCSC slide 59

60 quay crane AGV yard crane 2 2 Introductionweek 2018, DCSC slide 60

61 2 2 Introductionweek 2018, DCSC slide 61

62 2 2 Introductionweek 2018, DCSC slide 62

63 2 2 Introductionweek 2018, DCSC slide 63

64 2 2 Synchronization 1 Introductionweek 2018, DCSC slide 64

65 2 2 Introductionweek 2018, DCSC slide 65

66 2 2 Synchronization 2 Introductionweek 2018, DCSC slide 66

67 2 2 Introductionweek 2018, DCSC slide 67

68 2 2 Introductionweek 2018, DCSC slide 68

69 Palletizer Introductionweek 2018, DCSC slide 69

70 Introductionweek 2018, DCSC slide 70

71 Synchronization 1 Introductionweek 2018, DCSC slide 71

72 Introductionweek 2018, DCSC slide 72

73 Synchronization 2 Introductionweek 2018, DCSC slide 73

74 Introductionweek 2018, DCSC slide 74

75 Introductionweek 2018, DCSC slide 75

76 Synchronization 3 Introductionweek 2018, DCSC slide 76

77 Introductionweek 2018, DCSC slide 77

78 Introductionweek 2018, DCSC slide 78

79 Introductionweek 2018, DCSC slide 79

80 Max-Plus Linear systems Introductionweek 2018, DCSC slide 80

81 Max-plus-linear zero-element and unit-element Max-plus zero: x ( )=max(x, )=x x ( )=x+( )= Max-plus unit: 0 x 0=x+0=x Introductionweek 2018, DCSC slide 81

82 Max-plus-linear eigenvalues and eigenvectors For a cyclic timetable the system description is given by x(k)= A x(k 1) d(k) Max-plus-linear eigenvalue λ and eigenvector v: Properties: A v=λ v λ = natural cycle time (For Dutch railway network: ± 57 minutes). v = natural timetable (For well-defined network: v d). Introductionweek 2018, DCSC slide 82

83 Switching max-plus linear system Railway systems: Change order of trains. Legged robot: Change gait of robot. Paper flow in printers: Change paper size/thickness. Container terminal: Change route of container. Production system: Choose machine for processing. The system can operate in a different modes x(k)= A (l) (k) x(k 1) B (l) (k) u(k) in which A (l) and B (l) are system matrices forl-th mode. Introductionweek 2018, DCSC slide 83

84 Max-min-plus-scaling systems Piecewise affine systems Ton van den Boom Introduction week 2017 Introductionweek 2018, DCSC slide 84

85 Max-min-plus-scaling (MMPS) systems System description x(k+ 1)= f x (x(k),u(k)) y(k)= f y (x(k),u(k)), where entries of f x and f y are MMPS expressions in x(k), u(k). Example MMPS system: x(k +1) = max( 2x+5, 3)+min(x 3, max( x+3, 2x 7)) Introductionweek 2018, DCSC slide 85

86 Max-min-plus-scaling system: x(k+ 1)=max( 2x+5,3)+min(x 3,max( x+3,x 7)) Introductionweek 2018, DCSC slide 86

87 Min-max canonical form: x(k+ 1)=min( max( x+2,x), max( x+6,x 4)) Introductionweek 2018, DCSC slide 87

88 Max-min canonical form: x(k+ 1)=max( x+2, min(x, x+6), x 4) Introductionweek 2018, DCSC slide 88

89 Difference canonical form: x(k+ 1)=max( 2x+3,1,2x 9) max(x 5, x+1) Introductionweek 2018, DCSC slide 89

90 MSc thesis subjects Max-plus linear systems Modeling & control of MPL systems (theory / application) Stochastic MPL systems. Application using MPL models. Max-min-plus-scaling linear systems Modeling & control of MMPS systems (theory / application) Stochastic MMPS systems. Introductionweek 2018, DCSC slide 90

91 Important courses SC Optimization in Systems & Control SC Model Predictive Control SC Modeling & Control of Hybrid Systems WI4062TU - Transport, Routing and Scheduling CIE Railway traffic management Introductionweek 2018, DCSC slide 91

92 Questions?? Introductionweek 2018, DCSC slide 92

Railway disruption management

Railway disruption management Railway disruption management 4 5 6 7 8 Delft Center for Systems and Control Railway disruption management For the degree of Master of Science in Systems and Control at Delft University of Technology

More information

Optimization in container terminals

Optimization in container terminals Ilaria Vacca (EPFL) - Integrated optimization in container terminal operations p. 1/23 Optimization in container terminals Hierarchical vs integrated solution approaches Michel Bierlaire Matteo Salani

More information

1111: Linear Algebra I

1111: Linear Algebra I 1111: Linear Algebra I Dr. Vladimir Dotsenko (Vlad) Lecture 7 Dr. Vladimir Dotsenko (Vlad) 1111: Linear Algebra I Lecture 7 1 / 8 Invertible matrices Theorem. 1. An elementary matrix is invertible. 2.

More information

A mathematical programming model to determine a set of operation lines at minimal costs M.T. Claessens

A mathematical programming model to determine a set of operation lines at minimal costs M.T. Claessens A mathematical programming model to determine a set of operation lines at minimal costs M.T. Claessens Abstract A mathematical program is developed in order to determine an optimal train allocation. This

More information

Frequency Selective Hybrid Precoding for. Limited Feedback Millimeter Wave Systems

Frequency Selective Hybrid Precoding for. Limited Feedback Millimeter Wave Systems Frequency Selective Hybrid Precoding for Limited Feedback Millimeter Wave Systems Ahmed Alkhateeb and Robert W. Heath, Jr. Invited Paper) arxiv:50.00609v4 [cs.it] 3 Aug 06 Abstract Hybrid analog/digital

More information

Model Predictive Current Control of a Grid Connected Converter With LCL-Filter

Model Predictive Current Control of a Grid Connected Converter With LCL-Filter Model Predictive Current Control of a Grid Connected Converter With LCL-Filter Joanie M.C. Geldenhuys, Hendrik du Toit Mouton, Arnold Rix and Tobias Geyer Department of Electrical and Electronic Engineering

More information

Lesson Objectives. Simplifying Algebraic Expressions with Polynomials Multiplying Monomials and Binomials

Lesson Objectives. Simplifying Algebraic Expressions with Polynomials Multiplying Monomials and Binomials UDM11L04BLM/AK_61519 8/11/03 5:15 PM Page 29 Lesson Objectives Find the product of two monomials. Find the product of a monomial and a binomial. Find the product of two binomials using the Distributive

More information

Eigenvalues and Eigenvectors in Array Antennas. Optimization of Array Antennas for High Performance. Self-introduction

Eigenvalues and Eigenvectors in Array Antennas. Optimization of Array Antennas for High Performance. Self-introduction Short Course @ISAP2010 in MACAO Eigenvalues and Eigenvectors in Array Antennas Optimization of Array Antennas for High Performance Nobuyoshi Kikuma Nagoya Institute of Technology, Japan 1 Self-introduction

More information

Gates and Circuits 1

Gates and Circuits 1 1 Gates and Circuits Chapter Goals Identify the basic gates and describe the behavior of each Describe how gates are implemented using transistors Combine basic gates into circuits Describe the behavior

More information

Math 3560 HW Set 6. Kara. October 17, 2013

Math 3560 HW Set 6. Kara. October 17, 2013 Math 3560 HW Set 6 Kara October 17, 013 (91) Let I be the identity matrix 1 Diagonal matrices with nonzero entries on diagonal form a group I is in the set and a 1 0 0 b 1 0 0 a 1 b 1 0 0 0 a 0 0 b 0 0

More information

Chapter 6.1. Cycles in Permutations

Chapter 6.1. Cycles in Permutations Chapter 6.1. Cycles in Permutations Prof. Tesler Math 184A Fall 2017 Prof. Tesler Ch. 6.1. Cycles in Permutations Math 184A / Fall 2017 1 / 27 Notations for permutations Consider a permutation in 1-line

More information

EE 650 Linear Systems Theory

EE 650 Linear Systems Theory EE 650 Linear Systems Theory 3-0-0 6 Essentials of linear algebra: vector spaces, subspaces, singular value decomposition; state variable modeling of linear dynamical systems; transfer function matrices;

More information

Permutations and Combinations Problems

Permutations and Combinations Problems Permutations and Combinations Problems Permutations and combinations are used to solve problems. Factorial Example 1: How many 3 digit numbers can you make using the digits 1, 2 and 3 without method (1)

More information

MATH 433 Applied Algebra Lecture 12: Sign of a permutation (continued). Abstract groups.

MATH 433 Applied Algebra Lecture 12: Sign of a permutation (continued). Abstract groups. MATH 433 Applied Algebra Lecture 12: Sign of a permutation (continued). Abstract groups. Permutations Let X be a finite set. A permutation of X is a bijection from X to itself. The set of all permutations

More information

FURUNO DEEPSEA WORLD Class-A Universal AIS Automatic Identification System. The future today with FURUNO's electronics technology.

FURUNO DEEPSEA WORLD Class-A Universal AIS Automatic Identification System. The future today with FURUNO's electronics technology. R FURUNO DEEPSEA WORLD Class-A Universal AIS Automatic Identification System Model FA-100 The AIS improves the safety of navigation by assisting in the efficient navigation of ships, protection of the

More information

MIMO Channel Capacity in Co-Channel Interference

MIMO Channel Capacity in Co-Channel Interference MIMO Channel Capacity in Co-Channel Interference Yi Song and Steven D. Blostein Department of Electrical and Computer Engineering Queen s University Kingston, Ontario, Canada, K7L 3N6 E-mail: {songy, sdb}@ee.queensu.ca

More information

UNISI Team. UNISI Team - Expertise

UNISI Team. UNISI Team - Expertise Control Alberto Bemporad (prof.) Davide Barcelli (student) Daniele Bernardini (PhD student) Marta Capiluppi (postdoc) Giulio Ripaccioli (PhD student) XXXXX (postdoc) Communications Andrea Abrardo (prof.)

More information

TRAINS ON TIME. Optimizing and Scheduling of railway timetables. Soumya Dutta. IIT Bombay. Students Reading Group. July 27, 2016

TRAINS ON TIME. Optimizing and Scheduling of railway timetables. Soumya Dutta. IIT Bombay. Students Reading Group. July 27, 2016 TRAINS ON TIME Optimizing and Scheduling of railway timetables Soumya Dutta IIT Bombay Students Reading Group July 27, 2016 Soumya Dutta TRAINS ON TIME 1 / 22 Outline Introduction to Optimization Examples

More information

Number system: the system used to count discrete units is called number. Decimal system: the number system that contains 10 distinguished

Number system: the system used to count discrete units is called number. Decimal system: the number system that contains 10 distinguished Number system: the system used to count discrete units is called number system Decimal system: the number system that contains 10 distinguished symbols that is 0-9 or digits is called decimal system. As

More information

Community Detection and Labeling Nodes

Community Detection and Labeling Nodes and Labeling Nodes Hao Chen Department of Statistics, Stanford Jan. 25, 2011 (Department of Statistics, Stanford) Community Detection and Labeling Nodes Jan. 25, 2011 1 / 9 Community Detection - Network:

More information

INTRODUCTION TO KALMAN FILTERS

INTRODUCTION TO KALMAN FILTERS ECE5550: Applied Kalman Filtering 1 1 INTRODUCTION TO KALMAN FILTERS 1.1: What does a Kalman filter do? AKalmanfilterisatool analgorithmusuallyimplementedasa computer program that uses sensor measurements

More information

Tree Diagrams and the Fundamental Counting Principle

Tree Diagrams and the Fundamental Counting Principle Objective: In this lesson, you will use permutations and combinations to compute probabilities of compound events and to solve problems. Read this knowledge article and answer the following: Tree Diagrams

More information

Achim J. Lilienthal Mobile Robotics and Olfaction Lab, AASS, Örebro University

Achim J. Lilienthal Mobile Robotics and Olfaction Lab, AASS, Örebro University Achim J. Lilienthal Mobile Robotics and Olfaction Lab, Room T1227, Mo, 11-12 o'clock AASS, Örebro University (please drop me an email in advance) achim.lilienthal@oru.se 1 2. General Introduction Schedule

More information

ECE411 - Laboratory Exercise #1

ECE411 - Laboratory Exercise #1 ECE411 - Laboratory Exercise #1 Introduction to Matlab/Simulink This laboratory exercise is intended to provide a tutorial introduction to Matlab/Simulink. Simulink is a Matlab toolbox for analysis/simulation

More information

Single-Stage BJT Amplifiers and BJT High-Frequency Model. Single-Stage BJT Amplifier Configurations

Single-Stage BJT Amplifiers and BJT High-Frequency Model. Single-Stage BJT Amplifier Configurations 1 Single-Stage BJT Amplifiers and BJT High-Frequency Model Asst. Prof. MONTREE SIRIPRUCHYANUN, D. Eng. Dept. of Teacher Training in Electrical Engineering, Faculty of Technical Education King Mongkut s

More information

Evolutionary robotics Jørgen Nordmoen

Evolutionary robotics Jørgen Nordmoen INF3480 Evolutionary robotics Jørgen Nordmoen Slides: Kyrre Glette Today: Evolutionary robotics Why evolutionary robotics Basics of evolutionary optimization INF3490 will discuss algorithms in detail Illustrating

More information

Dynamic Prediction Method with Schedule Recovery Impact for Bus Arrival Time

Dynamic Prediction Method with Schedule Recovery Impact for Bus Arrival Time Dynamic Prediction Method with Schedule Recovery Impact for Bus Arrival Time Mei Chen, Xiaobo Liu, and Jingxin Xia This study develops a dynamic bus arrival time prediction model using the data collected

More information

Modern Control Theoretic Approach for Gait and Behavior Recognition. Charles J. Cohen, Ph.D. Session 1A 05-BRIMS-023

Modern Control Theoretic Approach for Gait and Behavior Recognition. Charles J. Cohen, Ph.D. Session 1A 05-BRIMS-023 Modern Control Theoretic Approach for Gait and Behavior Recognition Charles J. Cohen, Ph.D. ccohen@cybernet.com Session 1A 05-BRIMS-023 Outline Introduction - Behaviors as Connected Gestures Gesture Recognition

More information

MODELLING AND CONTROL OF OFFSHORE CRANE SYSTEMS

MODELLING AND CONTROL OF OFFSHORE CRANE SYSTEMS UNIVERSITY OF TECHNOLOGY, SYDNEY Faculty of Engineering and Information Technology MODELLING AND CONTROL OF OFFSHORE CRANE SYSTEMS by R.M.T. Raja Ismail A Thesis Submitted in Partial Fulfillment of the

More information

Why is scramble needed for DFE. Gordon Wu

Why is scramble needed for DFE. Gordon Wu Why is scramble needed for DFE Gordon Wu DFE Adaptation Algorithms: LMS and ZF Least Mean Squares(LMS) Heuristically arrive at optimal taps through traversal of the tap search space to the solution that

More information

Prediction error identification with rank-reduced output noise

Prediction error identification with rank-reduced output noise Prediction error identification with rank-reduced output noise Paul M.J. Van den Hof with Harm Weerts (TU/e) and Arne Dankers (Calgary) 2017 American Control Conference, Seattle, WA, 24 May 2017 Introduction

More information

The analysis and optimization of methods for determining traffic signal settings

The analysis and optimization of methods for determining traffic signal settings MASTER The analysis and optimization of methods for determining traffic signal settings Schutte, M. Award date: 2011 Link to publication Disclaimer This document contains a student thesis (bachelor's or

More information

Available theses in industrial robotics (October 2016) Prof. Paolo Rocco Prof. Andrea Maria Zanchettin

Available theses in industrial robotics (October 2016) Prof. Paolo Rocco Prof. Andrea Maria Zanchettin Available theses in industrial robotics (October 2016) Prof. Paolo Rocco Prof. Andrea Maria Zanchettin Politecnico di Milano - Dipartimento di Elettronica, Informazione e Bioingegneria Industrial robotics

More information

Short-Circuit Analysis IEC Standard Operation Technology, Inc. Workshop Notes: Short-Circuit IEC

Short-Circuit Analysis IEC Standard Operation Technology, Inc. Workshop Notes: Short-Circuit IEC Short-Circuit Analysis IEC Standard 1996-2009 Operation Technology, Inc. Workshop Notes: Short-Circuit IEC Purpose of Short-Circuit Studies A Short-Circuit Study can be used to determine any or all of

More information

LECTURE 8: DETERMINANTS AND PERMUTATIONS

LECTURE 8: DETERMINANTS AND PERMUTATIONS LECTURE 8: DETERMINANTS AND PERMUTATIONS MA1111: LINEAR ALGEBRA I, MICHAELMAS 2016 1 Determinants In the last lecture, we saw some applications of invertible matrices We would now like to describe how

More information

MPC Design for Power Electronics: Perspectives and Challenges

MPC Design for Power Electronics: Perspectives and Challenges MPC Design for Power Electronics: Perspectives and Challenges Daniel E. Quevedo Chair for Automatic Control Institute of Electrical Engineering (EIM-E) Paderborn University, Germany dquevedo@ieee.org IIT

More information

Optimization Techniques for Alphabet-Constrained Signal Design

Optimization Techniques for Alphabet-Constrained Signal Design Optimization Techniques for Alphabet-Constrained Signal Design Mojtaba Soltanalian Department of Electrical Engineering California Institute of Technology Stanford EE- ISL Mar. 2015 Optimization Techniques

More information

Some results on optimal estimation and control for lossy NCS. Luca Schenato

Some results on optimal estimation and control for lossy NCS. Luca Schenato Some results on optimal estimation and control for lossy NCS Luca Schenato Networked Control Systems Drive-by-wire systems Swarm robotics Smart structures: adaptive space telescope Wireless Sensor Networks

More information

IN357: ADAPTIVE FILTERS

IN357: ADAPTIVE FILTERS R 1 IN357: ADAPTIVE FILTERS Course book: Chap. 9 Statistical Digital Signal Processing and modeling, M. Hayes 1996 (also builds on Chap 7.2). David Gesbert Signal and Image Processing Group (DSB) http://www.ifi.uio.no/~gesbert

More information

Augment the Spatial Resolution of Multispectral Image Using PCA Fusion Method and Classified It s Region Using Different Techniques.

Augment the Spatial Resolution of Multispectral Image Using PCA Fusion Method and Classified It s Region Using Different Techniques. Augment the Spatial Resolution of Multispectral Image Using PCA Fusion Method and Classified It s Region Using Different Techniques. Israa Jameel Muhsin 1, Khalid Hassan Salih 2, Ebtesam Fadhel 3 1,2 Department

More information

17. Symmetries. Thus, the example above corresponds to the matrix: We shall now look at how permutations relate to trees.

17. Symmetries. Thus, the example above corresponds to the matrix: We shall now look at how permutations relate to trees. 7 Symmetries 7 Permutations A permutation of a set is a reordering of its elements Another way to look at it is as a function Φ that takes as its argument a set of natural numbers of the form {, 2,, n}

More information

Two-stage column generation and applications in container terminal management

Two-stage column generation and applications in container terminal management Two-stage column generation and applications in container terminal management Ilaria Vacca Matteo Salani Michel Bierlaire Transport and Mobility Laboratory EPFL 8th Swiss Transport Research Conference

More information

Optimal Voltage Control using Singular Value Decomposition of Fast Decoupled Load Flow Jacobian

Optimal Voltage Control using Singular Value Decomposition of Fast Decoupled Load Flow Jacobian Optimal Voltage Control using Singular Value Decomposition of Fast Decoupled Load Flow Jacobian Talha Iqbal, Ali Dehghan Banadaki, Ali Feliachi Lane Department of Computer Science and Electrical Engineering

More information

Introductory Probability

Introductory Probability Introductory Probability Combinations Nicholas Nguyen nicholas.nguyen@uky.edu Department of Mathematics UK Agenda Assigning Objects to Identical Positions Denitions Committee Card Hands Coin Toss Counts

More information

Adaptive Linear Predictive Frequency Tracking and CPM Demodulation

Adaptive Linear Predictive Frequency Tracking and CPM Demodulation Adaptive Linear Predictive Frequency Tracking and CPM Demodulation Malay Gupta and Balu Santhanam Department of Electrical and Computer Engineering University of New Mexico Albuquerque, New Mexico 873

More information

D3K YANGZHOU YANGJIE ELECTRONIC TECHNOLOGY CO.,LTD.

D3K YANGZHOU YANGJIE ELECTRONIC TECHNOLOGY CO.,LTD. D3K 1) Inner Box Drawing 2) Inner Box Label TYPE: XXXXXXX P/N: XXXXXXXXXXX-XXXX-XXXX---Yangjie Internal Control Part No. Exp. XXXXXX -XX-XXXXX Exp. CDR1106300050 Means this is the 50 th orders on June.

More information

Quality indicators for embedded stochastic subspace identification algorithms in wireless structural health monitoring systems

Quality indicators for embedded stochastic subspace identification algorithms in wireless structural health monitoring systems Quality indicators for embedded stochastic subspace identification algorithms in wireless structural health monitoring systems Stalin Ibáñez and Kosmas Dragos Chair of Computing in Civil Engineering Bauhaus

More information

Networked and Distributed Control Systems Lecture 1. Tamas Keviczky and Nathan van de Wouw

Networked and Distributed Control Systems Lecture 1. Tamas Keviczky and Nathan van de Wouw Networked and Distributed Control Systems Lecture 1 Tamas Keviczky and Nathan van de Wouw Lecturers / contact information Dr. T. Keviczky (Tamas) Office: 34-C-3-310 E-mail: t.keviczky@tudelft.nl Prof.dr.ir.

More information

Power allocation for Block Diagonalization Multi-user MIMO downlink with fair user scheduling and unequal average SNR users

Power allocation for Block Diagonalization Multi-user MIMO downlink with fair user scheduling and unequal average SNR users Power allocation for Block Diagonalization Multi-user MIMO downlink with fair user scheduling and unequal average SNR users Therdkiat A. (Kiak) Araki-Sakaguchi Laboratory MCRG group seminar 12 July 2012

More information

Performance Evaluation of Multilevel Inverter using Embedded and Digital Control

Performance Evaluation of Multilevel Inverter using Embedded and Digital Control Performance Evaluation of Multilevel Inverter using and Digital Control S. Shama Department of EEE Arunai Engg. College Tiruvannamalai, India Dr. S. P. Natarajan Department of EIE Annamalai University

More information

LAMC Junior Circle January 22, Oleg Gleizer. The Hanoi Tower. Part 2

LAMC Junior Circle January 22, Oleg Gleizer. The Hanoi Tower. Part 2 LAMC Junior Circle January 22, 2012 Oleg Gleizer The Hanoi Tower Part 2 Definition 1 An algorithm is a finite set of clear instructions to solve a problem. An algorithm is called optimal, if the solution

More information

How (Information Theoretically) Optimal Are Distributed Decisions?

How (Information Theoretically) Optimal Are Distributed Decisions? How (Information Theoretically) Optimal Are Distributed Decisions? Vaneet Aggarwal Department of Electrical Engineering, Princeton University, Princeton, NJ 08544. vaggarwa@princeton.edu Salman Avestimehr

More information

Logic diagram: a graphical representation of a circuit

Logic diagram: a graphical representation of a circuit LOGIC AND GATES Introduction to Logic (1) Logic diagram: a graphical representation of a circuit Each type of gate is represented by a specific graphical symbol Truth table: defines the function of a gate

More information

Travel time uncertainty and network models

Travel time uncertainty and network models Travel time uncertainty and network models CE 392C TRAVEL TIME UNCERTAINTY One major assumption throughout the semester is that travel times can be predicted exactly and are the same every day. C = 25.87321

More information

Resource Management in QoS-Aware Wireless Cellular Networks

Resource Management in QoS-Aware Wireless Cellular Networks Resource Management in QoS-Aware Wireless Cellular Networks Zhi Zhang Dept. of Electrical and Computer Engineering Colorado State University April 24, 2009 Zhi Zhang (ECE CSU) Resource Management in Wireless

More information

CS302 Digital Logic Design Solved Objective Midterm Papers For Preparation of Midterm Exam

CS302 Digital Logic Design Solved Objective Midterm Papers For Preparation of Midterm Exam CS302 Digital Logic Design Solved Objective Midterm Papers For Preparation of Midterm Exam MIDTERM EXAMINATION 2011 (October-November) Q-21 Draw function table of a half adder circuit? (2) Answer: - Page

More information

Constructing local discriminative features for signal classification

Constructing local discriminative features for signal classification Constructing local discriminative features for signal classification Local features for signal classification Outline Motivations Problem formulation Lifting scheme Local features Conclusions Toy example

More information

Link and Link Impedance 2018/02/13. VECTOR DATA ANALYSIS Network Analysis TYPES OF OPERATIONS

Link and Link Impedance 2018/02/13. VECTOR DATA ANALYSIS Network Analysis TYPES OF OPERATIONS VECTOR DATA ANALYSIS Network Analysis A network is a system of linear features that has the appropriate attributes for the flow of objects. A network is typically topology-based: lines (arcs) meet at intersections

More information

Design of General Order Digital Phase Locked Loop Dr. P.H.Tandel, Anuradha P. Gharge

Design of General Order Digital Phase Locked Loop Dr. P.H.Tandel, Anuradha P. Gharge Design of General Order Digital Phase Locked Loop Dr. P.H.Tandel, Anuradha P. Gharge Abstract This paper presents an analysis of a novel structure proposed for the conventional digital phase locked loop

More information

December 12, W. O r,n r

December 12, W. O r,n r SPECTRALLY ARBITRARY PATTERNS: REDUCIBILITY AND THE n CONJECTURE FOR n = LUZ M. DEALBA, IRVIN R. HENTZEL, LESLIE HOGBEN, JUDITH MCDONALD, RANA MIKKELSON, OLGA PRYPOROVA, BRYAN SHADER, AND KEVIN N. VANDER

More information

A PageRank Algorithm based on Asynchronous Gauss-Seidel Iterations

A PageRank Algorithm based on Asynchronous Gauss-Seidel Iterations Simulation A PageRank Algorithm based on Asynchronous Gauss-Seidel Iterations D. Silvestre, J. Hespanha and C. Silvestre 2018 American Control Conference Milwaukee June 27-29 2018 Silvestre, Hespanha and

More information

Circuit level, 32 nm, 1-bit MOSSI-ULP adder: power, PDP and area efficient base cell for unsigned multiplier

Circuit level, 32 nm, 1-bit MOSSI-ULP adder: power, PDP and area efficient base cell for unsigned multiplier LETTER IEICE Electronics Express, Vol.11, No.6, 1 7 Circuit level, 32 nm, 1-bit MOSSI-ULP adder: power, PDP and area efficient base cell for unsigned multiplier S. Vijayakumar 1a) and Reeba Korah 2b) 1

More information

Joint Congestion Control and Routing Subject to Dynamic Interruptions in Cognitive Radio Networks

Joint Congestion Control and Routing Subject to Dynamic Interruptions in Cognitive Radio Networks Joint Congestion Control and Routing Subject to Dynamic Interruptions in Cognitive Radio Networks Husheng Li Department of EECS University of Tennessee Knoxville, TN 37996 Email: husheng@eecs.utk.edu Lijun

More information

9-1: Circle Basics GEOMETRY UNIT 9. And. 9-2: Tangent Properties

9-1: Circle Basics GEOMETRY UNIT 9. And. 9-2: Tangent Properties 9-1: Circle Basics GEOMETRY UNIT 9 And 9-2: Tangent Properties CIRCLES Content Objective: Students will be able to solve for missing lengths in circles. Language Objective: Students will be able to identify

More information

Address for Correspondence

Address for Correspondence Research Paper MODEL PREDICTIVE CONTROL LAW OF SEPIC CONVERTER 1 P. Annapandi, 2 S.Selvaperumal, Address for Correspondence 1 Professor, Dept. of Electrical and Electronics Engineering, FRANCIS XAVIER

More information

A Novel Loss Recovery and Tracking Scheme for Maneuvering Target in Hybrid WSNs

A Novel Loss Recovery and Tracking Scheme for Maneuvering Target in Hybrid WSNs sensors Article A Novel Loss Recovery and Tracking Scheme for Maneuvering Target in Hybrid WSNs Hanwang Qian 1,2, Pengcheng Fu 1,2, Baoqing Li 1, Jianpo Liu 1 and Xiaobing Yuan 1, * 1 Science and Technology

More information

ADAPTIVE CONSENSUS-BASED DISTRIBUTED DETECTION IN WSN WITH UNRELIABLE LINKS

ADAPTIVE CONSENSUS-BASED DISTRIBUTED DETECTION IN WSN WITH UNRELIABLE LINKS ADAPTIVE CONSENSUS-BASED DISTRIBUTED DETECTION IN WSN WITH UNRELIABLE LINKS Daniel Alonso-Román and Baltasar Beferull-Lozano Department of Information and Communication Technologies University of Agder,

More information

PROFILE REPORT. Tenure Track position Optimization for engineering systems

PROFILE REPORT. Tenure Track position Optimization for engineering systems PROFILE REPORT Tenure Track position Optimization for engineering systems Faculty of Science and Engineering, University of Groningen Engineering and Technology Institute Groningen (ENTEG) Profile report:

More information

Model predictive control for rail condition-based maintenance: A multilevel approach

Model predictive control for rail condition-based maintenance: A multilevel approach Delft University of Technology Delft Center for Systems and Control Technical report 6- Model predictive control for rail condition-based maintenance: A multilevel approach Z. Su, A. Núñez, S. Baldi, and

More information

JRC MODIFIED VOLTAGE CONTROL LAW FOR LOW FREQUENCY RAILWAY POWER SYSTEMS

JRC MODIFIED VOLTAGE CONTROL LAW FOR LOW FREQUENCY RAILWAY POWER SYSTEMS Proceedings of the 27 IEEE/ASME Joint Rail Conference JRC27 April 4-7, 27, Philadelphia, PA, USA JRC27-2224 MODIFIED VOLTAGE CONTROL LAW FOR LOW FREQUENCY RAILWAY POWER SYSTEMS John Laury Electric Power

More information

Fair scheduling and orthogonal linear precoding/decoding. in broadcast MIMO systems

Fair scheduling and orthogonal linear precoding/decoding. in broadcast MIMO systems Fair scheduling and orthogonal linear precoding/decoding in broadcast MIMO systems R Bosisio, G Primolevo, O Simeone and U Spagnolini Dip di Elettronica e Informazione, Politecnico di Milano Pzza L da

More information

A New Approach to Current Differential Protection for Transmission Lines

A New Approach to Current Differential Protection for Transmission Lines A New Approach to Current Differential Protection for Transmission Lines CURRENT DIFFERENTIAL MODEL Normal Condition: I 1 + I 2 = I C - the line charging current Fault Condition: I 1 + I 2 = I C Can be

More information

SIMULATIONS OF ADAPTIVE ALGORITHMS FOR SPATIAL BEAMFORMING

SIMULATIONS OF ADAPTIVE ALGORITHMS FOR SPATIAL BEAMFORMING SIMULATIONS OF ADAPTIVE ALGORITHMS FOR SPATIAL BEAMFORMING Ms Juslin F Department of Electronics and Communication, VVIET, Mysuru, India. ABSTRACT The main aim of this paper is to simulate different types

More information

Frequently Asked Questions GE6252 BEEE UNIT I ELECTRICAL CIRCUITS AND MEASUREMENTS

Frequently Asked Questions GE6252 BEEE UNIT I ELECTRICAL CIRCUITS AND MEASUREMENTS Frequently Asked Questions GE6252 BEEE UNIT I ELECTRICAL CIRCUITS AND MEASUREMENTS 1. What is charge? 2. Define current. 3. Under what condition AC circuit said to be resonant? 4. What do you meant by

More information

Approaches for Angle of Arrival Estimation. Wenguang Mao

Approaches for Angle of Arrival Estimation. Wenguang Mao Approaches for Angle of Arrival Estimation Wenguang Mao Angle of Arrival (AoA) Definition: the elevation and azimuth angle of incoming signals Also called direction of arrival (DoA) AoA Estimation Applications:

More information

Robust cyclic berth planning of container vessels

Robust cyclic berth planning of container vessels OR Spectrum DOI 10.1007/s00291-010-0198-z REGULAR ARTICLE Robust cyclic berth planning of container vessels Maarten Hendriks Marco Laumanns Erjen Lefeber Jan Tijmen Udding The Author(s) 2010. This article

More information

A Multidisciplinary Approach to Cooperative Robotics

A Multidisciplinary Approach to Cooperative Robotics A Multidisciplinary Approach to Cooperative Pedro U. Lima Intelligent Systems Lab Instituto Superior Técnico Lisbon, Portugal WHERE ARE WE? ISR ASSOCIATE LAB PARTNERS Multidisciplinary R&D in and Information

More information

The Pennsylvania State University The Graduate School ENHANCEMENTS TO THE FLOQUET METHOD FOR ANALYSIS AND DESIGN OF POWER CONVERTER SYSTEMS

The Pennsylvania State University The Graduate School ENHANCEMENTS TO THE FLOQUET METHOD FOR ANALYSIS AND DESIGN OF POWER CONVERTER SYSTEMS The Pennsylvania State University The Graduate School ENHANCEMENTS TO THE FLOQUET METHOD FOR ANALYSIS AND DESIGN OF POWER CONVERTER SYSTEMS A Dissertation in Electrical Engineering by Mu He c 216 Mu He

More information

On uniquely k-determined permutations

On uniquely k-determined permutations Discrete Mathematics 308 (2008) 1500 1507 www.elsevier.com/locate/disc On uniquely k-determined permutations Sergey Avgustinovich a, Sergey Kitaev b a Sobolev Institute of Mathematics, Acad. Koptyug prospect

More information

POSITION AND SPEED ESTIMATION OF A STEPPING MOTOR AS AN ACTUATOR OF DIESEL ENGINE FUEL RACK

POSITION AND SPEED ESTIMATION OF A STEPPING MOTOR AS AN ACTUATOR OF DIESEL ENGINE FUEL RACK Ivana Golub Medvešek Ante Cibilić Vinko Tomas ISSN 0007-215X eissn 1845-5859 POSITION AND SPEED ESTIMATION OF A STEPPING MOTOR AS AN ACTUATOR OF DIESEL ENGINE FUEL RACK Summary UDC 629.5.062.3 Professional

More information

Algebra Adventure Directions. Format: Individual or Pairs (works best)

Algebra Adventure Directions. Format: Individual or Pairs (works best) Algebra Adventure Directions Format: Individual or Pairs (works best) Directions: Each student will receive an Algebra Adventure WS that they will keep track of their stations and work. Each pair will

More information

How to Measure the Robustness of Shunting Plans

How to Measure the Robustness of Shunting Plans How to Measure the Robustness of Shunting Plans Roel van den Broek Department of Computer Science, Utrecht University Utrecht, The Netherlands r.w.vandenbroek@uu.nl Han Hoogeveen Department of Computer

More information

Influence of Electrical Eigenfrequencies on Damped Voltage Resonance Based Sensorless Control of Switched Reluctance Drives

Influence of Electrical Eigenfrequencies on Damped Voltage Resonance Based Sensorless Control of Switched Reluctance Drives Influence of Electrical Eigenfrequencies on Damped Voltage Resonance ased Sensorless Control of Switched Reluctance Drives K.R. Geldhof, A. Van den ossche and J.A.A. Melkebeek Department of Electrical

More information

Bus Travel Time Prediction Model for Dynamic Operations Control and Passenger Information Systems

Bus Travel Time Prediction Model for Dynamic Operations Control and Passenger Information Systems November 15, 2002 Bus Travel Time Prediction Model for Dynamic Operations Control and Passenger Information Systems Amer Shalaby, Ph.D., P.Eng. Assistant Professor, Department of Civil Engineering University

More information

= X must be in a set of A or in a set of not A.

= X must be in a set of A or in a set of not A. Traditional (crisp) logic Traditional (crisp) logic In 300 B.C. ristotle formulated the law of the ecluded middle, which is now the principle foundation of mathematics. = X X must be in a set of or in

More information

Revision of Lecture Twenty-Eight

Revision of Lecture Twenty-Eight ELEC64 Advanced Wireless Communications Networks and Systems Revision of Lecture Twenty-Eight MIMO classification: roughly three classes create diversity, increase throughput, support multi-users Some

More information

and : Principles of Autonomy and Decision Making. Prof Brian Williams, Prof Emilio Frazzoli and Sertac Karaman September, 8 th, 2010

and : Principles of Autonomy and Decision Making. Prof Brian Williams, Prof Emilio Frazzoli and Sertac Karaman September, 8 th, 2010 16.410 and 16.412: Principles of Autonomy and Decision Making Prof Brian Williams, Prof Emilio Frazzoli and Sertac Karaman September, 8 th, 2010 1 1 Assignments Homework: Class signup, return at end of

More information

Real-time Volt/Var Optimization Scheme for Distribution Systems with PV Integration

Real-time Volt/Var Optimization Scheme for Distribution Systems with PV Integration Grid-connected Advanced Power Electronic Systems Real-time Volt/Var Optimization Scheme for Distribution Systems with PV Integration 02-15-2017 Presenter Name: Yan Chen (On behalf of Dr. Benigni) Outline

More information

Characteristics of Routes in a Road Traffic Assignment

Characteristics of Routes in a Road Traffic Assignment Characteristics of Routes in a Road Traffic Assignment by David Boyce Northwestern University, Evanston, IL Hillel Bar-Gera Ben-Gurion University of the Negev, Israel at the PTV Vision Users Group Meeting

More information

Teaching Portfolio MR. ROHIT MATHUR DEPT. OF E&CE. MANIPAL UNIV. JAIPUR. RAJ.

Teaching Portfolio MR. ROHIT MATHUR DEPT. OF E&CE. MANIPAL UNIV. JAIPUR. RAJ. Teaching Portfolio MR. ROHIT MATHUR DEPT. OF E&CE. MANIPAL UNIV. JAIPUR. RAJ. Content v My Teaching Philosophy. v Experimental Course Details. v Activity Based Learning. v Technology Use. v Course Assessment.

More information

a b y UC Berkeley CS61C : Machine Structures Hello Helo,world!

a b y UC Berkeley CS61C : Machine Structures Hello Helo,world! CS61C L23 Representations of Combinatorial Logic Circuits (1) inst.eecs.berkeley.edu/~cs61c UC Berkeley CS61C : Machine Structures Lecture 23 Representations of Combinatorial Logic Circuits 2006-10-20

More information

DIRECTION OF ARRIVAL ESTIMATION IN WIRELESS MOBILE COMMUNICATIONS USING MINIMUM VERIANCE DISTORSIONLESS RESPONSE

DIRECTION OF ARRIVAL ESTIMATION IN WIRELESS MOBILE COMMUNICATIONS USING MINIMUM VERIANCE DISTORSIONLESS RESPONSE DIRECTION OF ARRIVAL ESTIMATION IN WIRELESS MOBILE COMMUNICATIONS USING MINIMUM VERIANCE DISTORSIONLESS RESPONSE M. A. Al-Nuaimi, R. M. Shubair, and K. O. Al-Midfa Etisalat University College, P.O.Box:573,

More information

MSC MASTER SIGNAL CONTROLLER

MSC MASTER SIGNAL CONTROLLER MSC MASTER SIGNAL CONTROLLER By The naling Solution W. S. Ataras Engineering, Inc. PO Box West Terre Haute, IN Rev. B, //00 Copyright 99-00 W. S. Ataras Engineering, Inc. All Rights Reserved TABLE OF CONTENTS

More information

Dynamic Programming. Objective

Dynamic Programming. Objective Dynamic Programming Richard de Neufville Professor of Engineering Systems and of Civil and Environmental Engineering MIT Massachusetts Institute of Technology Dynamic Programming Slide 1 of 43 Objective

More information

DATA SHEET NPN SILICON TRIPLE DIFFUSED TRANSISTOR FOR HIGH-SPEED HIGH-VOLTAGE SWITCHING 15 A

DATA SHEET NPN SILICON TRIPLE DIFFUSED TRANSISTOR FOR HIGH-SPEED HIGH-VOLTAGE SWITCHING 15 A DATA SHEET SILICON POWER TRANSISTOR 2SC2335 NPN SILICON TRIPLE DIFFUSED TRANSISTOR FOR HIGH-SPEED HIGH-VOLTAGE SWITCHING The 2SC2335 is a mold power transistor developed for high-speed high-voltage switching,

More information

Gates and and Circuits

Gates and and Circuits Chapter 4 Gates and Circuits Chapter Goals Identify the basic gates and describe the behavior of each Describe how gates are implemented using transistors Combine basic gates into circuits Describe the

More information

Radar Signal Classification Based on Cascade of STFT, PCA and Naïve Bayes

Radar Signal Classification Based on Cascade of STFT, PCA and Naïve Bayes 216 7th International Conference on Intelligent Systems, Modelling and Simulation Radar Signal Classification Based on Cascade of STFT, PCA and Naïve Bayes Yuanyuan Guo Department of Electronic Engineering

More information

Lecture 2: Digital Logic Basis

Lecture 2: Digital Logic Basis Lecture 2: Digital Logic Basis Xufeng Kou School of Information Science and Technology ShanghaiTech University 1 Outline Truth Table Basic Logic Operation and Gates Logic Circuits NOR Gates and NAND Gates

More information

MULTISPECTRAL IMAGE PROCESSING I

MULTISPECTRAL IMAGE PROCESSING I TM1 TM2 337 TM3 TM4 TM5 TM6 Dr. Robert A. Schowengerdt TM7 Landsat Thematic Mapper (TM) multispectral images of desert and agriculture near Yuma, Arizona MULTISPECTRAL IMAGE PROCESSING I SENSORS Multispectral

More information

CARD GAMES AND CRYSTALS

CARD GAMES AND CRYSTALS CARD GAMES AND CRYSTALS This is the extended version of a talk I gave at KIDDIE (graduate student colloquium) in April 2011. I wish I could give this version, but there wasn t enough time, so I left out

More information