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Course: CS 4368, Fall 2009
School: U. Houston
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of Classification Search Problems http://www.cis.temple.edu/~ingargio/cis587/readings/constraints.html State Space Search Constraint Satisfaction Problems Optimization Problems Search Uninformed Search Heuristic Search Ch. Eick: Introduction to Search Example: State Space Search Figure Goal: find an operator sequence that leads from the start state to the goa State Space: a 3x3 matrix containing the...

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of Classification Search Problems http://www.cis.temple.edu/~ingargio/cis587/readings/constraints.html State Space Search Constraint Satisfaction Problems Optimization Problems Search Uninformed Search Heuristic Search Ch. Eick: Introduction to Search Example: State Space Search Figure Goal: find an operator sequence that leads from the start state to the goa State Space: a 3x3 matrix containing the numbers 1,...,8 and *(empty) Operators: North, South, East, West Ch. Eick: Introduction to Search Constraint Satisfaction Problems Problem: Assign value to a set of variable V, such that a set of constraints is satisfied. Example: 8Queen Problem: Place queens on a 8x8 chessboard so that they cannot capture each other Find values for: (x1,y1),...,(x8,y8) such that: ( i,j (xi,yi) (xj,yj) Attack((xi,yi),(xj,yj)) ) i j (xi,yi) (xj,yj) Ch. Eick: Introduction to Search Optimization Problems Maximize f(x,y,z)=|xy0.2|*|x*z0.8|*|0.3z*z*y| with x,y,z in [0,1] Characteristics: No explicit operators the path that leads to the solution is not important Frequently involves real numbers number of solutions is not finite Problems might be complicated by additionally requiring that the solution satisfies a set of contraints. Life is easier if the function is continuous and differentiable e.g. classical numerical optimization techniques can directly be applied AI and evolutionary computing are more attractive for "nasty" optimization problems. Ch. Eick: Introduction to Search Heuristic Search augment General Search Algorithms Domain-specific Knowledge Ch. Eick: Introduction to Search Classification of Search Algorithms To be discussed in depth Next Tueseday!! State Space Search Expansion Search Hill Climbing Backtracking Best First Search Uniform Cost Breadth First Depth First A* f(n)=g(n)+h(n) Greedy Search f(n)=h(n) Expand the best state/node with respect To an evaluation function f:NR Remark: Many other search algorithms exist that do not appear above Ch. Eick: Introduction to Search Characterization of State Space Search Algorithms A search strategy consists of the following: A state space S, set of operators O: SS, an initial state, and a (set of) goal state(s). A control strategy that determines how the search space will be searched; it consists of an operator selection and state selection function: Operator selection function: selects which operator(s) is applied to a given state State selection function: selects the state to which an operator (selected by the operator selection function) is applied next. Remarks: Operator selection functions only return operators that have not been applied yet, and state selection functions return only states that have not been completely expanded yet (some applicable operators have not been applied to this state yet); moreover, we assume that ties are broken randomly. Ch. Eick: Introduction to Search Example: Strategies Search for the 8 Puzzle Strategy 1 (Breadth First): Operator Selection Function: select all operators State Selection Function: Select a state s giving preference to states that are closer to the initial state i(closeness is evaluated by the number of operator applications it took to reach s from i) Strategy 2 (Backtracking with depth bound set to 3): Operator Selection Function : Select (applicable) operator by priorities: N>S>E>W State Selection Function : If the most recently created state is less than 3 operator applications away from the initial state, use this state; otherwise, use the predecessor of the most recent state. Strategy 3 (Greedy Search) Operator Selection Function: select all operators State Selection Function: Select the state s that is closest to the goal state g using a distance function d(s,g)="number of positions in which in which s and g disagree" Ch. Eick: Introduction to Search Un-graded Homework1 2004 Assume you have to search a labyrinth of interconnected rooms trying to find a particular room that contain a red flower. There will be many intersections of walkways that connect rooms all of which look completely the same; you will not know if you entered a particular crossing before; however, you will be given a piece of chalk that allow you to mark the to put signs of your own choosing on a wall. Devise a search strategy that will find a room with a red flower assuming that such a room exists. To be discussed on Sept. 30, 2004 in class! Goal State Ch. Eick: Introduction to Search Use of search strategies that are not suitable for real-time search problem (e.g. breadth first search as explained in the textbook or best first search) --- you cannot jump between stat...

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U. Houston - CS - 6367
Evolution strategiesChapter 4A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing Evolution StrategiesES quick overview Developed: Germany in the 1970's Early names: I. Rechenberg, H.-P. Schwefel Typically applied to:numeri
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Complex 9: Beta = 1.01 Eta = 1 Purity 1 Quality 0.9741 Time 5416.1 No of Clusters 17The left corner part contains 7 clusters, the outside convex shape outside belong to class 1 and two spots inside belong to class 0. By calculate the average distan
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CHAPTER ONE Introduction The introduction should start by some general ideas explaining the importance of your work. The idea is to convince your reader that you are addressing an interesting topic and that he should keep reading. You should give her
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SYLLABUS Numerical Methods II COSC 3362 Section 14080 Room 350 PGH Olin Johnson, Professor 596 PGH johnson@cs.uh.edu Office Hours: 4:00 - 5:00 T Th & by appointment Lecture Topic _ _ 1 Course Overview 2 Floating Point Arithmetic 3 Floating Point Arit
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* | ICTAI '95 REGISTRATION FORM | * > Register Today! <Please, complete and return this form and fee to: J. Vassilopoulos ICTAI'95 Registration Chair Tulane Universi
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Supplement to Assignment #3, COSC 1304, Fall 1999/*PROGRAMMER: Robert AndersonFILENAME: payroll.cDATE: March 8, 1994DESCRIPTION: This program inputs the hours worked and rate of pay for a series of employees and computes and outputs each empl
U. Houston - COSC - 1304
Practice Problems for COSC 1304 (exercises enclosed in parentheses are for reference) Chapter 1: 1.3 1.6 1.7 Chapter 2: 2.9 2.10 2.12 2.15 2.17 2.18 2.22 2.24 Chapter 3: 3.11 3.13 3.14 3.
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COSC 1304 Labs - Fall 1999 100 3.75% 3.75% 3.75% 4.50% 3.00% 3.75% 7.50% 20% 20% 30% of 100Code Lab#1 Lab#2 Lab#3 Lab#4 Lab#5
U. Houston - COSC - 1304
practice Problems for chapter 8Do the self-review exercises which have answers in the book.Do exercises: 8.6 8.8 8.9 (8.16 8.21 for reference)Answers to the Selected Exercises=8.6 #include <stdio.h> #include <ctype.h> /*for protot
U. Houston - COSC - 1304
Practice Problems for Chapter 10 and 11Do the self-review exercises which have answers in the book.Do exercises: 10.5 c) d) 10.6 a) b) e) f) g) h) i) j) 11.5 g) h) i) k) (11.7 extra question for reference)Answers to the Sel
U. Houston - COSC - 1304
Practice Problems for Chapter 6Do the self-review exercises which have answers in the book.Do exercises: 6.8 6.9 6.12 6.15 6.*1 6.*2 (You should practice these two problems.)Answers to the Selected E
U. Houston - COSC - 3351
COSC 3351 Software Design An Introduction to UML (I)This lecture contains material from: www.omg.org/docs/omg/01-03-02.ppt http:/wps.prenhall.com/esm_pfleeger_softengtp_2 http:/dn.codegear.com/article/31863 http:/sunset.usc.edu/classes/cs577a_2000/l
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COSC 3351 Software Design An Introduction to UML (II)This lecture contains material from: http:/dn.codegear.com/article/31863Edgar Gabriel Spring 2008Edgar GabrielPackages Classes can be grouped into packagesCOSC 3351 Software Design Edgar
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COSC 6343 Pattern RecognitionHW1 IntroductionProf. Shishir Shah14Prof. Shishir ShahImage Segmentation statistical learning only makes sense when you try it on data we will test what we learn on an image processing problem given the cheet
U. Houston - COSC - 6343
COSC 6343 Pattern RecognitionHW1 IntroductionProf. Shishir Shah14Prof. Shishir ShahImage Segmentation statistical learning only makes sense when you try it on data we will test what we learn on an image processing problem given the cheet
U. Houston - COSC - 4352
Agile Software DocumentationSoftware Documentation2What are the various types of documentationSpecification/Planning Architecture/Design verview of software O elationship and entity diagrams with respect to environment and R software compone
U. Houston - COSC - 6380
COSC 4393/6380 Digital Image Processing Department of Computer Science University of Houston Assignment #1 Due: 10/14/081. Write a program to binarize a gray-level image based on the assumption that the image has a bimodal histogram. You are to impl
U. Houston - COSC - 4352
Visual Numerics Company ConfidentialComputational Benchmarking of the IMSL LibraryIntroduction: Benchmarking is not a fixed project based exercise in which a set of tests are run, data gathered and the project is complete. Performance behavior var
U. Houston - COSC - 2008
COSC6343 PatternRecognition DepartmentofComputerScience UniversityofHouston Assignment1:ProbabilisticImageSegmentation Due:3/3/08The goal of this assignment is to apply statistical decision theory for the purpose of segmenting an image into two comp
U. Houston - COSC - 6343
COSC6343 PatternRecognition DepartmentofComputerScience UniversityofHouston Assignment1:ProbabilisticImageSegmentation Due:3/3/08The goal of this assignment is to apply statistical decision theory for the purpose of segmenting an image into two comp
U. Houston - COSC - 4352
Geographical Information System (GIS) Mapping and Selection Utilities Project Description BackgroundGeographical Information Systems (GIS) have become very important in recent years in industry, government and academia. Furthermore, the interest of
U. Houston - COSC - 4352
PlanetTeach.comisasocialnetworkingsiteinwhichpeoplecanTeachtheirPassionand LearntheirDream.PlanetTeachhascompleteditsBetaandisreadyfordevelopmentof version 1.5. The goal of version 1.5 is to simplify the UI and to improve the strategic capabilities
U. Houston - COSC - 4352
International Cancer Congress & The University of Houston .NET Team ProjectThe International Cancer Congress (ICC), a foundation sponsored by Extensions, Inc., will be a Houston-based organization established to further the progress of cancer resear
U. Houston - COSC - 4352
Software Development Practices Course Pre-requisite Certification Form Name: _ Student#:_ Pre-requisite: Must have completed either COSC 6318 or COSC 4351 Must have completed COSC 1320 or equivalent Must have completed COSC 2320 or equivalent I ce
U. Houston - COSC - 6380
COSC4393/6380 DigitalImageProcessing DepartmentofComputerScience UniversityofHouston Assignment#2 Due:11/12/08 1) Write a function to read input images provided with this assignment. Compute the histogram of the original image and perform histogram e
U. Houston - COSC - 4352
Software Tales: Storyboards for DevelopersMission (should you choose to accept it) Design and write an extraordinary application for creating and viewing storyboards. General Description There are many tools for documenting requirements and UI desig
U. Houston - COSC - 2008
COSC 6343 Pattern Recognition Spring 2008 MATLAB Introduction Exercise In the following, let A = [1 5 4; 2 7 8; 3 2 5] and B = [1 0 0; 0 4 2; 0 2 9] be matrices, C = [1 3 3] be a vector and D = [4 7 9 3 12 11 2 8 8] be data. 1. Compute the mean of da
U. Houston - COSC - 6343
COSC 6343 Pattern Recognition Spring 2008 MATLAB Introduction Exercise In the following, let A = [1 5 4; 2 7 8; 3 2 5] and B = [1 0 0; 0 4 2; 0 2 9] be matrices, C = [1 3 3] be a vector and D = [4 7 9 3 12 11 2 8 8] be data. 1. Compute the mean of da
U. Houston - COSC - 4352
Deloitte Data Analysis and Sharing Tool Deloitte professionals are servicing engagements for different industries all over the world. The common theme critical to the success of each of these engagements is the reliability of the source data and coll
U. Houston - COSC - 4352
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U. Houston - COSC - 2008
U. Houston - COSC - 6343
U. Houston - COSC - 6380
COSC4393/6380 DigitalImageProcessing DepartmentofComputerScience UniversityofHouston Assignment#3 Due:12/07/081. Write a program to detect lines in an image using the Hough Transform. Use the polar parameterization of a line for your implementation.
U. Houston - COSC - 6380
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U. Houston - COSC - 4352
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U. Houston - COSC - 4352
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U. Houston - COSC - 6385
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U. Houston - COSC - 6397
COSC 4397/6397 Network Systems Labs (Spring 2008) Project 1 Installing Debian on the Compaq Armada 110 Objective This assignment is designed to teach you to install Linux, configure and manage the network Description In this assignment, you will inst
U. Houston - COSC - 6397
More on Gaussian Distribution( x )2 f ( x) = exp( ) 2 2 2 2 1X ~ (x,2x, Y ~ (y, 2y), X & Y are independent, then X+Y ~ (x+y, 2x+2y) X ~ (x,2x), then aX ~ (ax,a22x) Error function erferf ( z ) = 2e0zt 2dt2 x 1 e t / 2 dt = [1 + erf
U. Houston - COSC - 6397
Wireless Application Protocol (WAP)Characteristics of mobile devices and mobile networksSmall screens Limited device memory Less powerful CPUs Limited bandwidth availability Unreliable connections High latencyOpen standard providing mobile users
U. Houston - COSC - 6397
Medium Access Control LogicStartPacket arrivalMedium idleMedium busydeferexpiredRandom BackoffMedium busyTransmitFreezeCollision detectedCW = min(2(CW+1) 1, CWmax)Interframe Space (IFS)Short IFS (SIFS)Shortest IFS (used for A
U. Houston - COSC - 6397
Lecture 2: Wireless Channel Propagation & Modulation Techniques13Police Radar14Basics IRandom variable XIf a probability distribution has density f(x), then intuitively the infinitesimal interval [x, x + dx] has probability f(x) dx. x Cumul
U. Houston - COSC - 6397
What is a computer program?"A combination of computer instructions and data definitions that enable computer hardware to perform computational or control functions"Instructions Data What is missing here?User Interface!inputInstructionsoutput
U. Houston - COSC - 6397
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U. Houston - COSC - 6397
Emerging Wireless Networks178OutlineScopeWireless sensor network Lower Power Personal area networks Wireless mesh networksFocusTechnology overview Representative projects/solutions179Adopted from D. Estrin's Mobicom'02 tutorialEmbedded
U. Houston - COSC - 6397
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U. Houston - COSC - 6397
Process MigrationMovement of a currently executing process to a new processor Process migration mechanism how the system migrates a process from source to destinationHomogenous Heterogeneous Distributed shared memoryRemote execution process exe
U. Houston - COSC - 6397
1COSC6397 Homework Assignment 1 (Larger-scale Fading)Solution Problem 1. Suppose a transmitter produces 50W of power. a. Express the transmit power in units of dBm and dBW. b. If the transmitters power is applied to a unity gain antenna with a 900
U. Houston - COSC - 6397
COSC6397 Homework Assignment 3September 22, 20041Part 1: Routing1. Find the shortest path tree from every node to node 1 for the graph of Fig.1 using the Bellman-ford and Dijkstra algorithms.10 2 4 6 3 4 5 3 9 6 3 2 3 4 3 2 6 5 2147Fig
U. Houston - COSC - 7397
Robust Rate Adaptation for 802.11 Wireless NetworksStarsky H.Y. Wong1 , Hao Yang2 , Songwu Lu1 and Vaduvur Bharghavan3Dept. of Computer Science, UCLA, 4732 Boelter Hall, Los Angeles, CA 90025 1 IBM T.J. Watson Research, 19 Skyline Drive, Hawthorne,
U. Houston - COSC - 6397
Destination Sequenced Distance Vector (DSDV)Based on Bellman-ford algorithm with the following changes:Each routing table entry keeps (destination, next-hop, hop-count, seq, install time), seq is assigned by a destination nodeDestination A B C D N
U. Houston - COSC - 6397
I/O Multiplexing and Posix ThreadsCOSC 6397Rong Zheng1Possible Mechanisms for Creating Concurrent Service1. ProcessesKernel automatically interleaves multiple logical flows. Each flow has its own private address space.2. I/O multip
U. Houston - COSC - 6377
Fall 2007 COSC 6377 Computer Networks Instructor: Rong Zheng Email: rzheng@cs.uh.edu Lecture time: 4:00pm 5:30pm, MW Location: 205 sec Office Hours: 2:00pm 4pm M; 5:30 6:30 W Class web: Go to www.uh.edu/webct, click on WebCTVista button Lab: PGH 5
U. Houston - COSC - 6397
GPSR: Greedy Perimeter Stateless Routing for Wireless NetworksProposed by Brad Karp and H.T. Kung Uses Position Information to make routing decisionsRouting based on destinations geographical location Only need to maintain neighbors location inform
U. Houston - COSC - 6397
COSC6397 Homework Assignment 3September 15, 20041Part 1: Routing1. Find the shortest path tree from every node to node 1 for the graph of Fig.1 using the Bellman-ford and Dijkstra algorithms.10 2 4 6 3 4 5 3 9 6 3 2 3 4 3 2 6 5 2Due date: Se
U. Houston - COSC - 6377
Fall 2005 COSC 6377 Computer Networks Instructor: Rong Zheng Email: rzheng@cs.uh.edu Lecture time: 4:00pm 5:30pm, MW Location: 138-SR Office Hours: 2:30pm 4pm, MW TA: Mohammad A Muqsith (muqsith1530@yahoo.com) TA Office Hours: 2:00pm 4pm, TTh Clas
U. Houston - COSC - 6397
COSC 6397 Wireless Networking and Mobile ComputingDr. Rong Zheng rzheng@cs.uh.edu1Mobile computing Wireless networkingMobile computing computing on the move, can be wired Wireless networking facilitates mobile computing with flexibility of mo
U. Houston - COSC - 6375
COSC 6375: Computer System Performance EvaluationSpring 2009 Instructors: Email: Lectures Office Hours: Class web site: Textbook: Hisashi Kobayashi and Brian L. Mark, System Modeling and Analysis: Foundations of System Performance Evaluation. Prent
U. Houston - COSC - 7397
Exploring Complex Networks S. H. Strogatz, Exploring complex networks, Nature, 1998Faloutsos3, On power-law relationship on the Internet topology, SIGCOMM, 1999 A. Lakhina et al, Sampling biases in IP topology measurement, INFOCOM, 200342Out
U. Houston - COSC - 6397
COSC 6397 Computer network system laboratory Instructor: Rong Zheng (PGH 565) TA: Song Wei (PGH 311) Time: Wed 4 7pm Location: SEC 205 Objectives: The main objective of the network system labs is to further the understanding of the network protocol