example
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example

Course Number: CECS 340, Fall 2008

College/University: CSU Long Beach

Word Count: 28735

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%!PS-Adobe-2.0 %%Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %%Title: example.dvi %%CreationDate: Wed Jan 07 17:18:35 2004 %%Pages: 6 %%PageOrder: Ascend %%BoundingBox: 0 0 596 842 %%DocumentPaperSizes: a4 %%EndComments %DVIPSWebPage: (www.radicaleye.com) %DVIPSCommandLine: dvips example.dvi %DVIPSParameters: dpi=600, compressed %DVIPSSource: TeX output 2004.01.07:1716 %%BeginProcSet: texc.pro %!...

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Coursehero >> California >> CSU Long Beach >> CECS 340

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CSU Long Beach - CECS - 340
%!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: dsa.dvi %CreationDate: Mon Jan 12 13:07:32 2004 %Pages: 10 %PageOrder: Ascend %BoundingBox: 0 0 596 842 %DocumentPaperSizes: a4 %EndComments %DVIPSWebPage: (www.radica
CSU Long Beach - CECS - 340
Queueing ModelsQueue Denitions customer: any entity that arrives to facility and requires service. server: entity that provides the requested service. calling population: the set of potential customers system capacity: the number of customers
CSU Long Beach - CECS - 340
Input ModelingFour basic steps to input modeling 1. Collect data and/or both seek expert opinion or make educated an educated guess. 2. If data has been collected, develop a frequency distribution and choose an appropriate family of probability dis
CSU Long Beach - CECS - 340
%!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: input.dvi %CreationDate: Thu Jan 15 16:18:37 2004 %Pages: 7 %PageOrder: Ascend %BoundingBox: 0 0 596 842 %DocumentPaperSizes: a4 %EndComments %DVIPSWebPage: (www.radic
CSU Long Beach - CECS - 340
%!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: fsm.dvi %CreationDate: Sat Mar 20 12:18:05 2004 %Pages: 8 %PageOrder: Ascend %BoundingBox: 0 0 596 842 %DocumentPaperSizes: a4 %EndComments %DVIPSWebPage: (www.radical
CSU Long Beach - CECS - 552
Queueing Systems and Discrete Event SimulationsQueueing System Terminology and Example In general, a queueing system consists of the following components: a calling population of customers which enter the system for the purpose of obtaining one or
CSU Long Beach - CECS - 328
Splay TreesSplay trees represent an alternative to AVL trees. A splay tree is simply a binary search tree. What makes it different however are its operations, in the sense that, every time an operation is performed on a node (e.g. find(), insert(),
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Introduction to Entropy, Codes and Human CodingEntropy as a Measure of Information Content Entropy of a random variable: Let X be a random variable that takes on values from the set {x1 , x2 , . . . , xn } with respective probabilities p1 , p2 , .
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Some NP-Complete ProblemsIn 1966 Stephen Cook was able to prove the remarkable fact that SAT is an NP-Complete decision problem. He did this by showing that for any non-deterministic, polynomial complexity Turing machine M and binary input x, there
CSU Long Beach - CECS - 340
CECS 340 Simulation Examples Assignment, Fall 2004, Professor Ebert1. Redo Example 1 from the lecture &quot;Simulation Examples&quot;. This time make a simulation table for 20 video customers. For the 20 customers, determine the average service time, average
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Lecture 3: Recursive AlgorithmsIntuitively, a recursive algorithm is one which solves a problem by reducing it to an instance of the same problem, but with smaller input. Like mathematical induction, a recursive algorithm has two components: base c
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Lecture 3: Mathematical ProofsAs a computer scientist it is not enough to dream up an algorithm and verify that it works for your favorite test cases. Mathematical proof is what converts todays intellectual gossip, speculation, and heresy into tomor
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Complexity of AlgorithmsOpening comments: The development of ingenious data structures (e.g. binary search trees, hash tables) and algorithms is motivated by the need for algorithms to run on machines as quickly as possible, for inputs of all sizes
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Criterion for optimal cost of BST: Each optimal tree consists of a root and at most 2 optimal subtrees (left and right). So the sub problem is to find out all possible optimal sub-trees (say from index i to index j and combining them to get the final
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AVL TreesAlthough the average depth of a binary search tree is O(log n), there exist important cases (e.g. when the data is mostly sorted) that cause the depth of a bst to approach O(n), thus rendering the tree little more ecient than an array or l
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Graph AlgorithmsLike trees, graphs represent a fundamental data structure used in computer science. We often hear about cyber space as being a new frontier for mankind, and if we look at the structure of cyberspace, we see that it is structured as a
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Introduction to Dynamic ProgrammingIn our study of divide-and-conquer algorithms, we noticed that a problem seemed conducive to a divide-and-conquer approach provided1. it could be divided into one or more subproblems of smaller size that could b
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Applications of Dynamic ProgrammingIn this lecture we examine some additional applications of dynamic programming. Below is a list of some of the problems which omit efficient dynamic programming solutions. Some applications include: Optimal Binary
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Behavioral ModelingBehavioral Modeling: describes functionality independent of technology describes functionality independent of structure (structural details are deferred to the synthesis phase) describes the relationship between the inputs and
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Sheet1 ID IA Time Arrive Rand A Serve B Serve Queue Time Time Time Time 1 0 0 99 6 5 2 2 2 55 4 3 3 4 6 30 3 2 4 4 10 69 5 4 5 2 12 2 3 2 6 2 14 77 5 4 7 3 17 53 4 3 8 3 20 24 3 2 9 3 23 24 3 2 10 1 24 50 4 3 11 1 25 13 3 2 12 2 27 95 6 5 13 2 29 100
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Sheet1 Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Rand Inventory Demand Inventory Order? Begin End 73 50 6 45 NO 74 45 6 39 NO 8 39 1 38 NO 92 38 9
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Sheet1 Player ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Player Door 2 3 2 2 2 1 1 1 3 2 2 3 1 3 3 3 1 3 2 2 1 2 1 1 1 1 1 1 2 3 1 1 2 3 3 2 1 2 3 3
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Probability ModelsPreliminariesRandom Variable: a variable X that instantiates to a particular value in accordance with a measure of likelihood for each possible value. Such a measure is called a probability measure or probability distribution.
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CECS 340 Quiz 2, Fall 2004, Professor Ebert Name: Show all work to receive full credit 1. Let T be a random variable that represents the high temperature for the city of Long Beach on January 1st of any given year. Give an appropriate range space for
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CECS 340 Quiz 3, Spring 2005, Professor Ebert Name: Show all work to receive full credit 1. Bookbargain.com provides free shipping for orders over $40. The estimated shipping time is stated as between 5 to 10 days. However, research has shown that th
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CECS 340 Quiz 4, Spring 2005, Professor Ebert Name: Show all work to receive full credit 1. Generate 5 random numbers using the LCG method, assuming that the modulus is 13, multiplier is 3, increment is 5, and seed is 0. (10 points)2. Suppose that
CSU Long Beach - QUIZ - 340
CECS 340 Quiz 5, Spring 2005, Professor Ebert Name: Show all work to receive full credit 1a. Show what will be stored in register R after the following declaration/initializations? (10 points) reg [15:0] R = -16'hAD; integer n=d12;1b. What will R &lt;
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Introduction to Dynamic ProgrammingIn our study of divide-and-conquer algorithms, we noticed that a problem seemed conducive to a divide-and-conquer approach provided 1. it could be divided into one or more subproblems of smaller size that could be
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Lecture 12: Bayesian LearningBayesian-learning methods draw their roots from a branch of probability and statistics known as decision theory, which invloves the theory of how to minimize risk and loss when making decisions based on uncertain informa
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Lecture 5: Game PlayingLike expert systems, game playing represents one of the rst historical applications of articial intelligence, and culminated in the 1997 defeat of world champion Gary Kasparov by IBMs Deep Blue.Two-person, perfect informatio
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CECS 228 Area 1 Quiz, Spring 2003, Professor Ebert Name: Show all work to receive full credit. 10 points for all correct, 6 for almost correct, 3 for attendance Part a. Translate the following statement into predicate logic: &quot;Try it the first time, a
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CECS 228 Areas 1 and 2 Quiz, Spring 2003, Professor Ebert Name: Show all work to receive full credit. 10 points for all correct, 6 for almost correct, 3 for attendance Part a. Translate the following statement into predicate logic: There is a student
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CECS 228 Area 4 Quiz, Spring 2003, Professor Ebert Name: Show all work to receive full credit. 10 points for all correct, 6 for almost correct, 3 for attendance Given the following hypotheses/assumptions and using only the tautologies and rules of in
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Computational ComplexityIn earlier lectures we saw that languages can be divided into three groups: decidable, Turing recognizable but undecidable, and those that are neither Turing recognizable nor decidable. Now if a problem is decidable, we would
CSU Long Beach - CECS - 340
Probability ModelsPreliminariesRandom Variable: a variable X that instantiates to a particular value in accordance with a measure of likelihood for each possible value. Such a measure is called a probability measure or probability distribution.
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Lecture 1: The Growth of Functions and Big-O NotationBig-O notation Purpose: to succinctly state the growth rate of a given function f (n) = O(g(n) iff there exist constants C &gt; 0 and k &gt; 0 such that |f (n)| C|g(n)| for every n &gt; k f (n) = (g(n)
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Java DEVS Programming and Design Tips by Todd Ebert with Editing by Susan JamesI. Know the JavaDEVS event model. Just as there is an object hierarchy in JavaDEVS that should be extended, so there is an event hierarchy that must be understood and ext
CSU Long Beach - CECS - 340
Modeling Finite-State Machines and ASM ChartsFinite-state machines are widely used for modeling memory components, such as flip-flops, as well as controllers for synchronous datapath and register operations of a computer. Hence they have both theor
CSU Long Beach - QUIZ - 328
CECS 328 Quiz 2, Fall 2003, Professor Ebert Name: Show all work to receive full credit 1. Insert the data with keys 100, 150, 50, 25, 80, 125, 90, 200, 75, and 85 into (in that order) an initially empty binary search tree. (10 points)2. For the tre
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CECS 328 Quiz 3, Fall 2003, Professor Ebert Name: Show all work to receive full credit 1. Use quicksort to transform the array 5,7,4,3,10,5,2,7,1,3,7 into one of the form aL M aR , where M is the median of three, every element in array aL is M , and
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CECS 406 Writing Assignment 8, Spring 2007, Todd EbertDirections. The completion of this assignment is to be demonstrated in-lab on Thursday May 17th. 1. Use a random variable generator to generate into a spreadsheet 500 observations of an exponenti
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CECS 174 Exam 2 Review, Fall 2003, Professor Ebert Review Topics Dening and implementing a function in C+ Using loops to examine each cell of an array; initializing arrays control structures: while and for loops; if, if-else, if-elseif, and if-els
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CECS 528 Recursion Assignment, Spring 2009, Dr. EbertRelated assessments: Exam 1 (February 25th) Book Problems. Section 4.1: 1, 2, 5, 6 Section 4.2: 1-4 Section 4.3: 1-4 End of Chapter 4 problems: 2, 3, 7Problems to submit for grading. 4.1-5,