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Course: CSE 352, Fall 2009School: Los Angeles Southwest...
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352 Problem EECE Set # 9 Due: November 10 Fall 1998 Task 1 (20%): Exercise 7.1 from the Weiss book. Task 2 (20%): Exercise 7.2 Task 3 (20%): Exercise 7.3, show your work. Task 4 (20%): Exercise 7.4, show the result each for k Task 5 (20%): Exercise 7.5, show your work. Give the precise time bound (i.e. not just the simplified value Department of Electrical and Computer Engineering University of South Carolina -1-
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352
Problem EECE Set # 9
Due: November 10
Fall 1998 Task 1 (20%): Exercise 7.1 from the Weiss book. Task 2 (20%): Exercise 7.2 Task 3 (20%): Exercise 7.3, show your work. Task 4 (20%): Exercise 7.4, show the result each for k Task 5 (20%): Exercise 7.5, show your work. Give the precise time bound (i.e. not just the simplified value
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Los Angeles Southwest College - CSE - 352
EECE 352Problem Set #5Due: September 29, 1998Fall 1998 Task 1 (30%): Exercise 4.23 from the book. Task 2 (30%): Exercise 4.30 from the book. Task 3 (40%): Exercise 4.35 from the book. Note: For the last two problems you do not have to type in and
Los Angeles Southwest College - CSE - 352
EECE 352Timer UsageFall 1998/ This is how you can use the Timers function: #include "Timers.h" CCpuTimer cTimer; cTimer.Start( ); / do stuff cTimer.Stop( ) cout < "elapsed CPU time: " < cTimer.Report( ) < endl;Department of Electrical and Comput
Los Angeles Southwest College - CHAP - 07
*fig7_12.txt*/* 1*/template <class Etype>/* 2*/void/* 3*/Quick_Sort( Etype A[ ], const unsigned int N )/* 4*/{/* 5*/ const unsigned int One = 1;/* 6*/ Q_Sort( A, One, N );/* 7*/ Insertion_Sort( A, N );/* 8*/}/* 9*/templ
Los Angeles Southwest College - CHAP - 352
*fig7_12.txt*/* 1*/template <class Etype>/* 2*/void/* 3*/Quick_Sort( Etype A[ ], const unsigned int N )/* 4*/{/* 5*/ const unsigned int One = 1;/* 6*/ Q_Sort( A, One, N );/* 7*/ Insertion_Sort( A, N );/* 8*/}/* 9*/templ
Los Angeles Southwest College - CHAP - 05
*fig5_9.txt*/* 1*/template <class Element_Type>/* 2*/inline void/* 3*/Hash_Table<Element_Type>:/* 4*/Insert( const Element_Type & Key )/* 5*/{/* 6*/ unsigned int Hash_Val = Hash( Key, H_Size );/* 7*/ if( !The_Lists[ Hash_Val ].
Los Angeles Southwest College - CHAP - 352
*fig5_9.txt*/* 1*/template <class Element_Type>/* 2*/inline void/* 3*/Hash_Table<Element_Type>:/* 4*/Insert( const Element_Type & Key )/* 5*/{/* 6*/ unsigned int Hash_Val = Hash( Key, H_Size );/* 7*/ if( !The_Lists[ Hash_Val ].
Los Angeles Southwest College - CHAP - 352
*fig3_16.txt*/* 1*// Delete an entire list./* 2*// Header assumed./* 3*/template <class Etype>/* 4*/void/* 5*/List<Etype>:/* 6*/Delete_List( )/* 7*/{/* 8*/ Node *P = List_Head->Next, *Temp;/* 9*/ while( P != NULL )/*10*/
Los Angeles Southwest College - CHAP - 352
*fig2_11.txt*/* 1*/Huge_Int/* 2*/Pow( const Huge_Int & X, const Huge_Int & N )/* 3*/{/* 4*/ if( N = 0 )/* 5*/ return Huge_Int( 1 );/* 6*/ if( N = 1 )/* 7*/ return X;/* 8*/ if( Even( N ) )/* 9*/ return
Los Angeles Southwest College - CHAP - 04
*fig4_20.txt*/* 1*/template <class Etype>/* 2*/Tree_Node<Etype>*/* 3*/Binary_Search_Tree<Etype>:/* 4*/Find_Min( Tree_Node<Etype> *T ) const/* 5*/{/* 6*/ if( T = NULL )/* 7*/ return NULL;/* 8*/ else/* 9*/ if( T->Lef
Los Angeles Southwest College - CHAP - 352
*fig4_20.txt*/* 1*/template <class Etype>/* 2*/Tree_Node<Etype>*/* 3*/Binary_Search_Tree<Etype>:/* 4*/Find_Min( Tree_Node<Etype> *T ) const/* 5*/{/* 6*/ if( T = NULL )/* 7*/ return NULL;/* 8*/ else/* 9*/ if( T->Lef
Los Angeles Southwest College - CHAP - 352
*fig6_26.txt*/* 1*/template <class Etype>/* 2*/Left_Node<Etype> */* 3*/Left_Heap<Etype>:/* 4*/Merge( Left_Node<Etype> *H1, Left_Node<Etype> *H2 )/* 5*/{/* 6*/ if( H1 = NULL )/* 7*/ return H2;/* 8*/ if( H2 = NULL )/* 9*
Los Angeles Southwest College - CHAP - 352
*fig3_51.txt*/* 1*/template <class Element_Type>/* 2*/inline const Element_Type &/* 3*/Stack<Element_Type>:/* 4*/Top( ) const/* 5*/{/* 6*/ if( Is_Empty( ) )/* 7*/ {/* 8*/ Error( "Empty stack" );/* 9*/ return 0;
Los Angeles Southwest College - CHAP - 04
*fig4_37.txt*/* 1*/template <class Etype>/* 2*/Avl_Node<Etype> */* 3*/Avl_Tree<Etype>:/* 4*/Copy( const Avl_Node<Etype> *T )/* 5*/{/* 6*/ if( T != NULL )/* 7*/ return new Avl_Node<Etype> ( /* 8*/ T->Element,/*
Los Angeles Southwest College - CHAP - 352
*fig4_37.txt*/* 1*/template <class Etype>/* 2*/Avl_Node<Etype> */* 3*/Avl_Tree<Etype>:/* 4*/Copy( const Avl_Node<Etype> *T )/* 5*/{/* 6*/ if( T != NULL )/* 7*/ return new Avl_Node<Etype> ( /* 8*/ T->Element,/*
Los Angeles Southwest College - CHAP - 352
*fig9_31.txt*/* 1*// Print shortest path to vertex V,/* 2*// After procedure Dijkstra has run./* 3*// Assume that the path exists./* 4*/void/* 5*/Print_Path( Vertex V, Table T )/* 6*/{/* 7*/ if( T[ V ].Path != Not_A_Vertex )/* 8*
Los Angeles Southwest College - CHAP - 04
*fig4_42.txt*/* 1*// This procedure can only be called if k3 has a left child./* 2*// And k3's left child has a right child./* 3*// Do the left-right double rotation./* 4*/template <class Etype>/* 5*/void/* 6*/D_Rotate_Left( Avl_Node<E
Los Angeles Southwest College - CHAP - 352
*fig4_42.txt*/* 1*// This procedure can only be called if k3 has a left child./* 2*// And k3's left child has a right child./* 3*// Do the left-right double rotation./* 4*/template <class Etype>/* 5*/void/* 6*/D_Rotate_Left( Avl_Node<E
Los Angeles Southwest College - CHAP - 352
*fig2_10.txt*/* 1*/unsigned int/* 2*/Gcd( unsigned int M, unsigned int N )/* 3*/{/* 4*/ unsigned int Rem;/* 5*/ while( N > 0 )/* 6*/ {/* 7*/ Rem = M % N;/* 8*/ M = N;/* 9*/ N = Rem;/*10*/ }
Los Angeles Southwest College - CSCE - 590
Interface Definition LanguagePresented by developerWorks, your source for great tutorials ibm.com/developerWorksTable of ContentsIf you're viewing this document online, you can click any of the topics below to link directly to that section.1. I
Los Angeles Southwest College - CSCE - 204
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Los Angeles Southwest College - DEVELOPMEN - 1
3.2.2.3Data Transport Data Transport is one of the key elements in the Data Management component of a Regional Coastal Ocean Observing System (RCOOS). Data Transport refers to transparent, interoperable access and delivery of data and data produc
Los Angeles Southwest College - CODEREPOSI - 1
USGS0208455560-76.4963156935.51794409USGS0208455155-76.6463180235.35683429USGS0208453300-76.8413273335.43099874USGS02084472-77.0616189635.54266282USGS0209265810-76.8096588534.94849391USGS0209262905-76.9429990334.99904827USGS0
Los Angeles Southwest College - CSV - 2
#!/usr/bin/perluse strict;if (scalar(@ARGV) < 1) {print "usage: perl csv2kml.pl <filename_in> <filename_out>\n\n";print "for example: perl csv2kml.pl test1.csv test1.kml\n";print "\n\n";print "column order is expected to be: name,description,
Los Angeles Southwest College - CODEREPOSI - 2
reference_to_MLLW = 0reference_to_MLW = 0.024reference_to_MSL = 0.416reference_to_MTL = 0.414reference_to_DTL = 0.437reference_to_MHW = 0.804reference_to_MHHW = 0.874reference_tide_datum_time_period = 'May 1990 - April 1998'reference_tid
Los Angeles Southwest College - CODEREPOSI - 1
netcdf ndbc_<STATION_ID>_buoy_latest {dimensions:time = 1 ;lat = 1 ;lon = 1 ;z = 4 ;variables:int time(time) ;time:short_name = "time" ;time:long_name = "Time" ;time:standard_name = "time" ;time:units = "seconds since 1970-1-1
Los Angeles Southwest College - XENIAPACKA - 2
#!/usr/bin/perluse DBI;#Enviroment$target_dir = '/usr2/home/jcothran/cc/obs';$target_file = "$target_dir/ndbc_adcp.sql";open(SQL_FILE,">$target_file");my $db_host = 'xxx.xxx.xxx.xxx';my $db_name = 'db_xenia_v2';my $db_user = 'postgres
Los Angeles Southwest College - RTAS - 08
From rtas08 at lists.cse.sc.edu Fri Oct 12 17:25:23 2007From: rtas08 at lists.cse.sc.edu (rtas08 at lists.cse.sc.edu)Date: Fri, 12 Oct 2007 17:25:23 -0400Subject: RTAS 2008 last call for papers - The extended submission deadlineon Oct 19 is fir
Los Angeles Southwest College - STAT - 110
Relationships between Two Variables (Chapters 14, 15, and Part of 24) Bivariate data have two variables are measured on each individual. We can study relationships between the two variables. How we describe relationships depends on the types of va
Los Angeles Southwest College - STAT - 110
Chapter 17:Thinking about ChanceRandomness of probability (p. 348) A phenomenon is random if individual outcomes are uncertain but there is nonetheless a regular distribution of outcomes in a large number of repetitions. The probability of any out
Los Angeles Southwest College - STAT - 110
Chapter 4: Sample Surveys in the Real World Sampling and statistics seem simple, but some problems can arise. Example: Prediction poll mistake of the 1948 presidential election that proclaimed Thomas Dewey as the winner over Harry Truman.Sampling
Los Angeles Southwest College - STAT - 110
Understanding prediction (p. 289) Prediction is based on fitting some model to the data. Prediction works best when the model fits the data closely. Will get better predictions if data have a tight linear relationship compare Figure 15.1 on p. 285
Los Angeles Southwest College - STAT - 110
Chapter 13: Normal DistributionsExploring data for one quantitative variable: Always plot the data: Histogram or stemplot Look for an overall pattern and for striking deviations such as outliers. Describe center and spread with the five-number su
Los Angeles Southwest College - STAT - 110
Chapter 5: Experiments, Good and Bad Three studies on pp. 71-72. Observational studies are passive data collections. Experiments are active data production. If properly designed, we can observe whether cause and effect relationships are present.
Los Angeles Southwest College - STAT - 110
Chapter 7: Basic Data Ethics (p. 110) Institutional review board: Reviews all planned studies in advance in order to protect subjects from possible harm. All organizations that conduct studies must have such a board. Informed consent: This means t
Los Angeles Southwest College - STAT - 110
Exam 1, Statistics 110 Spring 2003Multiple Choice Circle the correct answer for each question. No partial credit will be given. Each question is worth 1 points. 1. A study of a drug to prevent hair loss showed that 86% of the men who took it mainta
Los Angeles Southwest College - STAT - 110
Chapter 24: Two-Way Tables (to p. 469) A two-way table is a way to display bivariate data when both variables are categorical. This is sometimes called a contingency table. With cross-classified data, one variable is displayed in rows and the othe
Los Angeles Southwest College - STAT - 110
Chapter 6: Experiments in the Real WorldWays to control for bias of people in experiments. Single Blind: An experiment is single blind if the units are unaware of the exact treatment being imposed on them. Controls for subject bias. Double Blind:
Los Angeles Southwest College - STAT - 110
Mean and Standard DeviationAnother type of numerical summary for a data set Mean: The mean of a set of n observations is the arithmetic average; it is the sum of the observations divided by the number of observations, n. (p. 227) Formula: x1 + x2
Los Angeles Southwest College - STAT - 110
Chapter 2: Samples, Good and Bad Biased design: Systematically favors certain outcomes. (p. 20) Sampling designs that are often biased:Convenience sample: Selects whichever individuals are easiest to reach. (p. 20) Example: Interviewing people goi
Los Angeles Southwest College - STAT - 110
Bias and VariabilityMargin of Error Surveys often report a percentage and a "margin of error". "Margin of error plus or minus three percentage points" means: If we took many samples using the same method we used to get this one sample, 95% of th
Los Angeles Southwest College - STAT - 110
Course Syllabus for STAT 110 Spring 2003Purpose of Course: To provide an integrated introduction to the basic statistical ideas that a person would encounter in everyday life. Instructor: R. Webster West, Leconte 217A, 777-3792 or 777-7800, websterw
Los Angeles Southwest College - STAT - 110
r h ss us k }2}yy@y`s h ss us k 2Q}2 }yy@y`h }2}yy@y`q h ss us k h x ss us k }2wyy@y`k x h ss us k o2wyy@y` g 2btxby4xdbtx4gb4x2"0bg2w024vu rs rxq u ux u id u sl ghs u s bswt24vsyys4x2}@m tx4s4v4xmyIsj" q g u u rk id x u gn
Los Angeles Southwest College - STAT - 110
Independent Events Two outcomes are independent if knowing the outcome of one does not change the probabilities for outcomes of the other. When two events are independent, we find the probability of both events happening by multiply their individua
Los Angeles Southwest College - STAT - 110
Chapter 15: Describing Relationships: Regression, Prediction, and Causation If we have a strong linear correlation between two variables, then we can use a linear regression model to predict the value of a response variable, y, based on an explanato
Los Angeles Southwest College - STAT - 110
Chapter 12: Describing Distributions with Numbers We create graphs to give us a picture of the data. We also need numbers to summarize the center and spread of a distribution. Two types of descriptive statistics for categorical variables:1) Count
Los Angeles Southwest College - STAT - 110
Inference (Chapter 22) Inference is the term used to describe the process of making decisions based on data. We would like to make decisions about population parameters based on sample statistics. We will discuss making an inference about a popula
Los Angeles Southwest College - K - 12
Title: Is it hot in here? Exploring the greenhouse effect and global climate changeAuthor: Kimberly Schneider, GK-12 Program, Department of Biological Sciences, University of South Carolina Implemented: St. Andrews Middle School, Columbia SCOvervi
Los Angeles Southwest College - K - 12
Who Polluted the Potomac?Introduction:As human populations have increased and land uses have changed, many of our rivers have become polluted. This example demonstrates that, just as we each contribute to the problem, we must also be part of the so
Los Angeles Southwest College - K - 12
Title: What Do We Have In Common? (Comparing Bivalves to Humans)Author: Kimberly Schneider (GK-12 program), Crystal Welch (Rising Tide), Nouran Ragaban (Rising Tide), Department of Biological Sciences, University of South Carolina Implemented: St. A
Los Angeles Southwest College - K - 12
What do we all have in common? A bivalve dissection!I. External Observations: 1. List two qualitative observations about your bivalve specimen: Various answers: black, smooth shell, funny strings coming out of one side, smells like fish etc 2. List
Los Angeles Southwest College - K - 12
Mussel Distribution Map of Mystery BaySample sites in Mystery Bay17 18 9 11 12 19 22 20 23 15 14 13 16 24 10 21Color Gallant galloTroublesome trossolus25 26 27 302933 342831 3235 36 37 3839 5 6 1 2 7 4 3 8 42 41 4043 44 50 45 49
Los Angeles Southwest College - K - 12
Student Question Sheet for Traits1. What would be the benefit in having more byssal threads?Since mussels use byssal threads to attach, more byssal threads means having a stronger hold. Therefore, mussels are less susceptible to being dislodged.2
Los Angeles Southwest College - K - 12
Who Polluted the Potomac?1. Who polluted the Potomac?_ _ 2. What effect did the increasing population have on the health of the river? _ _ 3. Think about the pollution contained in the canisters. Could something be done to prevent those types of m
Los Angeles Southwest College - K - 12
BIVALVE DISSECTIONMETHODS: 1. Follow ALL the safety guidelines that your teacher has set in your classroom. 2. Carefully pull the shell apart. You may need to cut the abductor mussels so you can lay out the shells side by side. 3. Get six (2 for let
Los Angeles Southwest College - K - 12
Wave Action Map of Mystery BayKeyHeavy wave action -Medium wave action -Light wave action -
Los Angeles Southwest College - K - 12
Los Angeles Southwest College - K - 12
Whos Dominant? Analyzing Traits of MusselsAuthor: Brice Gill, St. Andrews Middle School, Columbia SC Implemented: St. Andrews Middle SchoolOverview: Students create a mock population of two competitive species of mussels in order to examine the ef
Los Angeles Southwest College - K - 12
The Greenhouse Effect1) What is the greenhouse effect, and why is it so called? . .. 2) What gases are the most responsible for causing the greenhouse effect? .. .. 3) What changes are likely to happen to the sea levels as the greenhouse effect gets