NYT crossword puzzle solver


 Rolf Webb
 1 years ago
 Views:
Transcription
1 NYT crossword puzzle solver 5. Mai NYT crossword puzzle solver
2 2 NYT crossword puzzle solver
3 1 Problem Description 2 Concept of Solution 3 Grid extraction 4 Box Classification 5 Solve puzzle 6 Results 3 NYT crossword puzzle solver
4 Problem Description Puzzle is a regular grid of quadratic boxes Box types I Empty Boxes II Numbered Boxes (include reference to hints) III Structure Boxes (entirely black) Assumption clues are known digitally project focused on image processing 4 NYT crossword puzzle solver
5 Problem Description Puzzle is a regular grid of quadratic boxes Box types I Empty Boxes II Numbered Boxes (include reference to hints) III Structure Boxes (entirely black) Assumption clues are known digitally project focused on image processing 4 NYT crossword puzzle solver
6 Problem Description Puzzle is a regular grid of quadratic boxes Box types I Empty Boxes II Numbered Boxes (include reference to hints) III Structure Boxes (entirely black) Assumption clues are known digitally project focused on image processing 4 NYT crossword puzzle solver
7 Concept of Solution Outline 1 Grid extraction image preprocessing extract lines find largest rectangle rectification grid validation box extraction 2 box classification 3 solve puzzle determine length of fitting words solve individual clues insert solutions 5 NYT crossword puzzle solver
8 Image preprocessing adaptive threshold filtered binary image 6 NYT crossword puzzle solver
9 Line extraction Hough transformation Clustering by θ Assumption: two largest clusters span the grid detected and clustered lines 7 NYT crossword puzzle solver
10 Find largest rectangle Problem Rectangle way too large Fortunately, detected lines are parallel to grid shrink rectangle Detected rectangle 8 NYT crossword puzzle solver
11 Grid validation Shrinkage based on row and column sums Peaks correspond to row and column numbers Warped binary image sum white pixel columns 9 NYT crossword puzzle solver
12 Problem Description Concept of Solution Grid extraction Box Classification Box extraction Back projection of Rectified grid cropped corner points Rectification based on DLT each box on regular grid just use a pair of scissors :) 10 NYT crossword puzzle solver Solve puzzle Results
13 Box classification Just consider the amount of black pixels within one box Structure Boxes: > 30% Empty Boxes: < 1% Numbered Boxes: remaining Mirror grid if numbers are not located in the top left corner Rectified binary grid 11 NYT crossword puzzle solver
14 Clue grid Assign numbers to numbered boxes Link numbered boxes with clues Determine length of solution Solve individual clues Send query to crossword puzzle solvers on the web and extract possible answers. Inset Solutions 1 Insert clues with single solution 2 Use letters in the grid to solve clues with multiple solutions 12 NYT crossword puzzle solver
15 Solution Back projection of solution 13 NYT crossword puzzle solver
16 Problem Description Concept of Solution Grid extraction Box Classification Limits of Algorithm (1) grid + pens 14 detected lines NYT crossword puzzle solver Solve puzzle Results
17 Problem Description Concept of Solution Grid extraction Box Classification Limits of Algorithm (2) Detected largest rectangle 15 Rectified grid NYT crossword puzzle solver Solve puzzle Results
18 Thank you for your attention! 16 NYT crossword puzzle solver
Automated Sudoku Solver. Marty Otzenberger EGGN 510 December 4, 2012
Automated Sudoku Solver Marty Otzenberger EGGN 510 December 4, 2012 Outline Goal Problem Elements Initial Testing Test Images Approaches Final Algorithm Results/Statistics Conclusions Problems/Limits Future
More informationTHIS MEANS THAT EACH NUMBER SHOULD ONLY APPEAR ONCE IN EACH ROW, COLUMN AND BOX! TOP TIPS FOR SOLVING SU DOKU
THE RULES Each row must contain each number from to 9 Each column must contain each number from to 9 Each box (small by grid shown by thicker lines) must contain each number from to 9 THIS MEANS THAT EACH
More information10 th POLISH SUDOKU CHAMPIONSHIP INSTRUCTION BOOKLET. February 22, 2015 IMPORTANT INFORMATION:
10 th POLISH SUDOKU CHAMPIONSHIP February 22, 2015 INSTRUCTION BOOKLET IMPORTANT INFORMATION: 1. Answer form can be sent many times, but only the last version will be considered. 2. In case of a tie, the
More informationAn Optical Sudoku Solver
An Optical Sudoku Solver Martin Byröd February 12, 07 Abstract In this report, a visionbased sudoku solver is described. The solver is capable of solving a sudoku directly from a photograph taken with
More informationSudoku Solver. Yixin Wang
Sudoku Solver Yixin Wang wangyix@stanford.edu Abstract An Android app was developed that allows a Sudoku puzzle to be extracted and solved in real time using captured images from the device s camera.
More informationPlot and Solve Equations
Plot and Solve Equations With SigmaPlot s equation plotter and solver, you can  plot curves of data from userdefined equations  evaluate equations for data points, and solve them for a data range. You
More informationSudoku puzzles and how to solve them
Sudoku puzzles and how to solve them Andries E. Brouwer 20060531 1 Sudoku Figure 1: Two puzzles the second one is difficult A Sudoku puzzle (of classical type ) consists of a 9by9 matrix partitioned
More informationFig. 1 Suitable data for a Crosstab Query.
Crosstab Queries A Crosstab Query is a special kind of query that summarizes data by plotting one field against one or more other fields. Crosstab Queries can handle large amounts of data with ease and
More informationDecision Trees from large Databases: SLIQ
Decision Trees from large Databases: SLIQ C4.5 often iterates over the training set How often? If the training set does not fit into main memory, swapping makes C4.5 unpractical! SLIQ: Sort the values
More informationClick to create a query in Design View. and click the Query Design button in the Queries group to create a new table in Design View.
Microsoft Office Access 2010 Understanding Queries Queries are questions you ask of your database. They allow you to select certain fields out of a table, or pull together data from various related tables
More informationWORDOKU SOLVER. Surya Chandra EENG510 December 3,2014
WORDOKU SOLVER Surya Chandra EENG510 December 3,2014 Outline Background Goal Steps to solve Algorithm used for each step Testing and Results Future work Questions Background A wordoku puzzle is similar
More informationMatt Cabot Rory Taca QR CODES
Matt Cabot Rory Taca QR CODES QR codes were designed to assist in the manufacturing plants of the automotive industry. These easy to scan codes allowed for a rapid way to identify parts and made the entire
More information2x 2x 2 8x. Now, let s work backwards to FACTOR. We begin by placing the terms of the polynomial inside the cells of the box. 2x 2
Activity 23 Math 40 Factoring using the BOX Team Name (optional): Your Name: Partner(s): 1. (2.) Task 1: Factoring out the greatest common factor Mini Lecture: Factoring polynomials is our focus now. Factoring
More informationResearch on Chinese financial invoice recognition technology
Pattern Recognition Letters 24 (2003) 489 497 www.elsevier.com/locate/patrec Research on Chinese financial invoice recognition technology Delie Ming a,b, *, Jian Liu b, Jinwen Tian b a State Key Laboratory
More information521466S Machine Vision Assignment #7 Hough transform
521466S Machine Vision Assignment #7 Hough transform Spring 2014 In this assignment we use the hough transform to extract lines from images. We use the standard (r, θ) parametrization of lines, lter the
More informationLesson 3  Processing a MultiLayer Yield History. Exercise 34
Lesson 3  Processing a MultiLayer Yield History Exercise 34 Objective: Develop yieldbased management zones. 1. FileOpen Project_33.map. 2. Double click the Average Yield surface component in the
More informationEnvironmental Remote Sensing GEOG 2021
Environmental Remote Sensing GEOG 2021 Lecture 4 Image classification 2 Purpose categorising data data abstraction / simplification data interpretation mapping for land cover mapping use land cover class
More informationAIP Factoring Practice/Help
The following pages include many problems to practice factoring skills. There are also several activities with examples to help you with factoring if you feel like you are not proficient with it. There
More informationBattleship. Big bands
Ball Fill in the grid so that every row, column (six smaller cells and three bigger circles or stars), outlined figures (eight smaller cells and a bigger circle), nine bigger circles and nine bigger stars
More informationMiSeq: Imaging and Base Calling
MiSeq: Imaging and Page Welcome Navigation Presenter Introduction MiSeq Sequencing Workflow Narration Welcome to MiSeq: Imaging and. This course takes 35 minutes to complete. Click Next to continue. Please
More informationMODULE 15 Clustering Large Datasets LESSON 34
MODULE 15 Clustering Large Datasets LESSON 34 Incremental Clustering Keywords: Single Database Scan, Leader, BIRCH, Tree 1 Clustering Large Datasets Pattern matrix It is convenient to view the input data
More informationPARALLELIZED SUDOKU SOLVING ALGORITHM USING OpenMP
PARALLELIZED SUDOKU SOLVING ALGORITHM USING OpenMP Sruthi Sankar CSE 633: Parallel Algorithms Spring 2014 Professor: Dr. Russ Miller Sudoku: the puzzle A standard Sudoku puzzles contains 81 grids :9 rows
More informationMultiplying with Multi digit Numbers. For example we know that 23 x 14 would look like this:
Multiplying with Multi digit Numbers Warm Up We have looked at multiplication using an area model for 2 digit by 2 digit numbers. For example we know that 23 x 14 would look like this: There is a short
More informationCVChess: Computer Vision Chess Analytics
CVChess: Computer Vision Chess Analytics Jay Hack and Prithvi Ramakrishnan Abstract We present a computer vision application and a set of associated algorithms capable of recording chess game moves fully
More informationBIG DATA VISUALIZATION. Team Impossible Peter Vilim, Sruthi Mayuram Krithivasan, Matt Burrough, and Ismini Lourentzou
BIG DATA VISUALIZATION Team Impossible Peter Vilim, Sruthi Mayuram Krithivasan, Matt Burrough, and Ismini Lourentzou Let s begin with a story Let s explore Yahoo s data! Dora the Data Explorer has a new
More informationWord Spotting in Cursive Handwritten Documents using Modified Character Shape Codes
Word Spotting in Cursive Handwritten Documents using Modified Character Shape Codes Sayantan Sarkar Department of Electrical Engineering, NIT Rourkela sayantansarkar24@gmail.com Abstract.There is a large
More informationDirections: Place greater than (>), less than (<) or equal to (=) symbols to complete the number sentences on the left.
Comparing Numbers Week 7 26) 27) 28) Directions: Place greater than (>), less than (
More informationCalculator Practice: Computation with Fractions
Calculator Practice: Computation with Fractions Objectives To provide practice adding fractions with unlike denominators and using a calculator to solve fraction problems. www.everydaymathonline.com epresentations
More informationApplication of Face Recognition to Person Matching in Trains
Application of Face Recognition to Person Matching in Trains May 2008 Objective Matching of person Context : in trains Using face recognition and face detection algorithms With a videosurveillance camera
More informationSMART NOTEBOOK 10. Instructional Technology Enhancing ACHievement
SMART NOTEBOOK 10 Instructional Technology Enhancing ACHievement TABLE OF CONTENTS SMART Notebook 10 Themes... 3 Page Groups... 4 Magic Pen... 5 Shape Pen... 6 Tables... 7 Object Animation... 8 Aligning
More informationState Spaces Graph Search Searching. Graph Search. CPSC 322 Search 2. Textbook 3.4. Graph Search CPSC 322 Search 2, Slide 1
Graph Search CPSC 322 Search 2 Textbook 3.4 Graph Search CPSC 322 Search 2, Slide 1 Lecture Overview 1 State Spaces 2 Graph Search 3 Searching Graph Search CPSC 322 Search 2, Slide 2 State Spaces Idea:
More informationProof of Crossword Puzzle Record
Proof of rossword Puzzle Record Kevin K. Ferland kferland@bloomu.edu Bloomsburg niversity, Bloomsburg, PA 17815 Abstract We prove that the maximum number of clues possible for a 15 15 daily New York imes
More informationLesson 3. Numerical Integration
Lesson 3 Numerical Integration Last Week Defined the definite integral as limit of Riemann sums. The definite integral of f(t) from t = a to t = b. LHS: RHS: Last Time Estimate using left and right hand
More informationPivot Tables How to Series by LACA 3/29/2016
Pivot Tables How to Series by LACA 3/29/2016 Pivot Tables With PivotTable reports, we can look at information in different ways with just a few mouse clicks. Data swings into place, answering questions,
More informationIENG2004 Industrial Database and Systems Design. Microsoft Access I. What is Microsoft Access? Architecture of Microsoft Access
IENG2004 Industrial Database and Systems Design Microsoft Access I Defining databases (Chapters 1 and 2) Alison Balter Mastering Microsoft Access 2000 Development SAMS, 1999 What is Microsoft Access? Microsoft
More informationHint for success: When tested in groups of students grade 3 and younger, this activity worked best as a centers activity.
Activities Grades 3 5 www.exploratorium.edu/geometryplayground/activities MAKING A TRANSLATION TESSELLATION Background: What is a tessellation? Suppose you wanted to cover a floor with tiles. You could
More informationSignature Region of Interest using Auto cropping
ISSN (Online): 16940784 ISSN (Print): 16940814 1 Signature Region of Interest using Auto cropping Bassam AlMahadeen 1, Mokhled S. AlTarawneh 2 and Islam H. AlTarawneh 2 1 Math. And Computer Department,
More informationA. V. Gerbessiotis CS Spring 2014 PS 3 Mar 24, 2014 No points
A. V. Gerbessiotis CS 610102 Spring 2014 PS 3 Mar 24, 2014 No points Problem 1. Suppose that we insert n keys into a hash table of size m using open addressing and uniform hashing. Let p(n, m) be the
More informationGraph Mining and Social Network Analysis
Graph Mining and Social Network Analysis Data Mining and Text Mining (UIC 583 @ Politecnico di Milano) References Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", The Morgan Kaufmann
More informationBuilding Queries in Microsoft Access 2007
Building Queries in Microsoft Access 2007 Description In this class we will explore the purpose, types and uses of Queries. Learn to design a query to retrieve specific data using criteria and operators.
More informationCHAPTER 5 SENDER AUTHENTICATION USING FACE BIOMETRICS
74 CHAPTER 5 SENDER AUTHENTICATION USING FACE BIOMETRICS 5.1 INTRODUCTION Face recognition has become very popular in recent years, and is used in many biometricbased security systems. Face recognition
More informationMATH2210 Notebook 1 Fall Semester 2016/2017. 1 MATH2210 Notebook 1 3. 1.1 Solving Systems of Linear Equations... 3
MATH0 Notebook Fall Semester 06/07 prepared by Professor Jenny Baglivo c Copyright 009 07 by Jenny A. Baglivo. All Rights Reserved. Contents MATH0 Notebook 3. Solving Systems of Linear Equations........................
More informationCS 585 Computer Vision Final Report Puzzle Solving Mobile App
CS 585 Computer Vision Final Report Puzzle Solving Mobile App Developed by Timothy Chong and Patrick W. Crawford December 9, 2014 Introduction and Motivation This project s aim is to create a mobile application
More informationNC DEPARTMENT OF PUBLIC INSTRUCTION
Rectangle Riddles Common Core Standard: Work with equal groups of objects to gain foundations for multiplication. 2.G.2 Partition a rectangle into rows and columns of samesize squares and count to find
More informationFragmentation of land by urbanisation, transport infrastructure and agriculture
Fragmentation of land by urbanisation, transport infrastructure and agriculture Background The survival of threatened species depends on populations which are large enough to maintain their genetic diversity
More informationData Mining and Data Warehousing on US Farmer s Data
Data Mining and Data Warehousing on US Farmer s Data Guide: Dr. Meiliu Lu Presented By, Yogesh Isawe Kalindi Mehta Aditi Kulkarni * Data Warehousing Project * Introduction * Background * Technologies Explored
More informationChessboard and Pieces Detection for Janggi Chess Playing Robot
16 GueeSang Lee: Chessboard and Pieces Detection for Janggi Chess Playing Robot http://dx.doi.org/10.5392/ijoc.2013.9.4.016 Chessboard and Pieces Detection for Janggi Chess Playing Robot Vo Quang Nhat,
More informationAdditional Fractions Problems With Solutions
6/6/009 Additional Fractions Problems With Solutions Ordering fractions with unlike denominators What is the first step? How do we do that? Then what? Find the lowest common denominator Write each denominator
More informationEvaluation of the Use of HighResolution Satellite Imagery in Transportation Applications
Evaluation of the Use of HighResolution Satellite Imagery in Transportation Applications Final Report Prepared by: Rocio AlbaFlores Department of Electrical and Computer Engineering University of Minnesota
More informationLargest FixedAspect, AxisAligned Rectangle
Largest FixedAspect, AxisAligned Rectangle David Eberly Geometric Tools, LLC http://www.geometrictools.com/ Copyright c 19982016. All Rights Reserved. Created: February 21, 2004 Last Modified: February
More informationMATHEMATICS Y3 Using and applying mathematics 3810 Solve mathematical puzzles and investigate. Equipment MathSphere www.mathsphere.co.
MATHEMATICS Y3 Using and applying mathematics 3810 Solve mathematical puzzles and investigate. Equipment Paper, pencil, ruler Dice, number cards, buttons/counters, boxes etc MathSphere 3810 Solve mathematical
More informationElectronic Trainer. Combined Series and Parallel Circuits
Electronic Trainer Combined Series and Parallel Circuits In this lab you will work with a circuit combining series and parallel elements. You will use six resistors to create a circuit with two parallel
More informationHandwritten Character Recognition from Bank Cheque
International Journal of Computer Sciences and Engineering Open Access Research Paper Volume4, Special Issue1 EISSN: 23472693 Handwritten Character Recognition from Bank Cheque Siddhartha Banerjee*
More informationLocating and Decoding EAN13 Barcodes from Images Captured by Digital Cameras
Locating and Decoding EAN13 Barcodes from Images Captured by Digital Cameras W3A.5 Douglas Chai and Florian Hock Visual Information Processing Research Group School of Engineering and Mathematics Edith
More informationCUSTOMER+ PURL Manager
CUSTOMER+ PURL Manager October, 2009 CUSTOMER+ v. 5.3.1 Section I: Creating the PURL 1. Go to Administration > PURL Management > PURLs 2. Click Add Personalized URL 3. In the Edit PURL screen, Name your
More informationOptical Flow. Thomas Pock
Optical Flow Thomas Pock 1 Optical Flow (II) Content Global approaches (HornSchunck, TVL1) Coarsetofine warping 2 The Horn and Schunck (HS) Method 3 The Horn and Schunck (HS) Method Global energy to
More informationLab 1 Introduction to Microsoft Project
Lab 1 Introduction to Microsoft Project Statement Purpose This lab provides students with the knowledge and skills to use Microsoft Project. This course takes students stepbystep through the features
More informationExcel I Sorting and filtering Revised February 2013
Excel I Sorting and filtering Revised February 2013 Nerd notes: total number of columns in a worksheet = 256 total number of rows in a worksheet = 65,536 (old)/1 million (Excel 2007) total number of characters
More informationSpecimen 2015 am/pm Time allowed: 1hr 30mins
SPECIMEN MATERIAL GCSE COMPUTER SCIENCE 8520/1 Paper 1 Specimen 2015 am/pm Time allowed: 1hr 30mins Materials There are no additional materials required for this paper. Instructions Use black ink or black
More informationKiller Sudoku Solver.
Killer Sudoku Solver Student: Mohammed Rizwan rizwanm4@cs.man.ac.uk Supervisor: Dr. Andrea Schalk a.schalk@cs.man.ac.uk Date: 29 th April 2008 1 Killer Sudoku Solver Student: Mohammed Rizwan Supervisor:
More informationA network flow algorithm for reconstructing. binary images from discrete Xrays
A network flow algorithm for reconstructing binary images from discrete Xrays Kees Joost Batenburg Leiden University and CWI, The Netherlands kbatenbu@math.leidenuniv.nl Abstract We present a new algorithm
More informationChess Vision. Chua Huiyan Le Vinh Wong Lai Kuan
Chess Vision Chua Huiyan Le Vinh Wong Lai Kuan Outline Introduction Background Studies 2D Chess Vision Realtime Board Detection Extraction and Undistortion of Board Board Configuration Recognition 3D
More informationBinary Image Scanning Algorithm for Cane Segmentation
Binary Image Scanning Algorithm for Cane Segmentation Ricardo D. C. Marin Department of Computer Science University Of Canterbury Canterbury, Christchurch ricardo.castanedamarin@pg.canterbury.ac.nz Tom
More informationGrade 3 Core Standard III Assessment
Grade 3 Core Standard III Assessment Geometry and Measurement Name: Date: 3.3.1 Identify right angles in twodimensional shapes and determine if angles are greater than or less than a right angle (obtuse
More informationMerged Cell. End of Row Marker Cell
Tables in Microsoft Word A table consists of rows and columns of cells that you can fill with text or graphics. When you insert a table, it is displayed as a grid, each section of which is referred to
More informationSudoku Puzzles: Medium
Puzzles: Medium The modern version of was invented in 1979 by Howard Garns in USA (where it was called `Number Place'). It became really popular in Japan in the 1980s and in the UK since late 2004. It
More informationThe Kakuro Kraze. The Rules of Kakuro. Kakuro puzzles are currently sweeping the world!
The Kakuro Kraze Kakuro puzzles are currently sweeping the world! In Japan, the puzzles are well known and second in popularity only to Sudoku puzzles, which spawned a puzzle publishing craze in Europe
More informationKnowledge Discovery and Data Mining. Structured vs. NonStructured Data
Knowledge Discovery and Data Mining Unit # 2 1 Structured vs. NonStructured Data Most business databases contain structured data consisting of welldefined fields with numeric or alphanumeric values.
More informationBy: Peter K. Mulwa MSc (UoN), PGDE (KU), BSc (KU) Email: Peter.kyalo@uonbi.ac.ke
SPREADSHEETS FOR MARKETING & SALES TRACKING  DATA ANALYSIS TOOLS USING MS EXCEL By: Peter K. Mulwa MSc (UoN), PGDE (KU), BSc (KU) Email: Peter.kyalo@uonbi.ac.ke Objectives By the end of the session, participants
More informationSkew Detection of Scanned Document Images
, March 1315, 2013, Hong Kong Skew Detection of Scanned Document Images Sepideh Barekat Rezaei, Abdolhossein Sarrafzadeh, and Jamshid Shanbehzadeh Abstract Skewing of the scanned image is an inevitable
More informationCentroid Distance Function and the Fourier Descriptor with Applications to Cancer Cell Clustering
Centroid Distance Function and the Fourier Descriptor with Applications to Cancer Cell Clustering By, Swati Bhonsle Alissa Klinzmann Mentors Fred Park Department of Mathematics Ernie Esser Department of
More information What is a feature?  Image processing essentials  Edge detection (Sobel & Canny)  Hough transform  Some images
Seminar: Feature extraction by André Aichert I Feature detection  What is a feature?  Image processing essentials  Edge detection (Sobel & Canny)  Hough transform  Some images II An Entropybased
More informationDeCyder Extended Data Analysis module Version 1.0
GE Healthcare DeCyder Extended Data Analysis module Version 1.0 Module for DeCyder 2D version 6.5 User Manual Contents 1 Introduction 1.1 Introduction... 7 1.2 The DeCyder EDA User Manual... 9 1.3 Getting
More informationThe Scientific Data Mining Process
Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In
More informationCognitive Abilities Test Practice Activities. Teacher Guide. Form 7. Quantitative Tests. Level. Cog
Cognitive Abilities Test Practice Activities Teacher Guide Form 7 Quantitative Tests Level 9 Cog Test 4: Number Analogies, Level 9 Part 1: Overview of Number Analogies An analogy draws parallels between
More information5. Binary objects labeling
Image Processing  Laboratory 5: Binary objects labeling 1 5. Binary objects labeling 5.1. Introduction In this laboratory an object labeling algorithm which allows you to label distinct objects from a
More informationKaleidaGraph Quick Start Guide
KaleidaGraph Quick Start Guide This document is a handson guide that walks you through the use of KaleidaGraph. You will probably want to print this guide and then start your exploration of the product.
More informationThe Role of Size Normalization on the Recognition Rate of Handwritten Numerals
The Role of Size Normalization on the Recognition Rate of Handwritten Numerals Chun Lei He, Ping Zhang, Jianxiong Dong, Ching Y. Suen, Tien D. Bui Centre for Pattern Recognition and Machine Intelligence,
More informationGalaxy Morphological Classification
Galaxy Morphological Classification Jordan Duprey and James Kolano Abstract To solve the issue of galaxy morphological classification according to a classification scheme modelled off of the Hubble Sequence,
More informationMultiplication and Division Fact Families
Multiplication and Division Fact Families Objectives To review fact families and the Multiplication/Division Facts Table; and to guide children as they practice multiplication and division facts. www.everydaymathonline.com
More informationWHAT S NEW IN MS EXCEL 2013
Contents Excel... 1 Filling empty cells using Flash Fill... 1 Filtering records using a Timeline... 2 Previewing with Quick Analysis... 4 Using Chart Advisor recommendations... 5 Finding errors and issues
More informationSection IV.1: Recursive Algorithms and Recursion Trees
Section IV.1: Recursive Algorithms and Recursion Trees Definition IV.1.1: A recursive algorithm is an algorithm that solves a problem by (1) reducing it to an instance of the same problem with smaller
More informationRelational Database: Additional Operations on Relations; SQL
Relational Database: Additional Operations on Relations; SQL Greg Plaxton Theory in Programming Practice, Fall 2005 Department of Computer Science University of Texas at Austin Overview The course packet
More informationSample Questions Csci 1112 A. Bellaachia
Sample Questions Csci 1112 A. Bellaachia Important Series : o S( N) 1 2 N N i N(1 N) / 2 i 1 o Sum of squares: N 2 N( N 1)(2N 1) N i for large N i 1 6 o Sum of exponents: N k 1 k N i for large N and k
More informationThe Application of Exact Cover to the. Creating of Sudoku Puzzle
Team # 3140 Page 1 of 24 The Application of Exact Cover to the Summary Creating of Sudoku Puzzle In this paper, we develop an algorithm to create a Sudoku puzzle of a desired difficulty level. The main
More informationQuiz 1 Solutions. (a) T F The height of any binary search tree with n nodes is O(log n). Explain:
Introduction to Algorithms March 9, 2011 Massachusetts Institute of Technology 6.006 Spring 2011 Professors Erik Demaine, Piotr Indyk, and Manolis Kellis Quiz 1 Solutions Problem 1. Quiz 1 Solutions True
More informationSudoku Puzzles and How to Solve Them
1 258 NAW 5/7 nr. 4 december 2006 Sudoku Puzzles and How to Solve Them Andries E. Brouwer Andries E. Brouwer Technische Universiteit Eindhoven Postbus 513, 5600 MB Eindhoven aeb@win.tue.nl Recreational
More informationPlant Identification Using Leaf Images
Plant Identification Using Leaf Images Sachin D. Chothe 1, V.R.Ratnaparkhe 2 P.G. Student, Department of EE, Government College of Engineering, Aurangabad, Maharashtra, India 1 Assistant Professor, Department
More information14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO)
Overview Kyrre Glette kyrrehg@ifi INF3490 Swarm Intelligence Particle Swarm Optimization Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) 3 Swarms in nature Fish, birds,
More informationContents WEKA Microsoft SQL Database
WEKA User Manual Contents WEKA Introduction 3 Background information. 3 Installation. 3 Where to get WEKA... 3 Downloading Information... 3 Opening the program.. 4 Chooser Menu. 46 Preprocessing... 67
More informationUsing Excel to Graph a Linear Equation
Using Excel to Graph a Linear Equation Level: LBS 5 Goal: To become familiar with Microsoft Excel and the Chart Wizard in order to create graphs of linear equations on the computer. Learning Outcomes:
More informationLeastSquares Intersection of Lines
LeastSquares Intersection of Lines Johannes Traa  UIUC 2013 This writeup derives the leastsquares solution for the intersection of lines. In the general case, a set of lines will not intersect at a
More informationChapter 5 More SQL: Complex Queries, Triggers, Views, and Schema Modification
Chapter 5 More SQL: Complex Queries, Triggers, Views, and Schema Modification Copyright 2011 Pearson Education, Inc. Publishing as Pearson AddisonWesley Chapter 5 Outline More Complex SQL Retrieval Queries
More informationCBA Fractions Student Sheet 1
Student Sheet 1 1. If 3 people share 12 cookies equally, how many cookies does each person get? 2. Four people want to share 5 cakes equally. Show how much each person gets. Student Sheet 2 1. The candy
More informationExcel Lab. Figure 1.1: Adding two numbers together in Excel
Excel Lab This document serves as an introduction to Microsoft Excel. Example 1: Excel is very useful for performing arithmetic operations. Suppose we want to add 2 + 3. We begin by entering the number
More informationTutorial on gplink. http://pngu.mgh.harvard.edu/~purcell/plink/gplink.shtml. PLINK tutorial, December 2006; Shaun Purcell, shaun@pngu.mgh.harvard.
Tutorial on gplink http://pngu.mgh.harvard.edu/~purcell/plink/gplink.shtml Basic gplink analyses Data management Summary statistics Association analysis Population stratification IBDbased analysis gplink
More informationMonitoring Creatures Great and Small: Computer Vision Systems for Looking at Grizzly Bears, Fish, and Grasshoppers
Monitoring Creatures Great and Small: Computer Vision Systems for Looking at Grizzly Bears, Fish, and Grasshoppers Greg Mori, Maryam Moslemi, Andy Rova, Payam Sabzmeydani, Jens Wawerla Simon Fraser University
More informationData Structure for RealTime Processing in 3D
Data Structure for RealTime Processing in 3D JeanFrançois Lalonde, Nicolas Vandapel and Martial Hebert Carnegie Mellon University Problem Dynamic processing of large 3D point cloud data from ladar
More information