'First pass' presentation slideshows

Chapter 14: Sorting and Searching

Chapter 14: Sorting and Searching. Objectives. After studying this chapter you should understand the following: orderings and the ordering of list elements; the simple sorting algorithms selection sort and bubble sort; how to generalize sort methods. the binary search algorithm.

822 views • 72 slides

COP 3540 Data Structures with OOP

COP 3540 Data Structures with OOP. Simple Sorting Chapter 3. Major Topics. Introductory Remarks Bubble Sort Selection Sort Insertion Sort Sorting Objects Comparing Sorts . Introductory Remarks. So many lists are better dealt if ordered. Will look at simple sorting first.

624 views • 47 slides

Evaluating interaction design

Evaluating interaction design. IMD07101: Introduction to Human Computer Interaction Brian Davison 2010/11 With material from Susan Turner. About evaluation. What it is Why and when to do it How to do it Heuristic (expert) evaluation Setting and using usability metrics

568 views • 47 slides

St. Eustatius drinking water production

St. Eustatius drinking water production . by means of Reverse Osmosis System. What is osmosis? Under normal pressure, solution passes from one side to the other until it is equal.

503 views • 40 slides

Nuclear Cardiology and you

Nuclear Cardiology and you. Anatomy & Physiology. Four cardiac chambers left ventricle of importance in NM Coronary arteries supply myocardium with blood NM detects quality of perfusion to myocardium, an indirect assessment of the coronary arteries. CLINICAL INDICATIONS.

728 views • 39 slides

Building a rich ontology from AGROVOC

Building a rich ontology from AGROVOC. Dagobert Soergel College of Information Studies, University of Maryland dsoergel@umd.edu , www.dsoergel.com. FAO Agricultural Ontology Server Workshop Beijing, April 27 - 29, 2004. The problem.

515 views • 35 slides

Why Study Assembler?

Why Study Assembler?. Why Should a Computer Science Major Learn Assembler Language? (Nobody uses it!) Edward L. Bosworth, PhD Columbus State University Columbus, GA. Outline of Talk. Introduce self Discuss levels of languages: high-level and assembler are the levels of interest here.

772 views • 34 slides

CS 552/652 Speech Recognition with Hidden Markov Models Winter 2011 Oregon Health & Science University Center for Sp

CS 552/652 Speech Recognition with Hidden Markov Models Winter 2011 Oregon Health & Science University Center for Spoken Language Understanding John-Paul Hosom Lecture 17 March 7 Search Strategies for Improved Performance, Part II. Next Topics: Improving Performance of an HMM.

475 views • 34 slides

Intro to Arena

Intro to Arena. A Fourth Simulation. Model 4.

506 views • 32 slides

Enhancing Drug Bioavailability by Overcoming Intestinal Metabolism

Enhancing Drug Bioavailability by Overcoming Intestinal Metabolism. Dr. Basavaraj K. Nanjwade M.Pharm ., Ph.D Professor of Pharmaceutics Department of Pharmaceutics KLE University, Belgaum, India E-mail: bknanjwade@yahoo.co.in Cell No: 00919742431000. INTRODUCTION.

1.04k views • 31 slides

Analysis 360: Blurring the line between EDA and PC

Analysis 360: Blurring the line between EDA and PC. Andrea Gibson , Product Director, Kroll Ontrack March 27, 2014. Discussion Overview. Pushing the Boundaries of Early Data Analysis (EDA) Examining Traditional EDA Tools Leveraging Predictive Coding (PC) for Analysis

443 views • 31 slides

Fast Algorithms For Mining Association Rules

Fast Algorithms For Mining Association Rules. By Rakesh Agrawal and R. Srikant Presented By: Chirayu Modi. What is Data mining ?.

551 views • 28 slides

Mapping Irregular Algorithms in a Custom Computing Image Processing Framework

366 views • 23 slides

Experience with XML Signature and Recommendations for future Development

341 views • 23 slides

Status of Mu2e Solenoids

Status of Mu2e Solenoids. Organization Technical Progress Cost and Schedule. Michael Lamm for the Mu2e Project. Working Group Meeting March 17, 2010. Mu2e Goals. Measure the Rare Process: m - + N ? e- + N relative to m - + N(A,Z) ? n + N(A, X)

352 views • 21 slides

Intermediate code generation

Intermediate code generation. Code Generation. Create linear representation of program Result can be machine code, assembly code, code for an abstract machine (e.g. the JVM ) threaded code, or anything in between. Common representation of intermediate code depend on target machine:

520 views • 19 slides

Instant Runoff Voting

325 views • 19 slides

Drug Stores

310 views • 15 slides

B + -Trees: Search

B + -Trees: Search. If there are n search-key values in the file, the path is no longer than ?log ? f /2? ( n )? (worst case). External Sort-Merge. Sorting phase : Sorts n B pages at a time n B = # of main memory pages buffer

181 views • 9 slides

CloudME Reports & Analytics

227 views • 1 slides