DOCSLIB.ORG

Information to Users

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Information to Users Adaptive digital image data compression using RIDPCM and a neural network for subimage classification Item Type text; Thesis-Reproduction (electronic) Authors Allan, Todd Stuart, 1964- Publisher The University of Arizona. Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. Download date 24/09/2021 23:55:43 Link to Item http://hdl.handle.net/10150/278109 INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6" x 9" black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. University Microfilms International A Bell & Howell Information Company 300 North Zeeb Road, Ann Arbor, Ml 48106-1346 USA 313/761-4700 800/521-0600 Order Number 1348472 Adaptive digital image data compression using RIDPCM and a neural network for subimage classification Allan, Todd Stuart, M.S. The University of Arizona, 1992 UMI 300 N. Zeeb Rd. Ann Artor, MI 48106 ADAPTIVE DIGITAL IMAGE DATA COMPRESSION USING RIDPCM AND A NEURAL NETWORK FOR SUBIMAGE CLASSIFICATION by Todd Stuart Allan A Thesis Submitted to the Faculty of the DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING In Partial Fulfillment of the Requirements For the Degree of MASTERS OF SCIENCE WITH A MAJOR IN ELECTRICAL ENGINEERING In the Graduate College THE UNIVERSITY OF ARIZONA 1 992 STATEMENT BY AUTHOR This thesis has been submitted in partial fulfillment of requirements for an advanced degree at The University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library. Brief quotations from this thesis are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interest of scholarship. In all other instances, however, permission must be obtained from the author. SIGNED: ^ . OS. APPROVAL BY THESIS DIRECTOR This thesis has been approved on the date shown below: Bobby R. Hunt Date Professor of Electrical and Computer Engineering 3 ACKNOWLEDGMENTS First and foremost, I would like to thank my thesis advisor, Dr. Bobby R. Hunt, for his support during this project. Without his knowledge and wisdom guiding me, this work would not have been possible. I would also like to thank Dr. Jeffrey J. Rodriguez and Dr. Robert A. Schowengerdt for their assistance throughout my graduate studies. Next, I would like to thank all of my fellow graduate students in the Neural Analysis and Imaging Lab for their support. I would especially like to thank Dave DeKruger, Eric Gifford, Michael Kiefer, and Phil Sementilli for sharing their knowledge and experience with C programming and the lab computer system. Finally, I would like to thank my family and friends for their continual support and encouragement. Special thanks goes to my wife, Maribel, for patiently and lovingly dealing with me during my graduate studies. 4 TABLE OF CONTENTS Page UST OF FIGURES 6 LIST OF TABLES 8 ABSTRACT 9 1. INTRODUCTION 10 1.1 Definition of a Digital Image 11 1.2 Concepts of Digital Image Data Compression 12 1.3 Predecessors of RIDPCM and ARIDPCM 13 1.4 Preview of Chapters 15 2. RECURSIVE IDPCM 18 2.1 Theory of RIDPCM 18 2.2 Optimum Quantization 20 2.2.1 Optimum Uniform Quantizer 21 2.2.2 Lloyd-Max Optimum Quantizer 21 2.3 Bit Rates for RIDPCM 22 2.4 Additional Algorithm Notes 23 2.5 Advantages 24 2.6 Disadvantages 25 3. ADAPTIVE RIDPCM 32 3.1 Theory of ARIDPCM 32 3.2 Subimage Classification 34 3.2.1 Image Feature Space Extraction 35 3.2.1.1 Subimage Mean 35 3.2.1.2 Subimage Variance 36 3.2.1.3 Subimage Entropy 36 3.2.1.4 Subimage Fractal Dimension 37 5 TABLE OF CONTENTS - Continued Page 3.2.2 Neural Network Classifier 41 3.2.2.1 Neural Network Overview 41 3.2.2.2 Neural Network Training 43 3.3 Bit Rates for ARIDPCM 44 3.4 Additional Algorithm Notes 45 3.5 Advantages 46 3.6 Disadvantages 47 4. SIMULATION RESULTS AND PERFORMANCE COMPARISONS 53 4.1 Neural Network Training Results 53 4.2 ARIDPCM and RIDPCM Performance Comparisons 55 4.2.1 Comparison Method 56 4.2.2 Test Results 58 5. CONCLUSIONS AND FUTURE WORK 84 LIST OF REFERENCES 86 6 USTOF FIGURES Figure Page 1.1 Block Diagram of IDPCM System 17 2.1 Block Diagram of RIDPCM System 27 2.2 Bilinear Kernels 28 2.3 Two Dimensional Representation of RIDPCM 29 2.4 Histogram of Subsample Values of Lenna 30 2.5 Histogram of First Set of Differences of Lenna 30 2.6 Histogram of Second Set of Differences of Lenna 31 2.7 Histogram of Third Set of Differences of Lenna 31 3.1 Block Diagram of ARIDPCM System 48 3.2 Blanket Covering Method for Estimating Fractal Dimension 49 3.3 Simple Processing Element 50 3.4 Sigmoidal Activation Function 50 3.5 Feedforward Network Configuration 51 3.6 Decision Regions as a Function of Network Configuration 52 4.1 Parity-3 Learning Curve 61 4.2 Original Lenna Image and Subimage Map 62 4.3 Histogram of Lenna 63 4.4 Training Data for Lenna 65 7 LIST OF FIGURES - Continued Figure Page 4.5 Learning Curve for Lenna 66 4.6 Class Label Map and Distribution-38 samples 67 4.7 Class Label Map and Distribution-113 samples 68 4.8 Approximate Quantization Bit Rates for Lenna: 6/4/4/4 69 4.9 Approximate Quantization Bit Rates for Lenna: 6/3/2/2 70 4.10 Approximate Quantization Bit Rates for Lenna: 6/3/2/1 71 4.11 Approximate Quantization Bit Rates for Lenna: 6/3/2/0 72 4.12 Approximate Quantization Bit Rates for Lenna: 6/1/1/1 74 4.13 Original Moon Image 75 4.14 Histogram of Moon 76 4.15 Class Label Map and Distribution for Moon 77 4.16 Approximate Quantization Bit Rates for Moon: 6/3/2/0 78 4.17 Approximate Quantization Bit Rates for Moon: 6/3/0/0 79 4.18 Original Rural Image 80 4.19 Histogram of Rural 81 4.20 Class Label Map and Distribution for Rural 82 4.21 Comparison of ARIDPCM and RIDPCM on Rural 83 8 LIST OF TABLES Table Page 2.1 Example of Equal Length, Natural Code Word Assignments 26 4.1 Parity-3 Training Data 61 4.2 ARIDPCM Subimage Class Labels 64 9 ABSTRACT Recursive Interpolated Differential Pulse Code Modulation (RIDPCM) is a fast and efficient method of digital image data compression. It is a simple algorithm which produces a high quality reconstructed image at a low bit rate. However, RIDPCM compresses the entire image the same regardless of image detail. This thesis introduces a variation on RIDPCM which adapts the bit rate according to the detail of the image. Adaptive RIDPCM (ARIDPCM) is accomplished by dividing the original image into smaller subimages and extracting features from them. These subimage features are passed through a trained neural network classifier. The output of the network is a class label which denotes the estimated subimage activity level or subimage type. Each class is assigned a specific bit rate and the subimage information is quantized accordingly. ARIDPCM produces a reconstructed image of higher quality than RIDPCM with the benefit of a further reduced bit rate. 10 CHAPTER 1 INTRODUCTION With the advent of sophisticated communication hardware and imaging systems, it is becoming increasingly more important to develop means of transmitting and storing the resulting massive amounts of data. This is the primary impetus of the image data compression field. There are two general categories of data compression, information preserving and non-information preserving [1]. Information preserving coding techniques retain all of the information of the original digital image. This allows the compressed image to be reconstructed into the exact original digital image. These coding techniques are used when the exact details of the image are required for further processing, such as fingerprint analysis. When some loss of image integrity is acceptable, the non-information preserving (lossy) techniques are much more efficient on storage space and transmission time.
Load more

Recommended publications
  • Image Processing with the Artificial Swarm Intelligence 86 Xiaodong Zhuang, Nikos E
    Advances in Image Analysis - Nature Inspired Methodology Dr. Xiaodong Zhuang 1 Associate Professor, Qingdao University, China 2 WSEAS Research Department, Athens, Greece Prof. Dr. Nikos E. Mastorakis 1 Professor, Technical University of Sofia, Bulgaria 2 Professor, Military Institutes of University Education, Hellenic Naval Academy, Greece 3 WSEAS Headquarters, Athens, Greece Published by WSEAS Press ISBN: 978-960-474-290-5 www.wseas.org Advances in Image Analysis – Nature Inspired Methodology Published by WSEAS Press www.wseas.org Copyright © 2011, by WSEAS Press All the copyright of the present book belongs to the World Scientific and Engineering Academy and Society Press. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the Editor of World Scientific and Engineering Academy and Society Press. All papers of the present volume were peer reviewed by two independent reviewers. Acceptance was granted when both reviewers' recommendations were positive. See also: http://www.worldses.org/review/index.html ISBN: 978-960-474-290-5 World Scientific and Engineering Academy and Society Preface The development of human society relies on natural resources in every area (both material and spiritual). Nature has enormous power and intelligence behind its common daily appearance, and it is generous. We learn in it and from it, virtually as part of it. Nature-inspired systems and methods have a long history in human science and technology. For example, in the area of computer science, the recent well-known ones include the artificial neural network, genetic algorithm and swarm intelligence, which solve hard problems by imitating mechanisms in nature.
    [Show full text]
  • CS 488/688 Fall 2017 Stephen Mann CONTENTS 2 Contents
    1 CS 488/688 Fall 2017 Stephen Mann CONTENTS 2 Contents 1 Administration 5 1.1 General Information . .5 1.2 Topics Covered . .6 1.3 Assignments . .6 2 Introduction 9 2.1 History . .9 2.2 Pipeline . 10 2.3 Primitives . 11 2.4 Algorithms . 12 2.5 APIs . 12 3 Devices and Device Independence 13 3.1 Calligraphic and Raster Devices . 13 3.2 How a Monitor Works . 13 3.3 Physical Devices . 14 4 Device Interfaces 17 4.1 Device Input Modes . 17 4.2 Application Structure . 17 4.3 Polling and Sampling . 18 4.4 Event Queues . 18 4.5 Toolkits and Callbacks . 19 4.6 Example for Discussion . 20 5 Geometries 23 5.1 Vector Spaces . 23 5.2 Affine Spaces . 23 5.3 Inner Product Spaces . 24 5.4 Euclidean Spaces . 24 5.5 Cartesian Space . 25 5.6 Why Vector Spaces Inadequate . 25 5.7 Summary of Geometric Spaces . 27 6 Affine Geometry and Transformations 29 6.1 Linear Combinations . 29 6.2 Affine Combinations . 29 6.3 Affine Transformations . 31 6.4 Matrix Representation of Transformations . 33 6.5 Geometric Transformations . 34 6.6 Compositions of Transformations . 35 6.7 Change of Basis . 36 6.8 Ambiguity . 39 6.9 3D Transformations . 40 CONTENTS 3 6.10 World and Viewing Frames . 42 6.11 Normals . 45 7 Windows, Viewports, NDC 47 7.1 Window to Viewport Mapping . 47 7.2 Normalized Device Coordinates . 49 8 Clipping 51 8.1 Clipping . 51 9 Projections and Projective Transformations 55 9.1 Projections . 55 9.2 Why Map Z? .
    [Show full text]
  • Deep Learning-Based Method for Classifying and Localizing Potato Blemishes
    Deep Learning-based Method for Classifying and Localizing Potato Blemishes Sofia Marino, Pierre Beauseroy and André Smolarz Institut Charles Delaunay/M2S, FRE 2019, Université de Technologie de Troyes, Keywords: Deep Learning, Potato Blemishes, Classification, Localization, Autoencoder, SVM. Abstract: In this paper we address the problem of potato blemish classification and localization. A large database with multiple varieties was created containing 6 classes, i.e., healthy, damaged, greening, black dot, common scab and black scurf. A Convolutional Neural Network was trained to classify face potato images and was also used as a filter to select faces where more analysis was required. Then, a combination of autoencoder and SVMs was applied on the selected images to detect damaged and greening defects in a patch-wise manner. The localization results were used to classify the potato according to the severity of the blemish. A final global evaluation of the potato was done where four face images per potato were considered to characterize the entire tuber. Experimental results show a face-wise average precision of 95% and average recall of 93%. For damaged and greening patch-wise localization, we achieve a False Positive Rate of 4.2% and 5.5% and a False Negative Rate of 14.2% and 28.1% respectively. Concerning the final potato-wise classification, we achieved in a test dataset an average precision of 92% and average recall of 91%. 1 INTRODUCTION is the difficulty to design a feature extractor adap- ted to each pattern, that require human expertise to Potato is one of the most important food crops con- suitable transform the raw input image into a good sumed all over the world with a total production that representation, exploitable to achieve the classifica- exceeds 374.000.000 tons (IPC, 2018).
    [Show full text]
  • Multiscene: a Large-Scale Dataset and Benchmark for Multi-Scene
    This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. arXiv:2104.02846v3 [cs.CV] 7 Sep 2021 SUBMITTED TO IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2 MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images Yuansheng Hua, Lichao Mou, Pu Jin, and Xiao Xiang Zhu, Fellow, IEEE Abstract—This is the preprint version. To read the final ver- sion, please go to IEEE Transactions on Geoscience and Remote woodland bridge Sensing. Aerial scene recognition is a fundamental research river problem in interpreting high-resolution aerial imagery. Over the woodland past few years, most studies focus on classifying an image into one scene category, while in real-world scenarios, it is more often that a single image contains multiple scenes. Therefore, parking lot residential parking lot in this paper, we investigate a more practical yet underexplored task—multi-scene recognition in single images. To this end, we create a large-scale dataset, called MultiScene, composed of bridge residential 100,000 unconstrained high-resolution aerial images. Considering river train station that manually labeling such images is extremely arduous, we resort to low-cost annotations from crowdsourcing platforms, e.g., OpenStreetMap (OSM). However, OSM data might suffer from incompleteness and incorrectness, which introduce noise into image labels. To address this issue, we visually inspect (a) Single-scene recognition (b) Multi-scene recognition 14,000 images and correct their scene labels, yielding a subset of cleanly-annotated images, named MultiScene-Clean.
    [Show full text]
  • Esoteric Programming Languages
    Esoteric Programming Languages An introduction to Brainfuck, INTERCAL, Befunge, Malbolge, and Shakespeare Sebastian Morr Braunschweig University of Technology [email protected] ABSTRACT requires its programs to look like Shakespearean plays, mak- There is a class of programming languages that are not actu- ing it a themed language. It is not obvious at all that the ally designed to be used for programming. These so-called resulting texts are, in fact, meaningful programs. For each “esoteric” programming languages have other purposes: To language, we are going describe its origins, history, and to- entertain, to be beautiful, or to make a point. This paper day’s significance. We will explain how the language works, describes and contrasts five influential, stereotypical, and give a meaningful example and mention some popular imple- widely different esoteric programming languages: The mini- mentations and variants. All five languages are imperative languages. While there mal Brainfuck, the weird Intercal, the multi-dimensional Befunge, the hard Malbolge, and the poetic Shakespeare. are esoteric languages which follow declarative programming paradigms, most of them are quite new and much less popu- lar. It is also interesting to note that esoteric programming 1. INTRODUCTION languages hardly ever seem to get out of fashion, because While programming languages are usually designed to be of their often unique, weird features. Whereas “real” multi- used productively and to be helpful in real-world applications, purpose languages are sometimes replaced by more modern esoteric programming languages have other goals: They can ones that add new features or make programming easier represent proof-of-concepts, demonstrating how minimal a in some way, esoteric languages are not under this kind of language syntax can get, while still maintaining universality.
    [Show full text]
  • Aerospace Medicine and Biology
    https://ntrs.nasa.gov/search.jsp?R=19760006642 2020-03-22T17:35:15+00:00Z NASA SP-7011 (147) AEROSPACE MEDICINE AND BIOLOGY A CONTINUING BIBLIOGRAPHY WITH INDEXES (Supplement 147) NOVEMBER 1975 NATIONAL AERONAUTICS AND SPACE ADMINISTRATION ACCESSION NUMBER RANGES Accession numbers cited in this Supplement fall within the following ranges: STAR (N-10000 Series) N75-28003—N75-29995 IAA (A-10000 Series) A75-38854—A75-41898 This bibliography was prepared by the NASA Scientific and Technical Information Facility operated for the National Aeronautics and Space Administration by Informatics Information Systems Company. NASA SP-7011 (147) AEROSPACE MEDICINE AND BIOLOGY A CONTINUING BIBLIOGRAPHY WITH INDEXES (Supplement 147) A selection of annotated references to unclas- sified reports and journal articles that were introduced into the NASA scientific and tech- nical information system and announced in October 1975 in • Scientific and Technical Aerospace Reports (STAR} • International Aerospace Abstracts (IAA) Scientific and Technical Information Office NOVEMBER 1975 NATIONAL AERONAUTICS AND SPACE ADMINISTRATION Washington, D.C. NASA SP-7011 and its supplements are available from the National Technical Information Service (NTIS). Questions on the availability of the predecessor publications, Aerospace Medicine and Biology (Volumes I - XI) should be directed to NTIS. This Supplement is available from the National Technical Information Service (NTIS), Springfield, Virginia 22161 for $4 00 For copies mailed to addresses outside the United States, add $2 50 per copy for handling and postage INTRODUCTION This Supplement to Aerospace Medicine and Biolog\ (NASA SP-7011) lists 253 reports, articles and other documents announced during October 1975 in Scientific and Technical Aerospace Reports (STAR) or in International Aerospace Abstracts HA A) The first issue of the bibliography was published in July 1964, since that time, monthly supplements have been issued.
    [Show full text]
  • Complexity & Verification: the History of Programming As Problem Solving
    COMPLEXITY & VERIFICATION: THE HISTORY OF PROGRAMMING AS PROBLEM SOLVING A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Joline Zepcevski IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Arthur L. Norberg February 2012 © Copyright by Joline Zepcevski 2012 All Rights Reserved Acknowledgments It takes the work of so many people to help a student finish a dissertation. I wish to thank Professor Arthur L. Norberg for postponing his retirement to be my advisor and my friend over the course of this project. Thank you to my committee, Professor Jennifer Alexander, Professor Susan Jones, Dr. Jeffery Yost, and Professor Michel Janssen, all of whom individually guided this dissertation at different times and in specific ways. Thank you also to Professor Thomas Misa for his guidance and assistance over many years. I had a great faculty and a great cohort of graduate students without whom this dissertation would never have been completed. I particularly want to thank Sara Cammeresi, whose unending support and friendship were invaluable to the completion of this project. I wish to thank my family, Jovan Zepcevski, Geraldine French, Nicole Zepcevski, and Brian Poff, who supported me and loved me throughout it all. I also want to thank my friends: Tara Jenson, Holly and Aaron Adkins, Liz Brophey, Jennifer Nunnelee, Jen Parkos, Vonny and Justin Kleinman, Zsuzsi Bork, AJ Letournou, Jamie Stallman, Pete Daniels, and Megan Longo who kept me sane. I need to thank Lisa Needham for all her assistance. Without your help, I wouldn’t sound nearly as smart.
    [Show full text]
  • Adversarial Continual Learning
    Adversarial Continual Learning Sayna Ebrahimi1;2, Franziska Meier1, Roberto Calandra1, Trevor Darrell2, and Marcus Rohrbach1 1Facebook AI Research, USA 2UC Berkeley EECS, Berkeley, CA, USA fsayna,[email protected], ffmeier,rcalandra,[email protected] Abstract. Continual learning aims to learn new tasks without forget- ting previously learned ones. We hypothesize that representations learned to solve each task in a sequence have a shared structure while containing some task-specific properties. We show that shared features are signifi- cantly less prone to forgetting and propose a novel hybrid continual learn- ing framework that learns a disjoint representation for task-invariant and task-specific features required to solve a sequence of tasks. Our model combines architecture growth to prevent forgetting of task-specific skills and an experience replay approach to preserve shared skills. We demon- strate our hybrid approach is effective in avoiding forgetting and show it is superior to both architecture-based and memory-based approaches on class incrementally learning of a single dataset as well as a sequence of multiple datasets in image classification. Our code is available at https: //github.com/facebookresearch/Adversarial-Continual-Learning. 1 Introduction Humans can learn novel tasks by augmenting core capabilities with new skills learned based on information for a specific novel task. We conjecture that they can leverage a lifetime of previous task experiences in the form of fundamental skills that are robust to different task contexts. When a new task is encountered, these generic strategies form a base set of skills upon which task-specific learning can occur. We would like artificial learning agents to have the ability to solve many tasks sequentially under different conditions by developing task-specific and task-invariant skills that enable them to quickly adapt while avoiding catas- trophic forgetting [24] using their memory.
    [Show full text]
  • Fortran 1957А–А2008
    Fortran 1957 ± 2008 : A Language with a Past, Present and Future Peter Crouch [email protected] Chairman Fortran Specialist Group www.fortran.bcs.org BCS Birmingham Branch meeting 19 May 2008 My Background 1968 - 1984 Industrial research chemist. Started programming in BASIC and Pascal in the late 1970s. Began to to use FORTRAN in the early 1980s. 1985 - 2001 Software developer for Computer Aided Design and Manufacturing systems using Fortran and C. 2003 - 2005 Civil servant in the Department for Work and Pensions. 1993 Joined the British Computer Society 1997 - 2002 Chairman of the BCS Birmingham Branch 2002 - 2008 Chairman of the BCS Fortran Specialist Group Presentation Outline In the Beginning Fortran Pioneers IBM Films Early Developments with example code Standardisation Modern Developments with example code Applications BCS Fortran Specialist Group In the Beginning In the beginning the only practical way to program computers was in machine code, which was extremely tedious. The source code used octal notation. By the 1950s assembly code had been developed, which was less tedious but still error prone and required a detailed knowledge of the computer hardware. FORTRAN Conceived In late 1953, John Backus sent a brief letter to his boss at IBM, asking that he be allowed to search for a "better way" of programming computers, with a project timescale of six months. He got the nod and began the research project that would eventually produce FORTRAN. As John Backus says in the film, ªproject completion was always six months awayº! Fortran
    [Show full text]
  • SE 016 990 Adaptive Schemes Called Sequential Reproductive Computers
    DOCUMENT RESUME ED 099 184 SE 016 990 AUTHOR Martin, Nancy TITLE Convergence Properties of a Class of Probabilistic Adaptive Schemes Called Sequential Reproductive Plans. Psychology and Education Series, Technical Report No. 210. INSTITUTION Stanford Univ., Calif. Inst. for Mathematical Studies in Social Science. SPONS AGENCY National Science Foundation, Washington, D.C. PUB DATE 31 Jul 73 NOTE 63p. EDRS PRICE EF-$0.75 HC-$3.15 PLUS POSTAGE DESCRIPTORS Computers; *Computer Science Education; Educational Research; *Genetics; Linear Programing; *Mathematical Models; Reproduction (Biology); Science Education; Social Sciences ABSTRACT Presented is a technical report concerning the use of a mathematical model describing certain aspects of the duplication and selection processes in natural genetic adaptation. This reproductive plan/model occurs in artificial genetics (the use of ideas from genetics to develop general problem solving techniques for computers). The reproductive plan is a sequential stochastic process involving n-tuples (corresponding to chromosomes in genetics) which may be simple numeric constants or complex structures such as computer algorithms. The plan also involves a sequence of probability distributions defined over n-tuples,, The report consists of five chapters: introduction; reproductive plans; deterministic problem bases; a chapter divided into sections on the search for an arena, the linear additive model, the linear models and pure problem bases; and conclusions. An appendix illustrating the theorem involved and a list of references conclude the report. (PEB) ! ..,ot Jr HI AL TN t A ALLF Alit .e.S1,Ttatt OF BEST COPYAVAILABLE t % -ttr . % % ; - % ftt ts` !CONVERGENCE PROPERTIES OF A CLASS OF PROBABILISTIC ADAPTIVESCHEMES CALLED SEQUENTIAL REPRODUCTIVE PLANS BY NANCY MARTIN TECHNICAL REPORT NO.
    [Show full text]
  • Making Construals in the CONSTRUIT Environment and Computing Education
    Making Construals In The CONSTRUIT Environment and Computing Education Kenneth Bugembe Discrete Mathematics - USCA-G4G1 19/20 Supervised by Mike Joy on behalf of Meurig Beynon (Emeritus Reader) 0 Introduction 3 Methodology (Theory) 7 Theoretical Content 9 Construals as an aspect of Computing Education 9 Construals as support for traditional Computing Education 13 Methodology (Technical) 16 Technical Discussion 17 Construal 1 - TPK Algorithm 17 Construal 2 - Turing Machine 23 Construals as “Objects to converse with” 30 Conclusions and Future Work 34 References 35 Appendix - Construals 38 1 Abstract There is growing recognition in the United Kingdom that everyone, regardless of their future occupation, will need some capacity to interact with computers as well as knowledge of both Computing and programming. However, there is increasing concern that current approaches to British Computing education are not adequate for this agenda. That the current curriculum is failing to properly equip students with the knowledge and skills to cope with an increasingly digital world and that the vast majority are choosing not to study Computing past Key Stage 3. Many argue that it is the difficulty of programming hindering the success and uptake of Computing education. Through the CONSTRUIT! project, the Empirical Modelling Research Group promote the theory of Empirical Modelling as a complementary experiential account of Computing. From this account, the practice of “making construals” arises; a mode of computer interaction they claim is more accessible to non-computing specialists and therefore better suited to an agenda of widely promoting Computing than conventional programming. This project aims to evaluate the theoretical and practical work of the CONSTRUIT! project in order to determine whether the principles and practice of making construals can be used to improve British Computing education.
    [Show full text]
  • Computer Graphics: Programming, Problem Solving, and Visual Communication
    Computer Graphics: Programming, Problem Solving, and Visual Communication Dr. Steve Cunningham Computer Science Department California State University Stanislaus Turlock, CA 95382 copyright © 2002, Steve Cunningham All rights reserved These notes are a draft of a textbook for an introductory computer graphics course that emphasizes graphics programming and is intended for undergraduate students who have a sound background in programming. Its goal is to introduce fundamental concepts and processes for computer graphics, give students experience in computer graphics programming using the OpenGL application programming interface (API), and show the power of visual communication and of computer graphics in the sciences. The contents below represent a relatively mature version of these notes, although some reorganization of the material is still expected and some additional topics may be developed. We hope you will find these notes to be helpful in learning computer graphics and encourage you to give us feedback with the feedback pages at the end of the notes. CONTENTS: Getting Started • What is a graphics API? • Overview of the book • What is computer graphics? • The 3D Graphics Pipeline - 3D model coordinate systems - 3D world coordinate system - 3D eye coordinate system - Clipping - Projections - 2D eye coordinates - 2D screen coordinates - Overall viewing process - Different implementation, same result - Summary of viewing advantages • A basic OpenGL program - The structure of the main() program using OpenGL - Model space - Modeling transformation
    [Show full text]