DOCSLIB.ORG

An Investigation of Storage and Communication Codes for an Electronic Library Mansour Alsulaiman Iowa State University

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
An Investigation of Storage and Communication Codes for an Electronic Library Mansour Alsulaiman Iowa State University Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1987 An investigation of storage and communication codes for an electronic library Mansour Alsulaiman Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Electrical and Electronics Commons, and the Library and Information Science Commons Recommended Citation Alsulaiman, Mansour, "An investigation of storage and communication codes for an electronic library " (1987). Retrospective Theses and Dissertations. 8608. https://lib.dr.iastate.edu/rtd/8608 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. INFORMATION TO USERS The most advanced technology has been used to photo­ graph and reproduce this manuscript from the microfilm master. UMI films the original text directly from the copy submitted. Thus, some dissertation copies are in typewriter face, while others may be from a computer printer. 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 copyrighted material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are re­ produced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each oversize page is available as one exposure on a standard 35 mm slide or as a 17" x 23" black and white photographic print for an additional charge. Photographs included in the original manuscript have been reproduced xerographically in this copy. 35 mm slides or 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. Accessing the World'sUMI Information since 1938 300 North Zeeb Road, Ann Arbor, Ml 48106-1346 USA Order Number 8805088 An investigation of storage and communication codes for an electronic library Alsulaiman, Mansour, Ph.D. Iowa State University, 1987 UMI 300 N. ZeebRd. Ann Arbor, MI 48106 PLEASE NOTE: In all cases this material has been filmed in the best possible way from the available copy. Problems encountered with this document have been identified here with a check mark V . 1. Glossy photographs or pages 2. Colored illustrations, paper or print 3. Photographs with dark background i/ 5. Pages with black marks, not original copy \/^ 6. Print shows through as there is text on both sides of page 7. Indistinct, broken or small print on several pages 8. Print exceeds margin requirements 9. Tightly bound copy with print lost in spine 10. Computer printout pages with indistinct print 11. Page(s/ lacking when material received, and not available from school or author. 12. Page(s) seem to be missing in numbering only as text follows. 13. Two pages numbered . Text follows. 14. Curling and wrinkled pages 15. Dissertation contains pages with print at a slant, filmed as received 16. Other UMI An investigation of storage and communication codes for an electronic library by Mansour Alsulaiman A Dissertation Submitted to the Graduate Faculty in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Department : Electrical Engineering and Computer Engineering Major: Computer Engineering Approved : Signature was redacted for privacy. In Charge of Major Work Signature was redacted for privacy. Signature was redacted for privacy. For tne uraauate College Iowa State University Ames, Iowa 1987 il TABLE OF CONTENTS Page l: INTRODUCTION 1 1.1. Statement of the Problem 1 1.2. Features and Assumptions of the Solution 1 1.3. Thesis Organization 2 2. LITERATURE REVIEW 4 2.1. Review of Facsimile Transmission 4 2.2. Review of the Lempel and Ziv Algorithm 21 3. CREATION OF THE IMAGE DATA BASE 34 3.1. Classification of the Library Informational Material 34 3.2. Device Description 37 3.3. Procedures of the Research 38 3.4. Creation of the Image Data Base 39 3.5. Classification of the Image Data Base 40 3.6. Results to Be Analyzed 41 3.7' Implementation Considerations 41 4. FACSIMILE CODING 43 4.1. Introduction 43 4.2. One Dimensional Compression Technique 43 4.3. Two Dimensional Compression Technique 46 4.4. MREAD Implementation and Results 53 4.5. Entropy Calculation of the One Dimensional Model 68 4.6. Entropy Calculation of the Two Dimensional Model 71 ill Page 4.7. Analysis of the Results 82 4.8. Conclusion 101 5. APPLICATION OF THE LEMPEL-ZIV-WELCH ALGORITHM 103 5.1. Description of the Lempel-Ziv-Welch Algorithm 103 5.2. Method LZWB 104 5.3. Method LZWBl 105 5.4. Method LZIfB2 108 5.5. Results of LZW and the Above Mentioned Modifications 109 5.6. LZW vs. FAX 120 5.7. LZyre and LZWB2 vs. LZW and FAX 120 5.8. LZWBl vs. LZWB 121 5.9. Conclusion 121 6. MODIFICATIONS TO THE LZW ALGORITHM 123 6.1. Method LZWl 125 6.2. Method LZW2 128 6.3. Method LZW3 129 6.4. Results of Compression Using LZWl, LZW2, and LZW3 130 7. METHODS R8, R4, AND BIG 140 7.1. Method R8 140 7.2. Method R4 143 7.3. Method BIG 143 7.4. Results and Analysis of R8 and R4 144 7.5. Results and Analysis of BIG 150 iv Page 8. GENERAL ANALYSES 161 8.1. Building the Screen 161 8.2. Screen Division 161 8.3. The Significance of the Groups Averages 164 8.4. Using the CCITT Documents for Comparison 130 8.5. Results of Group 5 183 8.6. Results of Group 8 183 0 0 The Significance of "Extracalls" 185 8.8. Table Size 186 8.9. Remarks about R8 and R4 187 9. CONCLUSION 189 9.1. Suggestions for Future Work 191 10. REFERENCES 193 11. ACKNOWLEDGMENTS 197 12. APPENDIX A. IMAGES USED IN THE DATA BASE 198 13. APPENDIX B. PROGRAM LIST OF THE CCITT ONE DIMENSIONAL COMPRESSION TECHNIQUE 273 13.1. File Main.c 274 13.2. File Cmprsln.c 277 13.3. File Cupdt.c 280 13.4. File Clast.c 284 13.5. File Dcmprsln.c 286 13.6. File Dupdtc.c 288 V Page 13.7. File Dupdtd.c 296 13.8. File Initscrn.c 301 13.9. File Gttime.c 303 13.10. File Print.c 304 13.11. File Geth.asm 305 13.12. File Puth.asm 313 13.13. File Swap.asm 321 13.14. File Mtchbts.asm 322 14. APPENDIX C. PROGRAM LISTINGS OF THE CODE OF THE CCITT TITO DIMENSIONAL COMPRESSION TECHNIQUE 324 14.1. File Main.c 325 14.2. File Cmprs2d.c 327 14.3. File Cupdt.c 336 14.4. File Dcmprs2d.c 340 14.5. File Dcmprsln.c 352 14.6. File Bitsrng.asm 354 15. APPENDIX D. PROGRAM LIST OF METHOD LZW 356 15.1. File Main.c 357 15.2. File Cmprs.c 359 15.3. File Dcmprs.c 362 15.4. File Tables.c 365 15.5. File Scanw.asm 366 15.6. File Scrinit.c 368 vi Page 15.7. File Print.c 372 15.8. File Fadjst.c 373 15.9. File Fradjst.c 374 16. APPENDIX E. PROGRAM LIST OF METHOD LZWB 377 16.1. File Main.c 378 16.2. File Contsym.c 381 16.3. File Dcmpsym.c 383 16.4. File Mraset.asm 387 16.5. File Swapfar.asm 388 17. APPENDIX F. PROGRAM LIST OF METHOD LZWBl 390 17.1. File Dcmpsym.c 391 17.2. File Contsym.c 394 17.3. File Scan2.asm 398 17.4. File ScanS.asm 399 18. APPENDIX G. PROGRAM LIST OF METHOD LZWB2 4U1 18.1. File Dcmprs.c 402 18.2. File Tables.c 404 19. APPENDIX H. PROGRAM LIST OF METHOD LZWl 406 19.1. File Tables.c 407 19.2. File Cmprs.c 408 19.3. File Dcmprs.c 411 19.4. File Dcorapose.c 412 19.5. File Scanw2.asm 414 19.6. File Scanw3.asm 416 vii Page 20. APPENDIX I. PROGRAM LIST OF METHOD LZW2 418 20.1. File Cmprs.c 419 21. APPENDIX J. PROGRAM LIST OF METHOD LZW3 423 21.1. File Cmprs.c 424 21.2. File Tables.c 426 21.3. File Scanw4.asm 428 22. APPENDIX K. TABLE USED IN METHOD LZWB-2 430 1 1. INTRODUCTION 1.1. Statement of the Problem The library plays an important role in the academic community and the community at large. With advancement in electronic technology, it is desirable to use this technology in order to make the library more accessible to its users. It is desirable to have a library system where the user can dial up the library and access its information. The data sent should be a complete duplicate of the data in the library and not part of it. This research tries to look at one aspect of this system, namely, at the methods of compressing these data for storage and trans­ mission. 1.2. Features and Assumptions of the Solution The receiver in this electronic library system is assumed to originate his connection from a microcomputer. The microcomputer was chosen, instead of a dump terminal, because it provides the following necessary services to the system: a) The receiver has a processing power which is needed to decompress the received data. b) The receiver has storage facility. This allows the sender to send more than one page to a receiver.
Load more

Recommended publications
  • The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree
    International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 6 (2018) pp. 3296-3414 © Research India Publications. http://www.ripublication.com The Pillars of Lossless Compression Algorithms a Road Map and Genealogy Tree Evon Abu-Taieh, PhD Information System Technology Faculty, The University of Jordan, Aqaba, Jordan. Abstract tree is presented in the last section of the paper after presenting the 12 main compression algorithms each with a practical This paper presents the pillars of lossless compression example. algorithms, methods and techniques. The paper counted more than 40 compression algorithms. Although each algorithm is The paper first introduces Shannon–Fano code showing its an independent in its own right, still; these algorithms relation to Shannon (1948), Huffman coding (1952), FANO interrelate genealogically and chronologically. The paper then (1949), Run Length Encoding (1967), Peter's Version (1963), presents the genealogy tree suggested by researcher. The tree Enumerative Coding (1973), LIFO (1976), FiFO Pasco (1976), shows the interrelationships between the 40 algorithms. Also, Stream (1979), P-Based FIFO (1981). Two examples are to be the tree showed the chronological order the algorithms came to presented one for Shannon-Fano Code and the other is for life. The time relation shows the cooperation among the Arithmetic Coding. Next, Huffman code is to be presented scientific society and how the amended each other's work. The with simulation example and algorithm. The third is Lempel- paper presents the 12 pillars researched in this paper, and a Ziv-Welch (LZW) Algorithm which hatched more than 24 comparison table is to be developed.
    [Show full text]
  • The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on Iot Nodes in Smart Cities
    sensors Article The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities Ammar Nasif *, Zulaiha Ali Othman and Nor Samsiah Sani Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science & Technology, University Kebangsaan Malaysia, Bangi 43600, Malaysia; [email protected] (Z.A.O.); [email protected] (N.S.S.) * Correspondence: [email protected] Abstract: Networking is crucial for smart city projects nowadays, as it offers an environment where people and things are connected. This paper presents a chronology of factors on the development of smart cities, including IoT technologies as network infrastructure. Increasing IoT nodes leads to increasing data flow, which is a potential source of failure for IoT networks. The biggest challenge of IoT networks is that the IoT may have insufficient memory to handle all transaction data within the IoT network. We aim in this paper to propose a potential compression method for reducing IoT network data traffic. Therefore, we investigate various lossless compression algorithms, such as entropy or dictionary-based algorithms, and general compression methods to determine which algorithm or method adheres to the IoT specifications. Furthermore, this study conducts compression experiments using entropy (Huffman, Adaptive Huffman) and Dictionary (LZ77, LZ78) as well as five different types of datasets of the IoT data traffic. Though the above algorithms can alleviate the IoT data traffic, adaptive Huffman gave the best compression algorithm. Therefore, in this paper, Citation: Nasif, A.; Othman, Z.A.; we aim to propose a conceptual compression method for IoT data traffic by improving an adaptive Sani, N.S.
    [Show full text]
  • Data Compression in Solid State Storage
    Data Compression in Solid State Storage John Fryar [email protected] Santa Clara, CA August 2013 1 Acknowledgements This presentation would not have been possible without the counsel, hard work and graciousness of the following individuals and/or organizations: Raymond Savarda Sandgate Technologies Santa Clara, CA August 2013 2 Disclaimers The opinions expressed herein are those of the author and do not necessarily represent those of any other organization or individual unless specifically cited. A thorough attempt to acknowledge all sources has been made. That said, we’re all human… Santa Clara, CA August 2013 3 Learning Objectives At the conclusion of this tutorial the audience will have been exposed to: • The different types of Data Compression • Common Data Compression Algorithms • The Deflate/Inflate (GZIP/GUNZIP) algorithms in detail • Implementation Options (Software/Hardware) • Impacts of design parameters in Performance • SSD benefits and challenges • Resources for Further Study Santa Clara, CA August 2013 4 Agenda • Background, Definitions, & Context • Data Compression Overview • Data Compression Algorithm Survey • Deflate/Inflate (GZIP/GUNZIP) in depth • Software Implementations • HW Implementations • Tradeoffs & Advanced Topics • SSD Benefits and Challenges • Conclusions Santa Clara, CA August 2013 5 Definitions Item Description Comments Open A system which will compress Must strictly adhere to standards System data for use by other entities. on compress / decompress I.E. the compressed data will algorithms exit the system Interoperability among vendors mandated for Open Systems Closed A system which utilizes Can support a limited, optimized System compressed data internally but subset of standard. does not expose compressed Also allows custom algorithms data to the outside world No Interoperability req’d.
    [Show full text]
  • Better Adaptive Text Compression Scheme
    Journal of Education and Science Vol. 27, No.2, 2018, pp. 48-57 ISSN 1812-125X http://edusj.mosuljournals.com Better Adaptive Text Compression Scheme Duha Amir Sultan Dept. of Biology/ Education College of Girls/ University of Mosul تاريخ اﻻستﻻم 17/04/2012 تاريخ القبول 2012/11/07 الخﻻصة ظهرت طر ق عدي دي لكبس البيدااداتو حاةدي أش ر ددددددددددددددهر طديق اللرق ر ط اللريقد ال قترة أش قبل العال ي ش Ziv ح Lempel حالت رطلق عليها اسددددددددد LZ77و ث طورت طيق اللريق أش قبددل العددال يش Storer و Szymanski لتظهر طريقدد يدديدددي ط LZSS و حالت رعلددن اتددا رفضدل أقارا ساداسقاتها سعي رط طبقن عل اصدو ثيير حخدة ف طاتيش اللريقتيش حي ي ع اللرق اﻷخرى الت تنضوي تحن تصنيف اللرق اليدناأيك ي لكبس البيااات دت أاح ال لف أش اليادددار ل الي يش اد اط رطول تلابق بيش أةزط البيااا ت )الن ص ال شدددمر أادددبقاص حالن ص اليي سدديشددمر اللريق ال قترة ف طيا البحث تعت ي أبير البح ث سإت اطيشو أش الياددار ل الي يش ث أش الي يش ل اليادددددددددددددارو عل الرر رط طيق اللريق تحتان حقتا رطول اوعا أا و ﻻ راها رعل ن اتي ثبس اكبر للبيااات Abstract A data compression scheme suggested by Ziv and Lempel, LZ77, is applied to text compression. A slightly modified version suggested by Storer and Szymanski ,LZSS, is found to achieve compression ratios as good as most existing schemes for a wide range of texts. In these two methods and all other dynamic methods, a text file is searched from left to right to find the longest match between the lookahead buffer (the previously encoded text) and the characters to be encoded.
    [Show full text]
  • These Algorithmes De Compression Et
    ~~··f.. ..• L Laboratoire d'Informatique L Fondamentale de Ulle ~/ Numéro d'ordre: 1663 THESE' Nouveau Régime Présentée à L'UNIVERSITÉ DES SCIENCES ET TECHNOLOGIES DE LILLE Pour obtenir le titre de DOCTEUR en INFORMATIQUE par Éric RIVALS ALGORITHMES DE COMPRESSION ET , APPLICATIONS A L'ANALYSE DE SEQUENCES / / GENETIQUES Thèse soutenue le 10 janvier 1996, devant la Commission d'Examen: Président Alain HÉNAUT Université de Versailles-Saint-Quentin Rapporteurs Maxime CROCHEMORE Université de Marne la Vallée Alain HÉNAUT Université de Versailles-Saint-Quentin Examinateurs Didier ARQUÈS Université de Marne la Vallée MaxDAUCHET Université de Lille I Jean-Paul DELAHAYE Université de Lille I Christian GAUTIER Université de Lyon 1 Mireille RÉGNIER INRIA Rocquencourt UNIVERSITE DES SCIENCES ET TECHNOLOGIES DE Lll...LE POYENS HONORAIRES PE L'ANCIENNE FACULTE DES SCIENCES M. H. LEFEBVRE, M. PARREAU PROFESSEURS HONORAIRES DES ANCIENNES FACULTES DE QROIT ET SCIENCES ECONOMIQUES. PES SCIENCES ET QES LETTRES MM. ARNOULT, BONTE, BROCHARD, CHAPPELON, CHAUDRON, CORDONNIER, DECUYPER, DEHEUVELS, DEHORS, DION, FAUVEL. FLEURY, GERMAIN, GLACET, GONTIER, KOURGANOFF, LAMOTTE, LASSERRE. LELONG. LHOMME, LIEBAERT, MARTINOT-LAGARDE, MAZET, MICHEL, PEREZ, ROIG, ROSEAU. ROCELLE, SCHILTZ, SAVARD, ZAMANSKI, Mes BEAUJEU, LELONG. PROFESSEUR EMERITE M. A. LEBRUN ANCIENS PRESIDENTS DE L'UNIVERSITE DES SCIENCES ET TECHNIQUES DE LILLE MM. M. PARREAU, J. LOMBARD, M. !\UGEO!'. J. CORTOIS. A.DlJBRULLE PRESIDENT PE L'UNIVERSITE PES SCIENCES ET TECHNOLOGIES DE LILLE M. P. LOUIS PROFESSEURS • CLASSE EXCEPTIONNELLE M. CHAMLEY Hervé G~otechnique M. CONSTANT Eugène Elecrronique M. ESCAIG Bertrand Physique du solide M. FOURET René Physique du solide M. GABll..LARD Roben Elecrroniqu~ M. LABLACHE COMBlER Alain Chimie M. LOMBARD Jacques Sociologie M.
    [Show full text]
  • State of the Art and Future Trends in Data Reduction for High-Performance Computing
    DOI: 10.14529/jsfi200101 State of the Art and Future Trends in Data Reduction for High-Performance Computing Kira Duwe1, Jakob L¨uttgau1, Georgiana Mania2, Jannek Squar1, Anna Fuchs1, Michael Kuhn1, Eugen Betke3, Thomas Ludwig3 c The Authors 2020. This paper is published with open access at SuperFri.org Research into data reduction techniques has gained popularity in recent years as storage ca- pacity and performance become a growing concern. This survey paper provides an overview of leveraging points found in high-performance computing (HPC) systems and suitable mechanisms to reduce data volumes. We present the underlying theories and their application throughout the HPC stack and also discuss related hardware acceleration and reduction approaches. After intro- ducing relevant use-cases, an overview of modern lossless and lossy compression algorithms and their respective usage at the application and file system layer is given. In anticipation of their increasing relevance for adaptive and in situ approaches, dimensionality reduction techniques are summarized with a focus on non-linear feature extraction. Adaptive approaches and in situ com- pression algorithms and frameworks follow. The key stages and new opportunities to deduplication are covered next. An unconventional but promising method is recomputation, which is proposed at last. We conclude the survey with an outlook on future developments. Keywords: data reduction, lossless compression, lossy compression, dimensionality reduction, adaptive approaches, deduplication, in situ, recomputation, scientific data set. Introduction Breakthroughs in science are increasingly enabled by supercomputers and large scale data collection operations. Applications span the spectrum of scientific domains from fluid-dynamics in climate simulations and engineering, to particle simulations in astrophysics, quantum mechan- ics and molecular dynamics, to high-throughput computing in biology for genome sequencing and protein-folding.
    [Show full text]
  • Appendix a Information Theory
    Appendix A Information Theory This appendix serves as a brief introduction to information theory, the foundation of many techniques used in data compression. The two most important terms covered here are entropy and redundancy (see also Section 2.3 for an alternative discussion of these concepts). A.1 Information Theory Concepts We intuitively know what information is. We constantly receive and send information in the form of text, sound, and images. We also feel that information is an elusive nonmathematical quantity that cannot be precisely defined, captured, or measured. The standard dictionary definitions of information are (1) knowledge derived from study, experience, or instruction; (2) knowledge of a specific event or situation; intelligence; (3) a collection of facts or data; (4) the act of informing or the condition of being informed; communication of knowledge. Imagine a person who does not know what information is. Would those definitions make it clear to them? Unlikely. The importance of information theory is that it quantifies information. It shows how to measure information, so that we can answer the question “how much information is included in this piece of data?” with a precise number! Quantifying information is based on the observation that the information content of a message is equivalent to the amount of surprise in the message. If I tell you something that you already know (for example, “you and I work here”), I haven’t given you any information. If I tell you something new (for example, “we both received a raise”), I have given you some information. If I tell you something that really surprises you (for example, “only I received a raise”), I have given you more information, regardless of the number of words I have used, and of how you feel about my information.
    [Show full text]
  • Syllable-Based Compression
    Charles University Faculty of Mathematics and Physics Syllable-based Compression Ph.D. Thesis Mgr. Jan L´ansk´y Department of Software Engineering Malostransk´en´amˇest´ı25 Prague, Czech Republic Supervisor: Prof. RNDr. Jaroslav Pokorn´y, CSc. Consultant: RNDr. Michal Zemliˇcka,ˇ Ph.D. 2008 Abstract: Classic textual compression methods work over the alphabet of characters or alphabet of words. For languages with rich morphology as well as for compression of smaller files it can be advantageous to use an alphabet of syllables. For some compression methods like the ones based on Burrows- Wheeler transformation the syllable is a reasonable solution also for large files - even for languages having quite simple morphology. Although the main goal of our research is the compression over the alpha- bet of syllables, all implemented methods can compress also over the alpha- bet of words. For small files we use the LZW method and Huffman coding. These methods were improved by the use of initialized dictionary containing characteristic syllables specific for given language. For the compression of very large files we implemented the project XBW allowing combination of compression methods BWT, MTF, RLE, PPM, LZC, and LZSS. We have also tried to compress XML files that are not well-formed. When compressing over a large alphabet, it is necessary to compress also the used alphabet. We have proposed two solutions. The first one works well especially for small documents. We initialize the compression method with a set of characteristic syllables whereas other syllables are coded when necessary character by character. The second solution is intended for com- pression of larger documents.
    [Show full text]
  • Image Compression Outline
    Image compression Outline Introduc2on ▶ What is image compression? ▶ Data and informa.on, irrelevant informa.on and redundancy ▶ Compression models, lossless vs lossy, fidelity criteria ▶ Major file formats, e.g. GIF, PNG, TIFF, JPEG… Basic compression methods ▶ Run-length coding ▶ Huffman coding ▶ Arithme.c coding ▶ LZW coding ▶ Block-transform coding CREDITS: The slides for this lecture contain material from the course of Prof. Václav Hlaváč Elaborazione di segnali e immagini per bioinforma2ca Alessandro Daducci What is image compression? (1/3) Data compression refers to the process of reducing the amount of data required to represent a given quan.ty of informa.on ▶ Data and informa.on are not the same thing ▶ Data are the means by which informaBon is conveyed e.g. “Today is sunny” and “Oggi c’é il sole” convey the same informa2on, but using two different representa2ons Usage: save storage space and reduce transmission Bme No2ce that various amounts of data can be used to represent the same amount of informaBon ▶ Representa2ons may contain repeated informa2on e.g. “…Today, May 24th 2018, which is the day aRer May 23rd 2018, is sunny…” ▶ Representa2ons may contain irrelevant informa2on e.g. “…Today is sunny, and the Sun is the star at the center of the solar system…” ▶ These representa2ons contain redundant data Elaborazione di segnali e immagini per bioinforma2ca Alessandro Daducci What is image compression? (2/3) In case of images, we want to reduce the amount of data needed to represent an image “without affec.ng its quality” Original (1.9MB)
    [Show full text]
  • Proceedings of the Dateso 2005 Workshop Group Ampho
    Dept. of Computer Science & Engineering, Czech Technical University, Prague Department of Computer Science, VSBˇ – Technical University of Ostrava Department of Software Engineering, Charles University, Prague Czech Society for Cybernetics and Informatics, Workgroup on Computer Science & Informatics, Prague Proceedings of the Dateso 2005 Workshop Databases, Texts Specifications,200 and Objects5 http://www.cs.vsb.cz/dateso/2005/ Group Amphor Sc. A R G AMPHORA RESEARCH GROUP April 13 – 15, 2005 Desn´a– Cern´aˇ R´ıˇckaˇ DATESO 2005 c K. Richta, V. Sn´aˇsel,J. Pokorn´y,editors This work is subject to copyright. All rights reserved. Reproduction or publication of this material, even partial, is allowed only with the editors’ permission. Technical editor: Pavel Moravec, [email protected] Faculty of Electrical Engineering and Computer Science, VSBˇ – Technical University of Ostrava Page count: 150 Impression: 150 Edition: 1st First published: 2005 This proceedings was typeset by PDFLATEX. Cover design by Tom´aˇsSkopal ([email protected]) and Pavel Moravec ([email protected]). Printed and bound in Ostrava, Czech Republic by TiskServis Jiˇr´ıPustina. Published by Faculty of Electrical Engineering, Czech Technical University in Prague Preface DATESO 2005, the international workshop on current trends on Databases, In- formation Retrieval, Algebraic Specification and Object Oriented Programming, was held on April 13 – 15, 2005 in Desn´a– Cern´aˇ R´ıˇcka.ˇ This was the 5th annual workshop organized by FEL CVUTˇ Praha, Department of Computer Science and Engineering, MFF UK Praha, Department of Software Engineering, and VSB-Technicalˇ University Ostrava, Department of Computer Science. The DATESO aims for strengthening the connection between this various areas of informatics.
    [Show full text]
  • Real-Time Lossless Compression of Soc Trace Data
    Jing Zhang Real-Time Lossless Compression of SoC Trace Data Trace SoC of Compression Lossless Real-Time Master’s Thesis Real-Time Lossless Compression of SoC Trace Data Jing Zhang Series of Master’s theses Department of Electrical and Information Technology LU/LTH-EIT 2015-478 Department of Electrical and Information Technology, http://www.eit.lth.se Faculty of Engineering, LTH, Lund University, December 2015. Master’s Thesis Real-Time Lossless Compression of SoC Trace Data Jing Zhang Department of Electrical and Information Technology Faculty of Engineering, LTH, Lund University SE-221 00 Lund, Sweden December 2015 1 Abstract Nowadays, with the increasing complexity of System-on-Chip (SoC), traditional debugging approaches are not enough in multi-core architecture systems. Hardware tracing becomes necessary for performance analysis in these systems. The problem is that the size of collected trace data through hardware- based tracing techniques is usually extremely large due to the increasing complexity of System-on-Chips. Hence on-chip trace compression performed in hardware is needed to reduce the amount of transferred or stored data. In this dissertation, the feasibility of different types of lossless data compression algorithms in hardware implementation are investigated and examined. A lossless data compression algorithm LZ77 is selected, analyzed, and optimized to Nexus traces data. In order to meet the hardware cost and compression performances requirements for the real-time compression, an optimized LZ77 compression algorithm is proposed based on the characteristics of Nexus trace data. This thesis presents a hardware implementation of LZ77 encoder described in Very High Speed Integrated Circuit Hardware Description Language (VHDL).
    [Show full text]
  • Introduction to Data Compression
    Introduction to Data Compression Guillaume Tochon [email protected] LRDE, EPITA Guillaume Tochon (LRDE) CODO - Introduction 1 / 9 Data compression is the process of modifying, encoding or converting the bits structure of some input data in order to reduce the storage size of this data. Input data, Compression algorithm Compressed data, large size smaller size Data decompression is the inverse process, namely restoring the compressed data back to its original form (or a close one) for usage. talking about the compression Talking about data compression , and the decompression algorithm. Data compression: whatizit? Guillaume Tochon (LRDE) CODO - Introduction 2 / 9 Input data, Compression algorithm Compressed data, large size smaller size Data decompression is the inverse process, namely restoring the compressed data back to its original form (or a close one) for usage. talking about the compression Talking about data compression , and the decompression algorithm. Data compression: whatizit? Data compression is the process of modifying, encoding or converting the bits structure of some input data in order to reduce the storage size of this data. Guillaume Tochon (LRDE) CODO - Introduction 2 / 9 Input data, Compression algorithm Compressed data, large size smaller size talking about the compression Talking about data compression , and the decompression algorithm. Data compression: whatizit? Data compression is the process of modifying, encoding or converting the bits structure of some input data in order to reduce the storage size of this data. Data decompression is the inverse process, namely restoring the compressed data back to its original form (or a close one) for usage. Guillaume Tochon (LRDE) CODO - Introduction 2 / 9 Input data, Compression algorithm Compressed data, large size smaller size Data compression: whatizit? Data compression is the process of modifying, encoding or converting the bits structure of some input data in order to reduce the storage size of this data.
    [Show full text]