Better Adaptive Text Compression Scheme
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Melinda's Marks Merit Main Mantle SYDNEY STRIDERS
SYDNEY STRIDERS ROAD RUNNERS’ CLUB AUSTRALIA EDITION No 108 MAY - AUGUST 2009 Melinda’s marks merit main mantle This is proving a “best-so- she attained through far” year for Melinda. To swimming conflicted with date she has the fastest her transition to running. time in Australia over 3000m. With a smart 2nd Like all top runners she at the State Open 5000m does well over 100k a champs, followed by a week in training, win at the State Open 10k consisting of a variety of Road Champs, another sessions: steady pace, win at the Herald Half medium pace, long slow which doubles as the runs, track work, fartlek, State Half Champs and a hills, gym work and win at the State Cross swimming! country Champs, our Melinda is looking like Springs under her shoes give hot property. Melinda extra lift Melinda began her sports Continued Page 3 career as a swimmer. By 9 years of age she was representing her club at State level. She held numerous records for INSIDE BLISTER 108 Breaststroke and Lisa facing racing pacing Butterfly. Her switch to running came after the McKinney makes most of death of her favourite marvellous mud moment Coach and because she Weather woe means Mo wasn’t growing as big as can’t crow though not slow! her fellow competitors. She managed some pretty fast times at inter-schools Brent takes tumble at Trevi champs and Cross Country before making an impression in the Open category where she has Champion Charles cheered steadily improved. by chance & chase challenge N’Lotsa Uthastuff Melinda credits her swimming background for endurance -
The Basic Principles of Data Compression
The Basic Principles of Data Compression Author: Conrad Chung, 2BrightSparks Introduction Internet users who download or upload files from/to the web, or use email to send or receive attachments will most likely have encountered files in compressed format. In this topic we will cover how compression works, the advantages and disadvantages of compression, as well as types of compression. What is Compression? Compression is the process of encoding data more efficiently to achieve a reduction in file size. One type of compression available is referred to as lossless compression. This means the compressed file will be restored exactly to its original state with no loss of data during the decompression process. This is essential to data compression as the file would be corrupted and unusable should data be lost. Another compression category which will not be covered in this article is “lossy” compression often used in multimedia files for music and images and where data is discarded. Lossless compression algorithms use statistic modeling techniques to reduce repetitive information in a file. Some of the methods may include removal of spacing characters, representing a string of repeated characters with a single character or replacing recurring characters with smaller bit sequences. Advantages/Disadvantages of Compression Compression of files offer many advantages. When compressed, the quantity of bits used to store the information is reduced. Files that are smaller in size will result in shorter transmission times when they are transferred on the Internet. Compressed files also take up less storage space. File compression can zip up several small files into a single file for more convenient email transmission. -
Data Compression for Remote Laboratories
Paper—Data Compression for Remote Laboratories Data Compression for Remote Laboratories https://doi.org/10.3991/ijim.v11i4.6743 Olawale B. Akinwale Obafemi Awolowo University, Ile-Ife, Nigeria [email protected] Lawrence O. Kehinde Obafemi Awolowo University, Ile-Ife, Nigeria [email protected] Abstract—Remote laboratories on mobile phones have been around for a few years now. This has greatly improved accessibility of these remote labs to students who cannot afford computers but have mobile phones. When money is a factor however (as is often the case with those who can’t afford a computer), the cost of use of these remote laboratories should be minimized. This work ad- dressed this issue of minimizing the cost of use of the remote lab by making use of data compression for data sent between the user and remote lab. Keywords—Data compression; Lempel-Ziv-Welch; remote laboratory; Web- Sockets 1 Introduction The mobile phone is now the de facto computational device of choice. They are quite powerful, connected devices which are almost always with us. This is so much so that about every important online service has a mobile version or at least a version compatible with mobile phones and tablets (for this paper we would adopt the phrase mobile device to represent mobile phones and tablets). This has caused online labora- tory developers to develop online laboratory solutions which are mobile device- friendly [1, 2, 3, 4, 5, 6, 7, 8]. One of the main benefits of online laboratories is the possibility of using fewer re- sources to serve more people i.e. -
Optimal Parsing for Dictionary Text Compression Alessio Langiu
Optimal Parsing for dictionary text compression Alessio Langiu To cite this version: Alessio Langiu. Optimal Parsing for dictionary text compression. Other [cs.OH]. Université Paris-Est, 2012. English. NNT : 2012PEST1091. tel-00804215 HAL Id: tel-00804215 https://tel.archives-ouvertes.fr/tel-00804215 Submitted on 25 Mar 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. UniversitadegliStudidiPalermo` Dipartimento di Matematica e Applicazioni Dottorato di Ricerca in Matematica e Informatica XXIII◦ Ciclo - S.S.D. Inf/01 UniversiteParis-Est´ Ecole´ doctorale MSTIC Th´ese de doctorat en Informatique Optimal Parsing for Dictionary Text Compression Author: LANGIU Alessio Ph.D. Thesis in Computer Science April 3, 2012 PhD Commission: Thesis Directors: Prof. CROCHEMORE Maxime University of Paris-Est Prof. RESTIVO Antonio University of Palermo Examiners: Prof. ILIOPOULOS Costas King’s College London Prof. LECROQ Thierry University of Rouen Prof. MIGNOSI Filippo University of L’Aquila Referees: Prof. GROSSI Roberto University of Pisa Prof. ILIOPOULOS Costas King’s College London Prof. LECROQ Thierry University of Rouen ii Summary Dictionary-based compression algorithms include a parsing strategy to transform the input text into a sequence of dictionary phrases. Given a text, such process usually is not unique and, for compression purposes, it makes sense to find one of the possible parsing that minimize the final compres- sion ratio. -
Acronis Revive 2019
Acronis Revive 2019 Table of contents 1 Introduction to Acronis Revive 2019 .................................................................................3 1.1 Acronis Revive 2019 Features .................................................................................................... 3 1.2 System Requirements and Installation Notes ........................................................................... 4 1.3 Technical Support ...................................................................................................................... 5 2 Data Recovery Using Acronis Revive 2019 .........................................................................6 2.1 Recover Lost Files from Existing Logical Disks ........................................................................... 7 2.1.1 Searching for a File ........................................................................................................................................ 16 2.1.2 Finding Previous File Versions ...................................................................................................................... 18 2.1.3 File Masks....................................................................................................................................................... 19 2.1.4 Regular Expressions ...................................................................................................................................... 20 2.1.5 Previewing Files ............................................................................................................................................ -
Archive and Compressed [Edit]
Archive and compressed [edit] Main article: List of archive formats • .?Q? – files compressed by the SQ program • 7z – 7-Zip compressed file • AAC – Advanced Audio Coding • ace – ACE compressed file • ALZ – ALZip compressed file • APK – Applications installable on Android • AT3 – Sony's UMD Data compression • .bke – BackupEarth.com Data compression • ARC • ARJ – ARJ compressed file • BA – Scifer Archive (.ba), Scifer External Archive Type • big – Special file compression format used by Electronic Arts for compressing the data for many of EA's games • BIK (.bik) – Bink Video file. A video compression system developed by RAD Game Tools • BKF (.bkf) – Microsoft backup created by NTBACKUP.EXE • bzip2 – (.bz2) • bld - Skyscraper Simulator Building • c4 – JEDMICS image files, a DOD system • cab – Microsoft Cabinet • cals – JEDMICS image files, a DOD system • cpt/sea – Compact Pro (Macintosh) • DAA – Closed-format, Windows-only compressed disk image • deb – Debian Linux install package • DMG – an Apple compressed/encrypted format • DDZ – a file which can only be used by the "daydreamer engine" created by "fever-dreamer", a program similar to RAGS, it's mainly used to make somewhat short games. • DPE – Package of AVE documents made with Aquafadas digital publishing tools. • EEA – An encrypted CAB, ostensibly for protecting email attachments • .egg – Alzip Egg Edition compressed file • EGT (.egt) – EGT Universal Document also used to create compressed cabinet files replaces .ecab • ECAB (.ECAB, .ezip) – EGT Compressed Folder used in advanced systems to compress entire system folders, replaced by EGT Universal Document • ESS (.ess) – EGT SmartSense File, detects files compressed using the EGT compression system. • GHO (.gho, .ghs) – Norton Ghost • gzip (.gz) – Compressed file • IPG (.ipg) – Format in which Apple Inc. -
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. -
Vital and Health Statistics; Series 10, No. 40
NATIONAL CENTER For HEALTH STATISTICS VITAL and HEALTH STATISTICS DATA FROM THE NATIONAL HEALTH SURVEY PROPEfW OFTHE PUBLICATIONSBRANCH EOIT(IRIAL LIBRARY characteristics of Persons With Diabetes United States =July 1964-June 1965 Statistics on the prevalence of diabetes as reported in inter- views, the socioeconomic and health characteristics of dia betics, their disabi Iity from diobetes and from all chronic con- ditions, and type of medical and hame care. Based on data collected in household interviews during the period July 1964- June 1965. Washington, D.C. Ocfober 1967 U.S. DEPARTMENT OF HEALTH, EDUCATION, AND WELFARE Public Health Service John W. Gardner William H. Stewart Secretary Surgeon General ~ublic Health Service Publication No. 10@O-Series IO-NO. 40 For sale by the Superintendent of Documents, U.S. flovernment F’rinting Office. \\’ashington, D.C. , !20402 - Price 35 cents NATIONAL CENTER FOR HEALTH STATISTICS FORREST E. LIND13R, PH. D., DirectoT THEODORE D. WOOLSEY, Deputy Dzrector OSWALD K. SAGEN, PH. D., Assistant Director /or State Relatzons WALT R. SIMMONS,M.A., Statzstt cal A dvz sor PHILIP S. LAWRENCE, .SC. D. , Pbzning o//zcer ALICE M. WATERHOUSE, M.D., MedrcaI Consultant JAMES E. KELLY, D.D.S., Dental Advzsor LOUIS R. STOLCIS, M.A., Executzve O//7cer DONALD GREEN, in/ormation O//icer DIVISION OF HEALTH INTERVIEW STATISTICS ELIJAH L. WHITE, AM., Dz~ecfo7 ROBERT R. FUCHSBERG, Chief Survey Methods Branch GERALDINE A. GLEESON, Chic/, Analysis and Reports Branch COOPERATION OF THE BUREAU OF THE CENSUS Under the legislation establishing the National Health Survey, the Public Health Service is authorized to use, insofar as possible, the services or facilities of other Federal, State, or private agencies. -
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. -
Data Compression for Remote Laboratories
CORE Metadata, citation and similar papers at core.ac.uk Provided by Online-Journals.org (International Association of Online Engineering) Paper—Data Compression for Remote Laboratories Data Compression for Remote Laboratories https://doi.org/10.3991/ijim.v11i4.6743 Olawale B. Akinwale Obafemi Awolowo University, Ile-Ife, Nigeria [email protected] Lawrence O. Kehinde Obafemi Awolowo University, Ile-Ife, Nigeria [email protected] Abstract—Remote laboratories on mobile phones have been around for a few years now. This has greatly improved accessibility of these remote labs to students who cannot afford computers but have mobile phones. When money is a factor however (as is often the case with those who can’t afford a computer), the cost of use of these remote laboratories should be minimized. This work ad- dressed this issue of minimizing the cost of use of the remote lab by making use of data compression for data sent between the user and remote lab. Keywords—Data compression; Lempel-Ziv-Welch; remote laboratory; Web- Sockets 1 Introduction The mobile phone is now the de facto computational device of choice. They are quite powerful, connected devices which are almost always with us. This is so much so that about every important online service has a mobile version or at least a version compatible with mobile phones and tablets (for this paper we would adopt the phrase mobile device to represent mobile phones and tablets). This has caused online labora- tory developers to develop online laboratory solutions which are mobile device- friendly [1, 2, 3, 4, 5, 6, 7, 8]. -
(12) United States Patent (10) Patent No.: US 8,013,862 B2 Mathe (45) Date of Patent: Sep
US0080 13862B2 (12) United States Patent (10) Patent No.: US 8,013,862 B2 Mathe (45) Date of Patent: Sep. 6, 2011 (54) TEXTURE CODEC 2004/O151372 A1* 8, 2004 Reshetov et al. ............. 382,166 2004/0227552 Al 11/2004 Markovic et al. 327/215 (75) Inventor: Zsolt Mathe, Issaquah, WA (US) 2005/0206784 A1* 9, 2005 Li et al. ......... ... 348/441 2006, OO17722 A1 1/2006 Hong et al. ... 345,419 2006/0092.169 A1 5, 2006 Wetzel ....... 345,582 (73) Assignee: Microsoft Corporation, Redmond, WA 2006/0268990 A1* 11, 2006 Lin et al. .... 375,240.24 (US) 2007/0070.083 A1* 3, 2007 Fouladi et al. ................ 345 604 2007, 0140554 A1* 6, 2007 Strom et al. .. ... 382,166 (*) Notice: Subject to any disclaimer, the term of this 2007/0172120 A1, 7/2007 Roimela et al... ... 382,166 patent is extended or adjusted under 35 2007/024208.6 A1* 10/2007 Tsujimoto et al. ............ 345,632 U.S.C. 154(b) by 890 days. OTHER PUBLICATIONS (21) Appl. No.: 11/941,337 DXTex Tool, (C) 2007. http://msdn.microsoft.com/archive, 3 pages, date unknown. (22) Filed: Nov. 16, 2007 van Waveren, J.M.P., “Real-Time DXT Compression'. May 20, 2006, http://cache-www.intel.com, 43 pages. (65) Prior Publication Data van Waveren, J.M.P., “Real-Time Texture Streaming & Decompres sion”, Nov. 11, 2006, http://softwarecommunity.intel.com, 57 pages. US 2009/O128576A1 May 21, 2009 * cited by examiner (51) Int. Cl. G09G 5/00 (2006.01) Primary Examiner — M Good Johnson (52) U.S. Cl. -
Compression: Putting the Squeeze on Storage
Compression: Putting the Squeeze on Storage Live Webcast September 2, 2020 11:00 am PT 1 | ©2020 Storage Networking Industry Association. All Rights Reserved. Today’s Presenters Ilker Cebeli John Kim Brian Will Moderator Chair, SNIA Networking Storage Forum Intel® QuickAssist Technology Samsung NVIDIA Software Architect Intel 2 | ©2020 Storage Networking Industry Association. All Rights Reserved. SNIA-At-A-Glance 3 3 | ©2020 Storage Networking Industry Association. All Rights Reserved. NSF Technologies 4 4 | ©2020 Storage Networking Industry Association. All Rights Reserved. SNIA Legal Notice § The material contained in this presentation is copyrighted by the SNIA unless otherwise noted. § Member companies and individual members may use this material in presentations and literature under the following conditions: § Any slide or slides used must be reproduced in their entirety without modification § The SNIA must be acknowledged as the source of any material used in the body of any document containing material from these presentations. § This presentation is a project of the SNIA. § Neither the author nor the presenter is an attorney and nothing in this presentation is intended to be, or should be construed as legal advice or an opinion of counsel. If you need legal advice or a legal opinion please contact your attorney. § The information presented herein represents the author's personal opinion and current understanding of the relevant issues involved. The author, the presenter, and the SNIA do not assume any responsibility or liability for damages arising out of any reliance on or use of this information. NO WARRANTIES, EXPRESS OR IMPLIED. USE AT YOUR OWN RISK. 5 | ©2020 Storage Networking Industry Association.