Hardware Compression in Storage and Network Attached Storage
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A Survey Paper on Different Speech Compression Techniques
Vol-2 Issue-5 2016 IJARIIE-ISSN (O)-2395-4396 A Survey Paper on Different Speech Compression Techniques Kanawade Pramila.R1, Prof. Gundal Shital.S2 1 M.E. Electronics, Department of Electronics Engineering, Amrutvahini College of Engineering, Sangamner, Maharashtra, India. 2 HOD in Electronics Department, Department of Electronics Engineering , Amrutvahini College of Engineering, Sangamner, Maharashtra, India. ABSTRACT This paper describes the different types of speech compression techniques. Speech compression can be divided into two main types such as lossless and lossy compression. This survey paper has been written with the help of different types of Waveform-based speech compression, Parametric-based speech compression, Hybrid based speech compression etc. Compression is nothing but reducing size of data with considering memory size. Speech compression means voiced signal compress for different application such as high quality database of speech signals, multimedia applications, music database and internet applications. Today speech compression is very useful in our life. The main purpose or aim of speech compression is to compress any type of audio that is transfer over the communication channel, because of the limited channel bandwidth and data storage capacity and low bit rate. The use of lossless and lossy techniques for speech compression means that reduced the numbers of bits in the original information. By the use of lossless data compression there is no loss in the original information but while using lossy data compression technique some numbers of bits are loss. Keyword: - Bit rate, Compression, Waveform-based speech compression, Parametric-based speech compression, Hybrid based speech compression. 1. INTRODUCTION -1 Speech compression is use in the encoding system. -
Arxiv:2004.10531V1 [Cs.OH] 8 Apr 2020
ROOT I/O compression improvements for HEP analysis Oksana Shadura1;∗ Brian Paul Bockelman2;∗∗ Philippe Canal3;∗∗∗ Danilo Piparo4;∗∗∗∗ and Zhe Zhang1;y 1University of Nebraska-Lincoln, 1400 R St, Lincoln, NE 68588, United States 2Morgridge Institute for Research, 330 N Orchard St, Madison, WI 53715, United States 3Fermilab, Kirk Road and Pine St, Batavia, IL 60510, United States 4CERN, Meyrin 1211, Geneve, Switzerland Abstract. We overview recent changes in the ROOT I/O system, increasing per- formance and enhancing it and improving its interaction with other data analy- sis ecosystems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly to improve experiment’s software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically in- creased during the LHC era. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly: there are sig- nificant trade-offs between the increased CPU cost for reading and writing files and the reduce storage space. 1 Introduction In the past years LHC experiments are commissioned and now manages about an exabyte of storage for analysis purposes, approximately half of which is used for archival purposes, and half is used for traditional disk storage. Meanwhile for HL-LHC storage requirements per year are expected to be increased by factor 10 [1]. arXiv:2004.10531v1 [cs.OH] 8 Apr 2020 Looking at these predictions, we would like to state that storage will remain one of the major cost drivers and at the same time the bottlenecks for HEP computing. -
PKZIP MVS User's Guide
PKZIP for MVS MVS/ESA, OS/390, & z/OS User’s Guide PKMU-V5R5000 PKWARE, Inc. PKWARE, Inc. 9009 Springboro Pike Miamisburg, Ohio 45342 Sales: 937-847-2374 Support: 937-847-2687 Fax: 937-847-2375 Web Site: http://www.pkzip.com Sales - E-Mail: [email protected] Support - http://www.pkzip.com/support 5.5 Edition (2003) PKZIP for MVS™, PKZIP for OS/400™, PKZIP for VSE™, PKZIP for UNIX™, and PKZIP for Windows™ are just a few of the many members in the PKZIP® family. PKWARE, Inc. would like to thank all the individuals and companies -- including our customers, resellers, distributors, and technology partners -- who have helped make PKZIP® the industry standard for Trusted ZIP solutions. PKZIP® enables our customers to efficiently and securely transmit and store information across systems of all sizes, ranging from desktops to mainframes. This edition applies to the following PKWARE of Ohio, Inc. licensed program: PKZIP for MVS™ (Version 5, Release 5, 2003) PKZIP(R) is a registered trademark of PKWARE(R) Inc. Other product names mentioned in this manual may be a trademark or registered trademarks of their respective companies and are hereby acknowledged. Any reference to licensed programs or other material, belonging to any company, is not intended to state or imply that such programs or material are available or may be used. The copyright in this work is owned by PKWARE of Ohio, Inc., and the document is issued in confidence for the purpose only for which it is supplied. It must not be reproduced in whole or in part or used for tendering purposes except under an agreement or with the consent in writing of PKWARE of Ohio, Inc., and then only on condition that this notice is included in any such reproduction. -
Implementing Compression on Distributed Time Series Database
Implementing compression on distributed time series database Michael Burman School of Science Thesis submitted for examination for the degree of Master of Science in Technology. Espoo 05.11.2017 Supervisor Prof. Kari Smolander Advisor Mgr. Jiri Kremser Aalto University, P.O. BOX 11000, 00076 AALTO www.aalto.fi Abstract of the master’s thesis Author Michael Burman Title Implementing compression on distributed time series database Degree programme Major Computer Science Code of major SCI3042 Supervisor Prof. Kari Smolander Advisor Mgr. Jiri Kremser Date 05.11.2017 Number of pages 70+4 Language English Abstract Rise of microservices and distributed applications in containerized deployments are putting increasing amount of burden to the monitoring systems. They push the storage requirements to provide suitable performance for large queries. In this paper we present the changes we made to our distributed time series database, Hawkular-Metrics, and how it stores data more effectively in the Cassandra. We show that using our methods provides significant space savings ranging from 50 to 95% reduction in storage usage, while reducing the query times by over 90% compared to the nominal approach when using Cassandra. We also provide our unique algorithm modified from Gorilla compression algorithm that we use in our solution, which provides almost three times the throughput in compression with equal compression ratio. Keywords timeseries compression performance storage Aalto-yliopisto, PL 11000, 00076 AALTO www.aalto.fi Diplomityön tiivistelmä Tekijä Michael Burman Työn nimi Pakkausmenetelmät hajautetussa aikasarjatietokannassa Koulutusohjelma Pääaine Computer Science Pääaineen koodi SCI3042 Työn valvoja ja ohjaaja Prof. Kari Smolander Päivämäärä 05.11.2017 Sivumäärä 70+4 Kieli Englanti Tiivistelmä Hajautettujen järjestelmien yleistyminen on aiheuttanut valvontajärjestelmissä tiedon määrän kasvua, sillä aikasarjojen määrä on kasvanut ja niihin talletetaan useammin tietoa. -
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. -
Incompressibility and Lossless Data Compression: an Approach By
Incompressibility and Lossless Data Compression: An Approach by Pattern Discovery Incompresibilidad y compresión de datos sin pérdidas: Un acercamiento con descubrimiento de patrones Oscar Herrera Alcántara and Francisco Javier Zaragoza Martínez Universidad Autónoma Metropolitana Unidad Azcapotzalco Departamento de Sistemas Av. San Pablo No. 180, Col. Reynosa Tamaulipas Del. Azcapotzalco, 02200, Mexico City, Mexico Tel. 53 18 95 32, Fax 53 94 45 34 [email protected], [email protected] Article received on July 14, 2008; accepted on April 03, 2009 Abstract We present a novel method for lossless data compression that aims to get a different performance to those proposed in the last decades to tackle the underlying volume of data of the Information and Multimedia Ages. These latter methods are called entropic or classic because they are based on the Classic Information Theory of Claude E. Shannon and include Huffman [8], Arithmetic [14], Lempel-Ziv [15], Burrows Wheeler (BWT) [4], Move To Front (MTF) [3] and Prediction by Partial Matching (PPM) [5] techniques. We review the Incompressibility Theorem and its relation with classic methods and our method based on discovering symbol patterns called metasymbols. Experimental results allow us to propose metasymbolic compression as a tool for multimedia compression, sequence analysis and unsupervised clustering. Keywords: Incompressibility, Data Compression, Information Theory, Pattern Discovery, Clustering. Resumen Presentamos un método novedoso para compresión de datos sin pérdidas que tiene por objetivo principal lograr un desempeño distinto a los propuestos en las últimas décadas para tratar con los volúmenes de datos propios de la Era de la Información y la Era Multimedia. -
Lossless Compression of Audio Data
CHAPTER 12 Lossless Compression of Audio Data ROBERT C. MAHER OVERVIEW Lossless data compression of digital audio signals is useful when it is necessary to minimize the storage space or transmission bandwidth of audio data while still maintaining archival quality. Available techniques for lossless audio compression, or lossless audio packing, generally employ an adaptive waveform predictor with a variable-rate entropy coding of the residual, such as Huffman or Golomb-Rice coding. The amount of data compression can vary considerably from one audio waveform to another, but ratios of less than 3 are typical. Several freeware, shareware, and proprietary commercial lossless audio packing programs are available. 12.1 INTRODUCTION The Internet is increasingly being used as a means to deliver audio content to end-users for en tertainment, education, and commerce. It is clearly advantageous to minimize the time required to download an audio data file and the storage capacity required to hold it. Moreover, the expec tations of end-users with regard to signal quality, number of audio channels, meta-data such as song lyrics, and similar additional features provide incentives to compress the audio data. 12.1.1 Background In the past decade there have been significant breakthroughs in audio data compression using lossy perceptual coding [1]. These techniques lower the bit rate required to represent the signal by establishing perceptual error criteria, meaning that a model of human hearing perception is Copyright 2003. Elsevier Science (USA). 255 AU rights reserved. 256 PART III / APPLICATIONS used to guide the elimination of excess bits that can be either reconstructed (redundancy in the signal) orignored (inaudible components in the signal). -
Zlib Home Site
zlib Home Site http://zlib.net/ A Massively Spiffy Yet Delicately Unobtrusive Compression Library (Also Free, Not to Mention Unencumbered by Patents) (Not Related to the Linux zlibc Compressing File-I/O Library) Welcome to the zlib home page, web pages originally created by Greg Roelofs and maintained by Mark Adler . If this page seems suspiciously similar to the PNG Home Page , rest assured that the similarity is completely coincidental. No, really. zlib was written by Jean-loup Gailly (compression) and Mark Adler (decompression). Current release: zlib 1.2.6 January 29, 2012 Version 1.2.6 has many changes over 1.2.5, including these improvements: gzread() can now read a file that is being written concurrently gzgetc() is now a macro for increased speed Added a 'T' option to gzopen() for transparent writing (no compression) Added deflatePending() to return the amount of pending output Allow deflateSetDictionary() and inflateSetDictionary() at any time in raw mode deflatePrime() can now insert bits in the middle of the stream ./configure now creates a configure.log file with all of the results Added a ./configure --solo option to compile zlib with no dependency on any libraries Fixed a problem with large file support macros Fixed a bug in contrib/puff Many portability improvements You can also look at the complete Change Log . Version 1.2.5 fixes bugs in gzseek() and gzeof() that were present in version 1.2.4 (March 2010). All users are encouraged to upgrade immediately. Version 1.2.4 has many changes over 1.2.3, including these improvements: -
PKZIP®/Securezip® for I5/OS® User's Guide
PKZIP®/SecureZIP® ® for i5/OS User’s Guide SZIU- V10R05M02 PKWARE, Inc. PKWARE, Inc. 648 N Plankinton Avenue, Suite 220 Milwaukee, WI 53203 Main office: 888-4PKWARE (888-475-9273) Sales: 937-847-2374 (888-4PKWARE / 888-475-9273) Sales: Email: [email protected] Support: 937-847-2687 Support: http://www.pkware.com/support/system-i Fax: 414-289-9789 Web Site: http://www.pkware.com 10.0.5 Edition (2010) SecureZIP for z/OS, PKZIP for z/OS, SecureZIP for i5/OS®, PKZIP for i5/OS, SecureZIP for UNIX, and SecureZIP for Windows are just a few of the members of the PKZIP family. PKWARE Inc. would like to thank all the individuals and companies—including our customers, resellers, distributors, and technology partners—who have helped make PKZIP the industry standard for trusted ZIP solutions. PKZIP enables our customers to efficiently and securely transmit and store information across systems of all sizes, ranging from desktops to mainframes. This edition applies to the following PKWARE Inc. licensed programs: PKZIP for i5/OS (Version 10, Release 0.5, 2010) SecureZIP for i5/OS (Version 10, Release 0.5, 2010) SecureZIP Partner for i5/OS (Version 10, Release 0.5, 2010) PKWARE, PKZIP and SecureZIP are registered trademarks of PKWARE, Inc. z/OS, i5/OS, zSeries, and iSeries are registered trademarks of IBM Corporation. Other product names mentioned in this manual may be trademarks or registered trademarks of their respective companies and are hereby acknowledged. This product includes software developed by the OpenSSL Project for use in the OpenSSL Toolkit (http://www.openssl.org/) Any reference to licensed programs or other material, belonging to any company, is not intended to state or imply that such programs or material are available or may be used. -
Comparison of Image Compressions: Analog Transformations P
Proceedings Comparison of Image Compressions: Analog † Transformations P Jose Balsa P CITIC Research Center, Universidade da Coruña (University of A Coruña), 15071 A Coruña, Spain; [email protected] † Presented at the 3rd XoveTIC Conference, A Coruña, Spain, 8–9 October 2020. Published: 21 August 2020 Abstract: A comparison between the four most used transforms, the discrete Fourier transform (DFT), discrete cosine transform (DCT), the Walsh–Hadamard transform (WHT) and the Haar- wavelet transform (DWT), for the transmission of analog images, varying their compression and comparing their quality, is presented. Additionally, performance tests are done for different levels of white Gaussian additive noise. Keywords: analog image transformation; analog image compression; analog image quality 1. Introduction Digitized image coding systems employ reversible mathematical transformations. These transformations change values and function domains in order to rearrange information in a way that condenses information important to human vision [1]. In the new domain, it is possible to filter out relevant information and discard information that is irrelevant or of lesser importance for image quality [2]. Both digital and analog systems use the same transformations in source coding. Some examples of digital systems that employ these transformations are JPEG, M-JPEG, JPEG2000, MPEG- 1, 2, 3 and 4, DV and HDV, among others. Although digital systems after transformation and filtering make use of digital lossless compression techniques, such as Huffman. In this work, we aim to make a comparison of the most commonly used transformations in state- of-the-art image compression systems. Typically, the transformations used to compress analog images work either on the entire image or on regions of the image. -
A Machine Learning Perspective on Predictive Coding with PAQ
A Machine Learning Perspective on Predictive Coding with PAQ Byron Knoll & Nando de Freitas University of British Columbia Vancouver, Canada fknoll,[email protected] August 17, 2011 Abstract PAQ8 is an open source lossless data compression algorithm that currently achieves the best compression rates on many benchmarks. This report presents a detailed description of PAQ8 from a statistical machine learning perspective. It shows that it is possible to understand some of the modules of PAQ8 and use this understanding to improve the method. However, intuitive statistical explanations of the behavior of other modules remain elusive. We hope the description in this report will be a starting point for discussions that will increase our understanding, lead to improvements to PAQ8, and facilitate a transfer of knowledge from PAQ8 to other machine learning methods, such a recurrent neural networks and stochastic memoizers. Finally, the report presents a broad range of new applications of PAQ to machine learning tasks including language modeling and adaptive text prediction, adaptive game playing, classification, and compression using features from the field of deep learning. 1 Introduction Detecting temporal patterns and predicting into the future is a fundamental problem in machine learning. It has gained great interest recently in the areas of nonparametric Bayesian statistics (Wood et al., 2009) and deep learning (Sutskever et al., 2011), with applications to several domains including language modeling and unsupervised learning of audio and video sequences. Some re- searchers have argued that sequence prediction is key to understanding human intelligence (Hawkins and Blakeslee, 2005). The close connections between sequence prediction and data compression are perhaps under- arXiv:1108.3298v1 [cs.LG] 16 Aug 2011 appreciated within the machine learning community. -
Adaptive On-The-Fly Compression
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 17, NO. 1, JANUARY 2006 15 Adaptive On-the-Fly Compression Chandra Krintz and Sezgin Sucu Abstract—We present a system called the Adaptive Compression Environment (ACE) that automatically and transparently applies compression (on-the-fly) to a communication stream to improve network transfer performance. ACE uses a series of estimation techniques to make short-term forecasts of compressed and uncompressed transfer time at the 32KB block level. ACE considers underlying networking technology, available resource performance, and data characteristics as part of its estimations to determine which compression algorithm to apply (if any). Our empirical evaluation shows that, on average, ACE improves transfer performance given changing network types and performance characteristics by 8 to 93 percent over using the popular compression techniques that we studied (Bzip, Zlib, LZO, and no compression) alone. Index Terms—Adaptive compression, dynamic, performance prediction, mobile systems. æ 1 INTRODUCTION UE to recent advances in Internet technology, the networking technology available changes regularly, e.g., a Ddemand for network bandwidth has grown rapidly. user connects her laptop to an ISDN link at home, takes her New distributed computing technologies, such as mobile laptop to work and connects via a 100Mb/s Ethernet link, and computing, P2P systems [10], Grid computing [11], Web attends a conference or visits a coffee shop where she uses the Services [7], and multimedia applications (that transmit wireless communication infrastructure that is available. The video, music, data, graphics files), cause bandwidth compression technique that performs best for this user will demand to double every year [21].