Principles of Communications ECS 332
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Data Compression: Dictionary-Based Coding 2 / 37 Dictionary-Based Coding Dictionary-Based Coding
Dictionary-based Coding already coded not yet coded search buffer look-ahead buffer cursor (N symbols) (L symbols) We know the past but cannot control it. We control the future but... Last Lecture Last Lecture: Predictive Lossless Coding Predictive Lossless Coding Simple and effective way to exploit dependencies between neighboring symbols / samples Optimal predictor: Conditional mean (requires storage of large tables) Affine and Linear Prediction Simple structure, low-complex implementation possible Optimal prediction parameters are given by solution of Yule-Walker equations Works very well for real signals (e.g., audio, images, ...) Efficient Lossless Coding for Real-World Signals Affine/linear prediction (often: block-adaptive choice of prediction parameters) Entropy coding of prediction errors (e.g., arithmetic coding) Using marginal pmf often already yields good results Can be improved by using conditional pmfs (with simple conditions) Heiko Schwarz (Freie Universität Berlin) — Data Compression: Dictionary-based Coding 2 / 37 Dictionary-based Coding Dictionary-Based Coding Coding of Text Files Very high amount of dependencies Affine prediction does not work (requires linear dependencies) Higher-order conditional coding should work well, but is way to complex (memory) Alternative: Do not code single characters, but words or phrases Example: English Texts Oxford English Dictionary lists less than 230 000 words (including obsolete words) On average, a word contains about 6 characters Average codeword length per character would be limited by 1 -
Bernard M. Oliver Oral History Interview
http://oac.cdlib.org/findaid/ark:/13030/kt658038wn Online items available Bernard M. Oliver Oral History Interview Daniel Hartwig Stanford University. Libraries.Department of Special Collections and University Archives Stanford, California November 2010 Copyright © 2015 The Board of Trustees of the Leland Stanford Junior University. All rights reserved. Note This encoded finding aid is compliant with Stanford EAD Best Practice Guidelines, Version 1.0. Bernard M. Oliver Oral History SCM0111 1 Interview Overview Call Number: SCM0111 Creator: Oliver, Bernard M., 1916- Title: Bernard M. Oliver oral history interview Dates: 1985-1986 Physical Description: 0.02 Linear feet (1 folder) Summary: Transcript of an interview conducted by Arthur L. Norberg covering Oliver's early life, his education, and work experiences at Bell Laboratories and Hewlett-Packard. Subjects include television research, radar, information theory, organizational climate and objectives at both companies, Hewlett-Packard's associations with Stanford University, and Oliver's association with William Hewlett and David Packard. Language(s): The materials are in English. Repository: Department of Special Collections and University Archives Green Library 557 Escondido Mall Stanford, CA 94305-6064 Email: [email protected] Phone: (650) 725-1022 URL: http://library.stanford.edu/spc Information about Access Reproduction is prohibited. Ownership & Copyright All requests to reproduce, publish, quote from, or otherwise use collection materials must be submitted in writing to the Head of Special Collections and University Archives, Stanford University Libraries, Stanford, California 94304-6064. Consent is given on behalf of Special Collections as the owner of the physical items and is not intended to include or imply permission from the copyright owner. -
Minimax Redundancy for Markov Chains with Large State Space
Minimax redundancy for Markov chains with large state space Kedar Shriram Tatwawadi, Jiantao Jiao, Tsachy Weissman May 8, 2018 Abstract For any Markov source, there exist universal codes whose normalized codelength approaches the Shannon limit asymptotically as the number of samples goes to infinity. This paper inves- tigates how fast the gap between the normalized codelength of the “best” universal compressor and the Shannon limit (i.e. the compression redundancy) vanishes non-asymptotically in terms of the alphabet size and mixing time of the Markov source. We show that, for Markov sources (2+c) whose relaxation time is at least 1 + √k , where k is the state space size (and c> 0 is a con- stant), the phase transition for the number of samples required to achieve vanishing compression redundancy is precisely Θ(k2). 1 Introduction For any data source that can be modeled as a stationary ergodic stochastic process, it is well known in the literature of universal compression that there exist compression algorithms without any knowledge of the source distribution, such that its performance can approach the fundamental limit of the source, also known as the Shannon entropy, as the number of observations tends to infinity. The existence of universal data compressors has spurred a huge wave of research around it. A large fraction of practical lossless compressors are based on the Lempel–Ziv algorithms [ZL77, ZL78] and their variants, and the normalized codelength of a universal source code is also widely used to measure the compressibility of the source, which is based on the idea that the normalized codelength is “close” to the true entropy rate given a moderate number of samples. -
Claude Elwood Shannon (1916–2001) Solomon W
Claude Elwood Shannon (1916–2001) Solomon W. Golomb, Elwyn Berlekamp, Thomas M. Cover, Robert G. Gallager, James L. Massey, and Andrew J. Viterbi Solomon W. Golomb Done in complete isolation from the community of population geneticists, this work went unpublished While his incredibly inventive mind enriched until it appeared in 1993 in Shannon’s Collected many fields, Claude Shannon’s enduring fame will Papers [5], by which time its results were known surely rest on his 1948 work “A mathematical independently and genetics had become a very theory of communication” [7] and the ongoing rev- different subject. After his Ph.D. thesis Shannon olution in information technology it engendered. wrote nothing further about genetics, and he Shannon, born April 30, 1916, in Petoskey, Michi- expressed skepticism about attempts to expand gan, obtained bachelor’s degrees in both mathe- the domain of information theory beyond the matics and electrical engineering at the University communications area for which he created it. of Michigan in 1936. He then went to M.I.T., and Starting in 1938 Shannon worked at M.I.T. with after spending the summer of 1937 at Bell Tele- Vannevar Bush’s “differential analyzer”, the an- phone Laboratories, he wrote one of the greatest cestral analog computer. After another summer master’s theses ever, published in 1938 as “A sym- (1940) at Bell Labs, he spent the academic year bolic analysis of relay and switching circuits” [8], 1940–41 working under the famous mathemati- in which he showed that the symbolic logic of cian Hermann Weyl at the Institute for Advanced George Boole’s nineteenth century Laws of Thought Study in Princeton, where he also began thinking provided the perfect mathematical model for about recasting communications on a proper switching theory (and indeed for the subsequent mathematical foundation. -
Digital Communication Systems 2.2 Optimal Source Coding
Digital Communication Systems EES 452 Asst. Prof. Dr. Prapun Suksompong [email protected] 2. Source Coding 2.2 Optimal Source Coding: Huffman Coding: Origin, Recipe, MATLAB Implementation 1 Examples of Prefix Codes Nonsingular Fixed-Length Code Shannon–Fano code Huffman Code 2 Prof. Robert Fano (1917-2016) Shannon Award (1976 ) Shannon–Fano Code Proposed in Shannon’s “A Mathematical Theory of Communication” in 1948 The method was attributed to Fano, who later published it as a technical report. Fano, R.M. (1949). “The transmission of information”. Technical Report No. 65. Cambridge (Mass.), USA: Research Laboratory of Electronics at MIT. Should not be confused with Shannon coding, the coding method used to prove Shannon's noiseless coding theorem, or with Shannon–Fano–Elias coding (also known as Elias coding), the precursor to arithmetic coding. 3 Claude E. Shannon Award Claude E. Shannon (1972) Elwyn R. Berlekamp (1993) Sergio Verdu (2007) David S. Slepian (1974) Aaron D. Wyner (1994) Robert M. Gray (2008) Robert M. Fano (1976) G. David Forney, Jr. (1995) Jorma Rissanen (2009) Peter Elias (1977) Imre Csiszár (1996) Te Sun Han (2010) Mark S. Pinsker (1978) Jacob Ziv (1997) Shlomo Shamai (Shitz) (2011) Jacob Wolfowitz (1979) Neil J. A. Sloane (1998) Abbas El Gamal (2012) W. Wesley Peterson (1981) Tadao Kasami (1999) Katalin Marton (2013) Irving S. Reed (1982) Thomas Kailath (2000) János Körner (2014) Robert G. Gallager (1983) Jack KeilWolf (2001) Arthur Robert Calderbank (2015) Solomon W. Golomb (1985) Toby Berger (2002) Alexander S. Holevo (2016) William L. Root (1986) Lloyd R. Welch (2003) David Tse (2017) James L. -
Randomized Lempel-Ziv Compression for Anti-Compression Side-Channel Attacks
Randomized Lempel-Ziv Compression for Anti-Compression Side-Channel Attacks by Meng Yang A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Applied Science in Electrical and Computer Engineering Waterloo, Ontario, Canada, 2018 c Meng Yang 2018 I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Abstract Security experts confront new attacks on TLS/SSL every year. Ever since the compres- sion side-channel attacks CRIME and BREACH were presented during security conferences in 2012 and 2013, online users connecting to HTTP servers that run TLS version 1.2 are susceptible of being impersonated. We set up three Randomized Lempel-Ziv Models, which are built on Lempel-Ziv77, to confront this attack. Our three models change the determin- istic characteristic of the compression algorithm: each compression with the same input gives output of different lengths. We implemented SSL/TLS protocol and the Lempel- Ziv77 compression algorithm, and used them as a base for our simulations of compression side-channel attack. After performing the simulations, all three models successfully pre- vented the attack. However, we demonstrate that our randomized models can still be broken by a stronger version of compression side-channel attack that we created. But this latter attack has a greater time complexity and is easily detectable. Finally, from the results, we conclude that our models couldn't compress as well as Lempel-Ziv77, but they can be used against compression side-channel attacks. -
Principles of Communications ECS 332
Principles of Communications ECS 332 Asst. Prof. Dr. Prapun Suksompong (ผศ.ดร.ประพันธ ์ สขสมปองุ ) [email protected] 1. Intro to Communication Systems Office Hours: Check Google Calendar on the course website. Dr.Prapun’s Office: 6th floor of Sirindhralai building, 1 BKD 2 Remark 1 If the downloaded file crashed your device/browser, try another one posted on the course website: 3 Remark 2 There is also three more sections from the Appendices of the lecture notes: 4 Shannon's insight 5 “The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point.” Shannon, Claude. A Mathematical Theory Of Communication. (1948) 6 Shannon: Father of the Info. Age Documentary Co-produced by the Jacobs School, UCSD- TV, and the California Institute for Telecommunic ations and Information Technology 7 [http://www.uctv.tv/shows/Claude-Shannon-Father-of-the-Information-Age-6090] [http://www.youtube.com/watch?v=z2Whj_nL-x8] C. E. Shannon (1916-2001) Hello. I'm Claude Shannon a mathematician here at the Bell Telephone laboratories He didn't create the compact disc, the fax machine, digital wireless telephones Or mp3 files, but in 1948 Claude Shannon paved the way for all of them with the Basic theory underlying digital communications and storage he called it 8 information theory. C. E. Shannon (1916-2001) 9 https://www.youtube.com/watch?v=47ag2sXRDeU C. E. Shannon (1916-2001) One of the most influential minds of the 20th century yet when he died on February 24, 2001, Shannon was virtually unknown to the public at large 10 C. -
Marconi Society - Wikipedia
9/23/2019 Marconi Society - Wikipedia Marconi Society The Guglielmo Marconi International Fellowship Foundation, briefly called Marconi Foundation and currently known as The Marconi Society, was established by Gioia Marconi Braga in 1974[1] to commemorate the centennial of the birth (April 24, 1874) of her father Guglielmo Marconi. The Marconi International Fellowship Council was established to honor significant contributions in science and technology, awarding the Marconi Prize and an annual $100,000 grant to a living scientist who has made advances in communication technology that benefits mankind. The Marconi Fellows are Sir Eric A. Ash (1984), Paul Baran (1991), Sir Tim Berners-Lee (2002), Claude Berrou (2005), Sergey Brin (2004), Francesco Carassa (1983), Vinton G. Cerf (1998), Andrew Chraplyvy (2009), Colin Cherry (1978), John Cioffi (2006), Arthur C. Clarke (1982), Martin Cooper (2013), Whitfield Diffie (2000), Federico Faggin (1988), James Flanagan (1992), David Forney, Jr. (1997), Robert G. Gallager (2003), Robert N. Hall (1989), Izuo Hayashi (1993), Martin Hellman (2000), Hiroshi Inose (1976), Irwin M. Jacobs (2011), Robert E. Kahn (1994) Sir Charles Kao (1985), James R. Killian (1975), Leonard Kleinrock (1986), Herwig Kogelnik (2001), Robert W. Lucky (1987), James L. Massey (1999), Robert Metcalfe (2003), Lawrence Page (2004), Yash Pal (1980), Seymour Papert (1981), Arogyaswami Paulraj (2014), David N. Payne (2008), John R. Pierce (1979), Ronald L. Rivest (2007), Arthur L. Schawlow (1977), Allan Snyder (2001), Robert Tkach (2009), Gottfried Ungerboeck (1996), Andrew Viterbi (1990), Jack Keil Wolf (2011), Jacob Ziv (1995). In 2015, the prize went to Peter T. Kirstein for bringing the internet to Europe. Since 2008, Marconi has also issued the Paul Baran Marconi Society Young Scholar Awards. -
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Arxiv:1306.1586V4 [Quant-Ph]
Strong converse for the classical capacity of entanglement-breaking and Hadamard channels via a sandwiched R´enyi relative entropy Mark M. Wilde∗ Andreas Winter†‡ Dong Yang†§ May 23, 2014 Abstract A strong converse theorem for the classical capacity of a quantum channel states that the probability of correctly decoding a classical message converges exponentially fast to zero in the limit of many channel uses if the rate of communication exceeds the classical capacity of the channel. Along with a corresponding achievability statement for rates below the capacity, such a strong converse theorem enhances our understanding of the capacity as a very sharp dividing line between achievable and unachievable rates of communication. Here, we show that such a strong converse theorem holds for the classical capacity of all entanglement-breaking channels and all Hadamard channels (the complementary channels of the former). These results follow by bounding the success probability in terms of a “sandwiched” R´enyi relative entropy, by showing that this quantity is subadditive for all entanglement-breaking and Hadamard channels, and by relating this quantity to the Holevo capacity. Prior results regarding strong converse theorems for particular covariant channels emerge as a special case of our results. 1 Introduction One of the most fundamental tasks in quantum information theory is the transmission of classical data over many independent uses of a quantum channel, such that, for a fixed rate of communica- tion, the error probability of the transmission decreases to zero in the limit of many channel uses. The maximum rate at which this is possible for a given channel is known as the classical capacity of the channel. -
MÁSTER UNIVERSITARIO EN INGENIERÍA DE TELECOMUNICACIÓN TRABAJO FIN DE MASTER Analysis and Implementation of New Techniques Fo
MÁSTER UNIVERSITARIO EN INGENIERÍA DE TELECOMUNICACIÓN TRABAJO FIN DE MASTER Analysis and Implementation of New Techniques for the Enhancement of the Reliability and Delay of Haptic Communications LUIS AMADOR GONZÁLEZ 2017 MASTER UNIVERSITARIO EN INGENIERÍA DE TELECOMUNICACIÓN TRABAJO FIN DE MASTER Título: Analysis and Implementation of New Techniques for the Enhancement of the Reliability and Delay of Haptic Communications Autor: D. Luis Amador González Tutor: D. Hamid Aghvami Ponente: D. Santiago Zazo Bello Departamento: MIEMBROS DEL TRIBUNAL Presidente: D. Vocal: D. Secretario: D. Suplente: D. Los miembros del tribunal arriba nombrados acuerdan otorgar la calificación de: ……… Madrid, a de de 20… UNIVERSIDAD POLITÉCNICA DE MADRID ESCUELA TÉCNICA SUPERIOR DE INGENIEROS DE TELECOMUNICACIÓN MASTER UNIVERSITARIO EN INGENIERÍA DE TELECOMUNICACIÓN TRABAJO FIN DE MASTER Analysis and Implementation of New Techniques for the Enhancement of the Reliability and Delay of Haptic Communications LUIS AMADOR GONZÁLEZ 2017 CONTENTS LIST OF FIGURES .....................................................................................2 LIST OF TABLES ......................................................................................3 ABBREVIATIONS .....................................................................................4 SUMMARY ............................................................................................5 RESUMEN .............................................................................................6 ACKNOWLEDGEMENTS -
Channel Coding
1 Channel Coding: The Road to Channel Capacity Daniel J. Costello, Jr., Fellow, IEEE, and G. David Forney, Jr., Fellow, IEEE Submitted to the Proceedings of the IEEE First revision, November 2006 Abstract Starting from Shannon’s celebrated 1948 channel coding theorem, we trace the evolution of channel coding from Hamming codes to capacity-approaching codes. We focus on the contributions that have led to the most significant improvements in performance vs. complexity for practical applications, particularly on the additive white Gaussian noise (AWGN) channel. We discuss algebraic block codes, and why they did not prove to be the way to get to the Shannon limit. We trace the antecedents of today’s capacity-approaching codes: convolutional codes, concatenated codes, and other probabilistic coding schemes. Finally, we sketch some of the practical applications of these codes. Index Terms Channel coding, algebraic block codes, convolutional codes, concatenated codes, turbo codes, low-density parity- check codes, codes on graphs. I. INTRODUCTION The field of channel coding started with Claude Shannon’s 1948 landmark paper [1]. For the next half century, its central objective was to find practical coding schemes that could approach channel capacity (hereafter called “the Shannon limit”) on well-understood channels such as the additive white Gaussian noise (AWGN) channel. This goal proved to be challenging, but not impossible. In the past decade, with the advent of turbo codes and the rebirth of low-density parity-check codes, it has finally been achieved, at least in many cases of practical interest. As Bob McEliece observed in his 2004 Shannon Lecture [2], the extraordinary efforts that were required to achieve this objective may not be fully appreciated by future historians. -
Jacob Wolfowitz 1910–1981
NATIONAL ACADEMY OF SCIENCES JACOB WOLFOWITZ 1910–1981 A Biographical Memoir by SHELEMYAHU ZACKS Any opinions expressed in this memoir are those of the author and do not necessarily reflect the views of the National Academy of Sciences. Biographical Memoirs, VOLUME 82 PUBLISHED 2002 BY THE NATIONAL ACADEMY PRESS WASHINGTON, D.C. JACOB WOLFOWITZ March 19, 1910–July 16, 1981 BY SHELEMYAHU ZACKS ACOB WOLFOWITZ, A GIANT among the founders of modern Jstatistics, will always be remembered for his originality, deep thinking, clear mind, excellence in teaching, and vast contributions to statistical and information sciences. I met Wolfowitz for the first time in 1957, when he spent a sab- batical year at the Technion, Israel Institute of Technology. I was at the time a graduate student and a statistician at the building research station of the Technion. I had read papers of Wald and Wolfowitz before, and for me the meeting with Wolfowitz was a great opportunity to associate with a great scholar who was very kind to me and most helpful. I took his class at the Technion on statistical decision theory. Outside the classroom we used to spend time together over a cup of coffee or in his office discussing statistical problems. He gave me a lot of his time, as though I was his student. His advice on the correct approach to the theory of statistics accompanied my development as statistician for many years to come. Later we kept in touch, mostly by correspondence and in meetings of the Institute of Mathematical Statistics. I saw him the last time in his office at the University of Southern Florida in Tampa, where he spent the last years 3 4 BIOGRAPHICAL MEMOIRS of his life.