Information Theory & the Digital Revolution 1
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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. -
Instructional Communication 8 the Emergence of a Field
Instructional Communication 8 The Emergence of a Field Scott A. Myers ince its inception, the field of instructional communication has Senjoyed a healthy existence. Unlike its related subareas of communi- cation education and developmental communication (Friedrich, 1989), instructional communication is considered to be a unique area of study rooted in the tripartite field of research conducted among educational psychology, pedagogy, and communication studies scholars (Mottet & Beebe, 2006). This tripartite field focuses on the learner (i.e., how students learn affectively, behaviorally, and cognitively), the instructor (i.e., the skills and strategies necessary for effective instruction), and the meaning exchanged in the verbal, nonverbal, and mediated messages between and among instructors and students. As such, the study of instructional com- munication centers on the study of the communicative factors in the teaching-learning process that occur across grade levels (e.g., K–12, post- secondary), instructional settings (e.g., the classroom, the organization), and subject matter (Friedrich, 1989; Staton, 1989). Although some debate exists as to the events that precipitated the emer- gence of instructional communication as a field of study (Rubin & Feezel, 1986; Sprague, 1992), McCroskey and McCroskey (2006) posited that the establishment of instructional communication as a legitimate area of scholarship originated in 1972, when the governing board of the Inter- national Communication Association created the Instructional Communi- cation Division. The purpose of the Division was to “focus attention on the role of communication in all teaching and training contexts, not just the teaching of communication” (p. 35), and provided instructional communication researchers with the opportunity to showcase their scholarship at the Association’s annual convention and to publish their research in Communication Yearbook, a yearly periodical sponsored by the Association. -
Information Technology Management 14
Information Technology Management 14 Valerie Bryan Practitioner Consultants Florida Atlantic University Layne Young Business Relationship Manager Indianapolis, IN Donna Goldstein GIS Coordinator Palm Beach County School District Information Technology is a fundamental force in • IT as a management tool; reshaping organizations by applying investment in • understanding IT infrastructure; and computing and communications to promote competi- • ȱǯ tive advantage, customer service, and other strategic ęǯȱǻȱǯȱǰȱŗşşŚǼ ȱȱ ȱ¢ȱȱȱ¢ǰȱȱ¢Ȃȱ not part of the steamroller, you’re part of the road. ǻ ȱǼ ȱ ¢ȱ ȱ ȱ ęȱ ȱ ȱ ¢ǯȱȱȱȱȱ ȱȱ ȱ- ¢ȱȱȱȱȱǯȱ ǰȱ What is IT? because technology changes so rapidly, park and recre- ation managers must stay updated on both technological A goal of management is to provide the right tools for ȱȱȱȱǯ ěȱ ȱ ě¢ȱ ȱ ȱ ȱ ȱ ȱ ȱȱȱȱ ȱȱȱǰȱȱǰȱ ȱȱȱȱȱȱǯȱȱȱȱ ǰȱȱȱȱ ȱȱ¡ȱȱȱȱ recreation organization may be comprised of many of terms crucial for understanding the impact of tech- ȱȱȱȱǯȱȱ ¢ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ ȱ ȱ ȱ ȱ ǯȱ ȱ - ȱȱęȱȱȱ¢ȱȱȱȱ ¢ȱǻ Ǽȱȱȱȱ¢ȱ ȱȱȱȱ ǰȱ ȱ ȱ ȬȬ ȱ ȱ ȱ ȱȱǯȱȱȱȱȱ ¢ȱȱǯȱȱ ȱȱȱȱ ȱȱ¡ȱȱȱȱȱǻǰȱŗşŞśǼǯȱ ȱȱȱȱȱȱĞȱȱȱȱ ǻȱ¡ȱŗŚǯŗȱ Ǽǯ ě¢ȱȱȱȱǯ Information technology is an umbrella term Details concerning the technical terms used in this that covers a vast array of computer disciplines that ȱȱȱȱȱȱȬȬęȱ ȱȱ permit organizations to manage their information ǰȱ ȱ ȱ ȱ Ȭȱ ¢ȱ ǯȱ¢ǰȱȱ¢ȱȱȱ ȱȱ ǯȱȱȱȱȱȬȱ¢ȱ a fundamental force in reshaping organizations by applying ȱȱ ȱȱ ȱ¢ȱȱȱȱ investment in computing and communications to promote ȱȱ¢ǯȱ ȱȱȱȱ ȱȱ competitive advantage, customer service, and other strategic ȱ ǻȱ ȱ ŗŚȬŗȱ ęȱ ȱ DZȱ ęȱǻǰȱŗşşŚǰȱǯȱřǼǯ ȱ Ǽǯ ȱ¢ȱǻ Ǽȱȱȱȱȱ ȱȱȱęȱȱȱ¢ȱȱ ȱ¢ǯȱȱȱȱȱ ȱȱ ȱȱ ȱȱȱȱ ȱ¢ȱ ȱȱǯȱ ȱ¢ǰȱ ȱȱ ȱDZ ȱȱȱȱǯȱ ȱȱȱȱǯȱ It lets people learn things they didn’t think they could • ȱȱȱ¢ǵ ȱǰȱȱǰȱȱȱǰȱȱȱȱȱǯȱ • the manager’s responsibilities; ǻȱǰȱȱ¡ȱĜȱȱĞǰȱȱ • information resources; ǷȱǯȱȱȱřŗǰȱŘŖŖŞǰȱ • disaster recovery and business continuity; ȱĴDZȦȦ ǯ ǯǼ Information Technology Management 305 Exhibit 14. -
Bioinformatics 1
Bioinformatics 1 Bioinformatics School School of Science, Engineering and Technology (http://www.stmarytx.edu/set/) School Dean Ian P. Martines, Ph.D. ([email protected]) Department Biological Science (https://www.stmarytx.edu/academics/set/undergraduate/biological-sciences/) Bioinformatics is an interdisciplinary and growing field in science for solving biological, biomedical and biochemical problems with the help of computer science, mathematics and information technology. Bioinformaticians are in high demand not only in research, but also in academia because few people have the education and skills to fill available positions. The Bioinformatics program at St. Mary’s University prepares students for graduate school, medical school or entry into the field. Bioinformatics is highly applicable to all branches of life sciences and also to fields like personalized medicine and pharmacogenomics — the study of how genes affect a person’s response to drugs. The Bachelor of Science in Bioinformatics offers three tracks that students can choose. • Bachelor of Science in Bioinformatics with a minor in Biology: 120 credit hours • Bachelor of Science in Bioinformatics with a minor in Computer Science: 120 credit hours • Bachelor of Science in Bioinformatics with a minor in Applied Mathematics: 120 credit hours Students will take 23 credit hours of core Bioinformatics classes, which included three credit hours of internship or research and three credit hours of a Bioinformatics Capstone course. BS Bioinformatics Tracks • Bachelor of Science -
Understanding RF Fundamentals and the Radio Design for 11Ac Wireless Networks Brandon Johnson Systems Engineer Agenda
Understanding RF Fundamentals and the Radio Design for 11ac Wireless Networks Brandon Johnson Systems Engineer Agenda • Physics - RF Waves • Antenna Selection • Spectrum, Channels & Channel widths • MIMO & Spatial Streams • Cisco 802.11ac features • Beyond 802.11ac Electromagnetic Spectrum • Radio waves • Micro waves Colour Frequency Wavelength • Infrared Radiation Violet 668-789 THz 380-450nm Blue 606-668 THz 450-495nm • Visible Light Green 526-606 THz 495-570nm • Ultraviolet Radiation Yellow 508-526 THz 570-590nm • X-Rays Orange 484-508 THz 590-620nm • Gamma Rays Red 400-484 THz 620-750nm Radio Frequency Fundamentals λ1 λ • Frequency and Wavelength 2 • f = c / λ c = the speed of light in a vacuum • 2.45GHz = 12.3cm • 5.0GHz = 6cm • Amplitude • Phase * ϕ A1 A2 Radio Frequency Fundamentals • Signal Strength • Wave Propagation • Gain and Amplification – Attenuation and Free Space Loss • Loss and Attenuation – Reflection and Absorption – Wavelength Physics of Waves • In phase, reinforcement • Out of phase, cancellation RF Mathematics • dB is a logarithmic ratio of values (voltages, power, gain, losses) • We add gains • We subtract losses • dBm is a power measurement relative to 1mW • dBi is the forward gain of an antenna compared to isotropic antenna Now we know that …. How? CSMA / CA – “Listen before talk” • Carrier sense, multiple access / open spectrum • (open, low-power ISM bands) no barrier to entry • Energy detection must detect channel available – Clear Channel Assessment • On collision, random back off timer before trying again. -
Physical Layer Overview
ELEC3030 (EL336) Computer Networks S Chen Physical Layer Overview • Physical layer forms the basis of all networks, and we will first revisit some of fundamental limits imposed on communication media by nature Recall a medium or physical channel has finite Spectrum bandwidth and is noisy, and this imposes a limit Channel bandwidth: on information rate over the channel → This H Hz is a fundamental consideration when designing f network speed or data rate 0 H Type of medium determines network technology → compare wireless network with optic network • Transmission media can be guided or unguided, and we will have a brief review of a variety of transmission media • Communication networks can be classified as switched and broadcast networks, and we will discuss a few examples • The term “physical layer protocol” as such is not used, but we will attempt to draw some common design considerations and exams a few “physical layer standards” 13 ELEC3030 (EL336) Computer Networks S Chen Rate Limit • A medium or channel is defined by its bandwidth H (Hz) and noise level which is specified by the signal-to-noise ratio S/N (dB) • Capability of a medium is determined by a physical quantity called channel capacity, defined as C = H log2(1 + S/N) bps • Network speed is usually given as data or information rate in bps, and every one wants a higher speed network: for example, with a 10 Mbps network, you may ask yourself why not 10 Gbps? • Given data rate fd (bps), the actual transmission or baud rate fb (Hz) over the medium is often different to fd • This is for -
Information Theory Techniques for Multimedia Data Classification and Retrieval
INFORMATION THEORY TECHNIQUES FOR MULTIMEDIA DATA CLASSIFICATION AND RETRIEVAL Marius Vila Duran Dipòsit legal: Gi. 1379-2015 http://hdl.handle.net/10803/302664 http://creativecommons.org/licenses/by-nc-sa/4.0/deed.ca Aquesta obra està subjecta a una llicència Creative Commons Reconeixement- NoComercial-CompartirIgual Esta obra está bajo una licencia Creative Commons Reconocimiento-NoComercial- CompartirIgual This work is licensed under a Creative Commons Attribution-NonCommercial- ShareAlike licence DOCTORAL THESIS Information theory techniques for multimedia data classification and retrieval Marius VILA DURAN 2015 DOCTORAL THESIS Information theory techniques for multimedia data classification and retrieval Author: Marius VILA DURAN 2015 Doctoral Programme in Technology Advisors: Dr. Miquel FEIXAS FEIXAS Dr. Mateu SBERT CASASAYAS This manuscript has been presented to opt for the doctoral degree from the University of Girona List of publications Publications that support the contents of this thesis: "Tsallis Mutual Information for Document Classification", Marius Vila, Anton • Bardera, Miquel Feixas, Mateu Sbert. Entropy, vol. 13, no. 9, pages 1694-1707, 2011. "Tsallis entropy-based information measure for shot boundary detection and • keyframe selection", Marius Vila, Anton Bardera, Qing Xu, Miquel Feixas, Mateu Sbert. Signal, Image and Video Processing, vol. 7, no. 3, pages 507-520, 2013. "Analysis of image informativeness measures", Marius Vila, Anton Bardera, • Miquel Feixas, Philippe Bekaert, Mateu Sbert. IEEE International Conference on Image Processing pages 1086-1090, October 2014. "Image-based Similarity Measures for Invoice Classification", Marius Vila, Anton • Bardera, Miquel Feixas, Mateu Sbert. Submitted. List of figures 2.1 Plot of binary entropy..............................7 2.2 Venn diagram of Shannon’s information measures............ 10 3.1 Computation of the normalized compression distance using an image compressor.................................... -
Combinatorial Reasoning in Information Theory
Combinatorial Reasoning in Information Theory Noga Alon, Tel Aviv U. ISIT 2009 1 Combinatorial Reasoning is crucial in Information Theory Google lists 245,000 sites with the words “Information Theory ” and “ Combinatorics ” 2 The Shannon Capacity of Graphs The (and) product G x H of two graphs G=(V,E) and H=(V’,E’) is the graph on V x V’, where (v,v’) = (u,u’) are adjacent iff (u=v or uv є E) and (u’=v’ or u’v’ є E’) The n-th power Gn of G is the product of n copies of G. 3 Shannon Capacity Let α ( G n ) denote the independence number of G n. The Shannon capacity of G is n 1/n n 1/n c(G) = lim [α(G )] ( = supn[α(G )] ) n →∞ 4 Motivation output input A channel has an input set X, an output set Y, and a fan-out set S Y for each x X. x ⊂ ∈ The graph of the channel is G=(X,E), where xx’ є E iff x,x’ can be confused , that is, iff S S = x ∩ x′ ∅ 5 α(G) = the maximum number of distinct messages the channel can communicate in a single use (with no errors) α(Gn)= the maximum number of distinct messages the channel can communicate in n uses. c(G) = the maximum number of messages per use the channel can communicate (with long messages) 6 There are several upper bounds for the Shannon Capacity: Combinatorial [ Shannon(56)] Geometric [ Lovász(79), Schrijver (80)] Algebraic [Haemers(79), A (98)] 7 Theorem (A-98): For every k there are graphs G and H so that c(G), c(H) ≤ k and yet c(G + H) kΩ(log k/ log log k) ≥ where G+H is the disjoint union of G and H. -
Harry Nyquist 1889-1976
Nyquist and His Seminal Papers Harry Nyquist 1889-1976 Karl Johan Åström A Gifted Scientist and Department of Mechanical Engineering Engineer University of California Santa Barbara Johnson-Nyquist noise The Nyquist frequency Nyquist’s Stability Criterion ASME Nyquist Lecture 2005 ASME Nyquist Lecture 2005 Introduction 1.Introduction 2.A Remarkable Career • Thank you for honoring Nyquist • Thank you for inviting me to give this lecture 3.Communications • Nyquist was awarded the Rufus Oldenburger 4.Johnson-Nyquist Noise medal in 1975 5.Stability Theory • The person and his contributions • What we can learn 6.Summary ASME Nyquist Lecture 2005 ASME Nyquist Lecture 2005 A Remarkable Career Born in Nilsby Sweden February 7 1889 6 years in school Emigrated to USA 1907 Farmhand Teachers College University of North Dakota PhD Physics Yale University 1917 AT&T Bell Labs 1917-1954 Consultant 1954-1965 ASME Nyquist Lecture 2005 ASME Nyquist Lecture 2005 Becoming a Teacher is my Dream Rubrik • Emigrated 1907 • Southern Minnesota Normal College, Austin Active as in teaching • Back to SMNC • Exam 1911 valedictarian • High School Teacher 1912 ASME Nyquist Lecture 2005 ASME Nyquist Lecture 2005 A Dream Comes True Academia Pulls University of North Dakota BS EE 1914 MS EE 1915 Very active in student organizations met Johnson Yale University PhD Physics 1917. Thesis topic: On the Stark effect in Helium and Neon. Largely experimental. ASME Nyquist Lecture 2005 ASME Nyquist Lecture 2005 The ATT Early Fax A Career in AT&T Bell commersial from 1925 • 1917 AT&T Engineering Department • 1919 Department of Development and Research • 1935 Bell Labs • World War II • 1952 Assistant director fo Systems Studies • 1954 Retired • 1954-1962 Consulting ASME Nyquist Lecture 2005 ASME Nyquist Lecture 2005 An Unusual Research Lab In His Right Environment Control the telephone monoply • Nyquist thrived, challenging problems, clever Key technologies collegues, interesting. -
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. -
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.