Michelle Effros | Shannon's Channel and Capacity

Sdílet
Vložit
  • čas přidán 30. 05. 2024
  • Claude Shannon Centennial Symposium
    Rackham Building Amphitheatre
    University of Michigan, Ann Arbor
    Michelle Effros, George Van Osdol Professor of Electrical Engineering at Caltech, speaks on Shannon's channel and capacity, calling it a mystery in three acts.
    Michelle Effros received the B.S. degree with distinction in 1989, the M.S. degree in 1990, and the Ph.D. degree in 1994, all in electrical engineering from Stanford University. During the summers of 1988 and 1989 she worked at Hughes Aircraft Company, researching modulation schemes, real-time implementations of fast data rate error-correction schemes, and future applications for fiber optics in space technology.
    She is currently Professor of Electrical Engineering at the California Institute of Technology; from 1994 - 2000 she was Assistant Professor of Electrical Engineering; and from 2000 - 2005, Associate Professor. Her research interests include information theory, data compression, communications, pattern recognition, speech recognition, and image processing.
    Professor Effros received Stanford's Frederick Emmons Terman Engineering Scholastic Award (for excellence in engineering) in 1989, the Hughes Masters Full-Study Fellowship in 1989, the National Science Foundation Graduate Fellowship in 1990, the AT&T Ph.D. Scholarship in 1993, the NSF CAREER Award in 1995, the Charles Lee Powell Foundation Award in 1997, and the Richard Feynman-Hughes Fellowship in 1997. She is a member of Tau Beta Pi, Phi Beta Kappa, Sigma Xi, and IEEE Information Theory, Signal Processing, and Communications societies. She served as the Editor of the IEEE Information Theory Society Newsletter from 1995-1998, as Co-Chair of the NSF Sponsored Workshop on Joint Source-Channel Coding in 1999, and has been a Member of the Board of Governors of the IEEE Information Theory Society since 1998.
    effros.caltech.edu/research.html
    For more lectures on demand, please visit the Alumni Engagement website:
    www.engin.umich.edu/college/in...
  • Věda a technologie

Komentáře • 8

  • @Tom-sp3gy
    @Tom-sp3gy Před 10 měsíci

    Fantastic presentation ! So crisp and clear and comprehensible!

  • @photon272
    @photon272 Před 3 lety +1

    Damn.. This is what i wanted! Wonderful!
    Everybody knows shannans formula and solve problems/numericals but they don't know true meaning of what shannans capacity is.
    Thank God you guys uploaded it.

  • @barbaraeffros4804
    @barbaraeffros4804 Před rokem

    Michelle , You make information technology fascinating and very practical.

  • @edwinfiguerres586
    @edwinfiguerres586 Před 3 lety

    I just accidentally got the video on Claude Shannon. I read an article in spectrum where I read the term, Shannon's limit. The article is focused on data transmission via fiber optic of 600 gigps by 3 male engineers
    from tum. at first I thought Claude Shannon is from Germany.
    My impression of the lecture is excellent and my hope for professor Effros is the future Deputy Director of NSF which is still vacant.

  • @hyperduality2838
    @hyperduality2838 Před 3 lety +1

    Syntropy (prediction) is dual to increasing entropy -- the 4th law of thermodynamics.
    The process of maximizing mutual information leads to optimizing your prediction -- syntropy.
    Predictions are used to track targets, goals -- teleology. All observers track targets.
    The word entropy means "a tendency to diverge or differentiate into new states".
    The word syntropy means "a tendency to converge", information is converged into a prediction by the brain.
    "Through imagination and reason we turn experience into foresight (prediction)" -- Spinoza describing syntropy.
    Convergence is dual to divergence, integration is dual to differentiation.
    Teleological physics is dual to non-teleological physics.
    Randomness (entropy) is dual to order (predictability).
    Entropy is dual to evolution (syntropy) -- Janna Levin, Astrophysicist.
    Energy is dual to mass -- Einstein.
    Dark energy is dual to dark matter.
    "Always two there are" -- Yoda.

  • @zeroonetime
    @zeroonetime Před měsícem

    010 for ever. From 0 to 01 Being ~ Beresheet.

  • @inxeoz
    @inxeoz Před rokem +1

    need more examples to understand 💀

  • @lenag3329
    @lenag3329 Před 2 lety

    Ellen Ripley before Nostromo