Computer Society Chapter

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Chairperson:Boian Berberov
Vice-Chairperson: Taha Mir

Upcoming Events

  • September, 2019: A tour of Sasktel’s Saskatoon data center during the second week of September. More details as they become available.
  • 2019: General Meeting and officer elections. More details as they become available.

Past Events

  • June 17, 2019:DVP speaker Michelle Thompson is speaking in Saskatoon.

    Michelle Thompson has an MSEE in Information Theory, professional experience in Globalstar, founded the Open Research Institute, and has contributed within multiple amateur radio payload projects since 2008. Michelle also has experience with machine learning and embedded programming, and is a practicing amateur musician.

    • An Open Source protocol for amateur radio satellites

      AMSAT stands for the Radio Amateur Satellite Corporation. After 50 years of steady success in launching and operating over 85 amateur radio satellites, AMSAT is taking a big step forward into microwave digital payloads at 5 and 10 GHz. Because of the increased complexity of digital microwave satellites, new radios must be developed for the ground. Phase 4 Ground is an all-volunteer open-source effort to bring a world-class DVB-protocol-based radio to life. Radios will range from do-it-yourself bespoke rigs to manufactured solutions. There are enormous and rewarding challenges and a wide variety of constraints and considerations.

      Event on vTools

      Links: …

      This presentation is co-sponsored by Innovation Place and Calian SED
      Innovation PlaceCalian SED

    • Algorithmic Music Composition

      Algorithmic composition of music is a sweeping intersection between mathematics, information theory, aesthetics, and artistry. Attempts to artificially create composed music that passes human muster has a long history. Current efforts in algorithmic composition include machine learning, deep learning, Markov chains, and biomimicry. Artificially intelligent music composition is not yet fully realized, but algorithms that assist human composers exist and are here to stay. The choices we make in how to represent and model music is of critical importance.

      Artificial intelligence is only as good as the input data.

      • What is it about music that makes it particularly challenging as a target of machine learning? Can music be treated simply as a time series?
      • What are the many areas of music practice that machine learning can address?
      • What might a computerized intelligent agent do for a busy composer?
      • Can and should machine learning archive the sound of particular artists, so that their mastery can be modeled and synthesized on demand for all time?
      • Are there hidden structures in sound that provide a mathematical theory of aesthetics?
      • What are the ethics and repercussions of replacing composers?

      Event on vTools

      Links: …