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[Author] Takashi MORITA(2hit)

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  • Implementation and Evaluation of an IVDLAN Transmission Circuit

    Takanori MIYAMOTO  Tohru KAZAWA  Toshiro SUZUKI  Takashi MORITA  

     
    INVITED PAPER

      Vol:
    E74-B No:9
      Page(s):
    2687-2695

    This paper presents experiments on a PR4 (Partial Response Class 4) transceiver aimed at using existing telephone-grade twisted pairs. The achievable loop length for typical line codes is first estimated for a wide range of bit rates, assuming that near-end crosstalk (NEXT) is the crucial degrading factor on TTPs. It is shown that a PR4 line code makes it possible to transmit 4 Mbps of data over a loop up to 450 meters long. A low-cost transceiver adopting a newly developed timing extraction method, called LDT (Logically Decided Timing extraction) , is implemented and evaluated. It is experimentally confirmed that 4Mbps transmission over existing TTPs was achieved in the presence of crosstalk and impulse noise.

  • Local Density Estimation Procedure for Autoregressive Modeling of Point Process Data Open Access

    Nat PAVASANT  Takashi MORITA  Masayuki NUMAO  Ken-ichi FUKUI  

     
    LETTER-Artificial Intelligence, Data Mining

      Pubricized:
    2024/07/11
      Vol:
    E107-D No:11
      Page(s):
    1453-1457

    We proposed a procedure to pre-process data used in a vector autoregressive (VAR) modeling of a temporal point process by using kernel density estimation. Vector autoregressive modeling of point-process data, for example, is being used for causality inference. The VAR model discretizes the timeline into small windows, and creates a time series by the presence of events in each window, and then models the presence of an event at the next time step by its history. The problem is that to get a longer history with high temporal resolution required a large number of windows, and thus, model parameters. We proposed the local density estimation procedure, which, instead of using the binary presence as the input to the model, performed kernel density estimation of the event history, and discretized the estimation to be used as the input. This allowed us to reduce the number of model parameters, especially in sparse data. Our experiment on a sparse Poisson process showed that this procedure vastly increases model prediction performance.