Nonlinear signal processing

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  • Nonlinear signal processing
  • Last Updated On: 
    Mon, 08/19/2019 - 04:52

    Reinforcement Learning (RL) agents can learn to control a nonlinear system without using a model of the system. However, having a model brings benefits, mainly in terms of a reduced number of unsuccessful trials before achieving acceptable control performance. Several modelling approaches have been used in the RL domain, such as neural networks, local linear regression, or Gaussian processes. In this article, we focus on a technique that has not been used much so far:\ symbolic regression, based on genetic programming.

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  • Computational Intelligence
  • Last Updated On: 
    Fri, 07/26/2019 - 03:46

    The proposed signals are used  for electromagnetic-based stroke classification.  Six realistic head phantom computed from MRI scans, is surrounded by an antenna array of 16 dipole antennas distributed uniformly around the head. These antennas are deployed in a fixed circular array around the head, at a distance of approximately 2-3 mm from the head. A Gaussian pulse covering the bandwidth from 0:7 to 2 GHz is emitted from each of the antennas, sequentially, while all of the antennas capture the scattered signals. Since 16 antennas were used, there are a total of 256 channel signals (i.e.

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  • Biophysiological Signals
  • Last Updated On: 
    Sat, 09/21/2019 - 20:32

    The proposed hardware architecture is modelled by Verilog HDL and synthesized by a Synopsys Design compiler with Semiconductor Manufacturing International Corporation (SMIC) 65-nm CMOS technology. The upload files are the systhesis reports.

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  • Nonlinear signal processing
  • Last Updated On: 
    Fri, 01/18/2019 - 12:44
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  • Communications
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    When heterogeneous honeycomb materials are cut using ultrasonic techniques, the ultrasonic frequency tends to vary over time, thereby degrading the quality of the machining. This variation can be addressed with a proportional–integral–derivative (PID) controller; however, these controllers perform relatively poorly in fixed parameter tracking scenarios. In contrast, this paper proposes a tracking model that combines fuzzy and PID control.

    112 views
  • Electric Utility
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34

    This is the Smulation Data for Power System State Estimation.

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  • Power and Energy
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34