Discrete-time signal processing

The dataset has 150 three-second sampling motor current signals from each synthetically-prepared motors. There are five motors with respective fault condition - bearing axis deviation (F1), stator coil inter-turn short circuit (F2), rotor broken strip (F3), outer bearing ring damage (F4), and healthy (H). The motors are run under five coupling loads - 0, 25, 50, 75, and 100%. The sampling signals are collected and processed into frequency occurrence plots (FOPs). Each image has a label, for example F2_L50_130, where F2 is the fault condition, L50 is the coupling load condition.

  • Sensors
  • Last Updated On: 
    Fri, 05/24/2019 - 22:27

    A VOR receiver based on Software-Defined Radio is presented. Experiments showed that the system indicated the radials of the VOR station of São José dos Campos with an average error rate of less than 1% and a standard deviation of less than 2.14% in relation to those calculated cartographically. The results suggest that low volume and weight SDR-based VOR receivers can be developed with processing on microcontrollers or FPGAs to equip drones that need to operate in aerodrome environments.

     

  • Signal Processing
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    R. Mello

    Audio dataset for Household Multimodal Environment (HoME). It is a collection of audio samples from the Freesound.org collaborative database of Creative Commons Licensed sounds.

  • Discrete-time signal processing
  • Last Updated On: 
    Tue, 08/07/2018 - 11:57

    This dataset includes  the Channels Switch Sequences of 300 IPTV viewers in Guangzhou, P.R. China, in Augest, 2014. There are 4 columns in the file, which represent viewer ID, the current channel number, the next channel number, the date of the month, respectively. The first column, the ID code of a viewer, ranks in descent with the times the viewer watched tv channels. The more times a viewer watches tv channels, the bigger the ID is. In a day, the rows are time series and generated step by step as the real watching tv behavior. 

     

     

     

     

  • Communications
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Sixuan Ren and Can Yang in South China University of Technology

    This dataset includes  the Channels Switch Sequences of 300 IPTV viewers in Guangzhou, P.R. China, in Augest, 2014.

    There are 4 columns in the file, which represent viewer ID, the current channel number, th next channel number, the date of the month, respectively.

    The first column, the ID code of a viewter, ranks with the times the viewer watched tv channels. The more times a viewer watches tv channels, the bigger

    the ID is. In a day, the rows are time series and generated step by step as the real watching tv behavior. 

     

     

     

     

  • Communications
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Sixuan Ren and Can Yang in South China University of Technology