Hardware Implementation of EMD Using DSP and FPGA for Online Signal Processing

@article{Lee2011HardwareIO,
  title={Hardware Implementation of EMD Using DSP and FPGA for Online Signal Processing},
  author={M. Lee and K. Shyu and P. Lee and C. Huang and Yun-Jen Chiu},
  journal={IEEE Transactions on Industrial Electronics},
  year={2011},
  volume={58},
  pages={2473-2481}
}
  • M. Lee, K. Shyu, +2 authors Yun-Jen Chiu
  • Published 2011
  • Computer Science, Engineering
  • IEEE Transactions on Industrial Electronics
  • This paper combines a digital signal processor (DSP) and a field programmable gate array (FPGA) to realize the online empirical mode decomposition (EMD)-based signal processing system. The EMD algorithm is a novel signal analysis technique, decomposing signals into a series of intrinsic mode functions. First, the EMD algorithm is implemented in the DSP, named the EMD processor, which has the ability to eliminate noise from the original signal. Next, in order to process the online sequential… CONTINUE READING
    FPGA Implementation for Real-Time Empirical Mode Decomposition
    • 37
    • Highly Influenced
    FPGA Based Real-Time Implementation of Online EMD With Fixed Point Architecture
    • 2
    FPGA-Based Real-Time Implementation of Bivariate Empirical Mode Decomposition
    • 3
    • PDF

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 21 REFERENCES
    Features, Design Tools, and Application Domains of FPGAs
    • 377
    An FPGA-Based Multiple-Axis Motion Control Chip
    • 95
    FPGA Design Methodology for Industrial Control Systems—A Review
    • 842
    • PDF
    Rotating machine fault diagnosis using empirical mode decomposition
    • 145
    Removal of power-line interference from the ECG: a review of the subtraction procedure
    • 168
    • PDF
    An FPGA-Based Novel Digital PWM Control Scheme for BLDC Motor Drives
    • 228
    EMD-based 60-Hz noise filtering of the ECG
    • 61
    R-peak Detection and Signal Averaging for Simulated Stress ECG using EMD
    • 47
    EMG signal filtering based on Empirical Mode Decomposition
    • 140