Finite Impulse Response

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Paul J. Hurst - One of the best experts on this subject based on the ideXlab platform.

  • A Passive Switched-Capacitor Finite-Impulse-Response Equalizer
    IEEE Journal of Solid-State Circuits, 2007
    Co-Authors: N.j. Guilar, F. Lau, Paul J. Hurst, Stephen H. Lewis
    Abstract:

    A passive CMOS switched-capacitor Finite-Impulse-Response equalizer is described. A sampling rate of 200 MS/s is achieved by six time-interleaved channels. Nonlinear parasitic capacitance scales the equalized output but does not affect the zero locations of the equalizer for a binary or ternary data signal. The 4-tap equalizer prototype is fully differential. At 200 MS/s, the equalizer dissipates 19.5 mW, which is virtually all consumed by clock drivers, and occupies an active area of 1.3 mm2 in a 0.35 mum CMOS process

  • Finite Impulse Response switched-capacitor decimation filters for the DSM D/A interface
    IEEE International Symposium on Circuits and Systems, 1
    Co-Authors: Paul J. Hurst, J.e.c. Brown
    Abstract:

    The existence of a class of switched-capacitor (SC) Finite-Impulse-Response (FIR) filters that can be used to decimate a delta-sigma modulator (DSM) output without decreasing the signal-to-noise ratio (SNR) in the baseband is shown. The filters have the property that all of their coefficients are integers and symmetric, which makes them especially suitable for MOS switched-capacitor implementation. >

B.m. Putter - One of the best experts on this subject based on the ideXlab platform.

Nor Hidayati Abdul Aziz - One of the best experts on this subject based on the ideXlab platform.

  • Single-agent Finite Impulse Response Optimizer vs Simulated Kalman Filter Optimizer
    Mekatronika, 2019
    Co-Authors: Tasiransurini Ab Rahman, Nor Azlina Ab. Aziz, Nor Hidayati Abdul Aziz
    Abstract:

    Single-agent Finite Impulse Response Optimizer (SAFIRO) is a new estimation-based optimization algorithm which mimics the work procedure of the ultimate unbiased Finite Impulse Response (UFIR) filter. In a real UFIR filter, the horizon length, N, plays an important role to obtain the optimal estimation. In SAFIRO, N represents the repetition number of estimation part that needs to be done in find-ing an optimal solution. On the other hand, Simulated Kalman Filter (SKF) is also an estimation- based optimization algorithm inspired by the estimation capability of Kalman filtering. In literature, substantial amount of works has been devoted to SKF, both in applied research and fundamental enhancements. Thus, in this paper, a performance comparison of both SAFIRO and SKF is presented. It is found that the SAFIRO outperforms the SKF significantly.

  • Evaluation of Different Horizon Lengths in Single-agent Finite Impulse Response Optimizer
    2019 International Conference on Computer and Information Sciences (ICCIS), 2019
    Co-Authors: Tasiransurini Ab Rahman, Zuwairie Ibrahim, Nor Azlina Ab. Aziz, Nor Hidayati Abdul Aziz, Mohd Saberi Mohamad, Mohd Ibrahim Shapiai
    Abstract:

    Single-agent Finite Impulse Response Optimizer (SAFIRO) is a newly single solution-based metaheuristic optimization algorithm which mimics the work procedure of the ultimate unbiased Finite Impulse Response (UFIR) filter. In a real UFIR filter, the horizon length, N plays an important role to obtain the optimal estimation. In SAFIRO, N represents the repetition number of estimation part that needs to be done in finding an optimal solution. In the original SAFIRO, N = 4 is assigned. In this study, the effect of N towards the performance of SAFIRO is evaluated by assigning N between the range of 4 to 10. The CEC 2014 benchmark test suite is used for performance evaluations. Statistical analysis using the nonparametric Friedman test was performed to observe the performance. Experimental results show that N is a function dependent parameter where for certain functions, SAFIRO performs better with a larger value of N. However, for certain functions, SAFIRO performs better with a minimum value of N.

  • Single-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems
    IEEE Access, 2018
    Co-Authors: Tasiransurini Ab Rahman, Zuwairie Ibrahim, Nor Azlina Ab. Aziz, Shunyi Zhao, Nor Hidayati Abdul Aziz
    Abstract:

    This paper introduces a new single-agent metaheuristic optimization algorithm, named single-agent Finite Impulse Response optimizer (SAFIRO). This proposed algorithm is inspired by the estimation ability of the ultimate iterative unbiased Finite Impulse Response (UFIR) filter. The UFIR filter is one of the variants of the Finite Impulse Response (FIR) filter, whereby in state space models, the FIR filter can be used as an option other than the Kalman filter (KF) for state estimation. Unlike the KF, the UFIR filter does not require any noise covariance, error covariance, and initial condition to calculate the state estimate. The UFIR filter also provides an iterative Kalman-like form to improve the estimation process. In the SAFIRO algorithm, the agent works as an individual UFIR to find an optimal or a near-optimal solution, where the agent needs to perform two main tasks; measurement and estimation. The performance of the SAFIRO algorithm is evaluated using the CEC 2014 Benchmark Test Suite for single-objective optimization and statistically compared with the several well-known metaheuristic optimization algorithms, such as Particle Swarm Optimization algorithm, Genetic Algorithm, and Grey Wolf Optimization algorithm. The experimental results show that the proposed SAFIRO algorithm is able to converge to the optimal and the near-optimal solutions, and significantly outperform all the aforementioned state-of-the-art metaheuristic algorithms.

Karel Volka - One of the best experts on this subject based on the ideXlab platform.

J.e.c. Brown - One of the best experts on this subject based on the ideXlab platform.

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