IIR GFs bring more flexibility to GF designers, as they can be designed and realized even when the graph spectrum is un- known. Numerical results show that IIR GFs are more accurate in approximating ideal GFs and more robust against network varia- tion than FIR GFs.
This letter proposes a family of GFs with infinite impulse response (IIR) and provides algorithms for their distributed realization in wireless sensor networks.
Jan 1, 2015 · This letter proposes a family of GFs with infinite impulse response (IIR) and provides algorithms for their distributed realization in wireless ...
Numerical results show that IIR GFs are more accurate in approximating ideal GFs and more robust against network variation than FIR GFs.
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This work focuses on the problem of operating graph filtering in random asymmetric WSNs. We show first that graph filtering with finite impulse response graph ...
Finite impulse response (FIR) graph filters (GFs) have received more attention in the literature because they enable distributed computation by the sensors.
The advantage of IIR filters over FIR filters is that IIR filters usually require fewer coefficients to execute similar filtering operations.
This paper proposes an efficient approach based on a variable length particle swarm optimization algorithm with a weighted sum fitness function (WS-VLPSO).
Jun 25, 2022 · Abstract—This study employs Infinite Impulse Response (IIR) Graph Neural Networks (GNN) to efficiently model the inherent graph network ...
May 3, 2023 · IIR (Infinite Impulse Response) filters are typically chosen for situations where memory is constrained and linear phase is not as critical.