Linear Convolution Filter to Reduce Computational Complexity Based on Discrete Hirschman Transform
| Autor: | Dingli Xue, Victor DeBrunner, Linda S. DeBrunner |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computational complexity theory
Computer science Applied Mathematics ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Fast Fourier transform Digital data 020206 networking & telecommunications 02 engineering and technology Filter (signal processing) Convolution Signal Processing 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Algorithm Computer Science::Distributed Parallel and Cluster Computing |
| Zdroj: | IEEE Signal Processing Letters. :1-1 |
| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/lsp.2019.2950111 |
| Popis: | A Fast linear convolution algorithm based on the Discrete Hirschman Transform (DHT) provides increased hardware flexibility and reduced computational complexity compared to those based on the Fast Fourier Transform (FFT). This DHT convolution can be realized by block-processing filters. We propose a hardware-efficient structure to implement the DHT convolution filter. A digital data example is used to discuss its improvement in computational complexity. Observation indicates that our proposed DHT convolution filter either enjoys the same peak performances as its FFT competitor, or even reduces more computational load with a slightly larger output size. This performance can be further enhanced using alternative DHT-based methods. |
| Databáze: | OpenAIRE |
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