Filtering and spectral processing of 1-D signals using cellular neural networks
Oscar Moreira-Tamayo, José Pineda de Gyvez · 2002
This paper presents cellular neural networks (CNN) for one-dimensional discrete signal processing. Although CNN has been extensively used in image processing applications, little has been done for 1-dimensional signal processing. We propose a novel CNN architecture to carry out these tasks. This architecture consists of a shift register, e.g., a charge coupled device, and a 1/spl times/n neural array. Each cell processes a sample of the input signal. By using appropriate templates and shifting the input signal the CNN array is capable of performing FIR filtering, discrete Fourier transform, and wavelet decomposition and reconstruction. Even though this implementation is not more efficient than conventional methods, the paper shows that an analog computer based on the CNN paradigm can also be used to perform the linear operations described above. Simulation results and comparisons for spectral audio applications are presented.