# Publication

A. A. Lazar,
T. Roska,
E. K. Simonyi, and
L. T. Toth

BibTex DOI PDF

Time encoding is a novel real-time asynchronous
mechanism for encoding amplitude information into a time
sequence. The analog bandlimited input is fed into a simple
nonlinear neuron-like circuit that generates a strictly increasing
time sequence based on which the signal can be reconstructed.
The heart of the reconstruction is solving a system of illconditioned linear equations. This contribution shows that the
equations can be manipulated so that the reconstruction becomes
feasible using a Cellular Neural Network (CNN) with a banded
system matrix. In particular, the system is ﬁrst transformed into a
well-conditioned smaller system; and then, the Lanczos process is
used to lay it out into a set of even smaller systems characterized
by a set of tridiagonal matrices. Each of these systems can directly
be solved by CNNs, whereas the preprocessing (transformation
and Lanczos algorithm) and simple postprocessing phases can be
partly or fully implemented by using the digital capabilities of the
CNN Universal Machine (CNN-UM). Each step of the proposed
formulation is conﬁrmed by numerical (digital) simulations
**A Time Decoding Realization with a CNN***7th Seminar on Neural Networks Applications in Electrical Engineering, Neurel 2004 ,*pp. 97-102 , September 23-25 , 2004 , BelgradeBibTex DOI PDF

# Reference

```
@inproceedings{LST04,
author = "A. A. Lazar and T. Roska and E. K. Simonyi and L. T. Toth",
title = "A Time Decoding Realization with a CNN",
year = 2004,
booktitle = "7th Seminar on Neural Networks Applications in Electrical Engineering, Neurel 2004",
pages = "97-102",
month = "Sep"
}
```