By Topic

Automatic Generation of Cellular Neural Networks for Distributed Sensor Data Processing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Chatziagorakis, P. ; Dept. of Electr. & Comput. Eng., Democritus Univ. of Thrace, Xanthi, Greece ; Sirakoulis, G.C. ; Lygouras, J.

In this paper, the main interest is the fusion and the control of data that is obtained from a set of sensors. This task requires the use of a both effective and versatile computational model. The chosen architecture is the already known for its suitability cellular neural network (CNN). This specific model, adopts some significant features, such as: continuous-time dynamics, local interconnection, reliability, simple implementation, low power consumption and as far as its behavior is concerned, great flexibility. Furthermore, it is taken into consideration, that depending on the application, the corresponding network dimension may vary. In order to confront this problem, a methodology is proposed for the automatic generation of CNNs of variable dimensions. The above task is achieved by developing an algorithm, which enables the combination of the basic CNN circuit counterparts, so as to produce the desired network dimensions.

Published in:

Informatics, 2009. PCI '09. 13th Panhellenic Conference on

Date of Conference:

10-12 Sept. 2009