Structural Design using Cellular Automata

Traditional parallel methods for structural design do not scale well. This paper discusses the application of massively scalable cellular automata (CA) techniques to structural design. There are two sets of CA rules, one used to propagate stresses and strains, and one to perform design analysis. These rules can be applied serially,periodically,or concurrently, and Jacobi or Gauss- Seidel style updating can be done. These options are compared with respect to convergence,speed, and stability

Pseudo-random Sequences Generated by Cellular Automata

International audienceGeneration of pseudo random sequences by cellular automata, as well as by hybrid cellular automata is surveyed. An application to the fast evaluation and FPGA implementation of some classes of boolean functions is sketched out

Optimal Rules Identification for a Random Number Generator Using Cellular Learning Automata

The cryptography is known as one of most essential ways for protecting information against threats. Among all encryption algorithms, stream ciphering can be indicated as a sample of swift ways for this purpose, in which, a generator is applied to produce a sequence of bits as the key stream. Although this sequence is seems to be random, severely, it contains a pattern that repeats periodically. Linear Feedback Shift Registers and cellular automata have been used as pseudo-random number generator. Some challenges such as error propagation and pattern dependability have motivated the designers to use CA for this purpose. The most important issue in using cellular automata includes determining an optimal set of rules for cells. This paper focuses on selecting optimal rules set for such this generator with using an open cellular learning automata, which is a cellular automata with learning capability and interacts with local and global environments

Integrating a Non-Uniformly Sampled Software Retina with a Deep CNN Model

We present a biologically inspired method for pre-processing images applied to CNNs that reduces their memory requirements while increasing their invariance to scale and rotation changes. Our method is based on the mammalian retino-cortical transform: a mapping between a pseudo-randomly tessellated retina model (used to sample an input image) and a CNN. The aim of this first pilot study is to demonstrate a functional retinaintegrated CNN implementation and this produced the following results: a network using the full retino-cortical transform yielded an F1 score of 0.80 on a test set during a 4-way classification task, while an identical network not using the proposed method yielded an F1 score of 0.86 on the same task. The method reduced the visual data by e×7, the input data to the CNN by 40% and the number of CNN training epochs by 64%. These results demonstrate the viability of our method and hint at the potential of exploiting functional traits of natural vision systems in CNNs