Analysis of one-dimensional linear hybrid cellular automata over GF(q)

1996 ◽  
Vol 45 (7) ◽  
pp. 782-792 ◽  
Author(s):  
K. Cattell ◽  
J.C. Muzio
Keyword(s):  
Cellular Automata ◽  
Hybrid Cellular Automata ◽  
One Dimensional ◽  
Linear Hybrid Cellular Automata

Determination of minimal cost one-dimensional linear hybrid cellular automata

1991 ◽  
Vol 27 (18) ◽  
pp. 1625 ◽  
Author(s):  
S. Zhang ◽  
D.M. Miller ◽  
J.C. Muzio
Keyword(s):  
Cellular Automata ◽  
Minimal Cost ◽  
Hybrid Cellular Automata ◽  
One Dimensional ◽  
Linear Hybrid Cellular Automata

Minimal cost one-dimensional linear hybrid cellular automata of degree through 500

1995 ◽  
Vol 6 (2) ◽  
pp. 255-258 ◽  
Author(s):  
Kevin Cattell ◽  
Shujian Zhang
Keyword(s):  
Cellular Automata ◽  
Minimal Cost ◽  
Hybrid Cellular Automata ◽  
One Dimensional ◽  
Linear Hybrid Cellular Automata

scholarly journals CellTCS:A Secure Threshold Cryptography Scheme based on Non-linear Hybrid Cellular Automata

2012 ◽  
Vol 6 ◽  
pp. 947-953 ◽  
Author(s):  
Jyoti Jakhar ◽  
Pami Dey ◽  
M. Dutta ◽  
D.K. Bhattacharyya
Keyword(s):  
Cellular Automata ◽  
Threshold Cryptography ◽  
Hybrid Cellular Automata ◽  
Non Linear ◽  
Linear Hybrid Cellular Automata

Quantitative analysis for linear hybrid cellular automata and LFSR as built-in self-test generators for sequential faults

1995 ◽  
Vol 7 (3) ◽  
pp. 209-221 ◽  
Author(s):  
Shujian Zhang ◽  
Rod Byrne ◽  
Jon C. Muzio ◽  
D. Michael Miller
Keyword(s):  
Quantitative Analysis ◽  
Cellular Automata ◽  
Hybrid Cellular Automata ◽  
Self Test ◽  
Built In Self Test ◽  
Linear Hybrid Cellular Automata

Low power BIST via non-linear hybrid cellular automata

2002 ◽  
Author(s):  
F. Corno ◽  
M. Rebaudengo ◽  
M.S. Reorda ◽  
G. Squillero ◽  
M. Violante
Keyword(s):  
Cellular Automata ◽  
Low Power ◽  
Hybrid Cellular Automata ◽  
Non Linear ◽  
Linear Hybrid Cellular Automata

scholarly journals A Novel Cellular Automata Classifier for COVID-19 Prediction

2020 ◽  
Author(s):  
Kiran Sree Pokkuluri ◽  
SSSN Usha Devi Nedunuri
Keyword(s):  
Cellular Automata ◽  
Short Term Memory ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Hybrid Cellular Automata ◽  
The World ◽  
Non Linear ◽  
Long Short Term Memory ◽  
Linear Hybrid Cellular Automata

Introduction: China has witnessed a new virus Corona,which is named COVID-19. It has become the world’s most concern as this virus has spread over the worldat a higher speed;the world has witnessed more than one lakh cases and one thousand deaths in a span of few days. Methods: We have developed a preliminary classifier with non-linear hybrid cellular automata, which is trained and tested to predict the effect of COVID-19 in terms of deaths, the number of people affected, the number of people being could be recovered, etc. This indirectly predicts the trend of this epidemic in India. We have collected the datasets from Kaggle and other standard websites. Results: The proposed classifier, hybrid non-linear cellular automata (HNLCA), was trained with 23,078 datasets and tested with 6785 datasets. HNLCA is compared with conventional methods of long short-term memory, AdaBoost, support vector machine, regression, and SVR and has reported an accuracy of 78.8%, which is better compared with the cited literature. This classifier can also predict the rate at which this virus spreads, transmission within the boundary, and of the boundary, etc.

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