Universality in Molecular and Cellular Computing

2015 ◽  
pp. 95-104
Author(s):  
Sergey Verlan
Keyword(s):  
Cellular Computing

Bacteria-based biocomputing with Cellular Computing Circuits to sense, decide, signal, and act

2011 ◽  
Vol 4 (12) ◽  
pp. 4907 ◽  
Author(s):  
Michaela A. TerAvest ◽  
Zhongjian Li ◽  
Largus T. Angenent
Keyword(s):  
Cellular Computing

scholarly journals A Hardware-Software Design Framework for Distributed Cellular Computing

2008 ◽  
pp. 71-82 ◽  
Author(s):  
Pierre-André Mudry ◽  
Julien Ruffin ◽  
Michel Ganguin ◽  
Gianluca Tempesti
Keyword(s):  
Software Design ◽  
Design Framework ◽  
Cellular Computing

scholarly journals MSB based Cellular Automata for Edge Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 1354-1358
Keyword(s):  
Cellular Automata ◽  
Edge Detection ◽  
Processing Time ◽  
Detection Methods ◽  
Detection Techniques ◽  
Detection Process ◽  
Cellular Computing ◽  
Finite State ◽  
Machine Learning Applications ◽  
Cluttered Background

A vitalcrucial pre-processing phase in image processing, computer vision and machine learning applications is Edge Detection which detects boundaries of foreground and background objects in an image. Discrimination between significant edges and not so important spurious edges highly affects the accuracy of edge detection process. This paper introduces an approach for extraction of significant edges present in images based on cellular automata. Cellular automata is a finite state machine where every cell has a state. Existing edge detection methods are complex to implement so they have large processing time. These methods tend to produce non-satisfactory results for noisy images which have cluttered background. Some methods are so trivial that they miss part of true edges and some methods are so complex that they tend to give spurious edges which are not required. The advantage of using cellular computing approach is to enhance edge detection process by reducing complexity and processing time. Parallel processing makes this method fast and computationally imple. MATLAB results of proposed method performed on images from Mendeley Dataset are compared with results obtained from existing edge detection techniques by evaluation of MSE and PSNR values Results indicate promising performance of the proposed algorithm. Visually compared, the proposed method produces better results to identify edges more clearly and is intelligent enough to discard spurious edges even for cluttered and complex images

A Biobrick Inversion Cellular Computing Model for Satisfiability Problem

2014 ◽  
Vol 36 (12) ◽  
pp. 2537-2544 ◽  
Author(s):  
Mei CHEN ◽  
Xiang-Qun CHEN ◽  
Lu ZHANG ◽  
Jin Xu
Keyword(s):  
Satisfiability Problem ◽  
Computing Model ◽  
Cellular Computing

Overview of Cellular Computing-Basic Principles and Applications

2016 ◽  
pp. 637-662
Author(s):  
Amit Das ◽  
Rakhi Dasgupta ◽  
Angshuman Bagchi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Life Sciences ◽  
Science Research ◽  
The Other ◽  
Computational Algorithms ◽  
Basic Principles ◽  
Computing Power ◽  
Cellular Computing ◽  
The Impact

Computers, due to their raw speed and massive computing power, have been highly used by biologists to expedite life science research whereas several computational algorithms like artificial neural network, genetic algorithm and many similar ones have been inspired by the behaviors of several biological or cellular entities. However till date both these disciplines i.e. life sciences and computer sciences have mostly progressed separately while recent studies are increasingly highlighting the impact of each discipline on the other. The chapter describes several features of biological systems which could be used for further optimizations of computer programs or could be engineered to harness necessary computational capabilities in lieu of traditional silico chip systems. We also highlight underlying challenges and avenues of implementations of cellular computing.

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