Study of Highway Crack Diagnosis Based on Cellular Neural Network

Road quality information detect system is an important component in architecture quality detect system, also is the basement of successfully working of other related project for the whole country. The study of detecting the road crack is the key to insure the security of accurately detect the road quality in transportation system. In this paper, we come up with a fixed way of road undersized rift image detection by using cellular neural networks. By image processing, building rift networks and details networks and adding the model of similarity undersized rift networks. It can avoid the problem that can not accurately detect undersized crack by only taking the crack feature value. The experiment proved that fixed crack detect computing is easy to do, more accurate to detect the undersized cracks on the road and can reach the standard level of current detect technique.

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CELLULAR NEURAL NETWORKS: ASYMMETRIC SPACE-DEPENDENT TEMPLATES, MOSAIC PATTERNS, AND SPATIAL CHAOS

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127404011041 ◽

2004 ◽

Vol 14 (08) ◽

pp. 2655-2665 ◽

Cited By ~ 1

Author(s):

LARRY TURYN

Keyword(s):

Neural Network ◽

Neural Networks ◽

Hexagonal Lattice ◽

Cellular Neural Network ◽

Cellular Neural Networks ◽

Integer Lattice ◽

Spatial Entropy ◽

Spatial Chaos

We consider a Cellular Neural Network (CNN), with a bias term, on the integer lattice ℤ2in the plane ℝ2. Space-dependent, asymmetric couplings (templates) appropriate for CNN in the hexagonal lattice on ℝ2are studied. We characterize the mosaic patterns and study their spatial entropy. It appears that for this problem, asymmetry of the template has a more robust effect on the spatial entropy than does the sign of a parameter in the templates.

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REALIZATION AND BIFURCATION OF BOOLEAN FUNCTIONS VIA CELLULAR NEURAL NETWORKS

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127405013277 ◽

2005 ◽

Vol 15 (07) ◽

pp. 2109-2129 ◽

Cited By ~ 12

Author(s):

FANGYUE CHEN ◽

GUANRONG CHEN

Keyword(s):

Neural Network ◽

Neural Networks ◽

Boolean Function ◽

Boolean Functions ◽

Cellular Neural Network ◽

Cellular Neural Networks ◽

Gene Bank ◽

Engineering Applications ◽

Binary Input

In this work, we study the realization and bifurcation of Boolean functions of four variables via a Cellular Neural Network (CNN). We characterize the basic relations between the genes and the offsets of an uncoupled CNN as well as the basis of the binary input vectors set. Based on the analysis, we have rigorously proved that there are exactly 1882 linearly separable Boolean functions of four variables, and found an effective method for realizing all linearly separable Boolean functions via an uncoupled CNN. Consequently, any kind of linearly separable Boolean function can be implemented by an uncoupled CNN, and all CNN genes that are associated with these Boolean functions, called the CNN gene bank of four variables, can be easily determined. Through this work, we will show that the standard CNN invented by Chua and Yang in 1988 indeed is very essential not only in terms of engineering applications but also in the sense of fundamental mathematics.

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CELLULAR NEURAL NETWORKS: SPACE-DEPENDENT TEMPLATE, MOSAIC PATTERNS AND SPATIAL CHAOS

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127402005510 ◽

2002 ◽

Vol 12 (08) ◽

pp. 1717-1730 ◽

Cited By ~ 3

Author(s):

JONQ JUANG ◽

SHIH-FENG SHIEH ◽

LARRY TURYN

Keyword(s):

Neural Network ◽

Neural Networks ◽

Hexagonal Lattice ◽

Cellular Neural Network ◽

Cellular Neural Networks ◽

Invariant Set ◽

Integer Lattice ◽

Translation Invariant ◽

Spatial Entropy ◽

Spatial Chaos

We consider a Cellular Neural Network (CNN) with a bias term in the integer lattice tenpoint ℤ2 on the plane tenpoint ℤ2. We impose a space-dependent coupling (template) appropriate for CNN in the hexagonal lattice on tenpoint ℤ2. Stable mosaic patterns of such CNN are completely characterized. The spatial entropy of a tenpoint (p1, p2)-translation invariant set is proved to be well-defined and exists. Using such a theorem, we are also able to address the complexities of resulting mosaic patterns.

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Dynamic properties of cellular neural networks

Journal of Applied Mathematics and Stochastic Analysis ◽

10.1155/s1048953393000103 ◽

1993 ◽

Vol 6 (2) ◽

pp. 107-116 ◽

Cited By ~ 3

Author(s):

Angela Slavova

Keyword(s):

Neural Network ◽

Neural Networks ◽

Dynamic Properties ◽

Cellular Neural Network ◽

State Equation ◽

Cellular Neural Networks ◽

New Approach ◽

New Class ◽

Continuous State Space ◽

Continuous State

Dynamic behavior of a new class of information-processing systems called Cellular Neural Networks is investigated. In this paper we introduce a small parameter in the state equation of a cellular neural network and we seek for periodic phenomena. New approach is used for proving stability of a cellular neural network by constructing Lyapunov's majorizing equations. This algorithm is helpful for finding a map from initial continuous state space of a cellular neural network into discrete output. A comparison between cellular neural networks and cellular automata is made.

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Traffic Analysis Using Artificial Neural Network

International Journal of Scientific Research in Science and Technology ◽

10.32628/cseit217494 ◽

2021 ◽

pp. 359-365

Author(s):

Mounica B ◽

Nithya B S ◽

Rakshitha N ◽

Sirisha M

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Processing ◽

Artificial Neural Network ◽

Conventional Method ◽

Image Sequences ◽

Testing Model ◽

Traffic Jam ◽

The Road ◽

On The Road

The vehicle traffic on the road is increasing progressively and managing such traffic on the roads are not stable by conventional method. To remove this traffic issue, we develop a project using machine learning in which we train the testing model as well as trained model of extracted traffic features. Extracted information from image sequences of testing model can give us real information to create the database which is the captured images like accident, foggy places, collision of the vehicles, traffic signal, no traffic jam, treefall etc. Choose any traffic image from the testing model, process and analyze the traffic image and the traffic image which was taken from the testing model is compared with the trained model of traffic images to determine the cause of the traffic. Image processing will be done to determine the cause of the traffic. This project is utilizing image processing methods designed to analyze and determine the cause of the traffic with the accuracy of the traffic caused. Thus, by using this project we can avoid the traffic and the time being wasted.

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CELLULAR NEURAL NETWORKS FOR EDGE DETECTION

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127407017811 ◽

2007 ◽

Vol 17 (04) ◽

pp. 1323-1328

Author(s):

GIUSEPPE GRASSI ◽

PIETRO VECCHIO ◽

EUGENIO DI SCIASCIO ◽

LUIGI A. GRIECO

Keyword(s):

Neural Network ◽

Neural Networks ◽

Edge Detection ◽

Cellular Neural Network ◽

Cellular Neural Networks ◽

Detection Technique ◽

Hardware Implementations ◽

Rigorous Model ◽

Better Than

This Letter presents an effective edge detection technique based on the cellular neural network paradigm. The approach exploits a rigorous model of the image contours and takes into account some electrical restrictions of existing hardware implementations. The method yields accurate results, better than the ones achievable by other cellular neural network-based techniques.

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HYPERCHAOS IN RTD-BASED CELLULAR NEURAL NETWORKS

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127408022470 ◽

2008 ◽

Vol 18 (11) ◽

pp. 3439-3446 ◽

Cited By ~ 2

Author(s):

FENG-JUAN CHEN ◽

JI-BIN LI

Keyword(s):

Neural Network ◽

Neural Networks ◽

Lyapunov Exponents ◽

Cellular Neural Network ◽

Cellular Neural Networks ◽

Phase Portraits

In this paper, a hyperchaotic RTD-based cellular neural network is proposed and its hyperchaotic dynamics is demonstrated. The Lyapunov exponents spectrum is presented, and some typical Lyapunov exponents are calculated in a range of parameters. Several important phase portraits are presented as well.

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STAR CELLULAR NEURAL NETWORKS FOR ASSOCIATIVE AND DYNAMIC MEMORIES

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127404010308 ◽

2004 ◽

Vol 14 (05) ◽

pp. 1725-1772 ◽

Cited By ~ 48

Author(s):

MAKOTO ITOH ◽

LEON O. CHUA

Keyword(s):

Neural Network ◽

Neural Networks ◽

Cellular Neural Network ◽

Cellular Neural Networks ◽

Phase Relations ◽

Associative Memories ◽

Central System ◽

Local Oscillators ◽

System A ◽

Output Pattern

In this paper, we propose a Star cellular neural network (Star CNN) for associative and dynamic memories. A Star CNN consists of local oscillators and a central system. All oscillators are connected to a central system in the shape of a Star, and communicate with each other through a central system. A Star CNN can store and retrieve given patterns in the form of synchronized chaotic states with appropriate phase relations between the oscillators (associative memories). Furthermore, the output pattern can occasionally travel around the stored patterns, their reverse patterns, and new relevant patterns which are called spurious patterns (dynamic memories).

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