Size modification of binary pictures can be mapped onto the CNN array using space variant linear templates. However, if all the parameters have to be set for each cell individually, then one of the CNN's main adva...
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Size modification of binary pictures can be mapped onto the CNN array using space variant linear templates. However, if all the parameters have to be set for each cell individually, then one of the CNN's main advantages will be lost in practice, the simple and quick parallel reprogrammability. In this paper, a general methodology is presented to derive the space variant templates of the complete weighting matrix from control pictures applying a simple nonlinear space invariant template. The straightforward design method presumes a modified CNN architecture (multiple input and specific nonlinear voltage-controlled current sources in every cell) and can be extended for a large class of sparse weighting matrices. Following this strategy the diminishment and enlargement process has been investigated using constant cell current and various bias maps in the transformations.
A vertex (edge) coloring c∶V → {1, 2, , t} (c′∶E → {1, 2, , t}) of a graph G=(V, E) is a vertex (edge) t-ranking if for any two vertices (edges) of the same color every path between them contains a vertex (edge) ...
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This paper sets out to provide a basis for a specification methodology for modelbased diagnostic systems (MBDS). The purpose of the methodology is to provide a mapping from the problem space of possible diagnostic app...
Model-based diagnosis is regarded by many as a way to overcome the limitations of first-generation knowledge-based systems which perform fault classification by means of empirical symptom-failure associations. Many di...
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Model-based diagnosis is regarded by many as a way to overcome the limitations of first-generation knowledge-based systems which perform fault classification by means of empirical symptom-failure associations. Many different approaches to model-based diagnosis exist. The ESPRIT ARTIST project, focusing on the development of model-based techniques for diagnosis of industrial systems, has tried to integrate these approaches within a common generic architecture so that a given application system could be generated by composing a particular diagnostic system from a set of predeveloped and hence reusable modules. There is an overview of the main results of the ARTIST project, its generic architecture and the three diagnostic systems developed and tested within the project. An industrial application to management of electrical transmission networks is presented.
An experimental system was built up and tested for optical path tracking of a robot, where a 16*16 connected component detector chip with direct optical input was used. The speed of computation in the experimental arc...
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An experimental system was built up and tested for optical path tracking of a robot, where a 16*16 connected component detector chip with direct optical input was used. The speed of computation in the experimental architecture was analyzed.< >
This paper introduces an analytic method to determine the sensitivity to random parameter variations of analog VLSI neural network architectures for linear image filtering. The authors compare the robustness of severa...
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This paper introduces an analytic method to determine the sensitivity to random parameter variations of analog VLSI neural network architectures for linear image filtering. The authors compare the robustness of several different circuit architectures for low pass filtering. This method can also determine which components within a particular architecture should specified the most precisely.< >
In this paper it is shown how the Cellular Neural Network (CNN) can be used to perform image and volume deblurring, with particular emphases on applications to microscopy. We discuss the basic linear theory of the CNN...
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In this paper it is shown how the Cellular Neural Network (CNN) can be used to perform image and volume deblurring, with particular emphases on applications to microscopy. We discuss the basic linear theory of the CNN including issues of stability and template size. It is observed that a CNN with a small template can be used to implement an Infinite Impulse Response filter. It is then shown how general deblurring problems can be addressed with a CNN when the blurring operator is known. The proposed application is to solve the basic 3-D confocal image reconstruction task about the form of the blurring operator, confocal behavior in microscope images can be obtained with only 3-5 acquired image planes. In addition, the stored program capability of the CNN Universal Machine would provide integration of several image processing and detection tasks in the same architecture.< >
In this paper we report on a fast, complex and efficient implementation of the Cellular Neural Network Universal Machine as an IC chip. The chip has continuous time analog dynamics, and has been designed to process 50...
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In this paper we report on a fast, complex and efficient implementation of the Cellular Neural Network Universal Machine as an IC chip. The chip has continuous time analog dynamics, and has been designed to process 500,000 image frames per second.< >
The theory of fuzzy sets and the development of qualitative reasoning have had similar motivations: coping with complexity in reasoning about the properties of physical systems. An approach is described that utilizes ...
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The theory of fuzzy sets and the development of qualitative reasoning have had similar motivations: coping with complexity in reasoning about the properties of physical systems. An approach is described that utilizes fuzzy sets to develop a fuzzy qualitative simulation algorithm that allows a semiquantitative extension to qualitative simulation, providing three significant advantages over existing techniques. Firstly, it allows a more detailed description of physical variables, through an arbitrary, but finite, discretisation of the quantity space. The adoption of fuzzy sets also allows common-sense knowledge to be represented in defining values through the use of graded membership, enabling the subjective element in system modelling to be incorporated and reasoned with in a formal way. Secondly, the fuzzy quantity space allows more detailed description of functional relationships in that both strength and sign information can be represented by fuzzy relations holding against two or multivariables. Thirdly, the quantity space allows ordering information on rates of change to be used to compute temporal durations of the state and the possible transitions. Thus, an ordering of the evolution of the states and the associated temporal durations are obtained. This knowledge is used to develop an effective temporal filter that significantly reduces the number of spurious behaviors. Experimental results with the algorithm are presented and comparison with other recently proposed methods is made.
The theory of fuzzy sets and the development of qualitative modelling have had similar motivations: coping with complexity in reasoning about the behaviour of physical systems. The paper presents a synthesis of these ...
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The theory of fuzzy sets and the development of qualitative modelling have had similar motivations: coping with complexity in reasoning about the behaviour of physical systems. The paper presents a synthesis of these techniques, providing a fuzzy qualitative modelling method for performing qualitative simulation that offers significant advantages over existing qualitative simulation methods. The resulting simulation algorithm is termed FuSim hereafter. This development makes a significant contribution towards the full-scale industrial applications of qualitative modelling. The paper shows a typical example of utilising fuzzy qualitative models in fault diagnosis of continuous dynamic systems, based on an iterative search technique.< >
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