The paper is concerned with the implementation of cryptographic hash functions on the regular array of simple cellular neural network (CNN) cells with periodic boundary conditions. Cryptographic hash functions enable ...
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The paper is concerned with the implementation of cryptographic hash functions on the regular array of simple cellular neural network (CNN) cells with periodic boundary conditions. Cryptographic hash functions enable message origin authentication and validation of message content integrity. A class of cryptographic hash functions-termed Cartesian authentication codes-provide provable (unconditional) security for message authentication between two mutually trustful parties sharing a secret key. We succeeded in implementing existing constructions of Cartesian authentication codes on today's CNN Universal Machine (CNN-UM) chips. Here we prove that rather complex (binary) arithmetic can be performed on a simple CNN chip, by providing an algorithm to implement a specific Cartesian authentication code based on the computation of a polynomial expression over a finite field. The bitrate of the computation is in the 100 Mbit/sec range with existing chips.
Novel types of analogic algorithms, using spatio-temporal CNN (cellular nonlinear/neural networks) operations are introduced. These algorithms make complex decisions in images without reading out the CNN chip. This ma...
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Novel types of analogic algorithms, using spatio-temporal CNN (cellular nonlinear/neural networks) operations are introduced. These algorithms make complex decisions in images without reading out the CNN chip. This makes them extremely time, area, and power effective. Two crucial effects are emphasized: diffusion type templates are applied during a finite time interval and local logic operates within well defined parts (patches) in the image plane. Hence, a new type of pattern recognition algorithm is introduced. The technique is demonstrated on an example. In our example we are dealing with an actual problem: how to avoid the counterfeiting on color copiers.< >
For pt.I, see ibid., p.1-10 (1992). The programmability (as a stored program) of the CNN universal machine is discussed. It is shown why and in which sense this machine is universal. The analogic type of algorithm is ...
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For pt.I, see ibid., p.1-10 (1992). The programmability (as a stored program) of the CNN universal machine is discussed. It is shown why and in which sense this machine is universal. The analogic type of algorithm is introduced. The application potential is reviewed and the biological relevance is analyzed. It is shown that the architecture is optimal not only for silicon implementations, but also for many biological information processing organs that have the same structure.< >
Various types of cellular neural networks (CNNs) are summarized, and a taxonomy of CNNs is given according to the different types of grids, processors, interactions, and modes of operation. The CNN universal machine i...
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Various types of cellular neural networks (CNNs) are summarized, and a taxonomy of CNNs is given according to the different types of grids, processors, interactions, and modes of operation. The CNN universal machine is introduced. The architecture and the key features of the CNN universal machine are outlined. An exhaustive list of references is given.< >
A multitarget tracking framework implemented on the Bi-i platform is presented. The demonstration applications include a target tracking system with laser actuation, an attention-selection algorithm and a laser dot de...
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A multitarget tracking framework implemented on the Bi-i platform is presented. The demonstration applications include a target tracking system with laser actuation, an attention-selection algorithm and a laser dot detection system with gaming applications [4]
In this paper a spatio-temporal analogic cellular neural network (CNN) algorithm is designed for front-end filtering, segmentation and object recognition. First, a generalized segmentation strategy is presented based ...
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In this paper a spatio-temporal analogic cellular neural network (CNN) algorithm is designed for front-end filtering, segmentation and object recognition. First, a generalized segmentation strategy is presented based on various diffusion models. Both PDE and non-PDE related schemes are discussed and their VLSI complexity is analyzed. In classification (object recognition) a CNN implementation of the autowave metric, a "nonlinear" variant of the Hausdorff metric, is used. This approach turned out to be superior compared to some other classification methods. A number of tests have been completed within the so-called "bubble/debris" segmentation experiments using original and artificial gray-scale images.
The visual navigation system of a UAV is a complex embedded device designed to modify the path of the platform depending on objects or events detected on the ground. In the visual field of the autopilot these events c...
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The visual navigation system of a UAV is a complex embedded device designed to modify the path of the platform depending on objects or events detected on the ground. In the visual field of the autopilot these events could be formalized as specific space-time signatures. Processing all pixels captured by the on-board camera(s) in real time with high frame rate needs huge computational effort that is often unnecessary. An adequate computational strategy would focus on the interesting locations only as in the visual system of various species. In this article we describe an automatic focusing mechanism relying on optical flow calculation for detecting moving objects on the ground, thus efficiently separating the motion of interest from ego-motion of the platform.
The printed circuit board layout inspection methods are mostly based on local geometric information, therefore it is well suited to the cellular neural network (CNN) paradigm. Two layout errors are detected here namel...
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The printed circuit board layout inspection methods are mostly based on local geometric information, therefore it is well suited to the cellular neural network (CNN) paradigm. Two layout errors are detected here namely, the breaks in the wires and some kind of short circuits. The designed analogic algorithms to solve the problems above were tested on real life examples using an experimental system based on our CNN-HAC1M digital multiprocessor add-on-board, with 1 million cell space and 2.0 /spl mu/s/cell/iteration speed.
computer Integrated Manufacturing (CIM) systems having determining role in the modern industry. These systems contain essentially two different equipments for transporting materials between workstations: - conveyors c...
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In this paper the parallel implementation of the Horn and Schunck motion estimation method in image sequences is presented, by using cellular neural networks (CNN). One of the drawbacks of the classical motion estimat...
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In this paper the parallel implementation of the Horn and Schunck motion estimation method in image sequences is presented, by using cellular neural networks (CNN). One of the drawbacks of the classical motion estimation algorithms is the computational time. The goal of the CNN implementation of the Horn & Schunck method is to increase the efficiency of the well-known classical implementation of this method, which is one of the most used algorithms among the motion estimation techniques. The aim is to obtain a smaller computation time and to include such an algorithm in motion compensation algorithms implemented using CNN, in order to obtain homogeneous algorithms for real-time applications in artificial vision or medical imaging
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