An effective method for visual pattern recognition using morphological techniques is presented. It is shown that it can be successfully used for the recognition of deformed letters. The method extracts morphological i...
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Church’s problem and the emptiness problem for Rabin automata on infinite trees, which represent basic paradigms for program synthesis and logical decision procedures, are formulated as a control problem for automata...
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As a first step to the control of paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is being studied. This paper deals with a neural control system, that has been develo...
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As a first step to the control of paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is being studied. This paper deals with a neural control system, that has been developed for this case. The control system has been tested for a model of the swinging lower leg using computer simulations. The neural controller was trained by supervised learning (SL) and by backpropagation through time (BTT). The performance of the controller with random initial weights was poor after training with BTT and fair after SL. BTT training of the neural controller with weights, which had been initialized by SL, resulted in good control. Training with BTT thus improved the performance of the controller that initially had been trained by SL. An adaptive neural control system based on BTT has been proposed and partially tested. The controller adapted relatively fast to the change of an important model parameter.
As a first step to the control of paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is being studied. This paper deals with a neural control system, that has been develo...
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As a first step to the control of paraplegic gait by functional electrical stimulation (FES), the control of the swinging lower leg is being studied. This paper deals with a neural control system, that has been developed for this case. The control system has been tested for a model of the swinging lower leg using computer simulations. The neural controller was trained by supervised learning (SL) and by backpropagation through time (BTT). The performance of the controller with random initial weights was poor after training with BTT and fair after SL. BTT training of the neural controller with weights, which had been initialized by SL, resulted in good control. Training with BTT thus improved the performance of the controller that initially had been trained by SL. An adaptive neural control system based on BTT has been proposed and partially tested. The controller adapted relatively fast to the change of an important model parameter.
An iteration method is presented for determining the largest singular value (2 - norm) of a matrix, and its corresponding singular vectors. Connections with the power method, and Bernoulli's method are presented. ...
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An architecture is presented for low-cost and flexible realisation of image processing and analysis algorithms of binary images. The flexibility of the architecture is due to reconfigurable network of simple boolean f...
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An architecture is presented for low-cost and flexible realisation of image processing and analysis algorithms of binary images. The flexibility of the architecture is due to reconfigurable network of simple boolean functions. It is shown that this architecture can readily be implemented by low-cost off-the-shelf components. This is illustrated by some simulations of a hypothetical realisation.< >
The authors present a general approach to determining the number of sinusoids present in measurements corrupted by additive white Gaussian and nonGaussian noise. The approach involves the simultaneous application of m...
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A nonlinear adaptive detector/estimator is introduced for single and multiple radar data processing. The problem of target detection from returns of monostatic radar(s) is formulated as a nonlinear joint detection/est...
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Many processes operate only around a limited number of operation points. In order to have adequate control around each operation point, an adaptive controller could be used. Then, if the operation point changes often,...
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Many processes operate only around a limited number of operation points. In order to have adequate control around each operation point, an adaptive controller could be used. Then, if the operation point changes often, a large number of parameters would have to be adapted over and over again. This prohibits application of conventional adaptive control, which is more suited for processes with slowly changing parameters. Furthermore, continuous adaptation is not always needed or desired. An extension of adaptive control is presented, in which for each operation point the process behaviour can be stored in a memory, retrieved from it and evaluated. These functions are coordinated by a "supervisor". This concept is referred to as supervisory control. It leads to an adaptive control structure which, after a learning phase, quickly adjusts the controller parameters based on retrieval of old information, without the need to fully relcam each time. This approach has been tested on an experimental set-up of a flexible beam, but it is directly applicable to processes in e.g. the (petro)chemical industry as well.
The design of binary hypothesis tests in the absence of any statistical information, based only on a set of available observations is studied. A Structured Adaptive Network (SAN) configuration for the design of such t...
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The design of binary hypothesis tests in the absence of any statistical information, based only on a set of available observations is studied. A Structured Adaptive Network (SAN) configuration for the design of such tests based on several criteria of optimality, is presented and certain key asymptotic properties of these criteria are established.
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