The process of reconstructing an original image from a compressed one is a difficult problem, since a large number of original images lead to the same compressed image and solutions to the inverse problem cannot be un...
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The process of reconstructing an original image from a compressed one is a difficult problem, since a large number of original images lead to the same compressed image and solutions to the inverse problem cannot be uniquely determined. Vector quantization is a compression technique that maps an input set of k-dimensional vectors into an output set of k-dimensional vectors, such that the selected output vector is closest to the input vector according to a selected distortion measure. In this paper, we show that adaptive 2D vector quantization of a fast discrete cosine transform of images using Kohonen neural networks outperforms other Kohonen vector quantizers in terms of quality (i.e. less distortion). A parallel implementation of the quantizer on a network of SUN Sparcstations is also presented.
A new adaptation algorithm is presented for adaptive control of robot manipulators. The passivity property of the proposed algorithm is first established, then the stability is proved based on the passivity properties...
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A new adaptation algorithm is presented for adaptive control of robot manipulators. The passivity property of the proposed algorithm is first established, then the stability is proved based on the passivity properties of the plant and those of the proposed algorithm. Because of the use of the past information and averaging effect, this algorithm gives a smoother tracking and parameter error and a parameter convergence under a weaker excitation condition.
An adaptive equalization technique using forward error control (FEC) coding is described and evaluated. Computer simulation is used to compare the FEC-assisted adaptive equalization to conventional adaptive equalizati...
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An adaptive equalization technique using forward error control (FEC) coding is described and evaluated. Computer simulation is used to compare the FEC-assisted adaptive equalization to conventional adaptive equalization. The system model employed is based on the proposed digital cellular communication standard for North America. The FEC-assisted adaptive equalization technique performs better than the conventional equalization method at low and moderate Doppler frequencies with the same level of redundancy in the transmitted data. For systems employing both FEC coding and adaptive equalization, the FEC-assisted adaptive equalization method successfully combines these two functions while using the redundancy required for only one of them. In other words, training required for adaptive equalization can be provided at no extra cost in terms of data redundancy in a system using FEC coding and vice versa.
Decision feedback equalizers (DFE's) are widely used in modern local network digital transmission systems to remove the intersymbol interference caused by slowly decaying pulse tails. This paper describes a simple...
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Decision feedback equalizers (DFE's) are widely used in modern local network digital transmission systems to remove the intersymbol interference caused by slowly decaying pulse tails. This paper describes a simple gradient descent algorithm for adapting a coefficient to model the slowly decaying portion of the tail. An equalization strategy is described that exploits a priori knowledge of the nature of the subscriber loop channel, together with the new adaptation algorithm, to result in reduced complexity DFE structures. The use of this new algorithm in both FIR and IIR equalizer structures is described. These structures can be dimensioned to give equivalent performance to a conventional DFE but with a reduced number of coefficients, or to give improved performance for a given complexity. The use of this new algorithm in both FIR and IIR equalizers is quantitively compared to a conventional DFE in terms of performance and implementation complexity. An analysis is presented describing the operation of the adaptation algorithm in the presence of noise. Simulation results are used to illustrate the trainup of the algorithm and its stability in the presence of near-end crosstalk noise.
The problem of separating two relatively compact classes of matrices by a linear function is investigated. The modality properties of the corresponding optimality criteria are considered. It is recommended that this s...
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The problem of separating two relatively compact classes of matrices by a linear function is investigated. The modality properties of the corresponding optimality criteria are considered. It is recommended that this sort of classifier is used for separating matrix descriptions of characters. The classifier is easy to implement and the sample size is smaller than in the case of a common vector classifier. Some simulation results show that it is possible to utilize the theory of matrix classification in solving a special character recognition problem.
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