The study presents a new classificationalgorithm and a new online training mode used for learning the parameters of a Bayesian RBFNN (radial basis function neural network) equaliser in a non-linear time-varying chann...
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The study presents a new classificationalgorithm and a new online training mode used for learning the parameters of a Bayesian RBFNN (radial basis function neural network) equaliser in a non-linear time-varying channel. The classificationalgorithm is used to determine the centres of the hidden layer neurons that are equal to the channel states. This proposed unsupervised classification algorithm is based on both the K-means and the rival penalised competitive algorithms. Its main advantage is neither an initialisation phase nor a knowledge of the channel states number is required. The connections of weights and the spread of the hidden neurons are learned by the gradient descent algorithm, which applies a new proposed training mode. This training mode combines the advantages of both the online and the offline training modes such as stability and good speed of convergence. The performances of the RBFNN equaliser trained by the proposed method are shown in comparison with the performances of the optimal Bayesian equaliser and those of the same equaliser trained by other known training modes. All these performances are studied by using different types of channels.
An unsupervised classification algorithm utilising both polarimetric scattering mechanisms (PSMs) of hybrid-polarity data and the Wishart classifier is proposed. The initial scattering categories of the proposed algor...
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An unsupervised classification algorithm utilising both polarimetric scattering mechanisms (PSMs) of hybrid-polarity data and the Wishart classifier is proposed. The initial scattering categories of the proposed algorithm are derived from the roll-invariant m-chi classificationalgorithm. Pixels with no clearly defined dominant PSM are excluded, and the resulting categories are expanded into a specified number of classes. These derived classes are taken as training samples of the Wishart classifier. The effectiveness of the proposed algorithm is validated with the dataset over San Francisco.
Hyperspectral image classification is an important part of the hyperspectral remote sensing information processing. The Iterative Selforganizing Data Analysis Techniques algorithm (ISODATA) clustering algorithm which ...
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ISBN:
(纸本)9781479958368
Hyperspectral image classification is an important part of the hyperspectral remote sensing information processing. The Iterative Selforganizing Data Analysis Techniques algorithm (ISODATA) clustering algorithm which is an unsupervised classification algorithm is considered as an effective measure in the area of processing hyperspectral images. In this paper, an improved ISODATA algorithm is proposed for hyperspectral images classification. The algorithm takes the maximum and minimum spectrum of the image into consideration and determines the initial cluster center by the stepped construction of spectrum accurately. The classification experiment results show that using the improved ISODATA algorithm can determine the initial cluster number adaptively. In comparison with the SAM (Spectral Angle Mapper) algorithm and the original ISODATA algorithm, a better performance of the proposed ISODATA method is shown in the part of results.
Polycomb-group (PcG) of proteins are evolutionarily conserved transcription factors necessary for the regulation of gene expression during the development and the safeguard of cell identity in adulthood. In the nucleu...
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Polycomb-group (PcG) of proteins are evolutionarily conserved transcription factors necessary for the regulation of gene expression during the development and the safeguard of cell identity in adulthood. In the nucleus, they form aggregates whose positioning and dimension are fundamental for their function. We present an algorithm, and its MATLAB implementation, based on mathematical methods to detect and analyze PcG proteins in fluorescence cell image z-stacks. Our algorithm provides a method to measure the number, the size, and the relative positioning of the PcG bodies in the nucleus for a better understanding of their spatial distribution, and thus of their role for a correct genome conformation and function. less
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