applications of artificialneuralnetworks to in situ assessment of water quality are considered by means of an online optical scatter nephelometer. Light scattered by suspensions of oil in water is investigated for t...
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applications of artificialneuralnetworks to in situ assessment of water quality are considered by means of an online optical scatter nephelometer. Light scattered by suspensions of oil in water is investigated for three different oils in the concentration range 0-100 parts per million by,volume. An artificialneural network is designed to recognise the oil species and output its concentration to within an accuracy of 5.4%. applications of the technique to more general classes of suspensions are discussed.
The analog CMOS circuit realization of cellular neuralnetworks with transconductance elements is presented. This realization can be easily adapted to various types of applications in imageprocessing just by choosing...
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The analog CMOS circuit realization of cellular neuralnetworks with transconductance elements is presented. This realization can be easily adapted to various types of applications in imageprocessing just by choosing the appropriate transconductance parameters according to the predetermined coefficients. The effectiveness of the designed circuits for connected component detection is shown by HSPICE simulations. For ''fixed function'' cellular neural network circuits the number of transistors are reduced further by using multi-input transconductance elements.
In this paper we describe some of the most important types of neuralnetworks applied in biomedical imageprocessing. The networks described are variations of well-known architectures but are including image-relevant ...
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In this paper we describe some of the most important types of neuralnetworks applied in biomedical imageprocessing. The networks described are variations of well-known architectures but are including image-relevant features in their structure. Convolutional neuralnetworks, modified Hopfield networks, regularization networks and nonlinear principal component analysis neuralnetworks are successfully applied in biomedical image classification, restoration and compression.
In this paper, we describe a genetic learning neural network system to vector quantize images directly to achieve data compression. The genetic learning algorithm is designed to have two levels: One is at the level of...
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ISBN:
(纸本)0819424412
In this paper, we describe a genetic learning neural network system to vector quantize images directly to achieve data compression. The genetic learning algorithm is designed to have two levels: One is at the level of code words in which each neural network is updated through reproduction every time an input vector is processed. The other is at the level of code-books in which five neuralnetworks are included in the gene pool. Extensive experiments on a group of image samples show that the genetic algorithm outperforms other vector quantization algorithms which include competitive learning, frequency sensitive learning and LBG.
In this paper we propose a new scalable predictive vector quantization (PVQ) technique for image and video compression, This technique has been implemented using neuralnetworks. A Kohonen self organized feature may, ...
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ISBN:
(纸本)0819424412
In this paper we propose a new scalable predictive vector quantization (PVQ) technique for image and video compression, This technique has been implemented using neuralnetworks. A Kohonen self organized feature may, is used to implement the vector quantizer, while a multilayer perceptron implements the predictor. Simulation results demonstrate that the proposed technique provides a 5-10% improvement in coding performance over the existing neuralnetworks based PVQ techniques.
Given that neuralnetworks have been widely reported in the research community of medical imaging we provide a focused literature survey on recent neural network developments in computer-aided diagnosis medical image ...
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Given that neuralnetworks have been widely reported in the research community of medical imaging we provide a focused literature survey on recent neural network developments in computer-aided diagnosis medical image segmentation and edge detection towards visual content analysts and medical image registration for Its pre-processing and post-processing with the aims of increasing awareness of how neuralnetworks can be applied to these areas and to provide a foundation for further research and practical development Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem (ii) how medical images could be analysed processed and characterised by neuralnetworks and (iii) how neuralnetworks could be expanded further to resolve problems relevant to medical imaging In the concluding section a highlight of comparison among many neural network applications is Included to provide a global view on computational intelligence with neuralnetworks in medical Imaging (C) 2010 Elsevier Ltd All rights reserved
neuralnetworks have been applied to many kinds of imageprocessing with well performance. When dealing with the large image, a large number of neurons is required so as to (i)make the construction model more complex ...
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ISBN:
(纸本)0819427470
neuralnetworks have been applied to many kinds of imageprocessing with well performance. When dealing with the large image, a large number of neurons is required so as to (i)make the construction model more complex (ii)make the speed of processing slower than the traditional methods due to heavy computation load. In this paper, an encoder-segmented neural network (ESNN) is constructed for image segmentation in which the available data can be obtained by a weight matrix containing maximum region information when a large number of input data are compressed by encoder network, meantime, the fuzzy clustering strategy applied on Hopfield neural network for the fine segmentation eliminates the tedious work of finding weighting factors. The experimental results indicate the performance of image segmentation can be improved effectively.
Interesting perspectives in imageprocessing with cellular neuralnetworks can be emphasized from an investigation into the internal states dynamics of the model. Most of the cellular neuralnetworks design methods in...
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ISBN:
(纸本)3540660682
Interesting perspectives in imageprocessing with cellular neuralnetworks can be emphasized from an investigation into the internal states dynamics of the model. Most of the cellular neuralnetworks design methods intend to control internal states dynamics in order to pet a straight processing result. The present one involves some kind of internal states preprocessing so as to finally achieve processing otherwise unrealizable. applications of this principle to the building of complex processing schemes, gray level preserving segmentation and selective brightness variation are presented.
This paper extends a recent and very appealing approach of computational learning to the field of image analysis. Recent works have demonstrated that the implementation of artificialneuralnetworks (ANN) could be sim...
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ISBN:
(纸本)9783642217371;9783642217388
This paper extends a recent and very appealing approach of computational learning to the field of image analysis. Recent works have demonstrated that the implementation of artificialneuralnetworks (ANN) could be simplified by using a large amount of neurons with random weights. Only the output weights are adapted, with a single linear regression. Supervised learning is very fast and efficient. To adapt this approach to image analysis, the novelty is to initialize weights, not as independent random variables, but as Gaussian functions with only a few random parameters. This creates smooth random receptive fields in the image space. These image Receptive Fields - neuralnetworks (IRF-NN) show remarkable performances for recognition applications, with extremely fast learning, and can be applied directly to images without pre-processing.
This paper is dedicated to the application of artificialneuralnetworks (ANN) in titanium alloys research, including: (i) time-temperature transformation (TTT) diagrams for titanium alloys;(ii) correlation between pr...
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This paper is dedicated to the application of artificialneuralnetworks (ANN) in titanium alloys research, including: (i) time-temperature transformation (TTT) diagrams for titanium alloys;(ii) correlation between processing parameters and properties in titanium alloys and gamma-TiAl-based alloys;(iii) fatigue stress life diagrams for Ti-6Al-4V alloy;(iv) corrosion resistance of titanium alloys. For each particular case, appropriate combination of inputs and outputs is chosen. Standard multilayer feedforward networks are created and trained using comprehensive datasets from published literature. Very good performances of the neuralnetworks are achieved. Different effects are modelled, among which are: (i) influence of the alloying elements on the transformation kinetics in titanium alloys;(ii) influence of the processing parameters, alloy composition and the work temperature on the mechanical properties for titanium alloys and titanium aluminides;(iii) influence of the microstructure, temperature, environment, surface treatment and the stress ratio on the fatigue life. The artificialneuralnetworks models are combined with computer programmes for optimisation of the inputs in order to achieve desirable combination of outputs. Graphical user interfaces are developed for use of the models. These models are convenient and powerful tools for practical applications in solving various problems in titanium alloys. (C) 2003 Elsevier B.V. All rights reserved.
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