作者:
Shaikh, Fayaz A.McClellan, StanCenter of Telecommunications
Education and Research Department of Electrical and Computer Engineering University of Alabama at Birmingham 1150 Tenth Avenue South BEC 253 BirminghamAL35294-4461 United States IP Architecture Group
Technology and Systems Engineering Compaq Telecommunications 1255 West Fifteenth Street Suite 8000 PlanoTX75075 United States
The primary challenges in the deployment of Voice over Internet Protocol technology include Quality of Service (QoS) guarantees in the form of stringent bounds on end-to-end delay, jitter and loss, as well as rigorous...
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The design of high-performance, high-precision, real-time digital signal processing (DSP) systems, such as those associated with wavelet signal processing, is a challenging problem. This paper reports on the innovativ...
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The design of high-performance, high-precision, real-time digital signal processing (DSP) systems, such as those associated with wavelet signal processing, is a challenging problem. This paper reports on the innovative use of the residue number system (RNS) for implementing high-end wavelet filter banks. The disclosed system uses an enhanced index-transformation defined over Galois fields to efficiently support different wavelet filter instantiations without adding any extra cost or additional lookup tables (LUT). An exhaustive comparison against existing two's complement (2C) designs for different custom IC technologies was carried out. These structures have been demonstrated to be well suited for field programmable logic (FPL) assimilation as well as for CBIC (cell-based integrated circuit) technologies.
In this paper we consider the successful hybridation of a two modern computational schemes, Clustering and Neural Networks, for the Predictive Classification of the future value of insect infestation levels for Integr...
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We propose a framework for constructing and training a radial basis function (RBF) neural network. The structure of the gaussian functions is modified using a pseudo-gaussian function (PG) in which two scaling paramet...
We propose a framework for constructing and training a radial basis function (RBF) neural network. The structure of the gaussian functions is modified using a pseudo-gaussian function (PG) in which two scaling parameters sigma are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. We propose a modified PG-BF (pseudo-gaussian basis function) network in which the regression weights are used to replace the constant weights in the output layer. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. A salient feature of the network systems is that the method used for calculating the overall output is the weighted average of the output associated with each receptive field. The superior performance of the proposed PG-BF system over the standard RBF are illustrated using the problem of short-term prediction of chaotic time series.
Many distributed applications executed on networks of workstations (NOWs) require the interconnection network to provide some quality of service (QoS) support. These networks must be able to support topology changes (...
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Networks of Workstations (NOWs) are usually arranged as a set of interconnected switches with hosts connected to switch ports through interface cards. Several commercial interconnects for high-speed NOWs use up✶/down✶...
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The growing number of applications for 3D graphics and imaging systems in the mass market requires the customized approach to the design of high-performance 3D graphics and imaging system architectures. That fact coup...
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Describes a structure to create a RBF neural network. This structure has 4 main characteristics. The first one is that the special RBF network architecture uses regression weights to replace the constant weights norma...
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Describes a structure to create a RBF neural network. This structure has 4 main characteristics. The first one is that the special RBF network architecture uses regression weights to replace the constant weights normally used. These regression weights are assumed to be functions of input variables. The second characteristic is the normalization of the activation of the hidden neurons (weighted average) before aggregating the activations, which, as observed by various authors, produces better results than the classical weighted sum architecture. The third aspect is that a new type of nonlinear function is proposed, the pseudo-gaussian function (PGBF). With this, the neural system gains flexibility, as the neurons possess an activation field that does not necessarily have to be symmetric with respect to the centre or to the location of the neuron in the input space. In addition to this new structure, we propose, as the fourth and final feature, a sequential learning algorithm, which is able to adapt the structure of the network, with this, it is possible to create new hidden units and also to detect and remove inactive units.
Because there are many possibilities for the set of basic functions, parameters and operators used in the design of an adaptive-network-based fuzzy inference system (ANFTS), the search for the most suitable operators ...
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Because there are many possibilities for the set of basic functions, parameters and operators used in the design of an adaptive-network-based fuzzy inference system (ANFTS), the search for the most suitable operators and functional blocks, together with their characterization and evaluation, is also an important topic in the field of neuro-fuzzy design. As shown in papers dealing with real applications, the designer has to select the operator to be used in each phase in the design of a neuro-fuzzy system, and this decision is usually taken in terms of the most common operations performed. Nevertheless, it is very important to determine which factors have the greatest influence on the behaviour and performance of the neuro-fuzzy system. Therefore, the designer should pay close attention to the phase in which the selection of the operator is most statistically significant. In this way, it is possible to obviate a detailed analysis of different configurations that lead to systems with very similar performance. In order to perform this analysis, an appropriate statistical tool has been used: the multifactorial analysis of the variance (ANOVA) which consists of a set of statistical techniques that enable the analysis and comparison of experiments, by describing the interactions and interrelations between either the quantitative or the qualitative variables of the neural network system. By applying this methodology to a great variety of neuro-fuzzy systems, it is possible to obtain general results about the most relevant factors defining the neural network design.
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