The Experts below are selected from a list of 174 Experts worldwide ranked by ideXlab platform
Dimitrios A. Karras - One of the best experts on this subject based on the ideXlab platform.
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ICONIP (1) - An improved modular neural network model for adaptive trajectory tracking control of robot manipulators
Advances in Neuro-Information Processing, 2009Co-Authors: Dimitrios A. KarrasAbstract:A novel approach is presented for adaptive trajectory tracking of robot manipulators using a three-stage hierarchical neural network model involving Support Vector Machines (SVM) and an adaptive unsupervised Neural Network. It involves a novel adaptive Self Organizing feature map (SOFM) in the first stage which aims at clustering the input variable space into smaller subspaces representative of the input space probability distribution and preserving its original topology, while rapidly increasing, on the other hand, cluster distances. Moreover, its Codebook Vector adaptation rule involves m-winning neurons dynamics and not the winner takes all approach. During convergence phase of the map a group of Support Vector Machines, associated with its Codebook Vectors, is simultaneously trained in an online fashion so that each SVM learns to positively respond when the input data belong to the topological sub-space represented by its corresponding Codebook Vector, taking into account similarity with that Codebook Vector. Moreover, it learns to negatively respond to input data not belonging to such a previously mentioned corresponding topological sub-space. The proposed methodology is applied, with promising results, to the design of a neural-adaptive trajectory tracking controller, by involving the computer-torque approach, which combines the proposed three-stage neural network model with a classical servo PD feedback controller. The results achieved by the suggested hierarchical SVM approach are favorably compared to the ones obtained by traditional (PD) and non-hierarchical neural network based controllers.
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ICANN (2) - A hierarchical support Vector machine based solution for off-line inverse modeling in intelligent robotics applications
Lecture Notes in Computer Science, 2005Co-Authors: Dimitrios A. KarrasAbstract:A novel approach is presented for continuous function approximation using a two-stage neural network model involving Support Vector Machines (SVM) and an adaptive unsupervised Neural Network to be applied to real functions of many variables. It involves an adaptive Kohonen feature map (SOFM) in the first stage which aims at quantizing the input variable space into smaller regions representative of the input space probability distribution and preserving its original topology, while rapidly increasing, on the other hand, cluster distances. During convergence phase of the map a group of Support Vector Machines, associated with its Codebook Vectors, is simultaneously trained in an online fashion so that each SVM learns to respond when the input data belong to the topological space represented by its corresponding Codebook Vector. The proposed methodology is applied, with promising results, to the design of a neural-adaptive controller, by involving the computer-torque approach, which combines the proposed two-stage neural network model with a servo PD feedback controller. The results achieved by the suggested SVM approach are favorably compared to the ones obtained if the role of SVMs is undertaken, instead, by Radial Basis Functions (RBF).
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Two-stage neural network models for MR image reconstruction from sparsely sampled k-space
2004 IEEE International Workshop on Imaging Systems and Techniques (IST) (IEEE Cat. No.04EX896), 1Co-Authors: Dimitrios A. Karras, Basil G. Mertzios, Danielle Graveron-demilly, D. Van OrmondtAbstract:A novel approach for magnetic resonance imaging (MRI) reconstruction using a two-stage neural network model, involving regularization techniques, is herein presented. The MRI reconstruction problem is considered when the k-space is sparsely scanned. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. The goal in such a case is to reduce the measurement time by omitting as many scanning trajectories as possible. The proposed model involves a regularized Kohonen feature map (SOFM) in the first stage which aims at quantizing the input variable space into smaller regions representative of the input space probability distribution and preserving its original topology, while increasing, on the other hand, cluster distances. This is achieved through adapting not only the winning neuron and its neighboring neurons weights but, also, loosing neurons weights during map's convergence phase. During convergence phase of the map, a group of support Vector machines (SVM), associated with its Codebook Vectors, is simultaneously trained in an online fashion so that each SVM learns to respond when the input data belong to the topological space represented by its corresponding Codebook Vector. Moreover, these SVMs follow a task specific regularization strategy which aims at incorporating additional information in their training process. It is found that such a model results in an improved image reconstruction performance very favourably compared to the one obtained by the trivial zero-filled k-space approach or traditional more sophisticated interpolation approaches.
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Neural network models based on regularization techniques for off-line robot manipulator path planning
2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 1Co-Authors: Dimitrios A. KarrasAbstract:A novel approach for continuous function approximation using a two-stage neural network model, involving regularization techniques, is herein presented. The suggested method can be applied to real functions of many variables as in robot path planning problems. It involves a regularized Kohonen feature map (SOFM) in the first stage which aims at quantizing the input variable space into smaller regions representative of the input space probability distribution and preserving its original topology, while increasing, on the other hand, cluster distances. This is achieved through adapting not only the winning neuron and its neighboring neurons weights but, also, losing neurons weights during map's convergence phase. Losing neurons weights are adapted in a manner similar to that of LVQ, by increasing the distance between these weights Vectors and the corresponding input data Vectors. During convergence phase of the map a group of support Vector machines (SVM), associated with its Codebook Vectors, is simultaneously trained in an online fashion so that each SVM learns to respond when the input data belongs to the topological space represented by its corresponding Codebook Vector. Moreover, these SVMs follow a task specific regularization strategy which aims at incorporating additional information in their training process. The proposed methodology is applied to the design of a neural-adaptive controller, by involving the computer-torque approach, which combines the regularized two-stage neural network model with a servo PD feedback controller. For this task, the regularization technique aims at filtering SVMs outputs so that their values become closer to that of a PD feedback controller, while compensating the nonlinear terms of the error, as regards the estimated torque, introduced in the robotic manipulator by employing the PD controller.
Arturo R.p. Ragozini - One of the best experts on this subject based on the ideXlab platform.
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Tree-structured product-Codebook Vector quantization
Signal Processing: Image Communication, 2001Co-Authors: Giovanni Poggi, Arturo R.p. RagoziniAbstract:To carry out Vector quantization (VQ) on large Vectors, and hence obtain a good performance, it is necessary to introduce some structural constraint in the encoder. Product-Codebook VQ reduces memory storage and encoding complexity. Tree-structured VQ reduces encoding complexity as well, and allows for progressive transmission. In this paper tree-structured product-Codebook VQ is proposed to carry out low-complexity, low-memory storage VQ, with progressive transmission. The joint design of the tree-structured component Codebooks is analyzed and a low-complexity greedy procedure is devised. The proposed approach has been implemented for two applications: the compression of gray-scale images, and the compression of multispectral images by means of the SPIHT algorithm, providing in both cases satisfactory experimental results.
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ICECS - Tree-structured product-Codebook Vector quantization
ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics Circuits and Systems (Cat. No.99EX357), 1Co-Authors: Giovanni Poggi, Arturo R.p. RagoziniAbstract:This paper presents a new compression technique based on Vector quantization. The proposed encoding algorithm uses a product Codebook in which both component Codebooks are tree-structured. This allows us to obtain low-complexity progressive encoding of large Vectors. We propose a simple, greedy, procedure for the Codebook design and assess its performance in the case of gray-scale images for gain-shape product VQ. Simulation results show that the proposed technique exhibits a significant performance gain at very low bit rates, while assuring a satisfactory image quality.
W.h. Holmes - One of the best experts on this subject based on the ideXlab platform.
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Application of sorted Codebook Vector quantization to spectral coding of speech
Proceedings of GLOBECOM '95, 1Co-Authors: Hamid Reza Sadegh Mohammadi, W.h. HolmesAbstract:A new Vector quantization method, namely sorted Codebook Vector quantization (SCVQ) is presented in this article. The paper explains the principles of this method, including training and optimization of the associated Codebook. It is shown that this quantizer can be implemented efficiently with almost similar computational complexity to tree-searched Vector quantization (TSVQ) and the storage cost of that is the same as unstructured VQ (i.e less than TSVQ). Application of SCVQ to quantization of Line Spectral Frequencies (LSFs), which are the most popular parameters for spectrum quantization in speech coders using linear prediction model, is described. Superior performance of the new method is verified through experimental simulations.
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ICASSP - Low cost Vector quantization methods for spectral coding in low rate speech coders
1995 International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: Hamid Reza Sadegh Mohammadi, W.h. HolmesAbstract:In low rate speech coders based on the linear prediction method, the quality of synthesized speech can be improved by enhancement of the short-term spectrum quantization stage. In this study, we propose two new efficient methods for coding the spectral parameters, namely sorted Codebook Vector quantization (SCVQ) and fine-coarse Vector quantization (FCVQ). The principles of these methods are presented along with the methods of training and optimizing the related Codebooks. The performance of the new schemes is compared experimentally with other efficient methods, such as tree-searched Vector quantization (TSVQ) and multi-stage Vector quantization (MSVQ). We demonstrate that the new methods offer significant cost reduction whilst achieving superior quality.
Hamid Reza Sadegh Mohammadi - One of the best experts on this subject based on the ideXlab platform.
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Spectral coding of speech based on generalized sorted Codebook Vector quantization
ICSP '98. 1998 Fourth International Conference on Signal Processing (Cat. No.98TH8344), 1Co-Authors: Hamid Reza Sadegh MohammadiAbstract:Sorted Codebook Vector quantization (SCVQ) is shown to be a very efficient Vector quantization method. Generalization of SCVQ is suggested and its application to the spectral coding of speech using the quantization of line spectral frequencies (LSF), which are the most popular parameters to represent the linear prediction model for spectrum quantization in speech coders, is described. Computer simulations are conducted to evaluate the performance of the new method. We demonstrate that the new method achieves superior quality and has low implementation costs.
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Application of sorted Codebook Vector quantization to spectral coding of speech
Proceedings of GLOBECOM '95, 1Co-Authors: Hamid Reza Sadegh Mohammadi, W.h. HolmesAbstract:A new Vector quantization method, namely sorted Codebook Vector quantization (SCVQ) is presented in this article. The paper explains the principles of this method, including training and optimization of the associated Codebook. It is shown that this quantizer can be implemented efficiently with almost similar computational complexity to tree-searched Vector quantization (TSVQ) and the storage cost of that is the same as unstructured VQ (i.e less than TSVQ). Application of SCVQ to quantization of Line Spectral Frequencies (LSFs), which are the most popular parameters for spectrum quantization in speech coders using linear prediction model, is described. Superior performance of the new method is verified through experimental simulations.
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ICASSP - Low cost Vector quantization methods for spectral coding in low rate speech coders
1995 International Conference on Acoustics Speech and Signal Processing, 1Co-Authors: Hamid Reza Sadegh Mohammadi, W.h. HolmesAbstract:In low rate speech coders based on the linear prediction method, the quality of synthesized speech can be improved by enhancement of the short-term spectrum quantization stage. In this study, we propose two new efficient methods for coding the spectral parameters, namely sorted Codebook Vector quantization (SCVQ) and fine-coarse Vector quantization (FCVQ). The principles of these methods are presented along with the methods of training and optimizing the related Codebooks. The performance of the new schemes is compared experimentally with other efficient methods, such as tree-searched Vector quantization (TSVQ) and multi-stage Vector quantization (MSVQ). We demonstrate that the new methods offer significant cost reduction whilst achieving superior quality.
Giovanni Poggi - One of the best experts on this subject based on the ideXlab platform.
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Tree-structured product-Codebook Vector quantization
Signal Processing: Image Communication, 2001Co-Authors: Giovanni Poggi, Arturo R.p. RagoziniAbstract:To carry out Vector quantization (VQ) on large Vectors, and hence obtain a good performance, it is necessary to introduce some structural constraint in the encoder. Product-Codebook VQ reduces memory storage and encoding complexity. Tree-structured VQ reduces encoding complexity as well, and allows for progressive transmission. In this paper tree-structured product-Codebook VQ is proposed to carry out low-complexity, low-memory storage VQ, with progressive transmission. The joint design of the tree-structured component Codebooks is analyzed and a low-complexity greedy procedure is devised. The proposed approach has been implemented for two applications: the compression of gray-scale images, and the compression of multispectral images by means of the SPIHT algorithm, providing in both cases satisfactory experimental results.
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ICECS - Tree-structured product-Codebook Vector quantization
ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics Circuits and Systems (Cat. No.99EX357), 1Co-Authors: Giovanni Poggi, Arturo R.p. RagoziniAbstract:This paper presents a new compression technique based on Vector quantization. The proposed encoding algorithm uses a product Codebook in which both component Codebooks are tree-structured. This allows us to obtain low-complexity progressive encoding of large Vectors. We propose a simple, greedy, procedure for the Codebook design and assess its performance in the case of gray-scale images for gain-shape product VQ. Simulation results show that the proposed technique exhibits a significant performance gain at very low bit rates, while assuring a satisfactory image quality.