Papers by Vlamos Panayiotis
Proceedings of the First International Conference on Software and Data Technologies, 2006
The aim of the paper is to develop a new learning by examples PCA-based algorithm for extracting ... more The aim of the paper is to develop a new learning by examples PCA-based algorithm for extracting skeleton information from data to assure both good recognition performances, and generalization capabilities. Here the generalization capabilities are viewed twofold, on one hand to identify the right class for new samples coming from one of the classes taken into account and, on the other hand, to identify the samples coming from a new class. The classes are represented in the measuremen /feature space by continuous repartitions, that is the model is given by the family of density functions t () f H h h ∈ , where H stands for the finite set of hypothesis (classes). The basis of the learning process is represented by samples of possible different sizes coming from the considered classes. The skeleton of each class is given by the principal components obtained for the corresponding sample.
Real time digital audio delivery over Wireless Local Area Networks (WLANs) represents an attracti... more Real time digital audio delivery over Wireless Local Area Networks (WLANs) represents an attractive, flexible and cost effective framework for realizing high-quality, multichannel home audio applications. However, the unreliable nature of WLANs IP link frequently imposes significant playback quality degradation, due to delay or permanent loss of a number of transmitted digital audio packets. In this paper, a novel packet error concealment technique is presented, based on the spectral reconstruction of the statistical equivalent of a previously successfully received audio data packet. It is shown that the proposed data reconstruction scheme outperforms previously published error concealment strategies, in both terms of objective and perceptual criteria.
Corr, 2008
The best previous solution for the general polygon retrieval problem uses $O(n^2)$ space and answ... more The best previous solution for the general polygon retrieval problem uses $O(n^2)$ space and answers a query in $O(k\log{n}+A)$ time, where $k$ is the number of vertices. It is also very complicated and difficult to be implemented in a standard imperative programming language such as C or C++.
Cognitive science: From molecular biology to brain function
2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), 2015
The recent years the interest in neurocognitive and brain science research is relatively increase... more The recent years the interest in neurocognitive and brain science research is relatively increased. In this study the relation of DNA and genetic factors that are interrelated to brain development and cognitive functions is analyzed. Author’s main purpose is to localize and analyze the function of the particular parts of brain which are related to specific cognitive functions. Especially the connection of specific brain cortexes with mathematical perception and mathematical learning difficulties also known as dyscalculia is analyzed and evaluated as well. Future directions in order to evaluate and enhance the theoretical outcomes by visualizing the brain activity within the use of brain imaging techniques are also discussed.
On the Efficiency of a Certain Class of Noise Removal Algorithms in Solving Image Processing Tasks
Icinco, 2004
Iceis, 2008
The aim of the research reported in the paper was twofold: to propose a new approach in cluster a... more The aim of the research reported in the paper was twofold: to propose a new approach in cluster analysis and to investigate its performance, when it is combined with dimensionality reduction schemes. Our attempt is based on group skeletons defined by a set of orthogonal and unitary eigen vectors (principal directions) of the sample covariance matrix. Our developments impose a set of quite natural working assumptions on the true but unknown nature of the class system. The search process for the optimal clusters approximating the unknown classes towards getting homogenous groups, where the homogeneity is defined in terms of the "typicality" of components with respect to the current skeleton. Our method is described in the third section of the paper. The compression scheme was set in terms of the principal directions corresponding to the available cloud. The final section presents the results of the tests aiming the comparison between the performances of our method and the standard k-means clustering technique when they are applied to the initial space as well as to compressed data.
Classical feature extraction and data projection methods have been extensively investigated in th... more Classical feature extraction and data projection methods have been extensively investigated in the pattern recognition and exploratory data analysis literature. Feature extraction and multivariate data projection allow avoiding the "curse of dimensionality", improve the generalization ability of classifiers and significantly reduce the computational requirements of pattern classifiers. During the past decade a large number of artificial neural networks and learning algorithms have been proposed for solving feature extraction problems, most of them being adaptive in nature and well-suited for many real environments where adaptive approach is required. Principal Component Analysis, also called Karhunen-Loeve transform is a well-known statistical method for feature extraction, data compression and multivariate data projection and so far it has been broadly used in a large series of signal and image processing, pattern recognition and data analysis applications.
A Connectionist Approach in Bayesian Classification
Iceis, 2007
Neural Approaches to Image Compression/Decompression Using PCA based Learning Algorithms
On a certain class of algorithms for noise removal in image processing: a comparative study
Proceedings. International Conference on Information Technology: Coding and Computing, 2002
Abstract The effectiveness of restoration techniques mainly depends on the accuracy of the image ... more Abstract The effectiveness of restoration techniques mainly depends on the accuracy of the image modeling. One of the most popular degradation models is based on the assumption that the image blur can be modeled as a superposition with an impulse response H that may be space ...
International Conference on Enterprise Information Systems, 2008
The aim of the research reported in the paper was twofold: to propose a new approach in cluster a... more The aim of the research reported in the paper was twofold: to propose a new approach in cluster analysis and to investigate its performance, when it is combined with dimensionality reduction schemes. Our attempt is based on group skeletons defined by a set of orthogonal and unitary eigen vectors (principal directions) of the sample covariance matrix. Our developments impose a set of quite natural working assumptions on the true but unknown nature of the class system. The search process for the optimal clusters approximating the unknown classes towards getting homogenous groups, where the homogeneity is defined in terms of the "typicality" of components with respect to the current skeleton. Our method is described in the third section of the paper. The compression scheme was set in terms of the principal directions corresponding to the available cloud. The final section presents the results of the tests aiming the comparison between the performances of our method and the standard k-means clustering technique when they are applied to the initial space as well as to compressed data.
Automated prediction procedure for Charcot-Marie-Tooth disease
13th IEEE International Conference on BioInformatics and BioEngineering, 2013
Image Restoration - Recent Advances and Applications, 2012
Principal directions - Based algorthm for classification tasks
Proceedings - 9th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2007, 2007
ABSTRACT In our approach, we consider a probabilistic class model where each class h isin H is re... more ABSTRACT In our approach, we consider a probabilistic class model where each class h isin H is represented by a probability density function defined on Rn where n is the dimension of input data and H stands for a given finite set of classes. The classes are learned by the algorithm using the information contained by samples randomly generated from them. The learning process is based on the set of class skeletons, where the class skeleton is represented by the principal axes estimated from data. Basically, for each new sample, the recognition algorithm classifies it in the class whose skeleton is the "nearest" to this example. For each new sample allotted to a class, the class characteristics are re-computed using a first order approximation technique. We introduce two principal directions based learning algorithms, a non-adaptive variant and an adaptive variant respectively. Comparative analysis is performed and experimentally derived conclusions concerning the performance of the new proposed methods are reported in the final section of the paper.
Mobile Networks and Applications, 2008
Real time digital audio delivery over Wireless Local Area Networks (WLANs) represents an attracti... more Real time digital audio delivery over Wireless Local Area Networks (WLANs) represents an attractive, flexible and cost effective framework for realizing highquality, multichannel home audio applications. However, the unreliable nature of WLANs IP link frequently imposes significant playback quality degradation, due to delay or permanent loss of a number of transmitted digital audio packets. In this paper, a novel packet error concealment technique is presented, based on the spectral reconstruction of the statistical equivalent of a previously successfully received audio data packet. It is shown that the proposed data reconstruction scheme outperforms previously published error concealment strategies, in both terms of objective and perceptual criteria.
Proceedings of the 3rd …, Jan 1, 2007
Real time digital audio delivery over Wireless Local Area Networks (WLANs) represents an attracti... more Real time digital audio delivery over Wireless Local Area Networks (WLANs) represents an attractive, flexible and cost effective framework for realizing high-quality, multichannel home audio applications. However, the unreliable nature of WLANs IP link frequently imposes significant playback quality degradation, due to delay or permanent loss of a number of transmitted digital audio packets. In this paper, a novel packet error concealment technique is presented, based on the spectral reconstruction of the statistical equivalent of a previously successfully received audio data packet. It is shown that the proposed data reconstruction scheme outperforms previously published error concealment strategies, in both terms of objective and perceptual criteria.
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Papers by Vlamos Panayiotis