Author
George Tzagkarakis
Other affiliations: DSM, French Alternative Energies and Atomic Energy Commission, University of Crete
Bio: George Tzagkarakis is an academic researcher from Foundation for Research & Technology – Hellas. The author has contributed to research in topics: Compressed sensing & Wireless sensor network. The author has an hindex of 13, co-authored 64 publications receiving 683 citations. Previous affiliations of George Tzagkarakis include DSM & French Alternative Energies and Atomic Energy Commission.
Papers published on a yearly basis
Papers
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01 Jan 2014TL;DR: A suite of novel indoor positioning techniques utilizing signal-strength fingerprints collected from access points and compressive sensing to perform sparsity-based accurate indoor localization, while reducing significantly the amount of information transmitted from a wireless device, possessing limited power, storage, and processing capabilities, to a central server is introduced.
Abstract: Accurate location awareness is of paramount importance in most ubiquitous and pervasive computing applications. Numerous solutions for indoor localization based on IEEE802.11, bluetooth, ultrasonic and vision technologies have been proposed. This paper introduces a suite of novel indoor positioning techniques utilizing signal-strength (SS) fingerprints collected from access points (APs). Our first approach employs a statistical representation of the received SS measurements by means of a multivariate Gaussian model by considering a discretized grid-like form of the indoor environment and by computing probability distribution signatures at each cell of the grid. At run time, the system compares the signature at the unknown position with the signature of each cell by using the Kullback-Leibler Divergence (KLD) between their corresponding probability densities. Our second approach applies compressive sensing (CS) to perform sparsity-based accurate indoor localization, while reducing significantly the amount of information transmitted from a wireless device, possessing limited power, storage, and processing capabilities, to a central server. The performance evaluation which was conducted at the premises of a research laboratory and an aquarium under real-life conditions, reveals that the proposed statistical fingerprinting and CS-based localization techniques achieve a substantial localization accuracy.
100 citations
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TL;DR: This paper presents a novel rotation-invariant image retrieval scheme based on a transformation of the texture information via a steerable pyramid using a joint alpha-stable sub-Gaussian model and a normalization process to Gaussianize the coefficients.
Abstract: This paper presents a novel rotation-invariant image retrieval scheme based on a transformation of the texture information via a steerable pyramid. First, we fit the distribution of the subband coefficients using a joint alpha-stable sub-Gaussian model to capture their non-Gaussian behavior. Then, we apply a normalization process in order to Gaussianize the coefficients. As a result, the feature extraction step consists of estimating the covariances between the normalized pyramid coefficients. The similarity between two distinct texture images is measured by minimizing a rotation-invariant version of the Kullback-Leibler Divergence between their corresponding multivariate Gaussian distributions, where the minimization is performed over a set of rotation angles
78 citations
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11 Nov 2010TL;DR: An approach to ℓp norm minimisation that employs the iteratively reweighted least squares (IRLS) algorithm but in which the parameter p is judiciously chosen by relating it to the characteristic exponent of the underlying alpha-stable distributed data.
Abstract: This paper introduces a novel framework for compressive sensing of biomedical ultrasonic signals based on modelling data with stable distributions. We propose an approach to l p norm minimisation that employs the iteratively reweighted least squares (IRLS) algorithm but in which the parameter p is judiciously chosen by relating it to the characteristic exponent of the underlying alpha-stable distributed data. Our results show that the proposed algorithm, which we prefer to call S±S-IRLS, outperforms previously proposed l 1 minimisation algorithms, such as basis pursuit or orthogonal matching pursuit, both visually and in terms of PSNR.
77 citations
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14 Mar 2010TL;DR: This paper develops a Bayesian CS (BCS) approach for estimating target bearings based on multiple noisy CS measurement vectors, where each vector results by projecting the received source signal on distinct over-complete dictionaries.
Abstract: Traditional bearing estimation techniques perform Nyquist-rate sampling of the received sensor array signals and as a result they require high storage and transmission bandwidth resources. Compressed sensing (CS) theory provides a new paradigm for simultaneously sensing and compressing a signal using a small subset of random incoherent projection coefficients, enabling a potentially significant reduction in the sampling and computation costs. In this paper, we develop a Bayesian CS (BCS) approach for estimating target bearings based on multiple noisy CS measurement vectors, where each vector results by projecting the received source signal on distinct over-complete dictionaries. In addition, the prior belief that the vector of projection coefficients should be sparse is enforced by fitting directly the prior probability distribution with a Gaussian Scale Mixture (GSM) model. The experimental results show that our proposed method, when compared with norm-based constrained optimization CS algorithms, as well as with single-measurement BCS methods, improves the reconstruction performance in terms of the detection error, while resulting in an increased sparsity.
39 citations
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17 Oct 2010TL;DR: This paper proposes a novel localization technique based on a multivariate Gaussian modeling of the signal strength measurements collected from several access points (APs) at different locations and considers a discretized grid-like form of the environment and computes a signature at each cell of the grid.
Abstract: This paper proposes a novel localization technique based on a multivariate Gaussian modeling of the signal strength measurements collected from several access points (APs) at different locations. It considers a discretized grid-like form of the environment and computes a signature at each cell of the grid. At run time the system compares the signature at the unknown position with the signature of each cell using the Kullback-Leibler Divergence estimation (KLD) between their corresponding probability densities. The paper evaluates the performance of the proposed technique and compares it with other statistical fingerprint-based localization systems. The performance analysis studies were conducted at the premises of a research laboratory and an aquarium under various conditions. Furthermore, the paper evaluates the impact of the number of APs and the size of the measurement datasets.
37 citations
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2,345 citations
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01 Jan 1990TL;DR: This chapter introduces the subject of statistical pattern recognition (SPR) by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier.
Abstract: This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.
1,189 citations
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TL;DR: This paper derives two sparse Bayesian learning algorithms, which have superior recovery performance compared to existing algorithms, especially in the presence of high temporal correlation, and provides analysis of the global and local minima of their cost function.
Abstract: We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlation and thus their performance degrades significantly with the correlation. In this paper, we propose a block sparse Bayesian learning framework which models the temporal correlation. We derive two sparse Bayesian learning (SBL) algorithms, which have superior recovery performance compared to existing algorithms, especially in the presence of high temporal correlation. Furthermore, our algorithms are better at handling highly underdetermined problems and require less row-sparsity on the solution matrix. We also provide analysis of the global and local minima of their cost function, and show that the SBL cost function has the very desirable property that the global minimum is at the sparsest solution to the MMV problem. Extensive experiments also provide some interesting results that motivate future theoretical research on the MMV model.
792 citations
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01 Jan 1992
335 citations