Maria Rangoussi

Person identification based on parametric processing of the EEG

Person identification based on parametric spectral analysis of the EEG signal is addressed in this work-a problem that has not yet been seen in a signal-processing framework, to the best of our knowledge. AR parameters are estimated from a signal containing only the alpha, rhythm activity of the EEG. These parameters are used as features in the classification step, which employs a learning vector quantizer network. The proposed method was applied on a set of real EEG recordings made on healthy individuals, in an attempt to experimentally investigate the connection between a persons EEG and genetically-specific information. Correct classification scores at the range of 72% to 84% show the potential of our approach for person classification/identification and are in agreement with previous research showing evidence that the EEG carries genetic information.

Parametric person identification from the EEG using computational geometry

Person identification based on features extracted parametrically from the EEG spectrum is investigated in this work. The method proposed utilizes computational geometry algorithms (convex polygon intersections), appropriately modified, in order to classify unknown EEGs. The signal processing step includes EEG spectral analysis for feature extraction, by fitting a linear model of the AR type on the alpha rhythm EEG signal. The correct classification scores obtained on real EEG data experiments (91% in the worst case) are promising in that they corroborate existing evidence that EEG carries genetically specific information and is therefore appropriate as a basis for person identification methods.

Neural network based person identification using EEG features

A direct connection between the electroencephalogram (EEG) and the genetic information of an individual has been suspected and investigated by neurophysiologists and psychiatrists since 1960. However, most of this early as well as more recent research focuses on the classification of pathological EEG cases, aiming to construct tests for purposes of diagnosis. On the contrary, our work focuses on healthy individuals and aims to establish an one-to-one correspondence between the genetic information of the individual and certain features of his/her EEG, as an intermediate step towards the further goal of developing a test for person identification based on features extracted from the EEG. Potential applications include, among others, information encoding and decoding and access to secure information. At the present stage the proposed method uses spectral information extracted from the EEG non-parametrically via the FFT and employs a neural network (a learning vector quantizer-LVQ) to classify unknown EEGs as belonging to one of a finite number of individuals. Correct classification scores ranging from 80% to 100% in experiments conducted on real data, show evidence that the EEG indeed carries genetic information and that the proposed method can be used to construct person identification tests based on EEG features.

On the use of EEG features towards person identification via neural networks

Person identification based on spectral information extracted from the EEG is addressed in this work-a problem that has not yet been seen in a signal processing framework. Spectral features are extracted non-parametrically from real EEG data recorded from healthy individuals. Neural network classification is applied on these features using a Learning Vector Quantizer in an attempt to experimentally investigate the connection between a persons EEG and genetically specific information. The proposed method, compared with previously proposed methods, has yielded encouraging correct classification scores in the range of 80% to 100% (case-dependent). These results are in agreement with previous research showing evidence that the EEG carries genetic information.Person identification based on spectral information extracted from the EEG is addressed in this work a problem that has not yet been seen in a signal processing framework. Spectral features are extracted non-parametrically from real EEG data recorded from healthy individuals. Neural network classification is applied on these features using a Learning Vector Quantizer in an attempt to experimentally investigate the connection between a persons EEG and genetically specific information. The proposed method, compared with previously proposed methods, has yielded encouraging correct classification scores in the range of 80% to 100% (case-dependent). These results are in agreement with previous research showing evidence that the EEG carries genetic information.

Review Article: Power line communication technologies for smart grid applications: A review of advances and challenges

This paper investigates the use of Power Line Communication (PLC) for Smart Grid (SG) applications. Firstly, an overview is done to define the characteristics of PLC and PLC-based SG applications are addressed to define the compatibility of PLC. Then, the advantages and disadvantages of PLC for SG applications are analyzed to improve the issues related to PLC. Due to the past standardization problem of PLC, new protocols and standards proposed for PLC are reviewed to see possible solutions toward its standardization. In addition, both completed and ongoing developments in the PLC technologies and their worldwide implementations are reviewed in this study. Finally, open research issues and future works are given.

Prediction of the air permeability of woven fabrics using neural networks

Purpose – The target of the current work is the creation of a model for the prediction of the air permeability of the woven fabrics and the water content of the fabrics after the vacuum drying.Design/methodology/approach – There have been produced 30 different woven fabrics under certain weft and warp densities. The values of the air permeability and water content after the vacuum drying have been measured using standard laboratory techniques. The structural parameters of the fabrics and the measured values have been correlated using techniques like multiple linear regression and Artificial Neural Networks (ANN). The ANN and especially the generalized regression ANN permit the prediction of the air permeability of the fabrics and consequently of the water content after vacuum drying. The performance of the related models has been evaluated by comparing the predicted values with the respective experimental ones.Findings – The predicted values from the nonlinear models approach satisfactorily the experiment...

Higher order statistics based Gaussianity test applied to on-line speech processing

Detection of speech in noisy recordings becomes a challenging problem when the noise does not follow the usual whiteness, stationarity and high signal-to-noise ratio assumptions. A robust speech detector can affect significantly the performance of several speech processing tasks, such as endpoint detection, segmentation, and finally recognition, if we deal with real life data, as opposed to laboratory or controlled environment recordings. The detector proposed is based on a Gaussianity test that employs third-order cumulants of the data to decide on the binary hypotheses of noise only versus speech plus noise. Speech intervals are detected by exploiting the third-order information present in the speech signal. The detector can handle a large family of additive noises, thanks to its third-order statistics basis. The sample-adaptive and decision feedback variations proposed, provide the detector with a tracking ability both with respect to the time variations of speech and the possible nonstationarity of noise. Experiments carried out using real data, recorded in a moving car interior, show satisfactory performance of the proposed algorithms down to -6 dB signal-to-noise ratio.<<ETX>>

On the use of higher-order statistics for robust endpoint detection of speech

Third order statistics of speech signals are not identically zero, as it would be expected based on the linear model for voice. This is due to quadratic harmonic coupling produced in the vocal tract. Based on this observation, third order cumulants are employed to address the endpoint detection problem in low SNR level recordings due to their immunity to (colored) additive non-skewed noise. The proposed method uses the maximum singular value of an appropriately formed cumulant matrix to distinguish between voiced parts of the speech signal, and silence (noise). Adaptive implementations are also proposed, making this method computationally attractive. Results of batch and adaptive forms are presented for real and simulated data.<<ETX>>

Sedimentation behaviour in electrorheological fluids based on suspensions of zeolite particles in silicone oil.

Sedimentation is a known and expected shortcoming of electrorheological fluids (ERFs) due to the inherent difference in the constituent densities. The long-term sedimentation causes loss of the electrorheological phenomenon and the exploitable electromechanical and viscoelastic properties despite the presence of the stimulating electric field. In this work, we report the effect of temperature and surfactant concentration on the stability of ERFs prepared from zeolite particles and silicone oil with primary focus on the sedimentation of the particles in the ERF. As the temperature stability of the ERFs is fundamentally important, we have studied three different ERF suspensions composed of different zeolite particles, in silicone oil. These ERFs have been comparatively evaluated for their sedimentation over time, across a wide range of temperatures (-40°C to +60°C). The influence of surfactant concentration on the colloidal stability of the ERFs has also been investigated. A novel method of acoustic stirring (kHz range) on the homogenisation of the ERFs has been proposed and its effect on the sedimentation process evaluated. These results are useful for assessment of alternative suspension methods for specific applications.

Artificial Neural Networks and Their Applications in the Engineering of Fabrics

Historically the main use of the textile fabrics has been limited mainly to clothing and domestic applications. The technical uses were of minor importance. However in the last decades the use of the textile structures has started to spread over other sectors like construction, medicine, vehicles, aeronautics, etc. The increased interest in technical applications have improved the fabric design and engineeringprocedures, given that the final products must be characterized by certain mechanical, electrical etc. properties. The performance of the fabrics should be predictable right from the design phase. The design of a fabric is focusing on the materials selection as well as on the definition of its structural parameters, so that the requirements of the end use be fulfilled. These changes in the application field of the textile structures caused a move from the esthetic design to the total technical design, where the fabric appearance and the particular properties affecting its final performance are taken in account. However, the textile structures are highly complex. A textile fabric consists of yarns; yarns in turn consist of fibres. Thus the mechanical performance of the fabrics is characterized by the structural geometrical complexity and non-linearity, as well as from the non-linearities of the materials themselves. This double non-linear bahaviour of the textile fabrics increases the difficulty in the fabric design and engineering processes. The complex structure and the difficulties introduced by the raw materials do not allow the use of precise analytical models for the technical design of the fabrics. Fabric engineering activities are increasingly based on computational models that aim at the prediction of the properties and the performance of the fabrics under consideration. Various computational tools have been used in order to represent the fabrics in a computational environment and to predict their final properties. Among others, Finite Element Method (FEM) analysis has supported mainly the prediction of the behaviour of the complex textile structures under mechanical loads. In the case of classification problems Artificial Neural Networks (ANNs) have proved a very efficient tool for the fast and precise solution. ANNs have found an increasing application in the textile field in the classification as well as in the