Sahar Javaher Haghighi

Effects of Side Information on Complexity Reduction in Superimposed Pilot Channel Estimation in OFDM Systems

A novel, reduced complexity iterative channel estimation algorithm for OFDM systems using superimposed pilots is proposed. It utilizes past channel estimations of double correlated channel as a side information to reduce number of iterations. Since pilots are available at all positions of the time-frequency OFDM grid in superimposed technique, the performance of the channel estimation does not degrade because of the variations of the fast fading channel between two pilots. On the other hand since no subcarrier is reserved for channel estimation purpose, superimposed pilot technique leads to improved spectral efficiency comparing to in-band OFDM pilots. However interference from data carrying signals made channel estimation more complex. In this paper, Least Square (LS) channel estimation followed by two dimensional Wiener filter for reducing OFDM symbol interference is done iteratively to achieve the Minimum Mean Square Error (MMSE). Small variations of the channel over each OFDM symbol duration are neglected due to a high data rate, but the values between different OFDM symbols are assumed correlated. The channel is modeled as a double selective, i.e. both frequency selectivity channel and Doppler shift are taken into consideration. Past channel estimates are used as side information for the present channel estimation to improve the forthcoming channel estimation at the first iteration and reduce the total number of iterations required.

The effect of Propofol induced anesthesia on human 40-Hz auditory steady state response

40-Hz auditory steady state responses (ASSR)s recorded from 12 human subjects during Propofol-induced anesthesia are studied in this paper. The 40-Hz ASSR signals are recorded in 8 channel stimulated electroencephalogram (EEG). The ASSR sweeps are extracted from 300 stimulated EEG epochs and updated every 0.5 seconds. Variations of the signal in time and frequency in 8 different channels are investigated both in constant times before and after anesthetic injection and relative to eyelash reflex disappearance in order to achieve a consistent result among all subjects. The latter demonstrates reduction in peak to peak amplitude 40 Hz and 80 Hz components of the signals after eyelash reflex disappearance in all 8 channels for all subjects.

On biometric systems: electrocardiogram Gaussianity and data synthesis

Electrocardiogram is a slow signal to acquire, and it is prone to noise. It can be inconvenient to collect large number of ECG heartbeats in order to train a reliable biometric system; hence, this issue might result in a small sample size phenomenon which occurs when the number of samples is much smaller than the number of observations to model. In this paper, we study ECG heartbeat Gaussianity and we generate synthesized data to increase the number of observations. Data synthesis, in this paper, is based on our hypothesis, which we support, that ECG heartbeats exhibit a multivariate normal distribution; therefore, one can generate ECG heartbeats from such distribution. This distribution is deviated from Gaussianity due to internal and external factors that change ECG morphology such as noise, diet, physical and psychological changes, and other factors, but we attempt to capture the underlying Gaussianity of the heartbeats. When this method was implemented for a biometric system and was examined on the University of Toronto database of 1012 subjects, an equal error rate (EER) of 6.71% was achieved in comparison to 9.35% to the same system but without data synthesis. Dimensionality reduction is widely examined in the problem of small sample size; however, our results suggest that using the proposed data synthesis outperformed several dimensionality reduction techniques by at least 3.21% in EER. With small sample size, classifier instability becomes a bigger issue and we used a parallel classifier scheme to reduce it. Each classifier in the parallel classifier is trained with the same genuine dataset but different imposter datasets. The parallel classifier has reduced predictors’ true acceptance rate instability from 6.52% standard deviation to 1.94% standard deviation.

An adaptive multi-level wavelet denoising method for 40-Hz ASSR

This paper presents a novel method for extracting auditory steady state response (ASSR) signals from background electroencephalogram. 40-Hz ASSR signals are sensitive to subjects state of consciousness and can be used as a monitor for the depth of anaesthesia. The suggested method is a multilevel adaptive wavelet denoising scheme that extracts ASSR cycles faster than the currently used averaging schemes and can monitor depth of anesthesia with minimum delay. It estimates the variance of noise and adapts the threshold at each denoising level. The algorithm benefits from the fact that wavelet transform preserves temporality and takes into consideration the correlation of the neighbor wavelet coefficients. Our method extracts ASSR from small number of epochs in a short time moreover, it does not neglect the variations of the signal from one epoch to the other and outperforms averaging. The performance of the proposed scheme is evaluated on the synthetic and on real data recorded during induction of anaesthesia ASSR signals in the paper.

Reference empirical mode decomposition

Proposed in this paper is a modified version of empirical mode decomposition (EMD) that guarantees unified signal representation after decomposition. Reference EMD (R-EMD), decomposes each signal with a set of reference sinusoids to achieve a wavelet-like frequency separation, while retaining the adaptive feature of the EMD algorithm. A brief proof is also provided on the role of the reference sinusoids in extracting frequencies at each level. R-EMD provides a solution for the problem of high dimensionality and complexity in decomposing multiple signals together.

On energy efficiency of pilot assisted modulation schemes

In this paper we investigate energy efficiency of basic modulation techniques in systems with partial channel state information (CSI) at the receiver, obtained via channel estimation. Both in-band and superimposed pilot aided channel estimation schemes are considered. Efficiency is defined as amount of energy per bit of information delivered. The efficiency is optimized with respect to the number and/or power of pilots for a given modulation. It is shown that there is an optimal power allocation for every modulation scheme and channel estimation scheme.

40-Hz ASSR depth of anaesthesia index

A novel method for defining an index based on multi-level clustering of 40-Hz auditory steady state response is presented in this paper. The index is a measure of depth of anaesthesia which can help monitoring depth of anaesthesia more closely and accurately. Multi-level expectation maximization (EM) is used for clustering the recorded 40-Hz auditory steady state response signals recorded from human subjects. The clustering information is used to define the depth of anaesthesia index. Rather than extracting the maximum amplitude and frequency at each cycle as clustering features, principal components analysis (PCA) is used for analyzing all samples of the cycles and projecting data into a lower dimension space. Both dimension reduction and clustering schemes are unsupervised methods, hence the algorithm does not need initial data labeling or training phase.

40-Hz ASSR fusion classification system for observing sleep patterns

This paper presents a fusion-based neural network (NN) classification algorithm for 40-Hz auditory steady state response (ASSR) ensemble averaged signals which were recorded from eight human subjects for observing sleep patterns (wakefulness W0 and deep sleep N3 or slow wave sleep SWS). In SWS, sensitivity to pain is the lowest relative to other sleep stages and arousal needs stronger stimuli. 40-Hz ASSR signals were extracted by averaging over 900 sweeps on a 30-s window. Signals generated during N3 deep sleep state show similarities to those produced when general anesthesia is given to patients during clinical surgery. Our experimental results show that the automatic classification system used identifies sleep states with an accuracy rate of 100% when the training and test signals come from the same subjects while its accuracy is reduced to 97.6%, on average, when signals are used from different training and test subjects. Our results may lead to future classification of consciousness and wakefulness of patients with 40-Hz ASSR for observing the depth and effects of general anesthesia (DGA).

Wireless Communications and Multitaper Analysis: Applications to Channel Modelling and Estimation

The goal of this Chapter is to review the applications of the Thomson Multitaper analysis (Percival and Walden; 1993b), (Thomson; 1982) for problems encountered in communications (Thomson; 1998; Stoica and Sundin; 1999). In particular we will focus on issues related to channel modelling, estimation and prediction. Sum of Sinusoids (SoS) or Sum of Cisoids (SoC) simulators (Patzold; 2002; SCM Editors; 2006) are popular ways of building channel simulators both in SISO and MIMO case. However, this approach is not a very good option when features of communications systems such as prediction and estimation are to be simulated. Indeed, representation of signals as a sum of coherent components with large prediction horizon (Papoulis; 1991) leads to overly optimistic results. In this Chapter we develop an approach which allows one to avoid this difficulty. The proposed simulator combines a representation of the scattering environment advocated in (SCM Editors; 2006; Almers et al.; 2006; Molisch et al.; 2006; Asplund et al.; 2006; Molish; 2004) and the approach for a single cluster environment used in (Fechtel; 1993; Alcocer et al.; 2005; Kontorovich et al.; 2008) with some important modifications (Yip and Ng; 1997; Xiao et al.; 2005). The problem of estimation and interpolation of a moderately fast fading Rayleigh/Rice channel is important in modern communications. TheWiener filter provides the optimum solution when the channel characteristics are known (van Trees; 2001). However, in real-life applications basis expansions such as Fourier bases and discrete prolate spheroidal sequences (DPSS) have been adopted for such problems (Zemen and Mecklenbrauker; 2005; Alcocer-Ochoa et al.; 2006). If the bases and the channel under investigation occupy the same band, accurate