Sharif Digital Repository

The measurement of PVT properties of natural gas in gas pipelines, gas storage systems, and gas reservoirs require accurate values of compressibility factor. Although equation of state and empirical correlations were utilized to estimate compressibility factor, but the demands for novel, more reliable, and easy-to-use models encouraged the researchers to introduce modern tools such as artificial intelligent systems.This paper introduces Particle swarm optimization (PSO) and Genetic algorithm (GA) as population-based stochastic search algorithms to optimize the weights and biases of networks, and to prevent trapping in local minima. Hence, in this paper, GA and PSO were used to minimize the... 

A general deflation framework is described for the separation of a desired signal subspace of arbitrary dimensions from noisy multichannel observations. The method simultaneously uses single and multichannel priors to split the desired and undesired subspaces, even for coplanar (intersecting) subspaces. By appropriate use of signal priors, it can even extract signals from degenerate mixtures of signals and noise recorded from a few number of channels in low SNR scenarios, without the reduction of the data dimensions. As a case study, the performance of the proposed method is studied for the problem of extracting fetal cardiac signals from maternal abdominal recordings, over simulated and... 

Abdominal recordings of fetal ECG (fECG) have lower signal-to-noise ratio (SNR) as compared with invasive procedures. In this paper we have combined two previously proposed methods, one for extracting fECG, called piCA and the other, a transformation based on Hilbert transform to enhance the R-peaks. The combination of these methods seems to work well in situations of noisy data and fetal repositioning. Also a comparison is done by using ICA in order to extract the fetal signals. Performance of both methods is studied separately. Results show that applying the transformation on the components extracted with the use of piCA (after maternal ECG cancellation), had a very good performance. Also,... 

Abdominal recordings of fetal ECG (fECG) have lower signal-to-noise ratio (SNR) as compared with invasive procedures. In this paper we have combined two previously proposed methods, one for extracting fECG, called πCA and the other, a transformation based on Hilbert transform to enhance the R-peaks. The combination of these methods seems to work well in situations of noisy data and fetal repositioning. Also a comparison is done by using ICA in order to extract the fetal signals. Performance of both methods is studied separately. Results show that applying the transformation on the components extracted with the use of πCA (after maternal ECG cancellation), had a very good performance. Also,... 

Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals. © 2008 Institute of Physics and Engineering in Medicine  

We present generalizations of our previously published artificial models for generating multi-channel ECG so that the simulation of abnormal rhythms is possible. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are then specified either as new dipoles, or as perturbations of the existing dipole. Switching between normal and abnormal beat types is achieved using a hidden Markov model (HMM). Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes form beat-to-beat... 

In this letter, we propose the application of the generalized eigenvalue decomposition for the decomposition of multichannel electrocardiogram (ECG) recordings. The proposed method uses a modified version of a previously presented measure of periodicity and a phase-wrapping of the RR-interval, for extracting the "most periodic" linear mixtures of a recorded dataset. It is shown that the method is an improved extension of conventional source separation techniques, specifically customized for ECG signals. The method is therefore of special interest for the decomposition and compression of multichannel ECG, and for the removal of maternal ECG artifacts from fetal ECG recordings. © 2006 IEEE  

In recent studies, Independent Component Analysis (ICA) has been used for the analysis of multi-channel ECG recordings. However most of these works have been carried out from the signal processing perspective. In this work, the single dipole vector theory of the heart and the ECG dimensionality are studied from the source separation viewpoint. Based on this study, the interpretation of the components extracted from multi-channel ECG and maternal abdominal recordings, and their relationship with the vectorcardiogram representation of the cardiac dipole are presented. The results of this study can be used for the extraction of meaningful clinical indexes, based on ICA techniques. © 2006 IEEE  

In some recent works, model-based filtering approaches have been proved as effective methods for extracting ECG signals from single channel noisy recordings. The previously developed methods, use a highly realistic nonlinear ECG model for the construction of Bayesian filters. In this work, a multi-channel extension of the previous approach is developed, by using a three dimensional model of the cardiac dipole vector. The results have considerable improvement compared with the single channel approach. The method is hence believed to be applicable to low SNR multi-channel recordings  

In this paper the Extended Kalman Filter (EKF) has been used for the filtering of Electrocardiogram (ECG) signals. The method is based on a previously nonlinear dynamic model proposed for the generation of synthetic ECG signals. The results show that the EKF may be used as a powerful tool for the extraction of ECG signals from noisy measurements; which is the state of the art in applications such as the noninvasive extraction of fetal cardiac signals from maternal abdominal signals. © 2005 IEEE  

In this paper several applications of the Independent Component Analysis (ICA) algorithm, for the analysis of biomedical signal recordings have been investigated. One of these applications is the removal of EEG artifacts such as the EOG. It is shown that ICA may serve as a powerful tool, which could help the analysis of biomedical recordings, and give better insights about the underlying sources of some disorders. Another application of the proposed method is the detection of sleep disorders in patients suffering from sleep apnea. The ultimate goal of this approach is to develop an automatic noninvasive data acquisition system, for clinical applications  

A three-dimensional dynamic model of the electrical activity of the heart is presented. The model is based on the single dipole model of the heart and is later related to the body surface potentials through a linear model which accounts for the temporal movements and rotations of the cardiac dipole, together with a realistic ECG noise model. The proposed model is also generalized to maternal and fetal ECG mixtures recorded from the abdomen of pregnant women in single and multiple pregnancies. The applicability of the model for the evaluation of signal processing algorithms is illustrated using independent component analysis. Considering the difficulties and limitations of recording long-term... 

In this paper, a nonlinear Bayesian filtering framework is proposed for the filtering of single channel noisy electrocardiogram (ECG) recordings. The necessary dynamic models of the ECG are based on a modified nonlinear dynamic model, previously suggested for the generation of a highly realistic synthetic ECG. A modified version of this model is used in several Bayesian filters, including the Extended Kalman Filter, Extended Kalman Smoother, and Unscented Kalman Filter. An automatic parameter selection method is also introduced, to facilitate the adaptation of the model parameters to a vast variety of ECGs. This approach is evaluated on several normal ECGs, by artificially adding white and... 

In this paper, a novel service that uses audio watermarking for covert communication through telephone system is presented. This system is suitable for ISDN and PSIN networks and it can be modified for using in mobile systems. We use a Direct sequence spread spectrum algorithem with perceptual modeling of HAS for embedding watermark into the audio or speed signals. The watermark is robust against telephone channel attacks such as AWGN , Low pass filtering, D/A-A/D conversion, Downsampling down to 64 slips and A-Law or μ-Law conversion. This system is also robust against audio format conversion such as wave (windows PCM) format to mp3 (down o 64 kbps compression. © 2006 IEEE  

In this paper an Extended Kalman Filter (EKF) has been proposed for the filtering of noisy ECG signals. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. An automatic parameter selection method has also been suggested, to adapt the model with a vast variety of normal and abnormal ECG signals. The results show that the EKF output is able to track the original ECG signal shape even in the most noisiest epochs of the ECG signal. The proposed method may serve as an efficient filtering procedure for applications such as the noninvasive extraction of fetal cardiac signals from maternal abdominal signals. © 2005 IEEE  

As a country's financing arm, the capital market can play a crucial role in long-term projects and be responsible for large-scale financing. While the development of rail transport has exceptionally positive and significant effects on the country's macroeconomy, and investment in this sector leads to the prosperity of other economic sectors, the return on investment may be delayed or unclear. Consequently, this uncertainty diminishes the investment appeal in this field. Thus, predicting the price and future value of railway company stocks on the stock market is vital due to the need for the foresight to take safe investment measures and achieve sustainable financing. To this end, this paper... 

The ability of Paraconiothyrium variabile, a laccase producing ascomycete recently isolated from soil, was studied to eliminate chlorophenol derivatives in submerged culture medium. Among the tested compounds, ρ-chlorophenol (ρ-CP) and pentachlorophenol (PCP) were found to have minimum and maximum toxic effects, respectively, on the growth of the microorganism and at the same time high and low bioelimination percentages. The fungal strain was able to remove 86% of ρ-CP (with initial concentration of 40mgl -1) and 56% of 2,4-dichlorophenol (2,4-DCP; with same concentration as ρ-CP) after 9 days of incubation while no elimination was observed in the presence of 2,4,6-trichlorophenol... 

Blind source separation (BSS) techniques have revealed to be promising approaches for the noninvasive extraction of fetal cardiac signals from maternal abdominal recordings. From previous studies, it is now believed that a carefully selected array of electrodes well-placed over the abdomen of a pregnant woman contains the required 'information' for BSS, to extract the complete fetal components. Based on this idea, previous works have involved array recording systems and sensor selection strategies based on the Mutual Information (MI) criterion. In this paper the previous works have been extended, by considering the 3-dimensional aspects of the cardiac electrical activity. The proposed method... 

Secure routing is a major key to service maintenance in ad hoc networks. Ad hoc nature exposes the network to several types of node misbehavior or attacks. As a result of the resource limitations in such networks nodes may have a tendency to behave selfishly. Selfish behavior can have drastic impacts on network performance. We have proposed a Misbehavior-Tolerant Multipath Routing protocol (MTMR) which detects and punishes all types of misbehavior such as selfish behavior, wormhole, sinkhole and grey-hole attacks. The protocol utilizes a proactive approach to enforce cooperation. In addition, it uses a novel data redirection method to mitigate the impact of node misbehavior on network...