Sharif Digital Repository

An active noise cancellation method is proposed for superconducting quantum interference devices (SQUID)-based magnetocardiography systems working out of magnetically shielded rooms. Using YBCO high-Tc rf-SQUID magnetometers as magnetic field sensors, an active shielding system was implemented based on this method. This method incorporates two different shielding frequency regimes of operation simultaneously. This is because the unwanted background magnetic field signals range from very low frequencies up to high frequencies with a wide range of amplitudes at the upper and lower frequency spectra. Therefore, the shielding system is designed in a bistage configuration, and each stage covers... 

There is growing interest in evaluating the impact of soft errors on multiprocessor system-on-chip (MPSoC) at application-level rather than architectural-level, particularly in multimedia applications to optimize system design. This has recently led to the concept of application-level correctness. In this paper, we consider the relationship between application-level correctness and system-level power management using voltage scaling technique with the aim to generate designs that are optimized in terms of power consumption, while providing acceptable application-level correctness and meeting real-time performance deadlines. We propose a novel voltage scaling technique based on linear... 

An optimal soft multiple-input multiple-output (MIMO) detector is proposed with linear complexity for a general spatial multiplexing system with two transmitting symbols and NR 2 receiving antennas. The computational complexity of the proposed scheme is independent of the operating signal-to-noise ratio and grows linearly with the constellation order. It provides the soft maximum-likelihood solution using an efficient log-likelihood ratio calculation method, avoiding the exhaustive search on all the candidate nodes. Moreover, an efficient pipelined hardware implementation of the detection algorithm is proposed, which is fabricated and fully tested in a 130-nm CMOS technology. Operating at... 

Next generation wideband and ultra-wideband radar antenna arrays need efficient true-time delay networks for array control. Optical beam-forming networks have been considered as a promising solution in such arrays. Performance analysis of optical beam-formers is thus quiet necessary. In this paper signal to noise ratio and dynamic range of simple optical beam-forming structures are investigated with a custom microwave CAD tool. Two structures with optical and RF power combining methods are discussed. With the proposed devices' parameters, the optical combining method shows better performance for low and moderate optical powers  

Next generation wideband and ultra-wideband radar antenna arrays need efficient true-time delay networks for array control. Following the great interest for optical control of antenna array beam steering, performance analysis of optical beam-formers is a must. In this paper signal to noise ratio and dynamic range of simple optical beamforming structures with components off the shelf are investigated theoretically. Especially the effects of certain power combining components such as arrayed wave guide gratings, broadband passive optical combiners and microwave combiners inspected. It turns up that an efficient microwave combiner would surpass the optical combiners in both SNR and dynamic... 

This paper proposes a novel error-feedback lattice recursive least-squares (EF-LRLS) filter for online estimation of power system oscillatory modes. The EF-LRLS filter is applied to ambient data provided by phasor measurement units to identify the autoregressive (AR) model parameters. This filter has a modular structure; accordingly, if the length of the filter equals N, it identifies AR(1) to AR(N) models concurrently. In the proposed method, removing very low and high frequencies and re-sampling steps are fulfilled in an online fashion. This adaptive filter has less computational complexity than standard RLS filter, making it an appropriate choice for online system identification. The... 

This paper proposes a novel error-feedback lattice recursive least-squares (EF-LRLS) filter for online estimation of power system oscillatory modes. The EF-LRLS filter is applied to ambient data provided by phasor measurement units to identify the autoregressive (AR) model parameters. This filter has a modular structure; accordingly, if the length of the filter equals $N$, it identifies AR(1) to ${m{AR(}}N)$ models concurrently. In the proposed method, removing very low and high frequencies and resampling steps are fulfilled in an online fashion. This adaptive filter has less computational complexity than standard RLS filter, making it an appropriate choice for online system identification.... 

We extend the result by Tse and Verdú on the optimum asymptotic multiuser efficiency of randomly spread code division multiple access (CDMA) with binary phase shift keying input. Random Gaussian and random binary antipodal spreading are considered. We obtain the optimum asymptotic multiuser efficiency of a K-user system with spreading gain N when K and N → ∞ and the loading factor, (K/N) , grows logarithmically with K under some conditions. It is shown that the optimum detector in a Gaussian randomly spread CDMA system has a performance close to the single user system at high signal-to-noise ratio when K and N → ∞ and the loading factor, (K/N), is kept less than (log3 K/2). Random binary... 

We have recently proposed a correlated model to provide a Gaussian mixture representation of the cardiovascular signals, with promising results in identifying rhythm disturbances. The approach provides a transformation of the data into a set of integrable Gaussians distributed over time. Looking into the model from a new joint modeling perspective, it is capable of assembling a filtered estimation, and can be used to derive temporal information of the waveforms. In this paper, we present a step-by-step derivation of the joint model putting correlation assumptions together to conclude a minimal joint description for a pair of ECG-ABP signals. We then probe novel applications of this model,... 

This paper presents efficient denoising and lossy compression schemes for electrocardiogram (ECG) signals based on a modified extended Kalman filter (EKF) structure. We have used a previously introduced two-dimensional EKF structure and modified its governing equations to be extended to a 17-dimensional case. The new EKF structure is used not only for denoising, but also for compression, since it provides estimation for each of the new 15 model parameters. Using these specific parameters, the signal is reconstructed with regard to the dynamical equations of the model. The performances of the proposed method are evaluated using standard denoising and compression efficiency measures. For... 

In this paper an efficient Altering procedure based on the Extended Kalman Filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. We have suggested simple dynamics as the governing equations for the model parameters. Since we have not any observation for these new state variables, they are considered as hidden states. Quantitative evaluation of the proposed algorithm on the MIT-BIH signals shows that an average SNR improvement of 12 dB is achieved for a signal of -5 dB. The results show improved output SNRs compared to the EKF outputs in the absence of these new dynamics. © 2007 IEEE  

An efficient Non-Destructive Evaluation algorithm has been developed in order to extract the required information for pattern recognition of defects in the conductive samples. Using high-Tc gradiometer RF-SQUIDs in unshielded environments and incorporating an automated two dimensional non-magnetic scanning robot, samples with different intentional defects have been tested. We have used a developed noise cancellation approach for the improvement of the effectiveness of the used inverse-problem technique. In this approach we have used a well examined Finite Element Method (FEM) to apply a noise reduction filtering on the obtained raw magnetic image data before incorporating the signal... 

The noise exists in almost all environments such as cellular mobile telephone systems. Various types of noise can be introduced such as speech additive noise which is the main factor of degradation in perceived speech quality. At some applications for example at the receiver of a telecommunication system, the direct value of interfering noise is not available and there is just access to noisy speech. In these cases the noise cannot be cancelled totally but it may be possible to reduce the noise in a sensible way by utilizing the statistics of the noise and speech signal. In this paper the proposed method for noise reduction is Bayesian recursive state-space Kalman filter, which is a method... 

In this paper an efficient method is proposed for blind detection of multiuser spread spectrum signal and pseudo noise (PN) length estimation in downlink scenario. This method is based on fluctuating observation of second order statistic (SOS) estimator of received signal; estimating length of spreading code with no need to primary information based on observing second order peaks of correlation estimator is presented. In low SNR, the fading multipath phenomenon especially in length of small codes leads to problem in detecting place of correlation estimator peaks. In this paper besides expanding blind estimation to multiuser environment, by using searching method in updating the length of... 

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 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  

Introduction: Cardiac contraction significantly degrades quality and quantitative accuracy of gated myocardial perfusion SPECT (MPS) images. In this study, we aimed to explore different techniques in motion-compensated temporal processing of MPS images and their impact on image quality and quantitative accuracy. Material and method: 50 patients without known heart condition underwent gated MPS. 3D motion compensation methods using Motion Freezing by Cedars Sinai (MF), Log-domain Diffeomorphic Demons (LDD) and Free-Form Deformation (FFD) were applied to warp all image phases to fit the end-diastolic (ED) phase. Afterwards, myocardial wall thickness, myocardial to blood pool contrast, and... 

In this paper we use a combination of fuzzy clustering estimation and sliding mode method to control a chaotic system, which its mathematical model is unknown. At first the model of chaotic system is identified without using any input noise signal. In this case the recurrent property of chaotic behavior is used for estimating its model. After estimating the fuzzy model of chaos, control and on-line identification of the input-related section is applied. In this step, we estimate the system model in normal form, such that the dynamic equations can be used in sliding mode control. Finally the proposed technique is applied to the Lorenz system as an example of chaotic system. The simulation... 

Denoising of corrupted images has been a classical problem in image processing. In this paper we propose a new approach for image noise reduction using wavelet transform. In this method an improved version of thresholding neural networks (TNN) is used to find the optimum threshold values in the sense of minimum mean square error (MMSE). Based on these optimum thresholds a novel cycle-spinning based method is used to reduce image artifacts. In this method, we utilize two thresholding schemes as the thresholding operator of cycle-spinning. A neighbor dependent thresholding scheme is employed as its first shrinkage step and a simple wavelet thresholding with the optimum derived threshold values... 

In this paper we propose a new method for image noise reduction based on wavelet transform. In this method we: introduce an improved version of thresholding neural networks. (TNN) by utilizing a new class of smooth nonlinear thresholding functions as the activation function. Using this approach we will find the best thresholds in the sense of minimum mean square error (MMSE). Then using, TNN with obtained thresholds, we employ a cycle-spinningbased technique to reduce image artifacts. Experimental results indicate that the proposed method outperforms several other established wavelet denoising techniques, in terms of Peak-Signal-to-Noise-Ratio (PSNR) and visual quality. © 2007 IEEE