Sharif Digital Repository

The accuracy of Photoplethysmographic signals is often not adequate due to motional artifacts induced in the recording site. Over recent decades there has been a widespread effort to reduce these artifacts and different methods are used for this aim. Nevertheless there are still some contradictory results reported by different methods about their effectiveness in artifact reduction. In this paper, we aim to compare three of established methods for PPG noise reduction on a unique dataset. Among different reported methods, we have chosen Adaptive Noise Cancellation (ANC), Discrete Wavelet Transform (DWT) and a newly developed method Cycle-by-cycle Fourier Series Analysis (CFSA) for denoising.... 

In this paper, we describe a Gaussian wave-based state space to model the temporal dynamics of electrocardiogram (ECG) signals. It is shown that this model may be effectively used for generating synthetic ECGs as well as separate characteristic waves (CWs) such as the atrial and ventricular complexes. The model uses separate state variables for each CW, i.e. P, QRS and T, and hence is capable of generating individual synthetic CWs as well as realistic ECG signals. The model is therefore useful for generating arrhythmias. Simulations of sinus bradycardia, sinus tachycardia, ventricular flutter, atrial fibrillation and ventricular tachycardia are presented. In addition, discrete versions of... 

ECG denoising has always been an important issue in medical engineering. The purposes of denoising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal. This paper proposes a method for removing white Gaussian noise from ECG signals. The concepts of singularity and local maxima of the wavelet transform modulus were used for analyzing singularity and reconstructing the ECG signal. Adaptive thresholding was used to remove white Gaussian noise modulus maximum of wavelet transform and then reconstruct the signal. ©2009 IEEE  

We investigate the performance of wavelet shrinkage methods for the denoising of symmetric- α -stable (S αS) self-similar stochastic processes corrupted by additive white Gaussian noise (AWGN), where α is tied to the sparsity of the process. The wavelet transform is assumed to be orthonormal and the shrinkage function minimizes the mean-square approximation error (MMSE estimator). We derive the corresponding formula for the expected value of the averaged estimation error. We show that the predicted MMSE is a monotone function of a simple criterion that depends on the wavelet and the statistical parameters of the process. Using the calculus of variations, we then optimize this criterion to... 

OCT skin images suffer from artifacts. Speckle is the main artifact while the other one is called background noise. In this study, we propose an algorithm that significantly reduces the background noise before applying a speckle reduction method. The results show that the diagnostically relevant features in the images become clearer after applying the proposed method. We used sub-pixel weighted median filtering for speckle reduction. The results from background noise removal in combination with the proposed speckle reduction algorithm show a significant improvement in the clarity of diagnostically relevant features in in-vivo human skin images. © 2017 SPIE  

Optical Coherence Optical coherence tomography is a powerful high-resolution imaging method with a broad biomedical application. Nonetheless, OCT images suffer from a multiplicative artefacts so-called speckle, a result of coherent imaging of system. Digital filters become ubiquitous means for speckle reduction. Addressing the fact that there still a room for despeckling in OCT, we proposed an intelligent speckle reduction framework based on OCT tissue morphological, textural and optical features that through a trained network selects the winner filter in which adaptively suppress the speckle noise while preserve structural information of OCT signal. These parameters are calculated for... 

Transcranial Doppler ultrasound can be used to detect circulating cerebral eraboli. Embolie signals have characteristic transient chirps suitable for wavelet analysis. We have implemented and evaluated the first online selective selective wavelet transient enhancement filter to amplify embolic signals in a preprocessing system. Our approach is similar to wavelet de-noising for signal enhancement, but, in order to retain blood flow information, we do not use traditional threshold methods. The selective wavelet amplifier uses the matched filter properties of wavelets to enhance embolic signals significantly and improve classification performance using a novel noise tolerant approach. Even the... 

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 recent studies, several methods have been suggested to decrease noise of magnetic resonance image (MRI) in order to raise the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM). In this paper, we propose a novel method based on a minimization problem in Ripplet domain that uses singular value decomposition (SVD) in low rank learning to eliminate the noise of MRI images. We reschedule the weighted nuclear norm minimization (WNNM) problem in any edges of Ripplet domain transform and using an adaptive weighting structure to denoise the patches of Ripplet component matrix. The parameters of the proposed method are divided into two groups, some of them are calculated... 

In digital imaging, especially in the process of data acquisition and transmission, images are often affected by impulsive noise. Therefore, it is essential to remove impulsive noise from images before any further processing. Due to the remarkable performance of deep neural networks in different applications of image processing and computer vision, we present an end-to-end fully convolutional neural network to remove impulsive noise from images. To train our network, we generate a customized dataset with various noise densities in which the highly corrupted images are more frequent. Hence, our convolutional neural network is blind since the percentage of impulsive noise is not required as... 

In order to safe design and optimize performance of some industrial systems, it's often needed to categorize two-phase flow into different regimes. In each flow regime, flow conditions have similar geometric and hydrodynamic characteristics. Traditionally, flow regime identification was carried out by flow visualization or instrumental indicators. In this research3 kind of neural networks have been used to predict system characteristic and flow regime, and results of them were compared: radial basis function neural networks, self organized and Multilayer perceptrons (supervised) neural networks. The data bank contains experimental pressure signalfor a wide range of operational conditions in... 

Background and objective Image registration is one of the fundamental and essential tasks for medical imaging and remote sensing applications. One of the most common challenges in this area is the presence of complex spatially varying intensity distortion in the images. The widely used similarity metrics, such as MI (Mutual Information), CC (Correlation Coefficient), SSD (Sum of Square Difference), SAD (Sum of Absolute Difference) and CR (Correlation Ratio), are not robust against this kind of distortion because stationarity assumption and the pixel-wise independence cannot be obeyed and captured by these metrics. Methods In this paper, we propose a new intensity-based method for...