Search for: de-noising
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    Investigation of online detected partial discharges in power transformer

    , Article 2008 Australasian Universities Power Engineering Conference, AUPEC 2008, Sydney, NSW, 14 December 2008 through 17 December 2008 ; April , 2008 ; 9781424441624 (ISBN) Ghaffarian, M ; Vakilian, M ; Parvin, V ; Ghaedi, A ; Sharif University of Technology
    In this paper some advantage of online monitoring in power transformer and the sensors that can be used for partial discharge detection are described. Several detected PD in one power transformer and one distribution transformer are depicted. Matlabs wavelet package is employed to de-noise the recorded signals. As it appear from the captured and the denoised signals, PD signals in an aged power transformer are often enough large that can be easily detected through the noisy measured signals. By examination of the frequency spectrum of several PD signal in this paper it is obvious that this signal contain two major frequency band, one under 1 MHz and the other one between 7 to 9 MHz. © 2008... 

    An optimal wavelet filtering method for noise suppression of PD measured signal and its location in power transformer winding

    , Article 2005 IEEE International Conference on Dielectric Liquids, ICDL 2005, Coimbra, 26 June 2005 through 1 July 2005 ; 2005 , Pages 269-272 ; 0780389549 (ISBN); 9780780389540 (ISBN) Naderi, M. S ; Vakilian, M ; Blackburn, T. R ; Phung, B. T ; Nam O, H ; Naderi, M. S ; Sharif University of Technology
    In this paper a method for selecting optimal wavelet is introduced, which is implemented for evaluating electrical measured partial discharges on a 66 kV/25 MVA fully interleaved winding of a power transformer. Applying wavelet transform to a signal produces a wavelet detail coefficient distribution throughout the time-scale, which depends on the wavelet chosen. This method is based on the capability of the chosen wavelet for generating coefficients with maximal values. The basic idea of this method is described and applications to partial discharge studies are explored. The paper demonstrates that the wavelet based de-noising method proposed in the paper can be employed in extracting the PD... 

    MMSE denoising of sparse and non-gaussian AR(1) processes

    , Article ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 20 March 2016 through 25 March 2016 ; Volume 2016-May , 2016 , Pages 4333-4337 ; 15206149 (ISSN) ; 9781479999880 (ISBN) Tohidi, P ; Bostan, E ; Pad, P ; Unser, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2016
    We propose two minimum-mean-square-error (MMSE) estimation methods for denoising non-Gaussian first-order autoregressive (AR(1)) processes. The first one is based on the message passing framework and gives the exact theoretic MMSE estimator. The second is an iterative algorithm that combines standard wavelet-based thresholding with an optimized non-linearity and cycle-spinning. This method is more computationally efficient than the former and appears to provide the same optimal denoising results in practice. We illustrate the superior performance of both methods through numerical simulations by comparing them with other well-known denoising schemes  

    A method to capture and de-noise partial discharge pulses using discrete wavelet transform and ANFIS

    , Article International Transactions on Electrical Energy Systems ; Volume 25, Issue 11 , September , 2015 , Pages 2696-2712 ; 20507038 (ISSN) Jahangir, H ; Hajipour, E ; Vakilian, M ; Akbari, A ; Blackburn, T ; Phung, B. T ; Sharif University of Technology
    John Wiley and Sons Ltd  2015
    Due to the presence of excessive noise in the recorded partial discharge (PD) current signals, de-noising of these signals is a crucial task for performing any investigation on the subject. Meanwhile, to accelerate this de-noising process a single PD pulse can be extracted from the train of those recorded pulses, followed by its de-noising. In this paper a single PD pulse is extracted from the train of recorded PD pulses, using noisy recorded data cumulative energy. A de-noising technique based on adaptive neuro-fuzzy inference systems is proposed. To verify the validity of the proposed method, four different sources of PD signals are physically simulated. The proposed method is applied on... 

    An improved image denoising technique using cycle spinning

    , Article 2007 IEEE International Conference on Telecommunications and Malaysia International Conference on Communications, ICT-MICC 2007, Penang, 14 May 2007 through 17 May 2007 ; February , 2007 , Pages 686-690 ; 1424410940 (ISBN); 9781424410941 (ISBN) Sahraeian, M. E ; Marvasti, F ; Sharif University of Technology
    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... 

    Impulsive noise removal from images using sparse representation and optimization methods

    , Article 10th International Conference on Information Sciences, Signal Processing and their Applications, ISSPA 2010, 10 May 2010 through 13 May 2010 ; May , 2010 , Pages 480-483 ; 9781424471676 (ISBN) Beygi Harchegani, S ; Kafashan, M ; Marvasti, F ; Sharif University of Technology
    In this paper, we propose a new method for impulsive noise removal from images. It uses the sparsity of natural images when they are expanded by mean of a good learned dictionary. The zeros in sparse domain give us an idea to reconstruct the pixels that are corrupted by random-value impulse noises. This idea comes from this reality that noisy image in sparse domain of original image will not have a sparse representation as much as original image sparsity. In this method we assume that the proper dictionary to achieve image in sparse domain is available  

    Denoising of genetic switches based on Parrondo's paradox

    , Article Physica A: Statistical Mechanics and its Applications ; Volume 493 , 2018 , Pages 410-420 ; 03784371 (ISSN) Fotoohinasab, A ; Fatemizadeh, E ; Pezeshk, H ; Sadeghi, M ; Sharif University of Technology
    Elsevier B.V  2018
    Random decision making in genetic switches can be modeled as tossing a biased coin. In other word, each genetic switch can be considered as a game in which the reactive elements compete with each other to increase their molecular concentrations. The existence of a very small number of reactive element molecules has caused the neglect of effects of noise to be inevitable. Noise can lead to undesirable cell fate in cellular differentiation processes. In this paper, we study the robustness to noise in genetic switches by considering another switch to have a new gene regulatory network (GRN) in which both switches have been affected by the same noise and for this purpose, we will use Parrondo's... 

    Image denoising using sparse representations

    , Article 8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009, Paraty, 15 March 2009 through 18 March 2009 ; Volume 5441 , 2009 , Pages 557-564 ; 03029743 (ISSN) Valiollahzadeh, S ; Firouzi, H ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
    The problem of removing white zero-mean Gaussian noise from an image is an interesting inverse problem to be investigated in this paper through sparse and redundant representations. However, finding the sparsest possible solution in the noise scenario was of great debate among the researchers. In this paper we make use of new approach to solve this problem and show that it is comparable with the state-of-art denoising approaches. © Springer-Verlag Berlin Heidelberg 2009  

    Noise cancelation of epileptic interictal EEG data based on generalized eigenvalue decomposition

    , Article 2012 35th International Conference on Telecommunications and Signal Processing, TSP 2012 - Proceedings ; 2012 , Pages 591-595 ; 9781467311182 (ISBN) Hajipour, S ; Shamsollahi, M. B ; Albera, L ; Merlet, I ; Sharif University of Technology
    Denoising is an important preprocessing stage in some Electroencephalography (EEG) applications such as epileptic source localization. In this paper, we propose a new algorithm for denoising the interictal EEG data. The proposed algorithm is based on Generalized Eigenvalue Decomposition of two covariance matrices of the observations. Since one of these matrices is related to the spike durations, we should estimate the occurrence time of the spike peaks and the exact spike durations. To this end, we propose a spike detection algorithm which is based on the available spike detection methods. The comparison of the results of the proposed algorithm with the results of two well-known ICA... 

    A brief comparison of adaptive noise cancellation, wavelet and cycle-by-cycle fourier series analysis for reduction of motional artifacts from PPG signals

    , Article IFMBE Proceedings, 30 April 2010 through 2 May 2010 ; Volume 32 IFMBE , April , 2010 , Pages 243-246 ; 16800737 (ISSN) ; 9783642149979 (ISBN) Malekmohammadi, M ; Moein, A ; Sharif University of Technology
    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.... 

    An adaptive thresholding approach for image denoising using redundant representations

    , Article Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009, 2 September 2009 through 4 September 2009, Grenoble ; 2009 ; 9781424449484 (ISBN) Sadeghipour, Z ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
    A frequently used approach for denoising is the shrinkage of coefficients of the noisy signal representation in a transform domain. Although the use of shrinkage is optimal for Gaussian white noise with complete and unitary transforms, it has already been shown that shrinkage has promising results even with redundant transforms. In this paper, we propose using adaptive thresholding of redundant representations of the noisy image for image denoising. In the proposed thresholding scheme, a different threshold is used for each representation coefficient of the noisy image in an overcomplete transform. In this method, each threshold is automatically set based on statistical properties of the... 

    Comparison of different electrocardiogram signal power line denoising methods based on SNR improvement

    , Article 2012 19th Iranian Conference of Biomedical Engineering, ICBME 2012 ; 2012 , Pages 159-162 ; 9781467331302 (ISBN) Amiri, M ; Afzali, M ; Vahdat, B. V ; Sharif University of Technology
    In order to access to an accurate detection of electrocardiogram signal in medical approaches especially mobile health and wearable medical devices, development of noise cancellation algorithms seems essential. In this study, the power line noise in ECG signal is filtered using several methods including DFT based, IIR, FIR, adaptive, Kalman, Wavelet and higher order statistics filters. Signal-to-noise ratio (SNR) improvements of the filters are then compared. It is found that FIR and IIR filtering show higher SNR improvement  

    ECG denoising using mutual information based classification of IMFs and interval thresholding

    , Article 2015 38th International Conference on Telecommunications and Signal Processing, TSP 2015, 9 July 2015 through 11 July 2015 ; July , 2015 , Page(s): 1 - 6 ; 9781479984985 (ISBN) Taghavi, M ; Shamsollahi, M. B ; Senhadji, L ; Molnar K ; Herencsar N ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    The Electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Therefore, the quality of information extracted from the ECG has a vital role. In real recordings, ECG is corrupted by artifacts such as prolonged repolarization, respiration, changes of electrode position, muscle contraction, and power line interface. In this paper, a denoising technique for ECG signals based on Empirical Mode Decomposition (EMD) is proposed. We use Ensemble Empirical Mode Decomposition (EEMD) to overcome the limitations of EMD. Moreover, to overcome the limitations of thresholding methods we use the combination of mutual information and two EMD based interval thresholding approaches. Our new method... 

    An efficient Jacobi-like Deflationary ICA algorithm: Application to EEG denoising

    , Article IEEE Signal Processing Letters ; Volume 22, Issue 8 , December , 2015 , Pages 1198-1202 ; 10709908 (ISSN) Sardouie, S. H ; Albera, L ; Shamsollahi, M. B ; Merlet, I ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    In this paper, we propose a Jacobi-like Deflationary ICA algorithm, named JDICA. More particularly, while a projection-based deflation scheme inspired by Delfosse and Loubaton's ICA technique (DelLR) is used, a Jacobi-like optimization strategy is proposed in order to maximize a fourth order cumulant-based contrast built from whitened observations. Experimental results obtained from simulated epileptic EEG data mixed with a real muscular activity and from the comparison in terms of performance and numerical complexity with the FastICA, RobustICA and DelLR algorithms, show that the proposed algorithm offers the best trade-off between performance and numerical complexity when a low number (∼... 

    A new approach in decomposition over multiple-overcomplete dictionaries with application to image inpainting

    , Article Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009, 2 September 2009 through 4 September 2009 ; 2009 ; 9781424449484 (ISBN) Valiollahzadeh, S ; Nazari, M ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
    Recently, great attention was intended toward overcomplete dictionaries and the sparse representations they can provide. In a wide variety of signal processing problems, sparsity serves a crucial property leading to high performance. Decomposition of a given signal over two or more dictionaries with sparse coefficients is investigated in this paper. This kind of decomposition is useful in many applications such as inpainting, denoising, demosaicing, speech source separation, high-quality zooming and so on. This paper addresses a novel technique of such a decomposition and investigates this idea through inpainting of images which is the process of reconstructing lost or deteriorated parts of... 

    Photoacoustic signal enhancement: Towards utilization of low energy laser diodes in real-time photoacoustic imaging

    , Article Sensors (Switzerland) ; Volume 18, Issue 10 , 2018 ; 14248220 (ISSN) Manwar, R ; Hosseinzadeh, M ; Hariri, A ; Kratkiewicz, K ; Noei, S ; Avanaki, M. R. N ; Sharif University of Technology
    MDPI AG  2018
    In practice, photoacoustic (PA) waves generated with cost-effective and low-energy laser diodes, are weak and almost buried in noise. Reconstruction of an artifact-free PA image from noisy measurements requires an effective denoising technique. Averaging is widely used to increase the signal-to-noise ratio (SNR) of PA signals; however, it is time consuming and in the case of very low SNR signals, hundreds to thousands of data acquisition epochs are needed. In this study, we explored the feasibility of using an adaptive and time-efficient filtering method to improve the SNR of PA signals. Our results show that the proposed method increases the SNR of PA signals more efficiently and with much... 

    A fast online bandwidth empirical mode decomposition scheme for avoidance of the mode mixing problem

    , Article Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science ; Volume 232, Issue 20 , 2018 , Pages 3652-3674 ; 09544062 (ISSN) Momeni Massouleh, S. H ; Hosseini Kordkheili, S. A ; Navazi, H. M ; Sharif University of Technology
    SAGE Publications Ltd  2018
    The main objective of this work is to propose a scheme to extract intrinsic mode functions of online data with an acceptable speed as well as accuracy. For this purpose, an individual block framework method is firstly employed to extract the intrinsic mode functions. In this method, buffers are selected such that they overlap with their neighbors to prevent the end effect errors with no need for the averaging process. And in order to avoid the mode mixing problem, a bandwidth empirical mode decomposition scheme is developed to effectively improve the results. Through this scheme, an auxiliary function made of both high- and low-frequency components corresponding to noise and dominant... 

    Multi-channel electrocardiogram denoising using a Bayesian filtering framework

    , Article 2006 Computers in Cardiology, CIC, Valencia, 17 September 2006 through 20 September 2006 ; Volume 33 , 2006 , Pages 185-188 ; 02766574 (ISSN); 1424425328 (ISBN); 9781424425327 (ISBN) Sameni, R ; Shamsollahi, M. B ; Jutten, C ; Sharif University of Technology
    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  

    Polarization maintaining optical fiber multi-intruder sensor

    , Article Optics and Laser Technology ; Volume 44, Issue 7 , October , 2012 , Pages 2026-2031 ; 00303992 (ISSN) Bahrampour, A. R ; Bathaee, M ; Tofighi, S ; Bahrampour, A ; Farman, F ; Vali, M ; Sharif University of Technology
    Elsevier  2012
    In this paper, an optical fiber multi-intruder sensor based on polarization maintaining optical fiber (PMF), without any interferometric fiber loop, is introduced. To map the local coordinates of intruders on the beating spectrum of the output modes, radiation from a ramp frequency modulated laser is injected at the input of PMF optical fiber sensor. It is shown that the local coordinates and some characteristics of intruders can be obtained by the measurement of the frequencies and amplitudes of the output mode beating spectrum. Generally the number of beating frequencies is more than the number of intruders. Among the beating frequencies, a group with maximum signal to noise ratio is... 

    Interictal EEG denoising using independent component analysis and empirical mode decomposition

    , Article 2015 38th International Conference on Telecommunications and Signal Processing, TSP 2015, 9 July 2015 through 11 July 2015 ; July , 2015 , Page(s): 1 - 6 ; 9781479984985 (ISBN) Salsabili, S ; Sardoui, S. H ; Shamsollahi, M. B ; Molnar K ; Herencsar N ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    Noise contamination is inevitable in biomedical recordings. In some cases biomedical recordings are highly contaminated with artifacts which make the effective recovering process hard to achieve. Many different methods have been proposed for artifact removal from biomedical signals but introducing an effective method which can present valuable data for medical analysis, is still an ongoing process. In this paper a new method for interictal EEG denoising is presented. Single-channel ICA denoising method based on EMD decomposition is used to improve the multi-channel ICA denoising results. This method is tested on simulated epileptic recordings which are contaminated with real muscle artifact...