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    Investigation of partial discharge propagation and location in multiple-α and single- α transformer windings using optimized wavelet analysis

    , Article Iranian Journal of Science and Technology, Transaction B: Engineering ; Volume 30, Issue 6 , 2006 , Pages 655-666 ; 03601307 (ISSN) Salay Naderi, M ; Vakilian, M ; Blackburn, T. R ; Phung, T. B ; Salay Naderi, M ; Sharif University of Technology
    2006
    Abstract
    Partial discharges (PD) are recognized as the main cause of the inner insulation deterioration process in power transformers. Therefore, the optimum inner insulation design is one of the challenges a transformer designer is faced with. Transformer strength, especially during transient conditions, is a criterion for transformer insulation designers. This challenge has made designers initiate and employ other types of winding, for example, rather than ordinary layer and disc windings employ the multiple-α windings. Multiple-α windings have a more complicated structure and are comprised of various parts with different physical structures and electrical characteristics. Typical partial discharge... 

    Semi-spatiotemporal fMRI brain decoding

    , Article Proceedings - 2013 3rd International Workshop on Pattern Recognition in Neuroimaging, PRNI 2013 ; 2013 , Pages 182-185 ; 9780769550619 (ISBN) Kefayati, M. H ; Sheikhzadeh, H ; Rabiee, H. R ; Soltani Farani, A ; Sharif University of Technology
    2013
    Abstract
    Functional behavior of the brain can be captured using functional Magnetic Resonance Imaging (fMRI). Even though fMRI signals have temporal and spatial structures, most studies have neglected the temporal structure when inferring mental states (brain decoding). This has two main side effects: 1. Degradation in brain decoding performance due to lack of temporal information in the model, 2. Inability to provide temporal interpretability. Few studies have targeted this issue but have had less success due to the burdening challenges related to high feature-to-instance ratio. In this study, a novel model for incorporating temporal information while maintaining a low feature-to-instance ratio, is... 

    Weighted sparse signal decomposition

    , Article ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings ; 2012 , Pages 3425-3428 ; 15206149 (ISSN) ; 9781467300469 (ISBN) Babaie Zadeh, M ; Mehrdad, B ; Giannakis, G. B ; Sharif University of Technology
    IEEE  2012
    Abstract
    Standard sparse decomposition (with applications in many different areas including compressive sampling) amounts to finding the minimum ℓ 0-norm solution of an underdetermined system of linear equations. In this decomposition, all atoms are treated 'uniformly' for being included or not in the decomposition. However, one may wish to weigh more or less certain atoms, or, assign higher costs to some other atoms to be included in the decomposition. This can happen for example when there is prior information available on each atom. This motivates generalizing the notion of minimal ℓ 0-norm solution to that of minimal weighted ℓ 0-norm solution. On the other hand, relaxing weighted ℓ 0-norm via... 

    Real-Time IDS using reinforcement learning

    , Article 2008 2nd International Symposium on Intelligent Information Technology Application, IITA 2008, Shanghai, 21 December 2008 through 22 December 2008 ; Volume 2 , January , 2008 , Pages 593-597 ; 9780769534978 (ISBN) Sagha, H ; Bagheri Shouraki, S ; Hosein, K ; Mahdi, D ; Sharif University of Technology
    2008
    Abstract
    In this paper we proposed a new real-time learning method. The engine of this method is a fuzzy-modeling technique which is called ink drop spread (IDS). IDS method has good convergence and is very simple and away from complex formula. The proposed method uses a reinforcement learning approach by an actor-critic system similar to Generalized Approximate Reasoning based Intelligent Control (GARIC) structure to adapt the IDS by delayed reinforcement signals. Our system uses Temporal Difference (TD) learning to model the behavior of useful actions of a control system. It is shown that the system can adapt itself, commencing with random actions. © 2008 IEEE  

    Investigation of high frequency signal propagation characteristics on HV XLPE cables

    , Article 7th International Power Engineering Conference, IPEC2005, Singapore, 29 November 2005 through 2 December 2005 ; Volume 2005 , 2005 ; 9810544693 (ISBN); 9789810544690 (ISBN) O, H. N ; Blackburn, T. R ; Phung, B. T ; Vakilian, M ; Naderi, M.S ; Zhang, H ; Sharif University of Technology
    2005
    Abstract
    The insulation lifetime of power cables is determined by several factors. One of the more important of these is the occurrence of partial discharge (PD) at the dielectric. The ability to detect and locate a PD source is limited by attenuation of the high frequency PD pulses as they propagate through the cable. Therefore it is necessary to understand the high frequency response of such cables. Further, to enable reconstruction of PD signals as emitted a viable high frequency model for simulation is needed. This paper presents results of measurements of PD calibration pulse and high frequency sinusoid propagation in HV XLPE cables. In addition to the tests a cable model was developed using the... 

    Maintenance Engineering and Management Joint Program

    , M.Sc. Thesis Sharif University of Technology Ahmad Khanloo, Afshin (Author) ; Modarres Yazdi, Mohammad (Supervisor)
    Abstract
    Despite of existing advanced routines and concepts of Maintenance, which are imported along with Metro System hardware, and valuable experience of maintenance personnel, which has been gathered during years of operation, there are some undeniable shortages in current Maintenance Policy of Tehran Metro. These shortages can be classified in two major groups: first, lake of reasonability in performing maintenance tasks and second, general reluctancy about runnig scientific works. Treating those two deficiencies, can improve the performance of Tehran Metro Maintenance System dramatically. Above mentioned shortages can be seen in whole Maintenance System of Tehran Metro, for the reason of having... 

    MRI image reconstruction via new K-space sampling scheme based on separable transform

    , Article Iranian Conference on Machine Vision and Image Processing, MVIP ; September , 2013 , Pages 127-130 ; 21666776 (ISSN) ; 9781467361842 (ISBN) Oliaiee, A ; Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
    IEEE Computer Society  2013
    Abstract
    Reducing the time required for MRI, has taken a lot of attention since its inventions. Compressed sensing (CS) is a relatively new method used a lot to reduce the required time. Usage of ordinary compressed sensing in MRI imaging needs conversion of 2D MRI signal (image) to 1D signal by some techniques. This conversion of the signal from 2D to 1D results in heavy computational burden. In this paper, based on separable transforms, a method is proposed which enables the usage of CS in MRI directly in 2D case. By means of this method, imaging can be done faster and with less computational burden  

    Analytical performance modeling of elastic optical links with aligned spectrum allocation

    , Article Computer Networks ; Volume 88 , September , 2015 , Pages 40-50 ; 13891286 (ISSN) Vaezi, K ; Akar, N ; Sharif University of Technology
    Elsevier  2015
    Abstract
    Abstract Elastic optical networking has recently been proposed for use in optical transport networks to cope with increasingly heterogeneous and dynamic demand patterns. In this paper, we study the blocking performance of a multi-class elastic optical link for which a demand needs to be allocated a contiguous subset of the entire spectrum. This problem is different than the well-known blocking problem in multi-class multi-server loss systems due to the contiguous allocation constraint. We first propose a non-work-conserving aligned spectrum allocation policy which is shown to outperform the conventional first fit-based work-conserving allocation policy without alignment. Subsequently, for... 

    Functional brain networks in parkinson's disease

    , Article 24th Iranian Conference on Biomedical Engineering and 2017 2nd International Iranian Conference on Biomedical Engineering, ICBME 2017, 30 November 2017 through 1 December 2017 ; 2018 ; 9781538636091 (ISBN) Akbari, S ; Fatemizadeh, E ; Reza Deevband, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Abstract
    Parkinson's disease (PD) is the second most common and progressive neurological disorder. Parkinson's signs are caused by dysfunction in PD patient's brain network. Newly, resting state functional magnetic resonance imaging has been utilized to assess the altered functional connectivity in PD patients. In this study, we investigated the properties of the brain network topology in 19 PD patients compared to 17 normal healthy group by means of graph theory. In addition, we used four different graph formation methods to explore linear and nonlinear relationships between fMRI signals. Each correlation measure created a weighted graph for each subject. Different graph characteristics have been... 

    Iterative learning control for the radio frequency subsystems of a free-electron laser

    , Article IEEE Transactions on Control Systems Technology ; 2017 ; 10636536 (ISSN) Rezaeizadeh, A ; Smith, R. S ; Sharif University of Technology
    Abstract
    In linear particle accelerators used for free-electron lasers, it is often required that the electron bunches experience the same electric field as they pass through the accelerating structures. For radio frequency (RF) pulsed mode machines, like the SwissFEL, this means that the amplitude and phase of the RF pulses feeding the structures through the waveguides should be kept constant over the pulselength. This raises an interesting problem that can be addressed by an iterative learning control (ILC) technique. This method manipulates the input waveforms iteratively, in order to generate flat amplitude and phase pulses at the output of the system. In this paper, we introduce two ILC... 

    Adaptive control of low-level radio frequency signals based on in-phase and quadrature components

    , Article IEEE Transactions on Nuclear Science ; Volume 64, Issue 4 , 2017 , Pages 1023-1028 ; 00189499 (ISSN) Rezaeizadeh, A ; Smith, R. S ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2017
    Abstract
    This research work presents a low-level radio frequency (RF) control method based on the in-phase, I , and quadrature, Q , components of the RF signal. The proposed method uses only the main four arithmetic operations, i.e., addition, subtraction, multiplication, and division, which makes this control method suitable for implementation on the field-programmable gate array. The control scheme is adaptive in the sense that it estimates the system response on-the-fly, and therefore, it is robust against changes in the loop phase and/or gain during the operation. © 2017 IEEE  

    fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease

    , Article Signal, Image and Video Processing ; 2020 Ahmadi, H ; Fatemizadeh, E ; Motie-Nasrabadi, A ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2020
    Abstract
    Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

    fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease

    , Article Signal, Image and Video Processing ; Volume 15, Issue 4 , 2021 , Pages 715-723 ; 18631703 (ISSN) Ahmadi, H ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2021
    Abstract
    Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

    fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease

    , Article Signal, Image and Video Processing ; Volume 15, Issue 4 , 2021 , Pages 715-723 ; 18631703 (ISSN) Ahmadi, H ; Fatemizadeh, E ; Motie-Nasrabadi, A ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2021
    Abstract
    Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

    Extraction and automatic grouping of joint and individual sources in multi-subject fMRI data using higher order cumulants

    , Article IEEE Journal of Biomedical and Health Informatics ; 24 May , 2018 ; 21682194 (ISSN) Pakravan, M ; Shamsollahi, M. B ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Abstract
    The joint analysis of multiple datasets to extract their interdependency information has wide applications in biomedical and health informatics. In this paper, we propose an algorithm to extract joint and individual sources of multi-subject datasets by using a deflation based procedure, which is referred to as joint/individual thin independent component analysis (JI-ThICA). The proposed algorithm is based on two cost functions utilizing higher order cumulants to extract joint and individual sources. Joint sources are discriminated by fusing signals of all subjects, whereas individual sources are extracted separately for each subject. Furthermore, JI-ThICA algorithm estimates the number of... 

    Extraction and automatic grouping of joint and individual sources in multisubject fMRI data using higher order cumulants

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 23, Issue 2 , 2019 , Pages 744-757 ; 21682194 (ISSN) Pakravan, M ; Shamsollahi, M. B ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Abstract
    The joint analysis of multiple data sets to extract their interdependency information has wide applications in biomedical and health informatics. In this paper, we propose an algorithm to extract joint and individual sources of multisubject data sets by using a deflation-based procedure, which is referred to as joint/individual thin independent component analysis (JI-ThICA). The proposed algorithm is based on two cost functions utilizing higher order cumulants to extract joint and individual sources. Joint sources are discriminated by fusing signals of all subjects, whereas individual sources are extracted separately for each subject. Furthermore, JI-ThICA algorithm estimates the number of... 

    Defining microRNA signatures of hair follicular stem and progenitor cells in healthy and androgenic alopecia patients

    , Article Journal of Dermatological Science ; Volume 101, Issue 1 , 2021 , Pages 49-57 ; 09231811 (ISSN) Mohammadi, P ; Nilforoushzadeh, M. A ; Youssef, K. K ; Sharifi Zarchi, A ; Moradi, S ; Khosravani, P ; Aghdami, R ; Taheri, P ; Hosseini Salekdeh, G ; Baharvand, H ; Aghdami, N ; Sharif University of Technology
    Elsevier Ireland Ltd  2021
    Abstract
    Background: The exact pathogenic mechanism causes hair miniaturization during androgenic alopecia (AGA) has not been delineated. Recent evidence has shown a role for non-coding regulatory RNAs, such as microRNAs (miRNAs), in skin and hair disease. There is no reported information about the role of miRNAs in hair epithelial cells of AGA. Objectives: To investigate the roles of miRNAs affecting AGA in normal and patient's epithelial hair cells. Methods: Normal follicular stem and progenitor cells, as well as follicular patient's stem cells, were sorted from hair follicles, and a miRNA q-PCR profiling to compare the expression of 748 miRNA (miRs) in sorted cells were performed. Further, we...