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    The online atlas of the languages of Iran: Design, methodology and initial results

    , Article Language Related Research ; Volume 12, Issue 2 , 2021 , Pages 231-291 ; 23223081 (ISSN) Taheri Ardali, M ; Anonby, E ; Hayes, A ; Azin, Z ; Ebrahimi, Z ; Öpengin, E ; Atabaki, N. A ; Stone, A ; Stilo, D ; Esmaeelpour, E ; Amani Babadi, M ; Ourang, M ; Izadi, E ; Oikle, R ; Bahrani, N ; Borjian, H ; Bahrami, A ; Poshtvan, H ; Piryaee, S ; Pishyardehkordi, P ; Sabethemmatabadi, P ; Jaafari Dehaghi, S ; Doab, M. J ; Jamaleddin, F ; Joulaei, K ; Shabaniyan, H. H ; Khanjani, J ; Dianat, L ; Rashidi, A ; Borujeni, R. R ; Rahnema, Z ; Zamani Gandomani, Z ; Schreiber, L ; Sherafat, N ; Sheyholislami, J ; Salehi, M ; Talebi Dastenaei, M ; Kasgari, A. A. A ; Ghiasian, M. S ; Fattahi, M ; Ghandi, S ; Gheitasi Doolabi, M ; Kamali, R ; Goshtasb, F ; Bahmani, H. M ; Mohammadi, M ; Moradi, R ; Meshkinfam, M ; Khoo, R. M ; Bahram, A. N ; Nemati, F ; Nourzaei, M ; Wang, E ; Hashemi Zarajabad, H ; Sharif University of Technology
    Tarbiat Modares University  2021
    Abstract
    Iran is home to a treasury of languages representing diverse language families: Iranic, Turkic, Semitic, Indic, Dravidian, Armenian, and Kartvelian, as well as sign languages. Despite valuable research carried out by Iranian and western scholars, there is still no comprehensive publication depicting the geographic distribution and linguistic status of language varieties in Iran. In order to work toward this goal, the Atlas of the Languages of Iran (ALI) (www.iranatlas.net) was officially launched in 2015 as an international, online research programme. The present study opens with a historical overview of the research context and underlines the ongoing necessity of constructing such an atlas... 

    Development of a Model to Investigate Energy Production Potential from Sea Applying GIS Technique

    , M.Sc. Thesis Sharif University of Technology Rahimi, Rahman (Author) ; Abbaspour, Majid (Supervisor)
    Abstract
    The growing requirements for renewable energy production lead to the development of a new series of systems, including wave and tidal energy conversion systems. Due to their sensitivity and the impact of the aggressive marine environment, the selection of the most adequate location for these systems is a major and very important task. Several factors, such as technological limitations, environmental conditions, administrative and logistic conditions, have to be taken into account in order to support the decision for best location. The aim of the present study is to provide an Atlas of IRAN Offshore Renewable Energy Resources (hereafter called ‘the Atlas’) to map out wave and tidal resources... 

    Application of a Thermo-Plastic Constitutive Model for Coupled THM Analysis of Behavior of Saturated Soils

    , M.Sc. Thesis Sharif University of Technology Afshari, Kioumars (Author) ; Pak, Ali (Supervisor)
    Abstract
    Effects of temperature on the behavior of soils and rocks have been studied widely in recent years. Following the need for understanding the effects of temperature on the behavior of clayey soils, several experimental and numerical studies on thermo-mechanical behavior of clayey soils have been carried out and a number of thermo-mechanical constitutive models have been developed for saturated and unsaturated clayey soils. In this research, a two-yield surface thermo-plastic constitutive model developed by Abuel-Naga et al. (2007, 2009), has been chosen for THM analysis of the behavior of saturated clayey soils. This constitutive model is based on Modified Cam-Clay (MCC) model. The MCC model... 

    A novel convolutional neural network with high convergence rate: Application to CT synthesis from MR images

    , Article 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2019, 26 October 2019 through 2 November 2019 ; 2019 ; 9781728141640 (ISBN) Bahrami, A ; Karimian, A ; Fatemizadeh, E ; Arabi, H ; Zaidi, H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Abstract
    Synthetic CT (sCT) generation from MR images is yet one of the major challenges in the context of MR-guided radiation planning as well as quantitative PET/MR imaging. Deep convolutional neural networks have recently gained special interest in large range of medical imaging applications including segmentation and image synthesis. In this study, a novel deep convolutional neural network (DCNN) model is presented for synthetic CT generation from single T1-weighted MR image. The proposed method has the merit of highly accelerated convergence rate suitable for applications where the number of training da-taset is limited while highly robust model is required. This algorithm exploits a Visual... 

    Glioma Tumor Segmentation in Brain MRI Using Atlas-based Learning and Graph Structures

    , M.Sc. Thesis Sharif University of Technology Barzegar, Zeynab (Author) ; Jamzad, Mansour (Supervisor) ; Beigy, Hamid (Co-Supervisor)
    Abstract
    Brain cancer is a lump or tumor in the brain caused by abnormal growth of cells. Glioma is a common type of tumor that develops in the brain. In order to plan precise treatment or accurate tumor removal surgery, brain tumor segmentation is critical for detecting all parts of tumor and its surrounding tissues. To visualize the brain anatomy and detect its abnormalities, we use Magnetic Resonance Imaging (MRI) as an input. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task. Moreover, due to the intensity inhomogeneity existing in brain MRI and gray... 

    Medical image registration using sparse coding of image patches

    , Article Computers in Biology and Medicine ; Volume 73 , 2016 , Pages 56-70 ; 00104825 (ISSN) Afzali, M ; Ghaffari, A ; Fatemizadeh, E ; Soltanian Zadeh, H ; Sharif University of Technology
    Elsevier Ltd 
    Abstract
    Image registration is a basic task in medical image processing applications like group analysis and atlas construction. Similarity measure is a critical ingredient of image registration. Intensity distortion of medical images is not considered in most previous similarity measures. Therefore, in the presence of bias field distortions, they do not generate an acceptable registration. In this paper, we propose a sparse based similarity measure for mono-modal images that considers non-stationary intensity and spatially-varying distortions. The main idea behind this measure is that the aligned image is constructed by an analysis dictionary trained using the image patches. For this purpose, we use... 

    Automated detection of autism spectrum disorder using a convolutional neural network

    , Article Frontiers in Neuroscience ; Volume 13 , 2020 Sherkatghanad, Z ; Akhondzadeh, M ; Salari, S ; Zomorodi Moghadam, M ; Abdar, M ; Acharya, U. R ; Khosrowabadi, R ; Salari, V ; Sharif University of Technology
    Frontiers Media S.A  2020
    Abstract
    Background: Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. Method: In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset. We detected ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data from a multi-site dataset named the Autism Brain Imaging Exchange (ABIDE). The proposed approach was able to classify ASD... 

    WLFS: Weighted label fusion learning framework for glioma tumor segmentation in brain MRI

    , Article Biomedical Signal Processing and Control ; Volume 68 , 2021 ; 17468094 (ISSN) Barzegar, Z ; Jamzad, M ; Sharif University of Technology
    Elsevier Ltd  2021
    Abstract
    Glioma is a common type of tumor that develops in the brain. Due to many differences in the shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor and its surrounding tissues in cancer detection is a challenging task in cancer detection. In recent researches, the combination of atlas-based segmentation and machine learning methods have presented superior performance over other automatic brain MRI segmentation algorithms. To overcome the side effects of limited existing information on atlas-based segmentation, and the long training and the time consuming phase of learning methods, we proposed a semi-supervised learning framework by introducing a... 

    Iran atlas of offshore renewable energies

    , Article Renewable Energy ; Volume 36, Issue 1 , January , 2011 , Pages 388-398 ; 09601481 (ISSN) Abbaspour, M ; Rahimi, R ; Sharif University of Technology
    2011
    Abstract
    The aim of the present study is to provide an Atlas of IRAN Offshore Renewable Energy Resources (hereafter called 'the Atlas') to map out wave and tidal resources at a national scale, extending over the area of the Persian Gulf and Sea of Oman. Such an Atlas can provide necessary tools to identify the areas with greatest resource potential and within reach of present technology development. To estimate available tidal energy resources at the site, a two-dimensional tidally driven hydrodynamic numerical model of Persian Gulf was developed using the hydrodynamic model in the MIKE 21 Flow Model (MIKE 21HD), with validation using tidal elevation measurements and tidal stream diamonds from... 

    A new deep convolutional neural network design with efficient learning capability: Application to CT image synthesis from MRI

    , Article Medical Physics ; Volume 47, Issue 10 , 2020 , Pages 5158-5171 Bahrami, A ; Karimian, A ; Fatemizadeh, E ; Arabi, H ; Zaidi, H ; Sharif University of Technology
    John Wiley and Sons Ltd  2020
    Abstract
    Purpose: Despite the proven utility of multiparametric magnetic resonance imaging (MRI) in radiation therapy, MRI-guided radiation treatment planning is limited by the fact that MRI does not directly provide the electron density map required for absorbed dose calculation. In this work, a new deep convolutional neural network model with efficient learning capability, suitable for applications where the number of training subjects is limited, is proposed to generate accurate synthetic computed tomography (sCT) images from MRI. Methods: This efficient convolutional neural network (eCNN) is built upon a combination of the SegNet architecture (a 13-layer encoder-decoder structure similar to the...