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    A comprehensive statistical study on daytime surface urban heat island during summer in urban areas, case study: Cairo and its new towns

    , Article Remote Sensing ; Volume 8, Issue 8 , 2016 ; 20724292 (ISSN) Taheri Shahraiyni, H ; Sodoudi, S ; El Zafarany, A ; Abou El Seoud, T ; Ashraf, H ; Krone, K ; Sharif University of Technology
    MDPI AG  2016
    Abstract
    Surface urban heat island (SUHI) is defined as the elevated land surface temperature (LST) in urban area in comparison with non-urban areas, and it can influence the energy consumption, comfort and health of urban residents. In this study, the existence of daytime SUHI, in Cairo and its new towns during the summer, is investigated using three different approaches; (1) utilization of pre-urbanization observations as LST references; (2) utilization of rural observations as LST references (urban-rural difference); and (3) utilization of the SIUHI (Surface Intra Urban Heat Island) approach. A time series of Landsat TM & ETM+ data (46 images) from 1984 to 2015 was employed in this study for... 

    Simultaneous Hypothesis Testing and False Discovery Rate

    , M.Sc. Thesis Sharif University of Technology Shahbazi, Mohammad (Author) ; Alishahi, Kasra (Supervisor)
    Abstract
    The purpose of this thesis is to introduce and review a recent methods in simultaneous hypothesis testing. False discovery rates, Benjamini and Hochberg’s FDR Control Algorithm, is the great success story of the new methodology. Much of what follows is an attempt to explain that success in empirical Bayes terms.The later chapters are at pains to show the limitations of current largescale statistical practice: Which cases should be combined in a single analysis? How do we account for notions of relevance between cases? What is the correct null hypothesis? How do we handle correlations? Some helpful theory is provided in answer, but much of the argumentation is by example, with graphs and... 

    Solving Simulation Optimization Problems Using Artificial Bee Colony and Ranking and Selection Methods

    , M.Sc. Thesis Sharif University of Technology Firooze, Hamid Reza (Author) ; Akhavan Niaki, Taghi (Supervisor)
    Abstract
    In this thesis the simulation optimization problems are solved by using Artificial Bee Colony (ABC). The main objective is to improve and adapt the ABC algorithm for solving the optimization problems in deterministic and stochastic environments. For solving deterministic problems, directed search in neighborhood and Nelder-Mead algorithm are combined with ABC algorithm to improve the convergence rate and solutions. Moreover; in stochastic environment, hypothesis test and Kim-Nelson (KN) indifference zone ranking and selection procedure are helping bees to produce solutions with better confidence level on the quality of the solution. Results of optimizing an extensive complex benchmark... 

    Covariance Statistic and a Significance Test for Selecting All Active Variables
    in Lasso

    , M.Sc. Thesis Sharif University of Technology Isakhani Mamaghani, Arman (Author) ;
    Abstract
    Testing the significance of the predictor variables and finding an appropriate method for inference on the coefficients is an important question in the sparse linear regression setting. Covariance statistic is a new method that tries to give an answer to this question. This statistic is defined based on lasso fitted values, and when the true model is linear, this statistic has an Exp(1) asymptotic distribution under the null hypothesis (the null being that all truly active variables are contained in the current lasso model). From classical statistics, we have known some methods like chi-squared test for testing the significance of an additional variable between two nested linear models. But... 

    Distributed binary majority voting via exponential distribution

    , Article IET Signal Processing ; Volume 10, Issue 5 , 2016 , Pages 532-542 ; 17519675 (ISSN) Salehkaleybar, S ; Golestani, S. J ; Sharif University of Technology
    Institution of Engineering and Technology 
    Abstract
    In the binary majority voting problem, each node initially chooses between two alternative choices. The goal is to design a distributed algorithm that informs nodes which choice is in majority. In this study, the authors formulate this problem as a hypothesis testing problem and propose fixed-size and sequential solutions using classical and Bayesian approaches. In the sequential version, the proposed mechanism enables nodes to test which choice is in majority, successively in time. Hence, termination of the algorithm is embedded within it, contrary to the existing approaches which require a monitoring algorithm to indicate the termination. This property makes the algorithm more efficient in... 

    Cooperative abnormality detection in fluidic medium molecular communication

    , Article 2020 Iran Workshop on Communication and Information Theory, IWCIT 2020 ; 26-28 May , 2020 Khaloopour, L ; Mirmohseni, M ; Nasiri Kenari, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    Abstract
    In this paper, we study the problem of cooperative abnormality detection using mobile sensors in a fluidic medium, based on a molecular communication setup. The sensors are injected into the medium to search the environment for the abnormality. To reduce the effects of sensor imperfection, we propose a cooperative scheme where the sensors activate each other by releasing some molecules (i.e., markers), into the medium after they sense an abnormality. A number of fusion centers (FC) are placed at specific locations in the medium, which absorb all sensors arrived at their locations. By observing the states of the received sensors, each FC decides whether an abnormality exists in its... 

    Model-based adaptive target detection in clutter using MIMO radar

    , Article 2006 CIE International Conference on Radar, ICR 2006, Shanghai, 16 October 2006 through 19 October 2006 ; 2006 ; 0780395824 (ISBN); 9780780395824 (ISBN) Sheikhi, A ; Zamani, A ; Norouzi, Y ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2006
    Abstract
    In this paper the problem of target detection using coherent pulse terrain in auto regressive (AR) clutter has been considered for MIMO radars. We have formulated this problem as a hypothesis test. Using the generalized likelihood ratio test (GLRT) an adaptive decision scheme has been developed for clutter with known order but unknown parameters. The performance of the proposed detectors have been evaluated using Monte-Carlo simulations. The results show the superiority of the MIMO radars with temporal coherent processing over the conventional phased arrays due to both angular spread and a newly presented phenomenon which is called doppler spread in this paper. © 2006 IEEE  

    Theoretical concept study of cooperative abnormality detection and localization in fluidic-medium molecular communication

    , Article IEEE Sensors Journal ; Volume 21, Issue 15 , 2021 , Pages 17118-17130 ; 1530437X (ISSN) Khaloopour, L ; Mirmohseni, M ; Nasiri Kenari, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2021
    Abstract
    In this paper, we propose a theoretical framework for cooperative abnormality detection and localization systems by exploiting molecular communication setup. The system consists of mobile sensors in a fluidic medium, which are injected into the medium to search the environment for abnormality. Some fusion centers (FC) are placed at specific locations in the medium, which absorb all sensors arrived at their locations, and by observing its state, each FC decides on the abnormality existence and/or its location. To reduce the effects of sensor imperfection, we propose a scheme where the sensors release some molecules (i.e., markers) into the medium after they sense an abnormality. If the goal... 

    Impact of initial ensembles on posterior distribution of ensemble-based assimilation methods

    , Article Journal of Petroleum Science and Engineering ; Volume 171 , 2018 , Pages 82-98 ; 09204105 (ISSN) Jahanbakhshi, S ; Pishvaie, M. R ; Bozorgmehry Boozarjomehry, R ; Sharif University of Technology
    Elsevier B.V  2018
    Abstract
    In this study, impact of initial ensembles on posterior distribution of ensemble-based assimilation methods is statistically analyzed. Along with, sampling performance as well as uncertainty quantification of these methods are compared in terms of their ability to accurately and consistently evaluate unknown reservoir model parameters and reservoir future performance. For this purpose, a synthetic test problem, which is a small but highly nonlinear reservoir model under two-phase flow, is utilized. Subsequently, different initial ensemble sets are considered and are updated through the assimilation process performed on the test problem using ensemble-based assimilation methods. Afterwards,... 

    Drug Effect on Brain Functional Connectivity Using EEG Signals

    , M.Sc. Thesis Sharif University of Technology Karimi, Sajjad (Author) ; Shamsollahi, Mohammad Bagher (Supervisor) ; Molaee-Ardekani, Behnam (Co-Advisor)
    Abstract
    In this study Donepezil effect on the brain functional connectivity investigated. In order to construct the brain functional network, EEG artifacts must firstly be removed because this step has important effects on the final interpretation of the results. Therefor, a new artifact removing method is proposed and better performance of the proposed method compared to other existing methods is stated using quantitative evaluations. After artifact removal, the functional brain network is extracted using conventional methods that were applied in the similar previous studies. The reasons for using conventional methods are their simplicity and reliability. Furtheremore, to study the recent... 

    Sparse Component Analysis and its Applications

    , Ph.D. Dissertation Sharif University of Technology Zayyani, Hadi (Author) ; Babaiezadeh, Massoud (Supervisor)
    Abstract
    Nowadays, using sparsity of signals has been utilized in diverse applications in signal processing community. Two important applications of signal sparsity are sparse source separation and sparse signal representation. These two problems are joined with a Sparse Component Analysis (SCA) framework. In SCA, the problem is divided into two subproblems which are matrix estimation and sparse vector estimation. In this thesis, a MAP-based algorithm is suggested for sparse vector estimation with a Bernoulli-Gaussian distribution for sparse vector elements. To reduce the complexity, an iterative Bayesian algoritm is used in which an steepest-ascent is utilized for maximization. A complete... 

    False Discovery Rate for Large Scale Hypothesis Testing

    , M.Sc. Thesis Sharif University of Technology Armandpour, Mohammad Reza (Author) ; Alishahi, Kasra (Supervisor)
    Abstract
    The chapter 1 begins the discussion of a theory of large-scale simultaneous hypothesis testing now under development in the statistics literature. Furthermore,this chapter introduces the False Discovery Rate (FDR) and Empirical Bayes approach. In chapter 2, the frequentist viewpoints to the simultaneous hypothesis testing is mentioned. apter 3 describes the break through paper of the Benjamini and Hochberg published in 1995. Chapter 4 provides new criteria for error and represents an outstanding method of controlling FDR by J.D. Storey. The first part of chapter 5 discusses a paper related to control of FDR for variable selection in linear model setting by E.Candes and R. Barber. In the rest... 

    Efficient Detection Schemes in Molecular Communication Networks

    , Ph.D. Dissertation Sharif University of Technology Mosayebi, Reza (Author) ; Nasiri-Kenari, Masoumeh (Supervisor) ; Aminzadeh Gohari, Amin (Supervisor) ; Mirmohseni, Mahtab (Co-Supervisor)
    Abstract
    The progress in the design of nano-scale machines over the past decade has motivated researchers to study the concept of nano-communications. Inspired by biological systems,diffusion-based molecular communication (MC) systems have been proposed as a potential solution for communication in nano-networks where molecules are used as information carriers.Nano-networks are envisioned to facilitate revolutionary applications in areas such as biological engineering, healthcare, and environmental monitoring. In recent years, there has been a significant amount of work on various aspects of MC systems, including transmitter and receiver design, multiple access protocols, and network layer issues.... 

    Information Theoretic Strategies in Private Distributed Hypothesis Testing

    , M.Sc. Thesis Sharif University of Technology Abbasalipour, Reza (Author) ; Mirmohseni, Mahtab (Supervisor)
    Abstract
    The problem of distributed binary hypothesis testing in the Gray-Wyner network with side information is studied in this dissertation. An observer agent has access to a discrete memoryless and stationary source and describes its observation to two detecting agents via one common and two private channels. The channels are considered error-free but rate-limited. Each detecting agent also has access to its own discrete memoryless and stationary source, i.e., the side information. The goal is to perform two distinct binary hypothesis testings on the joint distribution of observations at detecting agents. Additionally, the observing agent aims to keep a correlated latent source private against the... 

    A statistical inference approach for the identification of dominant parameters in immiscible nitrogen injection

    , Article Energy Sources, Part A: Recovery, Utilization and Environmental Effects ; Vol. 36, Issue. 12 , 2014 , Pages 1285-1295 ; ISSN: 15567036 Moradi, S ; Ghazvini, M. G ; Dabir, B ; Emadi, M. A ; Rashtchian, D ; Sharif University of Technology
    Abstract
    Screening analysis is a useful guideline that helps us with proper field selection for different enhanced oil recovery processes. In this work, reservoir simulation is combined with experimental design to estimate the effect of reservoir rock and fluid properties on performance of immiscible nitrogen injection. Reservoir dip, thickness, and horizontal permeability are found to be the most influential parameters. Possible interactions of parameters are also discussed to increase reliability and robustness of screening results. Finally, significance of both main effects and interactions are evaluated by employing a statistical inference approach (hypothesis testing) and results are compared to... 

    Two dimensional compressive classifier for sparse images

    , Article Proceedings of the 2009 6th International Conference on Computer Graphics, Imaging and Visualization: New Advances and Trends, CGIV2009, 11 August 2009 through 14 August 2009, Tianjin ; 2009 , Pages 402-405 ; 9780769537894 (ISBN) Eftekhari, A ; Moghaddam, H. A ; Babaie Zadeh, M ; Sharif University of Technology
    Abstract
    The theory of compressive sampling involves making random linear projections of a signal. Provided signal is sparse in some basis, small number of such measurements preserves the information in the signal, with high probability. Following the success in signal reconstruction, compressive framework has recently proved useful in classification, particularly hypothesis testing. In this paper, conventional random projection scheme is first extended to the image domain and the key notion of concentration of measure is closely studied. Findings are then employed to develop a 2D compressive classifier (2D-CC) for sparse images. Finally, theoretical results are validated within a realistic... 

    Bayesian hypothesis testing detector for one bit diffusion LMS with blind missing samples

    , Article Signal Processing ; Volume 146 , May , 2018 , Pages 61-65 ; 01651684 (ISSN) Zayyani, H ; Korki, M ; Marvasti, F ; Sharif University of Technology
    Elsevier B.V  2018
    Abstract
    This paper proposes a sparse distributed estimation algorithm when missing data occurs in the measurements over adaptive networks. Two classes of measurement models are considered. First, the traditional linear regression model is investigated and second the sign of the linear regression model is studied. The latter is referred to as one-bit model. We utilize the diffusion LMS strategy, in the proposed methods, where a set of nodes cooperates with each other to estimate a vector model parameter. In both models, it is shown that replacing the missing sample with a simple estimate is equivalent to removing the missing sample from the distributed diffusion algorithm. We consider two cases,... 

    A MSWF root-MUSIC based on Pseudo-noise resampling technique

    , Article Electronics Letters ; Volume 57, Issue 17 , 2021 , Pages 675-678 ; 00135194 (ISSN) Johnny, M ; Aref, M. R ; Sharif University of Technology
    John Wiley and Sons Inc  2021
    Abstract
    This paper uses the shift-invariance property of uniform linear array in root-MUSIC estimator for obtaining signal and noise subspaces by applying multistage Wiener filter (MSWF) procedure. Also, the MSWF root-MUSIC based on the pseudo-noise resampling process for estimating the direction of arrival (DOA) of signals is proposed. By this process, a root estimator bank and a corresponding DOA estimator bank are constructed. Then, a hypothesis test is applied to the DOA estimator bank to detect the normal DOA estimators from abnormal DOA estimators called outliers. By averaging the corresponding root estimators of normal DOA estimators, the final DOAs can be determined more accurately. When all... 

    Compressed-domain detection and estimation for colocated MIMO radar

    , Article IEEE Transactions on Aerospace and Electronic Systems ; Volume 56, Issue 6 , 2020 , Pages 4504-4518 Tohidi, E ; Hariri, A ; Behroozi, H ; Nayebi, M. M ; Leus, G ; Petropulu, A. P ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    Abstract
    This article proposes a compressed-domain signal processing (CSP) multiple-input multiple-output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First, compressive sensing is applied at the receive antennas, followed by a Capon beamformer, which is designed to suppress clutter. Exploiting the sparse nature of the beamformer output, a second compression is applied to the filtered data. Target detection is subsequently conducted by formulating and solving a hypothesis testing problem at each grid point of the... 

    Wideband spectrum sensing in unknown white Gaussian noise

    , Article IET Communications ; Volume 2, Issue 6 , 2008 , Pages 763-771 ; 17518628 (ISSN) Taherpour, A ; Gazor, S ; Nasiri Kenari, M ; Sharif University of Technology
    2008
    Abstract
    The spectrum sensing of a wideband frequency range is studied by dividing it into multiple subbands. It is assumed that in each subband either a primary user (PU) is active or absent in a additive white Gaussian noise environment with an unknown variance. It is also assumed that at least a minimum given number of subbands are vacant of PUs. In this multiple interrelated hypothesis testing problem, the noise variance is estimated and a generalised likelihood ratio detector is proposed to identify possible spectrum holes at a secondary user (SU). Provided that it is known that a specific PU can occupy a subset of subbands simultaneously, a grouping algorithm which allows faster spectrum...