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    Predicting the Optimal Operation Pattern of Municipal Wastewater Treatment Plant Using Artificial Intelligence Approaches

    , M.Sc. Thesis Sharif University of Technology Hakimi, Mahdi (Author) ; Torkian, Ayoub (Supervisor)
    Abstract
    With the growth of the population and the significant expansion of industries in the last century, many environmental problems have plagued developed and developing countries. One of these environmental problems is water pollution. Observing the effects of water pollution over time, sanitary and industrial wastewater treatment was proposed as a reliable solution. With technology development, wastewater treatment requirements have become stricter. The increase in energy consumption and wastewater treatment costs due to population growth and industrialization on the one hand and strict regulations, on the other hand, have forced those involved in this field to employ a variety of technical and... 

    Designing an Estimation of Distribution Algorithm Based on Data Mining Methods

    , M.Sc. Thesis Sharif University of Technology Akbari Azirani, Elham (Author) ; Beigy, Hamid (Supervisor)
    Abstract
    Estimation of distribution algorithms (EDA) are optimization methods that search the solution space by building and sampling probabilistic models. The linkage tree genetic algorithm (LTGA), which can be considered an estimation of distribution algorithm, uses hierarchical clustering to build a hierarchical linkage model called the linkage tree, and gene-pool optimal mixing algorithm to generate new solutions. While the LTGA performs very well on problems with separable sub-problems, its performance deteriorates on ones with overlapping sub-problems. This thesis presents a comparison of the effect of different pre-constructed models in the LTGA's performance. Several important factors that... 

    Recognizing Center of Siezur with Clustering Algorithm

    , M.Sc. Thesis Sharif University of Technology Akhshi, Amin (Author) ; Rahimitabar, Mohammad Reza (Supervisor)
    Abstract
    Complex systems are composed of a large number of subsystems behaving in a collective manner. In such systems, which are usually far from equilibrium, collective behavior arises due to self-organization and results in the formation of temporal, spatial, spatio-temporall structures. Examples of complex systems are turbulent flow, stock markets, dynamics of a brain, etc. In study of the complex systems, we always encounter with handling and analysing of a Big-Data set. There are several approaches to overcome this problem, among which the most powerful method is the clustering analysis. Clustering algorithm is based on the classifying of dynamics of complex system using some similarity... 

    On Different Information Theoretic Security Measures in Communication Network

    , M.Sc. Thesis Sharif University of Technology Kavian, Masoud (Author) ; Aref, Mohammad Reza (Supervisor) ; Mirmohseni, Mahtab (Co-Supervisor)
    Abstract
    Reliable and secure communication requires low error probability and low information leakage, respectively. In this thesis we study different security parameters in communication networks. Three important security metrics are noted by weak, strong, and perfect secrecy. A weak condition on secrecy requires the percentage of the message leaked to the adversary vanish as the code length increases, while a strong condition on secrecy requires the total amount of leaked information (not its percentage) vanish as length increases. Perfect secrecy requires absoloutely zero leakage of information for every given block length. An intriguing question is to determine the secrecy rate under secrecy... 

    Quantum Estimation: Properties and Calculation of the Quantum Fisher Information and Adaptive Scheme

    , Ph.D. Dissertation Sharif University of Technology Hassani, Majid (Author) ; Rezakhani, Ali (Supervisor)
    Abstract
    In this thesis , we study quantum estimation , its primary tools and different approaches . To this end , along with reviewing the main concepts of classical and quantum estimation , the Cramer-Rao inequlity which establishes a lower bound on the precision of estimation has been used . This lower bound can be calculated using the quantum Fisher information . In order to explore properties of the quantum Fisher information , we study the continuity relation of this quantity in the most general case and we calculate it for different quantum states . To underline the importance of the continuity relation , one can demonstrate an estimate of the value of the quantum Fisher information... 

    Non-Rigid Medical Image Registration Based on Information Theory

    , M.Sc. Thesis Sharif University of Technology Khorsandi, Rahman (Author) ; Fatemizadeh, Emadoddin (Supervisor)
    Abstract
    The registration of images is a fundamental task in numerous applications in medical image processing. The importance of medical image registraiton due to the imaging systems development in last decades is obvious to every one. Especially the wide employment and different capabilities of these systems has caused more attention to this field of image processing. Th e application of medical image registraiton is extended from clinical diagnosis and treatment evaluation, to image guided surgery. The dimension of images as well as modalities of imaging and imaging subjectshas made a wide variety of problems in this branch of image processing. Registration, briefly speaking, is a geometrical... 

    Multivariate Mutual Information via Secret-key Agreement

    , M.Sc. Thesis Sharif University of Technology Mostafa Zadflah Chobari, Mohammad (Author) ; Ebrahimi, Javad (Supervisor)
    Abstract
    Shannon (1948) for the first time defined the "mutual information'' parameter for two random variables, but still there is no common definition for multivariate mutual information has been agreed upon, despite the multitude of research on the subject and various proposed definitions. In 2015, a study suggested that the maximum rate of secret-key, in the secret-key agreement problem, is a suitable candidate for defining multivariate mutual information. Csiszár and Narayan's research on the secret-key agreement problem provides an accessible bound for the maximum rate of secret-key rate, which in the bivariate case is the shannon's mutual information. The proposed definition has all expected... 

    Using the Echo of Rotating Parts to Recognize a Radar Target

    , M.Sc. Thesis Sharif University of Technology Johari, Mohammad Mahdi (Author) ; Nayebi, Mohammad Mahdi (Supervisor)
    Abstract
    Target recognition techniques based on micro Doppler phenomenon are popular because they are applicable even on low resolution radars, in contrast to other techniques such as High Resolution Range Profile (HRRP) which need high resolution in range or angle. Usually, main purpose of such techniques is generating robust features against target initial state, velocity, aspect angle, etc. rather than features which exactly identify a target. Main approaches in the literature are based on time-frequency transforms (TFT) such as spectrogram in order to generate features to classify targets, but in this thesis, we propose a totally different method using Recurrence Plot for generating features... 

    Φ-Entropic measures of correlation

    , Article IEEE Transactions on Information Theory ; Volume 64, Issue 4 , 2018 , Pages 2193-2211 ; 00189448 (ISSN) Beigi, S ; Gohari, A ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Abstract
    A measure of correlation is said to have the tensorization property if it does not change when computed for i.i.d. copies. More precisely, a measure of correlation between two random variables X, Y denoted by rho (X, Y), has the tensorization property if ρ(Xn, Yn)=ρ (X, Y) where (Xn, Yn) denotes n i.i.d. copies of (X, Y). Two well-known examples of such measures are the maximal correlation and the hypercontractivity ribbon (HC ribbon). We show that the maximal correlation and the HC ribbon are special cases of the new notion of Φ-ribbons, defined in this paper for a class of convex functions Φ. Φ-ribbon reduces to the HC ribbon and the maximal correlation for special choices of Φ, and is a... 

    Zero error coordination

    , Article ITW 2015 - 2015 IEEE Information Theory Workshop, 11 October 2015 through 15 October 2015 ; 2015 , Pages 202-206 ; 9781467378529 (ISBN) Abroshan, M ; Gohari, A ; Jaggi, S ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    Abstract
    In this paper, we consider a zero error coordination problem wherein the nodes of a network exchange messages to be able to perfectly coordinate their actions with the individual observations of each other. While previous works on coordination commonly assume an asymptotically vanishing error, we assume exact, zero error coordination. Furthermore, unlike previous works that employ the empirical or strong notions of coordination, we define and use a notion of set coordination. This notion of coordination bears similarities with the empirical notion of coordination. We observe that set coordination, in its special case of two nodes with a one-way communication link is equivalent with the Hide... 

    Video activity analysis based on 3D wavelet statistical properties

    , Article 11th International Conference on Advanced Communication Technology, ICACT 2009, Phoenix Park, 15 February 2009 through 18 February 2009 ; Volume 3 , 2009 , Pages 2054-2058 ; 17389445 (ISSN); 9788955191387 (ISBN) Omidyeganeh, M ; Ghaemmagham, S ; Khalilain, H ; IEEE Communications Society, IEEE ComSoc; IEEE Region 10 and IEEE Daejeon Section; Korean Institute of Communication Sciences, KICS; lEEK Communications Society, IEEK ComSoc; Korean Institute of Information Scientists and Engineers, KIISE; et al ; Sharif University of Technology
    2009
    Abstract
    A video activity analysis is presented based on 3D wavelet transform. Marginal and joint statistics as well as mutual information estimates are extracted. Marginal histograms are approximated by Generalized Gaussian Density (GGD) functions. The mutual information between coefficients -as a quantitative estimate of joint statistics- decreases when the activity in the video increases. The relationship between kurtosis graphs, extracted from joint distributions and video activity, is deduced. Results show that the type of activity in the video can be figured out from Kurtosis curves. The GGD and the Kullback-Leibler distance (KLD) are used to retrieve and locate 96% of videos properly  

    Unsupervised image segmentation by mutual information maximization and adversarial regularization

    , Article IEEE Robotics and Automation Letters ; Volume 6, Issue 4 , 2021 , Pages 6931-6938 ; 23773766 (ISSN) Mirsadeghi, S. E ; Royat, A ; Rezatofighi, H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2021
    Abstract
    Semantic segmentation is one of the basic, yet essential scene understanding tasks for an autonomous agent. The recent developments in supervised machine learning and neural networks have enjoyed great success in enhancing the performance of the state-of-the-art techniques for this task. However, their superior performance is highly reliant on the availability of a large-scale annotated dataset. In this letter, we propose a novel fully unsupervised semantic segmentation method, the so-called Information Maximization and Adversarial Regularization Segmentation (InMARS). Inspired by human perception which parses a scene into perceptual groups, rather than analyzing each pixel individually, our... 

    Unreliability of mutual information as a measure for variations in total correlations

    , Article Physical Review A ; Volume 101, Issue 4 , 2020 Alipour, S ; Tuohino, S ; Rezakhani, A. T ; Ala-Nissila, T ; Sharif University of Technology
    American Physical Society  2020
    Abstract
    Correlations disguised in various forms underlie a host of important phenomena in classical and quantum systems, such as information and energy exchanges. The quantum mutual information and the norm of the correlation matrix are both considered as proper measures of total correlations. We demonstrate that, when applied to the same system, these two measures can actually show significantly different behavior except at least in two limiting cases: when there are no correlations and when there is maximal quantum entanglement. We further quantify the discrepancy by providing analytic formulas for time derivatives of the measures for an interacting bipartite system evolving unitarily. We argue... 

    Three easy ways for separating nonlinear mixtures?

    , Article Signal Processing ; Volume 84, Issue 2 , 2004 , Pages 217-229 ; 01651684 (ISSN) Jutten, C ; Babaie Zadeh, M ; Hosseini, S ; Sharif University of Technology
    2004
    Abstract
    In this paper, we consider the nonlinear Blind Source Separation BSS and independent component analysis (ICA) problems, and especially uniqueness issues, presenting some new results. A fundamental difficulty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they are nonunique without a suitable regularization. In this paper, we mainly discuss three different ways for regularizing the solutions, that have been recently explored. © 2003 Elsevier B.V. All rights reserved  

    Structural image representation for image registration

    , Article 2015 International Symposium on Artificial Intelligence and Signal Processing, AISP 2015, 3 March 2015 through 5 March 2015 ; March , 2015 , Pages 95-100 ; 9781479988174 (ISBN) Aghajani, K ; Shirpour, M ; Manzuri, M. T ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    Abstract
    Image registration is an important task in medical image processing. Assuming spatially stationary intensity relation among images, conventional area based algorithms such as CC (Correlation Coefficients) and MI (Mutual Information), show weaker results alongside spatially varying intensity distortion. In this research, a structural representation of images is introduced. It allows us to use simpler similarity metrics in multimodal images which are also robust against the mentioned distortion field. The efficiency of this presentation in non-rigid image registration in the presence of spatial varying distortion field is examined. Experimental results on synthetic and real-world data sets... 

    Sparse-induced similarity measure: Mono-modal image registration via sparse-induced similarity measure

    , Article IET Image Processing ; Volume 8, Issue 12 , 1 December , 2014 , Pages 728-741 ; ISSN: 17519659 Ghaffari, A ; Fatemizadeh, E ; Sharif University of Technology
    Abstract
    Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g. sum-of-squared-differences, correlation coefficient, mutual information and correlation ratio) assume a stationary image and pixel-by-pixel independence. These similarity measures ignore the correlation among pixel intensities; hence, a perfect image registration cannot be achieved especially in the presence of spatially varying intensity distortions and outlier objects that appear in one image but not in the other. It is supposed here that non-stationary intensity distortion (such as bias field) has a sparse representation in the transformation domain. Based on this... 

    Separating convolutive mixtures by mutual information minimization

    , Article 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001, Granada, 13 June 2001 through 15 June 2001 ; Volume 2085 LNCS, Issue PART 2 , 2001 , Pages 834-842 ; 03029743 (ISSN); 9783540422372 (ISBN) Babaie Zadeh, M ; Jutten, C ; Nayebi, K ; Sharif University of Technology
    Springer Verlag  2001
    Abstract
    Blind Source Separation (BSS) is a basic problem in signal processing. In this paper, we present a new method for separating convolutive mixtures based on the minimization of the output mutual information. We also introduce the concept of joint score function, and derive its relationship with marginal score function and independence. The new approach for minimizing the mutual information is very efficient, although limited by multivariate distribution estimations. © Springer-Verlag Berlin Heidelberg 2001  

    Sensitivity of the secrecy capacity of a wiretap channel to the channel gains with imperfect channel information

    , Article IWCIT 2017 - Iran Workshop on Communication and Information Theory, 3 May 2017 through 4 May 2017 ; 2017 ; 9781509047833 (ISBN) Sedighizad, M ; Bafghi, H. G ; Seyfe, B ; Sharif University of Technology
    Abstract
    In this paper, the impact of a small variations in the channel gains on the secrecy rate of the wiretap channel is studied, in which it is assumed that the imperfect channel knowledge is available at the transmitter. First, we consider general additive noise model for both legitimate and eavesdropper channels in the wiretap channel, and compute the variation of the secrecy rate resulting from the small variations in the channel gains. Then, we focus on the Gaussian wiretap channel, as a special case and calculate the sensitivity of the secrecy capacity to the channel gains with imperfect channel knowledge. Interestingly, it is shown that in some situations the effect of the channel variation... 

    Scalable feature selection via distributed diversity maximization

    , Article 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 4 February 2017 through 10 February 2017 ; 2017 , Pages 2876-2883 Abbasi Zadeh, S ; Ghadiri, M ; Mirrokni, V ; Zadimoghaddam, M ; Sharif University of Technology
    Abstract
    Feature selection is a fundamental problem in machine learning and data mining. The majority of feature selection algorithms are designed for running on a single machine (centralized setting) and they are less applicable to very large datasets. Although there are some distributed methods to tackle this problem, most of them are distributing the data horizontally which are not suitable for datasets with a large number of features and few number of instances. Thus, in this paper, we introduce a novel vertically distributable feature selection method in order to speed up this process and be able to handle very large datasets in a scalable manner. In general, feature selection methods aim at... 

    Robust blind separation of smooth graph signals using minimization of graph regularized mutual information

    , Article Digital Signal Processing: A Review Journal ; Volume 132 , 2022 ; 10512004 (ISSN) Einizade, A ; Hajipour Sardouie, S ; Sharif University of Technology
    Elsevier Inc  2022
    Abstract
    The smoothness of the graph signals on predefined/constructed graphs appears in many natural applications of processing unstructured (i.e., graph-based) data. In the case of latent sources being smooth graph signals, blind source separation (BSS) quality can be significantly improved by exploiting graph signal smoothness along with the classic measures of statistical independence. In this paper, we propose a BSS method benefiting from the minimization of mutual information as a well-known independence criterion and also graph signal smoothness term of the estimated latent sources, and show that its performance is superior and fairly robust to the state-of-the-art classic and Graph Signal...