Sharif Digital Repository

Given a social network modeled as a weighted graph G, the influence maximization problem seeks k vertices to become initially influenced, to maximize the expected number of influenced nodes under a particular diffusion model. The influence maximization problem has been proven to be NP-hard, and most proposed solutions to the problem are approximate greedy algorithms, which can guarantee a tunable approximation ratio for their results with respect to the optimal solution. The state-of-the-art algorithms are based on Reverse Influence Sampling (RIS) technique, which can offer both computational efficiency and non-trivial (1-1/e-ϵ)-approximation ratio guarantee for any ϵ>0. RIS-based... 

Wireless communications often requires accurate knowledge of the underlying channel be­ tween transmitter and receiver which leads to channel estimation problem. Wireless chan­ nels are mostly multipath channels and have sparse impulse responses in time domain. One popular method in this field is to estimate the channel using training symbols. Also, Orthog­ onal Frequency Division Multiplexing (OFDM) and Multiple-Input Multiple-Output OFDM (MIMO-OFDM) systems are two popular and widely used systems. Since in training-based channel estimation methods some resources which can be utilized for data transmission is used for the channel estimation process, decreasing the number of pilots or... 

Iranian Household Expenditures and Income Survay(IHEIS) in urban area shows that, average of real hourly wage against the schooling years is declining in the right tail of wage distribution between 1390 and 1384(2005 and 2011). A tentative justification for this observation is that the private rate of return to education at upper quantiles and higher years of schooling have been reduced in the mentioned period. Also, It is observed that the real wage inequality between upper and lower deciles in 1390 is less than 1384. This study intend to investigate the changes in wage inequality between different deciles by different levels of education and rates of return to education in the time... 

The main purpose in this study is the determination of offshore structures reliability index over time by considering the simultaneous effects of natural hazards. Importance of this study stems from the effect of events associated with time which induces high amount of uncertainties on offshore structures performance during their life time. Extreme wave, marine current, earthquake, fatigue, corrosion and marine growth hazards are considered in this paper. The intensity and occurrence time of all hazards are time dependent parameter. In addition some of these hazards have the possibility to coincidence and the interaction effect. Whereas, the previous studies in this field, the effect of each... 

This research investigated the seismic performance of gravity dam in the reliability and sensitivity analysis framework under earthquake loading. The performance criteria were defined using crest displacement and tensile stress. The uncertainty due to the material properties of the dam was considered for the reliability approach, in addition to the different ground motion intensity levels for parametric analysis. The exceedance probability of the limit-state functions was computed for varying threshold values through Mean-Value Second-Moment, First-Order Reliability Method and improved Latin Hypercube sampling. Approximation methods were applied along with the sampling method for considering... 

This thesis presents the seismic hazard and seismic risk analysis of the City of Golpayegan using reliability methods and multiple interacting probabilistic models. These models evaluate the seismic hazard intensity by simulating the hazard event and evaluate the seismic risk by simulating the building portfolio and infrastructure systems under the hazard event and quantifying the ensuing consequences. In this context, hazard is the exceedance probability of a ground shaking intensity measure, and risk is the exceedance probability of social and economic losses. This is the first time in the literature that reliability methods are applied in evaluating the seismic risk of a real-world city... 

Compressed sensing theory with the goal of sparse recovery using as few measurements as possible, has found much attention in the past decade. A fundamental assumption in compresses sensing is the linear nature of the measurements, that establishes a matrix equation between the unknowns and unknowns. However, in some applications, the physics of the problem may impose some type of nonlinear relationship between the unknowns and the measurements. Some solutions concerned with this type of problems,approximate the nonlinear relationship using a second order Taylor expansion. Using this approximation besides some modifications in the way the second order relationship is presented, a more simple... 

The primary objective of this research is probabilistic quantification of the fatigue life of tension-leg platforms (TLP) using reliability methods. The need for such methods stems from the significant uncertainty in the loads exerted on offshore structures. The scope of this paper is limited to the study of fatigue in TLP tendons. For this purpose, nonlinear force time history response of the TLP tendon under random wave loads is computed and the damage due to fatigue is estimated in accordance with the Palmgren-Miners rule. Assuming a Rayleigh distribution for stress variation and eight different sea states, the ultimate fatigue damage is computed by accumulating the damage over all... 

Fabrication of bulk superconductors with high critical current density widely has been studied because they have many applications, for example, Fault Current Limiter (SFCL), Superconducting Magnetic Energy Storage (SMES), and magnetic levitation. Providing pinning center by addings impurity into superconductor is usual and simple solution for increasing the superconductor critical current density. In this project we have fabricated superconductor YBCO bulk and optimized its critical current density. There are two major stream in this thesis, first design of Vibrating System Magnetization (VSM) for critical current measurements and then fabrication of YBCO bulk and optimization of its... 

Patient scheduling is one of the most important problems in healthcare optimization which has been in center of attention in various environments including outpatient clinics and operating rooms. Effective scheduling systems have the goal of matching the demand with capacity so that resources are better utilized and patient waiting times are minimized. Furthermore, patient scheduling can be merged with another problem in which the main goal is to find the optimal sequence of a given list of patients. Moreover, there are several sources of uncertainty such as patients’ unpunctuality, absence, and service time in this problem resulting in having a very complex problem. In this research,... 

This study considers an online multi-period assortment optimization problem over multiple replenishment cycles. The retailer chooses a subset from N substitutable products and decides how many of each product to order and sell at each time period. Retailer is constrained by a total inventory capacity, a cardinality constraint on the product variety (display space), and predetermined replenishment time intervals. The assortment selection is modeled as a Multi-armed Bandit problem and the customers' choice is modeled by the Multinomial Logit (MNL) choice model with dynamic substitution. The objective is to optimize the revenue by actively learning the censored demand and improving the offering... 

In this thesis, Particle Filter Methods are investigated in the context of moving targets tracking and the associated performance analysis. The application scope of this algorithm which is a particular case of Sequential Monte Carlo Method is far broader than tracking of moving targets. This algorithm can be used for mathematical calculations such as estimation of mathematical expectations, integrals, surface area of curves and many other mathematical calculations. In addition, it has applications in other branches of science like genetics. This algorithm is based on random sampling of a probability density function and resampling from the extracted samples. We change this algorithm in... 

Loess as a kind of collapsible soils is a well-known Aeolian deposit, which is characterized by some specific Engineering properties including high initial void ratio, relatively low initial density and water content and high percentage of fine-grained particles. Collapsible soils such as loess, sustain large stress under dry condition. However, they experience a high and sudden decrease in their volume by reaching to their critical moisture content. As a result, their natural condition is not suitable for construction works if the possibility of the exposure of the soil to excessive water is present. This research presents the utilization of an innovative method for stabilization and... 

This thesis proposes a novel method for enhancing the speech signal based on compressed sensing. Compressed sensing, as a new rapidly growing research field, promises to effectively recover a sparse signal at the rate of below Nyquist rate. This revolutionary technology strongly relies on the sparsity of the signal and incoherency between sensing basis and representation basis. Exact recovery of a sparse signal will be occurred in a situation that the signal of interest sensed randomly and the measurements are also taken based on sparsity level and log factor of the signal dimension.
In this research, compressed sensing method is proposed to reconstruct speech signal and for noise... 

Deep learning methods have become more popular in the past years. These methods use complex network architectures to model rich, hierarchical datasets. Although most of the research has been centered around Discriminative models, however, recently a lot of research is focused on Deep Generative Models. Two of the pioneering models in this field are Generative Adversarial Networks and Variational Auto-Encoders. In addition, knowing the structure of data helps models to search in a narrower hypothesis space. Most of the structure in datasets are models using Probabilistic Graphical Models. Using this structural information, one can achieve better parameter estimations. In the case of... 

This thesis is dedicated to examine performance of compressive sensing based MIMO radar systems. MIMO radars have the ability to achieve higher target detection and parameter estimation as well as better performance in noisy and clutter environments than SISO or phased array radars. The need for high-speed analog to digital converters is one of the weaknesses in implementation of radar systems. With the advent of compressive sensing providing necessary guarantees for reconstruction of sparse signal using fewer samples than what Nyquist thorem describes, the need for such a high-speed converters that are either not available or too expensive is resolved. What allowes us to use compressive... 

By far, the most expensive part of the production optimization process is the evaluation of the objective function because this requires computationally expensive reservoir simulations to be performed.One way to reduce this high computational cost isby using surrogates or proxies for the reservoir simulator. There are different methods for constructing a surrogate that their aims are mimicking the reservoir behavior with high accuracy and low computational cost. In reservoir engineering surrogate modeling has been used for the problem of well placement optimization,while in the context of production optimization it has not yet been investigated in the literature. Moreover, most of surrogate... 

Level crossing sampling is a sampling method in which a sample is taken whenever signal crosses predefined and specific levels. In this dissertation, some recommendations are made in order to increase the sampler’s performance in the sampling, data compression, and reconstruction stages.The IMATMirror algorithm is introduced in the sample reconstruction stage. This algorithm is derived from the IMAT reconstruction method. However, additional data processing is done in each iteration, causing the reconstructed signal to satisfy some properties of the Level-Crossing samples.In order to solve the problem of level crossing sampling of extremely bursty signals (ECG signals for example), a sampler... 

Converting analog signal to digital one is one of the most important issues in signal processing which can be done by using analog to digital converter (A/D). In the first part of this thesis, sigma delta converters based on minimum support filter are investigated. Then, we will show that iterative algorithm can be used in order to improve the performance of the overall system significantly. Asynchronous converters can be utilized for the sake of decreasing power in the process of analog to digital converting. In these converters, a few numbers of samples will be taken from the regions which signal has high autocorrelation. In other words, samples in the asynchronous converters have more... 

Biometrics has been long known to recognize persons based on their physical and behavioral characteristics. Face recognition (FR) is one of such biometrics that has received a considerable attention in recent years both from the industry and research communities. As the boosting framework has shown good performance in face recognition, it has been adopted in this work. This thesis deals with pattern recognition methods such as linear discriminant analysis (LDA) and machine learning approaches such as boosting which are integrated to overcome the technical limitation of existing FR methods. However, LDA-based methods often suffer from the so-called “small-sample-size” (SSS) problem arising...