Sharif Digital Repository

Control charts are the most popular Statistical Process Control tools used to monitor process changes. However, they are not capable of identifying the real time of a process change, which is essential for diagnosing assignable causes of the change. Therefore, a number of methods of change-point estimation have been developed. In the literature, relatively little study has been done on multiple changes. In this research, a new method based on Maximum Likelihood Estimator (MLE) is introduced to identify linear drifts and step changes in multivariate multiple linear profiles. Due to the massive increase in the amount and time of the calculations along with the growth of the number of the... 

Vehicle headway distribution plays an important role in various traffic engineering aspects such as capacity and level of service analysis and simulation issues. Although many headway models have been developed over the past decades, few have investigated the effects of vehicle’s type and its position in the traffic lanes on headway distributions. In this research, time headway data were collected at three different freeways using video camera. The collected data were analyzed and modeled for four different categories (i.e., car-car, car-truck, truck-car, and truck-truck) and under two traffic conditions of platooning and non-platoonig. Four types of probability distribution functions of... 

Predicting the probability distribution of asset remaining useful life is an essential procedure in the intelligent maintenance. It also plays an important role in improving system reliability and optimizing further decisions. The main concern of this project is to estimate the probability distribution of rolling element bearing remaining useful life. For this purpose, the bearing degradation process is modeled through the statistical models, considering the major variabilities in the degradation process. The models parameters are updated, once a new measurement of the equipment is available. Then, the constructed model is utilized in order to predict the probabitity distribution of... 

This effort attempts to study the change point problem in the area of non-linear profiles. Two methods for estimating the time of a single step change is proposed. In the first method a model consisting of two networks which is based on artificial neural networks is proposed. These networks are different only in their training data. One network is trained for ascending segments of the profile and the other is trained for descending segments of the profile. In the second method the maximum likelihood estimator (MLE) of the single step change is analyzed. Due to the complexity of estimating the parameters of the non-linear model by MLE, this estimator is based on the difference between the... 

In this thesis, some algorithms for PAPR reduction of OFDM systems are investigated and some of these methods are extended to MIMO-OFDM systems. Two types of multiple antenna OFDM systems are considered; OFDM systems with spatial multiplexing for increasing the data rate and OFDM systems with spatial diversity for improvement of the symbol error rate. For the systems of the first type we have proposed two PAPR reduction algorithms. In the first method, the signals of transmitter antennas are clipped and filtered independently. Then, at the receiver side an iterative algorithm is proposed to compensate for the clipping noise. In the second method, we have extended TR method to spatial... 

Image Registration is spatially alignment of two images in a wide range of applications such as remote sensing, computer assisted surgery, and medical image analysis and processing. In general, registration algorithms can be categorized as either intensity based or feature based. The feature based methods use the alignment between the extracted features in two images. The simplest feature is images intensity which is directly used in the intensity based method via similarity measure. This similarity measure quantifies the matching of two images.Similarity measure is main core of image registration algorithms. Spatially varying intensity dis-tortion is an important challenge in a wide range... 

The purpose of Direction-of-arrival(DOA) estimation in MIMO communication networks is to find the direction of signals that are mixed together and mixed with noise by using an array of multiple receiving antennas. DOA estimation is an important issue in array signal processing. The traditional methods for DOA estimation try to solve this problem with mathematical algorithms. For example, some of these methods are based on parameter estimation; such as DOA estimation methods using maximum likelihood estimation methods. Some of other traditional methods are based on subspace decomposition; Like the famous MUSIC method. The traditional methods of DOA estimation usually have a drawback, and that... 

Opinion formation is one of the major topics in social networks analysis and mining. A number of methods have been introduced for describing and simulating this phenomenon. In this thesis, we proposed a novel optimization framework based on an opinion formation model that used the Deffuant’s model with some changes in neighbors’ selection policy. Its efficiency was compared with a number of benchmark optimization methods including genetic algorithms, differential evolution and particle swarm optimization.The proposed model demonstrated better performance than the others.In the proposed, each person changes his opinion based on one of his neighbors who has the best performance in terms of the... 

In this research, we estimate the change point in multistage processes using maximum likelihood estimation approach. We first model a multistage process with one quality characteristic in each stage, with both AR(1) and ARMA(1,1) time series model and then a maximum likelihood estimator for linear trend change point is developed. Also, a multivariate multistage process is modeled with VAR(1) time series model and the step change point is estimated using maximum likelihood estimator for multivariate multistage process  

The closed-form formulation of MDCEV has become popular in past years, as it accounts for satiation effects through a novel consumption-based utility structure. The model, however, suffers from an “empirical identification” problem. The problem is mainly due to entangled roles of two model parameters in capturing satiation effects. Thus far, the best known solution to the problem is fixing either or both of the parameters to pre-defined values and estimate a restricted satiation profile. This paper, presents a new hybrid optimization algorithm (named HEMPME) to eliminate the need for imposing such restrictions, and disentangle the two effects through estimating a “mixed profile” model.... 

Control charts are one of the strongest optimization tools. Control charts issue warning due to out of control processby recorded data. As soon as charts warn, attempt start to find the changes reason.Finding out the issue on time save a lot of time and cost. By determining the time of this change, finding out the problem reason get faster. The real time of process change called change point. There exist many papers in change point field finding real time of change in literature. Each one considers the problem with specific assumptions. These assumptions consist of distribution function, change type, parameters and solution procedure. One kind of existing papers are about to determine normal... 

This thesis investigates various modulation recognition systems both in analogue and digital forms. We are looking for a recognition technique considering practical implementation issues. To recognize the modulation, the career frequency is supposed to be known and a bandpass signal is received. Besides, we investigate some digital Automatic Modulation Recognition (AMR) methods. The techniques based on the features of the signal have less computational complexity compared to Maximum Likelihood (ML) techniques; hence they are more desirable for practical implementation. In this project, analog modulations are implemented using C++ software. For the convenience of users, here, two graphical... 

Knowing the time of change would narrow the search to find and identify the variables disturbing a process. Having this information, an appropriate corrective action could be implemented and valuable time could be saved. Multistage processes that are often observed in current manufacturing processes must be monitored to assure quality products. The change-point detection of such processes has not been proposes investigated yet. Thus, this dissertation proposes maximum likelihood step-change estimators of two kinds of these processes. First, a multistage process with variable quality characteristics is considered and formulated by the first-order auto-regressive model. For the location... 

Control charts are one of the most important statistical process control tools used in monitoring processes and improving the quality by decreasing the variability of processes. In spite of various applications for multi-attribute control charts in industries and service sectors, only a few research efforts have been performed in developing this type of control charts. The developed multivariate control charts are all based on the assumption that the quality characteristics follow a multivariate Normal distribution while, in many applications the correlated quality characteristics that have to be monitored simultaneously are of attribute type and follow distributions such as multivariate... 

In this thesis, the problem of locating a source based on the time difference of arrival (TDOA) has been studied and three different methods have been presented to improve the precision of the positioning. The first method is a solution to the least square error (LSE) by utilizing a convex problem. Generally, the mentioned problem is a non-convex problem; however, it is possible to convert it to a convex one by some relaxation in the constraints. By some justifiable and insightful relaxation, not only we obtain a convex problem, but also the exact solution of the main problem is derived. The second method is using high perturbed TDOA measurements which severely affected the localization.... 

Due to its extensive applications in financial analysis, stock market volatility modeling is a considerably important issue for stock market practitioners and academics. Using GARCH models to formulate the conditional variance heteroskedasticity with respect to the advantages of panel data technique such as higher degrees of freedom, more flexibility in the control of the omitted or unobserved variables effects, lead to a significant increase in the accuracy of estimations. In this thesis, in order to examine the similarities and differences between the conditional variance structure of stocks in the inter and intra industries, we have used a general to specific methodology of nested tests... 

Target localization in multiple-input multiple-output (MIMO) radar systems has been an active research topic during the recent years. Generally speaking, MIMO radars can be classified, based on the antennas geometry, into colocated and distributed architectures.The former provides waveform diversity and the latter makes use of spatial diversity provided by widely separated configuration of antennas, both of which improve localization and detection performance. In this study, we focus on the latter case. In this thesis, we investigate the localization problem in two parts, i.e., positioning based on bistatic time delay measurements and hybrid localization using combination of time delay,... 

Precise leak detection by acoustic signal analysis calls for robust similarity measures to estimate the time delay between recorded leakage signals and minimize the probability of false alarms, in the face of dispersive propagation, multiple reflections, and unknown correlated, uncorrelated and impulsive background noise. Providing evidence that higher order modes and multi-reflected signals behave like sets of correlated noise, this thesis uses a regression model to optimize residual complexity in the presence of both correlated and uncorrelated noise. This optimized residual complexity (ORC) is highly robust since it takes into account both the level and complexity of noise and signal,...