Sharif Digital Repository

In this thesis, the problem of conflict detection was investigated with a probabilistic approach. The focus of the new solutions presented in this treatise is the accurate conflict estimation with low computational cost. For this purpose, several algorithms were developed. In the first step, a method for grouping aircraft to find aircraft pairs that are in conflict was presented. This work ends to reduction in the workload of conflict checking systems. Because there is no need to check all aircraft pairs in the scene. In the next step, an algorithm was presented for the analytical calculation of the relative position error of the aircraft by considering the wind correlation effect. Using... 

In this thesis, the problem of conflict detection was investigated with a probabilistic approach. The focus of the new solutions presented in this treatise is the accurate conflict estimation with low computational cost. For this purpose, several algorithms were developed. In the first step, a method for grouping aircraft to find aircraft pairs that are in conflict was presented. This work ends to reduction in the workload of conflict checking systems. Because there is no need to check all aircraft pairs in the scene. In the next step, an algorithm was presented for the analytical calculation of the relative position error of the aircraft by considering the wind correlation effect. Using... 

In this study, the changes in the mean vector and the covariance matrix of engineering demand parameter (EDP) distributions for different intensity measures and ground motion selections is evaluated. Although, the changes in the mean distribution of EDPs have been extensively studied in the literature, but the changes in the covariance matrix of EDPs distributions caused by ground motion selections has not been paid the due attention. In other words, no study has specifically looked into the effect of intensity measure changes on the covariance matrix; therefore, for the purpose of this study, several steel frame models were considered with perimeter special moment resisting frames (SMRF).... 

In today’s world, knowledge of the statistical behavior of noise can tremendously affect the accuracy of target detection in radar systems. Therefore, radar systems commonly collect a secondary dataset of homogeneous noise and estimate the statistics of the gathered data, prior to attempting target detection. Specifically, in the case of Gaussian noise with a mean of zero, the entire statistical information of the noise is encoded in its covariance matrix. In practice, however, the challenge is that the training samples do not purely contain homogeneous noise. In fact, some samples contain non-homogeneous outlier signals that do not have the same distribution as the noise samples. In this... 

Nowadays we are witnessing the emergence of machine learning in various applications. One of the key problems in data science and machine learning is the problem of dimensionality reduction, which deals with finding a mapping that embeds samples to a lower-dimensional space such that, the relationships between the samples and their properties are preserved in the secondary space as much as possible. Obtaining such mapping is essential in today's high-dimensional settings. Moreover, due to the large volume of data and high-dimensional samples, it is infeasible or insecure to process and store all data in a single machine. As a result, we need to process data in a distributed manner.In this... 

Estimating direction of arrival (DOA) is one of the most important problems in array signal processing to solve which various methods have been proposed. The older methods for estimating signal DOA were divided into three main groups: beamforming, maximum likelihood-based and subspace-based methods. By applying sparse representation techniques to the DOA estimation problem, a new group of methods for solving this problem are introduced. In this thesis, two grid-based methods, which are tow sub groups of sparse methods for estimation of DOA, are proposed. Each of these methods uses singular value decomposition to reduce the power of noise. Also proposed methods are compared with the multiple... 

Brain-Computer Interface (BCI) presents a way for brain’s direct connection with external world. BCI system is composed of three parts: 1) Signal acquisition, 2) Signal processing and 3) External device control. The main part of this system is signal processing which includes three subparts: 1) Feature extraction, 2) Dimension reduction and 3) Signal separation and classification. In this thesis, we focus on the signal processing section in BCI systems. One of the most successful works done in signal processing is the use of covariance matrices in feature extraction from brain signals. Since covariance matrices are positive semi-definite and symmetric, they belong to certain manifolds called... 

Multiple Input Multiple Output (MIMO) radar is an emerging technology which has attracted many researchers recently. The problem of beampattern design of a MIMO radar in uniform arrays and with the covariance based method of point targets has been investigated by many papers so far. In this thesis it is desirable to consider this problem in non-uniform arrays. Many authors have designed the transmitted beampattern by means of designing the cross correlation matrix of transmitted signals elements, but in this paper optimizing the locations of transmitted antennas will be done as well as cross correlation matrix of transmitted signals to achieve a better results  

Employing array of antennas in amny signal processing application has received considerable attention in recent years due to major advances in design and implementation of large dimentional antennas. In many applications we deal with such large dimentional antennas which challenge the traditional signal processing algorithms. Since most of traditional signal processing algorithms assume that the number of samples is much more than the number of array elements while it is not possible to collect so many samples due to hardware and time constraints.
In this thesis we exploit new results in random matrix theory to charachterize and describe the properties of Sample Covariance Matrices...