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Design and Implementation of Distributed Dimensionality Reduction Algorithms under Communication Constraints
, M.Sc. Thesis Sharif University of Technology ; Maddah Ali, Mohammad Ali (Supervisor) ; Salehkaleybar, Saber (Supervisor)
Abstract
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...
Beampattern Design in Non-uniform MIMO Radars
, M.Sc. Thesis Sharif University of Technology ; Bastani, Mohammad Hassan (Supervisor)
Abstract
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
Classification of Different Mental Activities Based on Riemannian Geometry
,
M.Sc. Thesis
Sharif University of Technology
;
Babaiezadeh, Massoud
(Supervisor)
Abstract
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...
Source Enumeration and Identification in Array Processing Systems
, Ph.D. Dissertation Sharif University of Technology ; Bastani, Mohammad Hasan (Supervisor)
Abstract
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...
In this thesis we exploit new results in random matrix theory to charachterize and describe the properties of Sample Covariance Matrices...
Outlier Censoring Based on Sparse Signal Recovery Algorithms
, M.Sc. Thesis Sharif University of Technology ; Karbasi, Mohammad (Supervisor)
Abstract
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...
Real-time Conflict Detection in Medium-term Flight Horizon using Probabilistic Approach and Dynamic Grouping Strategy
, Ph.D. Dissertation Sharif University of Technology ; Nobahari, Hadi (Supervisor)
Abstract
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...
Real-time Conflict Detection in Medium-term Flight Horizon using Probabilistic Approach and Dynamic Grouping Strategy
, M.Sc. Thesis Sharif University of Technology ; Nobahari, Hadi (Supervisor)
Abstract
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...
Direction of Arrival (DOA)Estimation based on Sparsity-Aware Signal Processing
, M.Sc. Thesis Sharif University of Technology ; Behnia, Fereidoon (Supervisor) ; Babaiezadeh, Massoud (Supervisor)
Abstract
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...
Evaluating the Changes in the Mean Vector and Covariance Matrix of EDP Distribution for different IM and Ground Motion Selections
, M.Sc. Thesis Sharif University of Technology ; Rahimzadeh Rofouei, Fayyaz (Supervisor)
Abstract
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)....
Using empirical covariance matrix in enhancing prediction accuracy of linear models with missing information
, Article 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017, 3 July 2017 through 7 July 2017 ; 2017 , Pages 446-450 ; 9781538615652 (ISBN) ; Shahsavari, S ; Esmaeili, A ; Marvasti, F ; Sharif University of Technology
Abstract
Inference and Estimation in Missing Information (MI) scenarios are important topics in Statistical Learning Theory and Machine Learning (ML). In ML literature, attempts have been made to enhance prediction through precise feature selection methods. In sparse linear models, LASSO is well-known in extracting the desired support of the signal and resisting against noisy systems. When sparse models are also suffering from MI, the sparse recovery and inference of the missing models are taken into account simultaneously. In this paper, we will introduce an approach which enjoys sparse regression and covariance matrix estimation to improve matrix completion accuracy, and as a result enhancing...
Transmit signal design in colocated MIMO radar without covariance matrix optimization
, Article IEEE Transactions on Aerospace and Electronic Systems ; Volume 53, Issue 5 , 2017 , Pages 2178-2186 ; 00189251 (ISSN) ; Nayebi, M. M ; Ghorashi, S. A ; Sharif University of Technology
Abstract
In this paper, the problem of the waveform design for colocated multiple-input multiple-output (MIMO) radars is considered in two parts. In the first part, we design transmit waveform in order to approximate the desired beampattern with low number of samples in the transmitter. Unlike the traditional waveform design methods, in our solution, waveforms are designed for a specific number of samples. Also, the constant envelope constraint that is an important practical constraint is considered. In the second part, we jointly design the transmit waveform and receive filter by a sequential algorithm, considering a priori information of target and interference angle locations. We have evaluated...
The strong tracking innovation filter
, Article IEEE Transactions on Aerospace and Electronic Systems ; Volume 58, Issue 4 , 2022 , Pages 3261-3270 ; 00189251 (ISSN) ; Ahmadvand, R ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2022
Abstract
Sliding innovation filter (SIF) has recently been introduced as a robust strategy for estimation of linear systems. The SIF has been extended to nonlinear systems via analytical linearization. However, as the performance of the extended SIF (ESIF) degrades in the presence of severe nonlinearities, this article has initially developed a derivative-free cubature SIF (CSIF) that uses statistical linearization for the error propagation. In addition, the SIF gain has been reformed to incorporate the innovation covariance matrix, thus reducing the estimation error. Furthermore, the adaptive fading factor has been employed to strengthen the robustness and convergence properties of the CSIF against...
The integration of principal component analysis and cepstral mean subtraction in parallel model combination for robust speech recognition
, Article Digital Signal Processing: A Review Journal ; Volume 21, Issue 1 , 2011 , Pages 36-53 ; 10512004 (ISSN) ; Sameti, H ; Sharif University of Technology
Abstract
This paper addresses the problem of automatic speech recognition in real applications in which the speech signal is altered by various noises. Feature compensation and model compensation robustness methods are studied. Parallel model combination (PMC) and its recent advances are reviewed and a novel algorithm called PC-PMC is proposed. This algorithm utilizes cepstral mean subtraction (CMS) normalization ability and principal component analysis (PCA) compression and de-correlation capability in the combination with PMC model transformation method. PC-PMC algorithm takes the advantages of additive noise compensation ability of PMC and convolutional noise removal capability of CMS and PCA. In...
Subsurface characterization with localized ensemble Kalman filter employing adaptive thresholding
, Article Advances in Water Resources ; Vol. 69, issue , 2014 , p. 181-196 ; Pishvaie, M. R ; Boozarjomehry, R. B ; Sharif University of Technology
Abstract
Ensemble Kalman filter, EnKF, as a Monte Carlo sequential data assimilation method has emerged promisingly for subsurface media characterization during past decade. Due to high computational cost of large ensemble size, EnKF is limited to small ensemble set in practice. This results in appearance of spurious correlation in covariance structure leading to incorrect or probable divergence of updated realizations. In this paper, a universal/adaptive thresholding method is presented to remove and/or mitigate spurious correlation problem in the forecast covariance matrix. This method is, then, extended to regularize Kalman gain directly. Four different thresholding functions have been considered...
Spectral distribution of the exponentially windowed sample covariance matrix
, Article ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 25 March 2012 through 30 March 2012, Kyoto ; 2012 , Pages 3529-3532 ; 15206149 (ISSN) ; 9781467300469 (ISBN) ; Bastani, M. H ; Gazor, S ; Sharif University of Technology
IEEE
2012
Abstract
In this paper, we investigate the effect of applying an exponential window on the limiting spectral distribution (l.s.d.) of the exponentially windowed sample covariance matrix (SCM) of complex array data. We use recent advances in random matrix theory which describe the distribution of eigenvalues of the doubly correlated Wishart matrices. We derive an explicit expression for the l.s.d. of the noise-only data. Simulations are performed to support our theoretical claims
Source enumeration in large arrays using moments of eigenvalues and relatively few samples
, Article IET Signal Processing ; Volume 6, Issue 7 , 2012 , Pages 689-696 ; 17519675 (ISSN) ; Gazor, S ; Bastani, H ; Sharif University of Technology
IET
2012
Abstract
This study presents a method based on minimum description length criterion to enumerate the incident waves impinging on a large array using a relatively small number of samples. The proposed scheme exploits the statistical properties of eigenvalues of the sample covariance matrix (SCM) of Gaussian processes. The authors use a number of moments of noise eigenvalues of the SCM in order to separate noise and signal subspaces more accurately. In particular, the authors assume a Marcenko-Pastur probability density function (pdf) for the eigenvalues of SCM associated with the noise subspace. We also use an enhanced noise variance estimator to reduce the bias leakage between the subspaces....
Source enumeration in large arrays based on moments of eigenvalues in sample starved conditions
, Article IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation, 17 October 2012 through 19 October 2012, Quebec ; October , 2012 , Pages 79-84 ; 15206130 (ISSN) ; 9780769548562 (ISBN) ; Bastani, M. H ; Gazor, S ; Sharif University of Technology
2012
Abstract
This paper presents a scheme to enumerate the incident waves impinging on a high dimensional uniform linear array using relatively few samples. The approach is based on Minimum Description Length (MDL) criteria and statistical properties of eigenvalues of the Sample Covariance Matrix (SCM). We assume that several models, with each model representing a certain number of sources, will compete and MDL criterion will select the best model with the minimum model complexity and maximum model decision. Statistics of noise eigenvalue of SCM can be approximated by the distributional properties of the eigenvalues given by Marcenko-Pastur distribution in the signal-free SCM. In this paper we use random...
Skewness reduction approach in multi-attribute process monitoring
, Article Communications in Statistics - Theory and Methods ; Volume 36, Issue 12 , 2007 , Pages 2313-2325 ; 03610926 (ISSN) ; Abbasi, B ; Sharif University of Technology
2007
Abstract
Since the product quality of many industrial processes depends upon more than one dependent variable or attribute, they are either multivariate or multi-attribute in nature. Although multivariate statistical process control is receiving increased attention in the literature, little work has been done to deal with multi-attribute processes. In this article, we develop a new methodology to monitor multi-attribute processes. To do this, first we transform multi-attribute data in a way that their marginal probability distributions have almost zero skewness. Then, we estimate the transformed covariance matrix and apply the well-known T2 control chart. In order to illustrate the proposed method...
Sequential Bayesian estimation of state and input in dynamical systems using output-only measurements
, Article Mechanical Systems and Signal Processing ; Volume 131 , 2019 , Pages 659-688 ; 08883270 (ISSN) ; Papadimitriou, C ; Teymouri, D ; Katafygiotis, L. S ; Sharif University of Technology
Academic Press
2019
Abstract
The problem of joint estimation of the state and input in linear time-invariant dynamical systems is revisited proposing novel sequential Bayesian formulations. An appealing feature of the proposed method is the promise it delivers for updating the covariance matrices of the process and measurement noise in a real-time fashion using asymptotic approximations. The proposed method avoids the direct transmission of the input into predictions of the state using a zero-mean Gaussian distribution for the input. This prior distribution aims to eliminate low-frequency drifts from estimations of the state and input. Moreover, the method is outlined in a computational algorithm offering real-time...
Seizure detection in EEG signals: a comparison of different approaches
, Article 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, 30 August 2006 through 3 September 2006 ; 2006 , Pages 6724-6727 ; 05891019 (ISSN); 1424400325 (ISBN); 9781424400324 (ISBN) ; Maghsoudi, A ; Shamsollahi, M. B ; Sharif University of Technology
2006
Abstract
In this paper, the performance of traditional variance-based method for detection of epileptic seizures in EEG signals are compared with various methods based on nonlinear time series analysis, entropies, logistic regression, discrete wavelet transform and time frequency distributions. We noted that variance-based method in compare to the mentioned methods had the best result (100%) applied on the same database. © 2006 IEEE