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A new algorithm for multimodal soft coupling
, Article 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, 21 February 2017 through 23 February 2017 ; Volume 10169 LNCS , 2017 , Pages 162-171 ; 03029743 (ISSN); 9783319535463 (ISBN) ; Babaie Zadeh, M ; Rivet, B ; Jutten, C ; Sharif University of Technology
Springer Verlag
2017
Abstract
In this paper, the problem of multimodal soft coupling under the Bayesian framework when variance of probabilistic model is unknown is investigated. Similarity of shared factors resulted from Nonnegative Matrix Factorization (NMF) of multimodal data sets is controlled in a soft manner by using a probabilistic model. In previous works, it is supposed that the probabilistic model and its parameters are known. However, this assumption does not always hold. In this paper it is supposed that the probabilistic model is already known but its variance is unknown. So the proposed algorithm estimates the variance of the probabilistic model along with the other parameters during the factorization...
Estimating Probability Distribution of Remaining Useful Life of Rolling Element Bearing, Using Data-driven Methods
, M.Sc. Thesis Sharif University of Technology ; Behzad, Mehdi (Supervisor)
Abstract
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...
Multimodal Blind Source Separation
, Ph.D. Dissertation Sharif University of Technology ; Babaie-Zadeh, Massoud (Supervisor)
Abstract
Blind Source Separation (BSS) is a challenging task in signal processing which aims to separate sources from their mixtures when no information is available about the sources or the mixing system. Different approaches have already been proposed for source separation.However, during the last decade, new approaches based on multimodal nature of phenomena have been proposed for source separation. Different aspects of a multimodal phenomenon can be measured by means of different instruments where each of the measured signals is called a modality of that phenomenon. Although the modalities are different signals with different features, due to the same physical origin, they usually have some...
Bayesian Model Class Selection and Peobabilistic System Identification Considering Model Complexity
, M.Sc. Thesis Sharif University of Technology ; Mahsuli, Mojtaba (Supervisor)
Abstract
This research proposes a Bayesian model selection framework using the stochastic filtering for rapid Bayesian identification of structures under seismic excitations. Structural identification after an earthquake at a regional scale entails a high computational effort. For rapid damage detection on a regional scale, using simplified and low-cost structural models is preferred over complex finite element models, due to the large amount of information needed for finite element modeling of numerous structures within a region as well as the high computational cost of such models. Timoshenko beams, shear beams, and shear buildings are examples of simplified structural models used in this study to...
A Bayesian approach to the data description problem
, Article Proceedings of the National Conference on Artificial Intelligence, 22 July 2012 through 26 July 2012 ; Volume 2 , July , 2012 , Pages 907-913 ; 9781577355687 (ISBN) ; Rabiee, H. R ; Manzuri, M. T ; Rohban, M. H ; Sharif University of Technology
2012
Abstract
In this paper, we address the problem of data description using a Bayesian framework. The goal of data description is to draw a boundary around objects of a certain class of interest to discriminate that class from the rest of the feature space. Data description is also known as one-class learning and has a wide range of applications. The proposed approach uses a Bayesian framework to precisely compute the class boundary and therefore can utilize domain information in form of prior knowledge in the framework. It can also operate in the kernel space and therefore recognize arbitrary boundary shapes. Moreover, the proposed method can utilize unlabeled data in order to improve accuracy of...
Bayesian pursuit algorithm for sparse representation
, Article 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009, Taipei, 19 April 2009 through 24 April 2009 ; 2009 , Pages 1549-1552 ; 15206149 (ISSN); 9781424423545 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Institute of Electrical and Electronics Engineers; Signal Processing Society ; Sharif University of Technology
2009
Abstract
In this paper, we propose a Bayesian Pursuit algorithm for sparse representation. It uses both the simplicity of the pursuit algorithms and optimal Bayesian framework to determine active atoms in sparse representation of a signal. We show that using Bayesian Hypothesis testing to determine the active atoms from the correlations leads to an efficient activity measure. Simulation results show that our suggested algorithm has better performance among the algorithms which have been implemented in our simulations in most of the cases. ©2009 IEEE
Distribution-aware block-sparse recovery via convex optimization
, Article IEEE Signal Processing Letters ; Volume 26, Issue 4 , 2019 , Pages 528-532 ; 10709908 (ISSN) ; Haddadi, F ; Amini, A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2019
Abstract
We study the problem of reconstructing a block-sparse signal from compressively sampled measurements. In certain applications, in addition to the inherent block-sparse structure of the signal, some prior information about the block support, i.e., blocks containing non-zero elements, might be available. Although many block-sparse recovery algorithms have been investigated in the Bayesian framework, it is still unclear how to incorporate the information about the probability of occurrence into regularization-based block-sparse recovery in an optimal sense. In this letter, we bridge between these fields by the aid of a new concept in conic integral geometry. Specifically, we solve a weighted...
Use of Data Assimilation Methods for Multiphase Flow in Porous Media
, M.Sc. Thesis Sharif University of Technology ; Rajabi Ghahnavieh, Abbas (Supervisor) ; Bazargan, Hamid (Co-Supervisor)
Abstract
The importance of optimizing the extraction process of available resources increases each day due to the increasing energy consumption and the lack of energy resources. Oil and gas are one of the most important sources of energy. Although existing oil and gas resources are thought to be sufficient to meet the growing energy demand for the next few decades, given the non-renewable nature of these resources and the growing demand for oil and gas, it will become much harder to meet the future energy demand. Many existing oil fields are now in the process of maturing, and the discovery of large new oil fields is rare. As a result, new technologies must be used in the future to meet this demand,...
Fiducial points extraction and characteristicwaves detection in ECG signal using a model-based bayesian framework
, Article ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings ; 2013 , Pages 1257-1261 ; 15206149 (ISSN) ; 9781479903566 (ISBN) ; Shamsollahi, M. B ; Jutten, C ; Sharif University of Technology
2013
Abstract
The automatic detection of Electrocardiogram (ECG) waves is important to cardiac disease diagnosis. A good performance of an automatic ECG analyzing system depends heavily upon the accurate and reliable detection of QRS complex, as well as P and T waves. In this paper, we propose an efficient method for extraction of characteristic points of ECG signal. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the...
Motion vector recovery with Gaussian process regression
, Article ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 22 May 2011 through 27 May 2011 ; May , 2011 , Pages 953-956 ; 15206149 (ISSN) ; 9781457705397 (ISBN) ; Bayati, A ; Rabiee, H. R ; Rohban, M. H ; Sharif University of Technology
2011
Abstract
In this paper, we propose a Gaussian Process Regression (GPR) framework for concealment of corrupted motion vectors in predictive video coding of packet video systems. The problem of estimating the lost motion vectors is modelled as a kernel construction problem in a Bayesian framework. First, to describe the similarity between the neighboring motion vectors, a kernel function is defined. Then the parameters of the kernel function is estimated as the coefficients of a linear Bayesian estimator. The experimental results verify the superiority of the proposed algorithm over the conventional and state of the art motion vector concealment methods. Moreover, noticeable improvements on both...