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Activation Detection in fMRI Using Nonlinear Time Series Analysis
, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emadeddin (Supervisor)
Abstract
Functional Magnetic Resonance Imaging (fMRI) is a recently developed neuroimaging technique with capacity to map neural activity with high spatial precision. To locate active brain areas, the method utilizes local blood oxygenation changes which are reflected as small intensity changes in a special type of MR images. The ability to non-invasively map brain functions provides new opportunities to unravel the mysteries and advance the understanding of the human brain, as well as to perform pre-surgical examinations in order to optimize surgical interventions. To obtain these goals the analysis of fMRI is the first condition which should be met. First methods were linear and assumed the...
Short Term Traffic State Forecasting for Travel Time Estimation
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Real-time travel time estimation is a major requirement in many transportation related systems. One of the main challeges is to estimate the traffic speed and then forecast it for a short time. A valuable data source for this task is instant location of moving cars that is captured using global positioning system (GPS) and sent through internet in online manner. The main problem is that the resulting traffic data is severely sparse and also contains a lot of noise. Previous researchs on this type of data are mostly based on matrix or tensor factorization. In this work it is shown that despite the large fraction of missing value it is possible to use neural network for this problem with some...
The Effect of Temporal Alignment in 3D Action Recognition Using Recurrent Neural Network
, M.Sc. Thesis Sharif University of Technology ; Behroozi, Hamid (Supervisor) ; Mohammadzadeh, Hoda (Co-Supervisor)
Abstract
Action recognition has a lot of applications in everyday human life. In the past, the researchers concentrated on using RGB frames, but since the advent of 3-dimensional sensors such as Kinect, 3D action recognition drew researchers' attention. Kinect can extract the joints of the body in action as time series. One of the main challenges of action recognition is that different individuals perform an action with various styles and speeds. Hence, the conventional methods such as calculating Euclidean distance seem inappropriate for this task. One solution is to use the techniques such as DTW, which aims to temporal aligning of the sequences. The DTW is not a metric distance; hence, in this...
Evaluating the Impact of Gasoline Price Change on the Passing Car Volume in the Provinces of Iran and Tehran and the Impact of CBD Entry Policy Change on the Passing Car Volume in Tehran
, M.Sc. Thesis Sharif University of Technology ; Amini, Zahra (Supervisor)
Abstract
Nowadays, various policies are adopted by transportation managers and planners. These policies aim to improve system performance, reduce user costs, control and reduce air pollution, reduce noise pollution, and ultimately reduce congestion. A set of these policies in the form of transportation demand management is presented in the literature. A common way to find the effect of a policy on user behavior is to use questionnaires. Other causal inference models have been proposed in disciplines such as statistics, political science, marketing science, epidemiology, and psychology. The purpose of these models is to find the causal effect of an intervention (treatment) on a system. These studies...
Complex Dynamics of Epileptic Brain and Turbulence :From Time Series to Information Flow
, Ph.D. Dissertation Sharif University of Technology ; Rahimi Tabar, Mohammad Reza (Supervisor) ; Karimipour, Vahid (Supervisor)
Abstract
Complex systems are composed of a large number of subsystems behaving in a collective manner. In such systems, which are usually far from equilibrium, collective behavior arises due to self-organization and results in the formation of temporal, spatial, spatio-temporal and functional structures. The dynamics of order parameters in complex systems are generally non-stationary and can interact with each other in nonlinear manner. As a result, the analysis of the behavior of complex systems must be based on the assessment of the nonlinear interactions, as well as the determination of the characteristics and the strength of the fluctuating forces. This leads to the problem of retrieving a...
Processing the Local Field Potential Signals in Comparison to Neighboring Simple and Complex Neurons of Primary Visual Cortex
, M.Sc. Thesis Sharif University of Technology ; Lashgari, Reza (Supervisor)
Abstract
In neural systems of living organism, moreover than differences in anatomic structure of cells, there is also differences in physiological functions of analogous cells.Specification and categorization of neurons based on physiological functions is one of objectives of neuroscience. Study of cognitive behaviors and systematic study of neural system, modeling and practical applications in neural prosthesis design are some of applications of categorizing neural cells. Neural signals can be studied by Spike rate of a single neuron activity or Local Field Potential (LFP) of a finite number of neurons. In previous studies neurons of first visual cortex are divided into two groups of simple and...
Bootstrap-based Ensemble Clustering of Resting-state fMRI Time Series
, M.Sc. Thesis Sharif University of Technology ; Vosoughi Vahdat, Bijan (Supervisor)
Abstract
Studies in recent years have shown formation of strongly functionally linked sub-networks during rest, networks that are often referred to as resting-state networks. RSNs not only have basic information about the brain but also play a key role in detecting brain disorders, such as Alzheimer and Autism; Consequently, they have been remarkably noticed by neuroscientists. Numerous methods have been used in order to extract RSNs using resting-states fMRI time series. Independent component analysis (ICA) is the most common method, whi have been reported to show a high level of consistency neurophysiology; however, its results is unstable in subject-level. is weakness restricted the ICA...
Time Series Analysis Using Deep Neural Networks Based on DTW Kernels and its Application in Blood Pressure Estimation Using PPG Signals
, M.Sc. Thesis Sharif University of Technology ; Mohammadzadeh, Narjesolhoda (Supervisor)
Abstract
This work presents a modification of deep neural networks for time series analysis. We used kernel layer(s), as a novel approach, at the beginning of the common deep neural networks. These kernels learn based on dynamic time warping (DTW). In each kernel, DTW is calculated between the kernel value and a part of input time series or a part of last layer output (if the kernel is not in the first layer). DTW also gives an alignment path for the input series. This alignment path is used to defining a loss function with the goal of getting better alignment (lower DTW distance) between the kernel and the other input. Besides getting better accuracy on the examined datasets, the other achievement...
House Value Forecasting Based on Time Series
, M.Sc. Thesis Sharif University of Technology ; Shavandi, Hassan (Supervisor) ; Khedmati, Majid (Supervisor)
Abstract
Making money and maintaining the value of assets has always been one of the most important concerns of people. Real estate is one of the essential human needs, but it is also considered an investment tool for individuals. In addition to individuals in a family, various groups and organizations such as policymakers, analysts, banks and financial institutions, taxpayers, and real estate investors are directly or indirectly affected by the dynamic characteristic of the housing market. Therefore, forecasting the exact amount of housing value in the future is very important. Factors that can improve this forecasting's accuracy include considering the relationship between housing value and...
Real Time Trend Forecasting of Noisy Signal Using Deep Recurrent LSTM Network
, M.Sc. Thesis Sharif University of Technology ; Vosoughi Vahdat, Bijan (Supervisor)
Abstract
Artificial neural networks are mathematical models inspired by the nervous system and brain. The types and applications of these networks are very widespread nowadays, and it seems that they can be used to track the signals well and estimate the data of the next. In this research, we try to present a model that can predict the future of the trend of noisy signals that have unpredictable behavior, or in other words, chaotic signals. Such research is also widely used in the medical sciences, including the diagnosis of epileptic seizures or heart attacks. In this research, a study with high volatility financial data has been done as an example on this issue and the proposed model tries to be...
Impact of mobility on COVID-19 spread – A time series analysis
, Article Transportation Research Interdisciplinary Perspectives ; Volume 13 , 2022 ; 25901982 (ISSN) ; Aminpour, N ; Ahmadian, M. A ; Samimi, A ; Saidi, S ; Sharif University of Technology
Elsevier Ltd
2022
Abstract
In this paper, we investigate the impact of mobility on the spread of COVID-19 in Tehran, Iran. We have performed a time series analysis between the indicators of public transit use and inter-city trips on the number of infected people. Our results showed a significant relationship between the number of infected people and mobility variables with both short-term and long-term lags. The long-term effect of mobility showed to have a consistent lag correlation with the weekly number of new COVID-19 positive cases. In our statistical analysis, we also investigated key non-transportation variables. For instance, the mandatory use of masks in public transit resulted in observing a 10% decrease in...
Speech enhancement using hidden Markov models in Mel-frequency domain
, Article Speech Communication ; Volume 55, Issue 2 , 2013 , Pages 205-220 ; 01676393 (ISSN) ; Sameti, H ; Sharif University of Technology
2013
Abstract
Hidden Markov model (HMM)-based minimum mean square error speech enhancement method in Mel-frequency domain is focused on and a parallel cepstral and spectral (PCS) modeling is proposed. Both Mel-frequency spectral (MFS) and Mel-frequency cepstral (MFC) features are studied and experimented for speech enhancement. To estimate clean speech waveform from a noisy signal, an inversion from the Mel-frequency domain to the spectral domain is required which introduces distortion artifacts in the spectrum estimation and the filtering. To reduce the corrupting effects of the inversion, the PCS modeling is proposed. This method performs concurrent modeling in both cepstral and magnitude spectral...
Transportation development and globalization trends: A comparative global assessment
, Article 1st International Symposium on Transportation and Development Innovative Best Practices 2008, TDIBP 2008, Beijing, 24 April 2008 through 26 April 2008 ; Volume 319 , 2008 , Pages X8-14 ; 9780784409619 (ISBN) ; Rezaee, A ; Sharif University of Technology
2008
Abstract
Globalization is shaping a new socio-economic order with profound, and still unfolding, implications for transportation development. A prerequisite for national competitiveness in the global market place is efficacious transportation when its recent technological advances have created an unprecedented rise in mobility and accessibility. Developments of efficient and effective transportation infrastructure and services are among the principal driving forces for the globalization process. Using international databanks, this paper describes an attempt to shed some light on national trends of globalization and transportation, and their relationships. Deploying a comparative macroscopic approach...
Chaos in the APFM nonlinear adaptive filter
, Article Signal Processing ; Volume 89, Issue 5 , 2009 , Pages 697-702 ; 01651684 (ISSN) ; Haeri, M ; Sharif University of Technology
2009
Abstract
In this paper, we show that an amplitude phase frequency model (APFM) which is used as a nonlinear adaptive filter in signal processing can exhibit chaotic behavior. It is illustrated that both frequency and amplitude of the input components have effect on the filter characteristics which can lead the filter to behave chaotically for some values of these parameters. We have exploited different measures such as the Lyapunov exponents, bifurcation diagram, sensitivity to initial condition, and 0-1 test on time series to confirm our claim. Existence of the chaotic behavior in APFM does not conform to the prior conjectures about this filter. © 2008 Elsevier B.V. All rights reserved
Searching for variable stars in the cores of five metal-rich globular clusters using EMCCD observations
, Article Astronomy and Astrophysics ; Volume 573 , January , 2015 ; 00046361 (ISSN) ; Bramich, D. M ; Figuera Jaimes, R ; Jørgensen, U. G ; Kains, N ; Arellano Ferro, A ; Alsubai, K. A ; Bozza, V ; Calchi Novati, S ; Ciceri, S ; D'Ago, G ; Dominik, M ; Galianni, P ; Gu, S. H ; Harpsøe, K. B. W ; Haugbølle, T ; Hinse, T. C ; Hundertmark, M ; Juncher, D ; Korhonen, H ; Liebig, C ; Mancini, L ; Popovas, A ; Rabus, M ; Rahvar, S ; Scarpetta, G ; Schmidt, R. W ; Snodgrass, C ; Southworth, J ; Starkey, D ; Street, R. A ; Surdej, J ; Wang, X. B ; Wertz, O ; Sharif University of Technology
EDP Sciences
2015
Abstract
Aims. In this paper, we present the analysis of time-series observations from 2013 and 2014 of five metal-rich ([Fe/H] > -1) globular clusters: NGC 6388, NGC 6441, NGC 6528, NGC 6638, and NGC 6652. The data have been used to perform a census of the variable stars in the central parts of these clusters. Methods. The observations were made with the electron-multiplying charge-couple device (EMCCD) camera at the Danish 1.54m Telescope at La Silla, Chile, and they were analysed using difference image analysis to obtain high-precision light curves of the variable stars. Results. It was possible to identify and classify all of the previously known or suspected variable stars in the central regions...
Predicting the competitive position of extended gates: The case of inland customs zones
, Article European Journal of Transport and Infrastructure Research ; Volume 18, Issue 4 , 2018 , Pages 433-456 ; 15677141 (ISSN) ; Saffarzadeh, M ; Tavasszy, L ; Fatemi Ardestani, S. F ; Sharif University of Technology
Editorial Board EJTIR
2018
Abstract
The extended gate concept aims to reduce the pressure on international ports by postponing administrative processes from these border gates to inland terminals. At present, this approach is used mainly in the container transport industry in European and Asian ports. In this paper we study an extended gate concept, where inland customs services are made available from all entry points of a country. Our aim is to predict the portion of the current flow through border gates that is diverted to these inland customs zones. We propose a time-series gravity models to predict these changes and estimate the parameters of this model using publicly available data for different cargo groups. The focus...
Levels of complexity in turbulent time series for weakly and high Reynolds number
, Article Physica A: Statistical Mechanics and its Applications ; Volume 391, Issue 11 , 2012 , Pages 3151-3158 ; 03784371 (ISSN) ; Sharif University of Technology
2012
Abstract
We use the detrended fluctuation analysis (DFA), the detrended cross correlation analysis (DCCA) and the magnitude and sign decomposition analysis to study the fluctuations in the turbulent time series and to probe long-term nonlinear levels of complexity in weakly and high turbulent flow. The DFA analysis indicate that there is a time scaling region in the fluctuation function, segregating regimes with different scaling exponents. We discuss that this time scaling region is related to inertial range in turbulent flows. The DCCA exponent implies the presence of power-law cross correlations. In addition, we conclude its multifractality for high Reynold's number in inertial range. Further, we...
The level crossing and inverse statistic analysis of German stock market index (DAX) and daily oil price time series
, Article Physica A: Statistical Mechanics and its Applications ; Volume 391, Issue 1-2 , 2012 , Pages 209-216 ; 03784371 (ISSN) ; Hölling, M ; Peinke, J ; Rahimi Tabar, M. Reza ; Sharif University of Technology
2012
Abstract
The level crossing and inverse statistics analysis of DAX and oil price time series are given. We determine the average frequency of positive-slope crossings, να+, where Tα=1να+ is the average waiting time for observing the level α again. We estimate the probability P(K,α), which provides us the probability of observing K times of the level α with positive slope, in time scale Tα. For analyzed time series, we found that maximum K is about ≈6. We show that by using the level crossing analysis one can estimate how the DAX and oil time series will develop. We carry out the same analysis for the increments of DAX and oil price log-returns (which is known as inverse statistics), and provide the...
A novel regression imputation framework for Tehran air pollution monitoring network using outputs from WRF and CAMx models
, Article Atmospheric Environment ; Volume 187 , 2018 , Pages 24-33 ; 13522310 (ISSN) ; Karimi, S ; Hosseini, V ; Yazgi, D ; Torbatian, S ; Sharif University of Technology
Elsevier Ltd
2018
Abstract
Missing or incomplete data in short or long intervals is a common problem in measuring air pollution. Severe issues may arise when dealing with missing data for time-series prediction schemes or mean analysis. This study aimed to develop a new regression imputation framework to impute missing values in the hourly air quality data set of Tehran and enhance the applicability of Tehran Air Pollution Forecasting System (TAPFS). The proposed framework was designed based on three types of features including measurements of other stations, WRF and CAMx physical models. In this framework, elastic net and neuro-fuzzy networks were efficiently combined in a two-layer structure. The framework was...
Stockwell transform of time-series of fMRI data for diagnoses of attention deficit hyperactive disorder
, Article Applied Soft Computing Journal ; Volume 86 , 2020 ; Kalbkhani, H ; Ghasemzadeh, P ; Shayesteh, M. G ; Sharif University of Technology
Elsevier Ltd
2020
Abstract
Attention deficit hyperactivity disorder (ADHD) is a common brain disorder among children. It presents various symptoms, hence, utilizing the information obtained from functional magnetic resonance imaging (fMRI) time-series data can be useful. Finding functional connections in typically developed control (TDC) and ADHD patients can be helpful in classification. The aim of this paper is to present a multifold method for the study of fMRI data to diagnose ADHD patients. In the proposed method, first, by applying the Stockwell transform (ST), we obtain detailed information about the time-series of the region of interests (ROIs) in the time and frequency domains. ST provides information about...