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A New Metaheuristic Algorithm Based on Particle Swarm Optimization for Discrete Time Resource Trade-off Problem
, M.Sc. Thesis Sharif University of Technology ; Kianfar, Farhad (Supervisor)
Abstract
In this research, a new metaheuristic algorithm is developed for solving the Discrete Time- Resource Trade off Problem in the field of project scheduling.In this problem ,a project contains activities interrelated by finish-start type precedence constraints and each has a specified work content and can be performed in different combinations of duration and resource requirement.Since the problem is NP-hard , the Particle Swarm Optimization is adopted due to minimization of the makespan subject to precedence relations and a single renewable resource. Basically PSO is used to solve continous problems and discrete problems have just begun to be solved by the discrete PSO.In proposed method,a...
Flooding numerical simulation of heterogeneous oil reservoir using different nanoscale colloidal solutions
, Article Journal of Molecular Liquids ; Volume 302 , 15 March , 2020 ; Hosseinizadeh, E ; Esfandeh, S ; Sharif University of Technology
Elsevier B.V
2020
Abstract
In this study, flooding of oil reservoir by implementing Nanoscale colloidal solutions as a working fluid is simulated. All oil reservoirs are heterogeneous porous media; therefore, in this investigation for more accurate prediction of the problem, the porous media is considered heterogeneous. The governing equations of this problem are solved by finite element method (FEM). Moreover two-phase equations of Darcy and mass transfer equations are used. In this paper, the effect of temperature and volume fractions of nanoparticles on the rate of oil recovery is investigated. The SiO2, Al2O3, and CuO nanoparticles are used in the enhanced oil recovery (EOR) process. Also, in order to solve the...
Seizure Detection in Generalized and Focal Seizure from EEG Signals
, M.Sc. Thesis Sharif University of Technology ; Hajipour, Sepideh (Supervisor)
Abstract
Epilepsy is one of the diseases that affects the quality of life of epileptic patients. Epileptic patients lose control during epileptic seizures and are more likely to face problems. Designing and creating a seizure detection system can reduce casualties from epileptic attacks. In this study, we present an automatic method that reduces the artifact from the raw signals, and then classifies the seizure and non-seizure epochs. At all stages, it is assumed that no information is available about the patient and this detection is made only based on the information of other patients. The data from this study were recorded in Temple Hospital and the recording conditions were not controlled, so...
Studying Time Perception in Musician and Non-musician Using Auditory Stimuli
, M.Sc. Thesis Sharif University of Technology ; Hajipour, Sepideh (Supervisor)
Abstract
Time perception is a concept that describes how a person interprets the duration of an event. Depending on the circumstances, people may feel that time passes quickly or slowly. So far, the understanding, comparison, and estimation of the time interval have been described using a simple model, a pacemaker accumulator, that is powerful in explaining behavioral and biological data. Also, the role of the frequency band, Contingent Negative Variation (CNV), and Event-Related Potential (ERP) components have been investigated in the passage of time and the perception of time duration. Still, the stimuli used in these studies were not melodic. Predicting is one of the main behaviors of the brain....
Evaluation Auditory Attention Using Eeg Signals when Performing Motion and Visual Tasks
, M.Sc. Thesis Sharif University of Technology ; Hajipour, Sepideh (Supervisor)
Abstract
Attention is one of the important aspects of brain cognitive activities, which has been widely discussed in psychology and neuroscience and is one of the main fields of research in the education field. The human sense of hearing is very complex, impactful and crucial in many processes such as learning. Human body always does several tasks and uses different senses simultaneously. For example, a student who listens to his/her teacher in the class, at the same time pays attention to the teacher, looks at a text or image, and sometimes writes a note.Using the electroencephalogram (EEG) signal for attention assessment and other cognitive activities is considered because of its facile recording,...
Emotion Recognition from EEG Signals using Tensor based Algorithms
, M.Sc. Thesis Sharif University of Technology ; Hajipour, Sepideh (Supervisor)
Abstract
The brain electrical signal (EEG) has been widely used in clinical and academic research, due to its ease of recording, non-invasiveness and precision. One of the applications can be emotion recognition from the brain's electrical signal. Generally, two types of parameters (Valence and Arousal) are used to determine the type of emotion, which, in turn, indicate "positive or negative" and "level of extroversion or excitement" for a specific emotion. The significance of emotion is determined by the effects of this phenomenon on daily tasks, especially in cases where the person is confronted with activities that require careful attention and concentration.In the emotion recognition problem,...
Diagnosis of Depressive Disorder using Classification of Graphs Obtained from Electroencephalogram Signals
, M.Sc. Thesis Sharif University of Technology ; Hajipour, Sepideh (Supervisor)
Abstract
Depression is a type of mental disorder that is characterized by the continuous occurrence of bad moods in the affected person. Studies by the World Health Organization (WHO) show that depression is the second disease that threatens human life, and eight hundred thousand people die due to suicide every year. In order to reduce the damage caused by depression, it is necessary to have an accurate method for diagnosing depression and its rapid treatment, in which electroencephalogram (EEG) signals are considered as one of the best methods for diagnosing depression. Until now, various researches have been conducted to diagnose depression using electroencephalogram signals, most of which were...
A Heuristic Method for Optimization of Node Placement in Wireless Sensor Network
,
M.Sc. Thesis
Sharif University of Technology
;
Hemmatyar, Ali Mohammad Afshin
(Supervisor)
Abstract
As the applications of wireless sensor networks are expanding at an increasing rate, design of these networks so that they conform to the requirements of a specific application is considered to be a major challenge. Regarding to the specific application, network designers consider various suitable parameters to improve and then to enhance the efficiency of the network. Parameters included are such as area coverage, lifetime, reliability and etc which can significantly improve with the precise placement of nodes. Node localization problem in wireless sensor networks aiming at improvement of the performance parameters has been solved by different methods. In this thesis, in order to solve this...
EEG-based Emotion Recognition Using Graph Learning
, M.Sc. Thesis Sharif University of Technology ; Hajipour Sardouie, Sepideh (Supervisor)
Abstract
The field of emotion recognition is a growing area with multiple interdisciplinary applications, and processing and analyzing electroencephalogram signals (EEG) is one of its standard methods. In most articles, emotional elicitation methods for EEG signal recording involve visual-auditory stimulation; however, the use of virtual reality methods for recording signals with more realistic information is suggested. Therefore, in the present study, the VREED dataset, whose emotional elicitation is virtual reality, has been used to classify positive and negative emotions. The best classification accuracy in the VREED dataset article is 73.77% ± 2.01, achieved by combining features of relative...
Detection of High Frequency Oscillations from Brain Electrical Signals Using Time Series and Trajectory Analysis
, M.Sc. Thesis Sharif University of Technology ; Hajipour Sardouie, Sepideh (Supervisor)
Abstract
The analysis of cerebral signals, encompassing both invasive and non-invasive electroencephalogram recordings, is extensively utilized in the exploration of neural systems and the examination of neurological disorders. Empirical research has indicated that under certain conditions, such as epileptic episodes, cerebral signals exhibit frequency components exceeding 80 Hz, which are designated as high frequency oscillations. Consequently, high frequency oscillations are recognized as a promising biomarker for epilepsy and the delineation of epileptic foci. The objective of this dissertation is to evaluate the existing methodologies for the detection of high frequency oscillations and to...
High Frequency Oscillation Detection in Brain Electrical Signals Using Tensor Decomposition
, M.Sc. Thesis Sharif University of Technology ; Hajipour, Sepideh (Supervisor)
Abstract
High-frequency oscillations (HFOs) in brain electrical signals are activities within the 80–500 Hz frequency range that are distinct from the baseline and include at least four oscillatory cycles. Research indicates that HFOs could serve as potential biomarkers for neurological disorders. Manual detection of HFOs is time-consuming and prone to human error, making automated HFO detection methods increasingly necessary. These automated methods typically rely on the signal's energy within the HFO frequency band. Tensor decompositions are mathematical models capable of extracting hidden information from multidimensional data. Due to the multidimensional nature of brain electrical signals, tensor...
Extraction of Event Related Potentials (ERP) from EEG Signals using Semi-blind Approaches
, M.Sc. Thesis Sharif University of Technology ; Hajipour Sardouie, Sepideh (Supervisor)
Abstract
Nowadays, Electroencephalogram (EEG) is the most common method for brain activity measurement. Event Related Potentials (ERP) which are recorded through EEG, have many applications. Detecting ERP signals is an important task since their amplitudes are quite small compared to the background EEG. The usual way to address this problem is to repeat the process of EEG recording several times and use the average signal. Though averaging can be helpful, there is a need for more complicated filtering. Blind source separation methods are frequently used for ERP denoising. These methods don’t use prior information for extracting sources and their use is limited to 2D problems only. To address these...
Design and Implementation of a P300 Speller System by Using Auditory and Visual Paradigm
, M.Sc. Thesis Sharif University of Technology ; Hajipour Sardouie, Sepideh (Supervisor)
Abstract
The use of brain signals in controlling devices and communication with the external environment has been very much considered recently. The Brain-Computer Interface (BCI) systems enable people to easily handle most of their daily physical activity using the brain signal, without any need for movement. One of the most common BCI systems is P300 speller. In this type of BCI systems, the user can spell words without the need for typing with hands. In these systems, the electrical potential of the user's brain signals is distorted by visual, auditory, or tactile stimuli from his/her normal state. An essential principle in these systems is to exploit appropriate feature extraction methods which...
An Investigation of Resting-State Eeg Biomarkers Derived from Graph of Brain Connectivity for Diagnosis of Depressive Disorder
, M.Sc. Thesis Sharif University of Technology ; Hajipour, Sepideh (Supervisor)
Abstract
Among the most costly diseases that affect a person's quality of life throughout his or her life, mental disorders (excluding sleep disorders) affect up to 25 percent of people in any community. One of the most common types of these disorders in Iran is depressive disorder, which according to official statistics, 13% of Iranians have some symptoms of it. Until now, the diagnosis of this disease has been traditionally done in clinics with interviews and questionnaires tests based on behavioral psychology and using symptom assessment. Therefore, there is a relatively low accuracy in the treatment process. Nowadays, with the help of functional brain imaging such as electroencephalogram (EEG)...
Design and Implementing an Evaluator Platforn for Cochlear Implent Devices
, M.Sc. Thesis Sharif University of Technology ; Hajipour, Sepideh (Supervisor) ; Molaei, Behnam (Co-Supervisor)
Abstract
The auditory system with its unique features has been considered by researchers in the past and its various parts from the outside of the body to its internal parts have been studied. The auditory nervous system, as the most important part of the auditory system, is responsible for receiving and processing information from the ear. The auditory system has different anatomical and physiological characteristics. The result of these characteristics is processing power in the field of time and frequency, which has received more attention in this dissertation. This processing power is most evident in the central auditory nervous system. This section includes nerve neurons and synapses from the...
Preparation and Characterization of Composites Based on Natural Fibers
, M.Sc. Thesis Sharif University of Technology ; Shojaei, Akbar (Supervisor) ; Esfandeh, Masoud (Supervisor) ; Rezadoust, Amir Masoud (Co-Supervisor)
Abstract
In this research, coating with polymer solution was considered for improve the surface adhesion of natural jute fibers with polymer matrix. This coating was performed by two polymers, polyurethane and polystyrene, in order to create compatibility between natural fibers and unsaturated polyester resin. Impregnation and coating of fibers were done by dissolving these polymers in a suitable solvent and creating a thin coating on the fibers. 2%, 5% and 10% solutions of each polymer were prepared for impregnating fibers and the pultrusion process of the resulting fibers was performed using unsaturated polyester resin. The polyurethane were not compatible with jute fibers and caused a loss of...
Detection of High Frequency Oscillations from ECoG Recordings in Epileptic Patients
, M.Sc. Thesis Sharif University of Technology ; Hajipour, Sepideh (Supervisor) ; Sinaei, Farnaz (Co-Supervisor)
Abstract
The processing of brain signals, including the electrocorticogram (ECoG) signal, is widely used in the investigation of neurological diseases. Conventionally, the ECoG signal has frequency components up to the range of 80 Hz. Studies have proven that in some conditions, such as epilepsy, the brain signal includes frequency components higher than 80 Hz, which are called high-frequency oscillations (HFO). Therefore, HFOs are recognized as a biomarker for epilepsy. The aim of this thesis is to review the previous methods of detecting HFOs and to present new methods with greater efficiency in the direction of diagnosis or treatment of epileptic patients. For this purpose, we used the ECoG data...
Improving CCA Based Methods for SSVEP Classification using Graph Signal Processing
, M.Sc. Thesis Sharif University of Technology ; Hajipour Sardouie, Sepideh (Supervisor) ; Einizadeh, Aref (Co-Supervisor)
Abstract
The Brain Computer Interface (BCI) translates brain signals into a series of commands, enabling individuals to fulfill many of their basic needs without physical activity. Electroencephalogram (EEG) signals are commonly used as input for BCI systems, because the recording of this signal is non-invasive, inexpensive, and also have an acceptable time resolution. One of the most prevalent methods in BCI systems is the brain-computer interface based on Steady State Visual Evoked Potentials (SSVEP). These systems provide high response speed and Information Transfer Rate (ITR) as well as a good signal-to-noise ratio (SNR). The main purpose of these systems is to detect the frequency of SSVEP in...
Multimodal Brain Source Localization
, Ph.D. Dissertation Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor) ; Hajipour Sardouei, Sepideh (Supervisor)
Abstract
In most of brain studies, the primary objective is to find dipole activities, an underdetermined problem that requires additional constraints. Adequate constraints can be added by using information from other modalities. This research aims to develop a platform that combines various noninvasive modalities to improve localization accuracy. To accomplish this, two novel general approaches to combining modalities are proposed. In the first approach, the result of localizing by different methods and in different modalities are processed and combined in intervals by Dempster Shaffer's combination law. The final amount of bipolar activity is obtained by cumulating the activities obtained at...
Utilizing Artificial Intelligence Technique in Acidizing Process of Asphaltenic Oil Wells
, M.Sc. Thesis Sharif University of Technology ; Ayatollahi, Shahaboddin (Supervisor) ; Pishvaie, Mahmoud Reza (Supervisor)
Abstract
The Oil wells are usually damaged because of the drilling process and production scenarios or fluid injection during EOR processes. These damages would critically affect the rate of production and injectivity of the well in the form of plugging damage. Different methods are used to fix these damages and increase the production flow from the oil wells. One of the most widely used well-stimulation methods to remediate this challenge is well acidizing. Although this method has very high efficiency in improving the ability of wells, if it is not designed and implemented correctly and optimally, it can cause induced damage and even lead to the well shutting. This challenge is especially reported...