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    Seizure Detection in Generalized and Focal Seizure from EEG Signals

    , M.Sc. Thesis Sharif University of Technology Mozafari, Mohsen (Author) ; 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 Niroomand, Niavash (Author) ; 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 Bagheri, Sara (Author) ; 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 Einizadeh, Aref (Author) ; 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 Moradi, Amir (Author) ; 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... 

    The effect of FSW parameters on the macro/micro-galvanic corrosion of the dissimilar joint between AZ31 and AA5052

    , Article Materials Chemistry and Physics ; Volume 305 , 2023 ; 02540584 (ISSN) Safari, S ; Nouripour, M ; Ghorbani, M ; Safari, H ; Sharif University of Technology
    Elsevier Ltd  2023
    Abstract
    In this study, dissimilar friction stir welding was performed between AA5052 aluminium and AZ31 magnesium alloys using a variety of parameters. The results revealed that the optimal range of welding parameter is between 20 and 38 rpm/mm.min−1 (the ratio of rotation speed to travel speed). The findings of the X-ray diffraction and Micro-hardness tests on the stir zone indicated that when the rotation speed is increased, it leads to the formation of intermetallic compounds such as Al12Mg17 and Al3Mg2. The polarization test was used to assess the corrosion rate in various zones. The corrosion rate in the stir zone is higher than in other zones due to macro-galvanic couple between Al and Mg.... 

    EEG-based Emotion Recognition Using Graph Learning

    , M.Sc. Thesis Sharif University of Technology Talaie, Sharareh (Author) ; 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 Gharabaghi, Ali (Author) ; 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 Yousefi Mashhoor, Reza (Author) ; 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... 

    Hyper-T-width and hyper-D-width: Stable connectivity measures for hypergraphs

    , Article Theoretical Computer Science ; Volume 463 , 2012 , Pages 26-34 ; 03043975 (ISSN) Safari, M ; Sharif University of Technology
    2012
    Abstract
    We introduce hyper-D-width and hyper-T-width as the first stable (see Definition 3) measures of connectivity for hypergraphs. After studying some of their properties and, in particular, proposing an algorithm for computing nearly optimal hyper-T-decomposition when hyper-T-width is constant, we introduce some applications of hyper-D-width and hyper-T-width in solving hard problems such as minimum vertex cover, minimum dominating set, and multicut  

    Extraction of Event Related Potentials (ERP) from EEG Signals using Semi-blind Approaches

    , M.Sc. Thesis Sharif University of Technology Jalilpour Monesi, Mohammad (Author) ; 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 Jalilpour, Shayan (Author) ; 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 Arabpour, Mohammad Reza (Author) ; 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 Asadian, Saeed (Author) ; 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... 

    Microstructure evolution and its influence on deformation mechanisms during high temperature creep of a nickel base superalloy

    , Article Materials Science and Engineering A ; Volume 499, Issue 1-2 , 2009 , Pages 445-453 ; 09215093 (ISSN) Safari, J ; Nategh, S ; Sharif University of Technology
    2009
    Abstract
    The interaction of dislocation with strengthening particles, including primary and secondary γ′, during different stages of creep of Rene-80 was investigated by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). During creep of the alloy at 871 °C under stress of 290 MPa, the dislocation network was formed during the early stages of creep, and the dislocation glide and climb process were the predominant mechanism of deformation. The density of dislocation network became more populated during the later stages of the creep, and at the latest stage of the creep, primary particles shearing were observed alongside with the dislocation glide and climb. Shearing of γ′... 

    Co-evolutionary reliability-oriented high-level synthesis

    , Article 2008 IEEE International Symposium on Circuits and Systems, ISCAS 2008, Seattle, WA, 18 May 2008 through 21 May 2008 ; 2008 , Pages 2026-2029 ; 02714310 (ISSN) ; 9781424416844 (ISBN) Safari, S ; Aminzadeh, S ; Sharif University of Technology
    2008
    Abstract
    The main contribution of this paper is utilizing bio-inspired evolutionary algorithm for reliability oriented high level synthesis. In this paper genetic algorithm is used to schedule a data-flow graph considering latency and resource allocation considering resource constraints and area overhead. Then a co-evolutionary strategy merges the results of these solutions to find the RT level design of the circuit which satisfies both performance and area constraints. To satisfy the user-defined reliability, another genetic algorithm is developed to insert some hardware redundancies to the resulted data-path. Experimental results show using the proposed approach results in an acceptable reliability... 

    On the heat treatment of Rene-80 nickel-base superalloy

    , Article Journal of Materials Processing Technology ; Volume 176, Issue 1-3 , 2006 , Pages 240-250 ; 09240136 (ISSN) Safari, J ; Nategh, S ; Sharif University of Technology
    2006
    Abstract
    Rene-80, as an alloy for production of the jet turbine blades, shows high mechanical properties as well as microstructure stability during the high temperature engine operation. In this study we tried to have a deep insight on the microstructure of the cast Rene-80 and the evolution of microstructure during the different stages of the relatively complex GE class-A heat treatment. Although the solution heat treatment homogenized the chemical alloy segregation resulted from the casting to some extent, it did not cause all the casting γ′ particles to dissolve completely in the matrix. Primary aging caused growth in the residue particles as well as growth of the new precipitated particles, which... 

    Inflammation and mental health disorders: immunomodulation as a potential therapy for psychiatric conditions

    , Article Current Pharmaceutical Design ; Volume 29, Issue 36 , 2023 , Pages 2841-2852 ; 13816128 (ISSN) Safari, H ; Mashayekhan, S ; Sharif University of Technology
    Bentham Science Publishers  2023
    Abstract
    Mood disorders are the leading cause of disability worldwide and their incidence has significantly increased after the COVID-19 pandemic. Despite the continuous surge in the number of people diagnosed with psychiatric disorders, the treatment methods for these conditions remain limited. A significant number of people either do not respond to therapy or discontinue the drugs due to their severe side effects. Therefore, alternative therapeutic interventions are needed. Previous studies have shown a correlation between immunological alterations and the occurrence of mental health disorders, yet immunomodulatory therapies have been barely investigated for combating psychiatric conditions. In... 

    Synthesis, Characterization, Crystal Structure Determination and Theoretical Studies of Some new Rhenium(I)-tricarbonyl Complexes with 2,2'-bipyridine and 2,9-dimethylphenanthroline Ligands

    , M.Sc. Thesis Sharif University of Technology Safari, Fatemeh (Author) ; Kia, Reza (Supervisor)
    Abstract
    The synthesis, characterization, structural and computational studies of mononuclear Re(I) tricarbonyl complexes of 2,2'-bipyridine (2,2′-bpy) and 2,9-dimethylphenanthtroline (2,9-Me2Phen), [Re(CO)3(NN)(X)], where NN=2,2′-bpy,X=Br (1) and X=–ONO (3); NN=2,9-Me2 Phen, X=Br (2) and X= –ONO (4), were reported. The complexes characterized by crystallographic and spectroscopic methods and elemental analyses. In each complex, the Re(I) center showed the distorted octahedral geometry. Single crystal X-ray diffraction data revealed the endo-nitrito (κ1–ONO) coordination in complexes 3 and 4. It has been shown that the replacement of the bromo ligand in complexes 1 and 2, either by AgOTf/NaNO2 in a... 

    Exploiting the Intrinsic Redundancy of Multicore Platforms to Achieve Low-power Fault-tolerance in Embedded Applications

    , M.Sc. Thesis Sharif University of Technology Safari, Sepideh (Author) ; Ejlali, Alireza (Supervisor)
    Abstract
    VLSI technology scaling has resulted in the integration of a larger number of cores in a single chip in successive technology nodes, offering a great potential to realize task-level redundancy for reliability enhancement in safety-critical applications. However, since battery technology no longer advances commensurately with integration density, multi-core platforms may have limited utility in battery-powered embedded systems. In this thesis, we propose an energy-budget-aware reliability management (enBudRM) method for multi-core embedded systems featuring hybrid energy source (with renewable and non-renewable energy sources). Our method is composed of two phases. In the offline phase, we...