Design and Simulation of Simulated Moving Beds

, M.Sc. Thesis Sharif University of Technology Yazdian Kashani, Sepideh (Author) ; Farhadi, Fathollah (Supervisor)

Abstract

In this thesis design and simulation of SMB for four different mixture was done with Aspen chromatography package. Simulation for three mixtures of D-Psicose and D-Fructose, racemic mixture of Bupivacaine and Ibuprofen racemate were done with the aim of validation, based on experimental data of articles. The simulation results matched well with the experimental results in articles. The separation of p-xylene from mixed C8 aromatics and Para Diethyl benzene (as desorbent), was simulated for one of the p-xylene producing units and results were validated against the PFD. The influence of various parameters on the purity and recovery of p-xylene were investigated. The studies showed high...

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...

Adaptive modulation technique for amplify & forward cooperative diversity and fairness analysis

, Article 2008 International Conference on Telecommunications, ICT, St. Petersburg, 16 June 2008 through 19 June 2008 ; October , 2008 ; 9781424420360 (ISBN) Yazdian, E ; Pakravan, M. R ; Sharif University of Technology

2008

Abstract

In recent years, cooperative diversity techniques have gained increased attention as a new method to combat degrading channel effects. Cooperation among two or more single antenna users generates a virtual diverse multiple-antenna transmission system in the wireless network. In this paper, we propose a scheme to improve the spectral efficiency of the Amplify and Forward system and investigate the application of adaptive modulation concept to the performance of cooperative techniques. We consider the adaptation of rate and power allocation with MQAM modulation and study the performance of Amplify & Forward scenarios in Rayleigh fading channels. We compare this system with its comparable...

Adaptive modulation technique for cooperative diversity in wireless fading channels

, Article 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, Helsinki, 11 September 2006 through 14 September 2006 ; 2006 ; 1424403294 (ISBN); 9781424403295 (ISBN) Yazdian, E ; Pakravan, M. R ; Sharif University of Technology

2006

Abstract

In recent years, cooperative diversity techniques have gained increased attention as a new way to combat degrading effects of Fading. Cooperation among two or more single antenna users generates a virtual diverse multiple-antenna transmission system in the wireless network. In this paper, we investigate the application of adaptive modulation concept to the performance of cooperative techniques. We consider the adaptation of rate and power allocation with M-QAM modulation, and study the performance of Amplify & Forward scenarios in Rayleigh fading channels. We compare these systems with their comparable adaptive non-cooperative scenario and evaluate the energy saving achieved through...

Source Enumeration and Identification in Array Processing Systems

, Ph.D. Dissertation Sharif University of Technology Yazdian, Ehsan (Author) ; 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...

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...

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) Yazdian, E ; 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) Yazdian, E ; 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...

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) Yazdian, E ; 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

Limiting spectral distribution of the sample covariance matrix of the windowed array data

, Article Eurasip Journal on Advances in Signal Processing ; Volume 2013, Issue 1 , 2013 ; 16876172 (ISSN) Yazdian, E ; Gazor, S ; Bastani, M. H ; Sharif University of Technology

2013

Abstract

In this article, we investigate the limiting spectral distribution of the sample covariance matrix (SCM) of weighted/windowed complex data. We use recent advances in random matrix theory and describe the distribution of eigenvalues of the doubly correlated Wishart matrices. We obtain an approximation for the spectral distribution of the SCM obtained from windowed data. We also determine a condition on the coefficients of the window, under which the fragmentation of the support of noise eigenvalues can be avoided, in the noise-only data case. For the commonly used exponential window, we derive an explicit expression for the l.s.d of the noise-only data. In addition, we present a method to...

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...

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)...

Performance improvement of a double stage switch mode AC voltage regulator

, Article 2012 3rd Power Electronics and Drive Systems Technology, PEDSTC 2012, 15 February 2012 through 16 February 2012 ; February , 2012 , Pages 181-186 ; 9781467301114 (ISBN) Hajimoradi, M. R ; Karimi, E ; Mokhtari, H ; Yazdian, A ; Sharif University of Technology

2012

Abstract

Widespread use of sensitive loads in industries has required the need for high efficiency voltage regulators in order to provide continuous and fast dynamic adjustable AC voltage. This paper introduces a new control strategy to improve the response time and performance of a double stage switch mode regulator. The proposed strategy responds to input voltage variations quickly and improves the efficiency and performance of the circuit. Results of some simulations are presented and analyzed. Experimental tests on a laboratory prototype verify the excellent performance of the proposed system