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    The Super Resolution Algorithm Based on Attributed Scattering Model Using Multi-Band and Multi-Angle Signals

    , M.Sc. Thesis Sharif University of Technology Seyedin, Mohammad Bagher (Author) ; Karbasi, Mohammad (Supervisor)
    Abstract
    The resolution is one of the most essential factors in radar imaging. The range and cross-range resolution are inversely proportional to the radar bandwidth and the angular width, respectively. Increasing the bandwidth requires upgrading the hardware of radars and the cost of this procedure is high. Also, to increase the angular width, we need more time to scan the scene from different angles but due to reasons such as movements of targets in the scene, it is unachievable.According to these limitations, we can utilize some signal processing techniques to Improve the radar resolution. Super resolution algorithms are the common approaches to achieving this goal. In this research, we proposed a... 

    Outlier Censoring Based on Sparse Signal Recovery Algorithms

    , M.Sc. Thesis Sharif University of Technology Bassak, Elaheh (Author) ; Karbasi, Mohammad (Supervisor)
    Abstract
    In today’s world, knowledge of the statistical behavior of noise can tremendously affect the accuracy of target detection in radar systems. Therefore, radar systems commonly collect a secondary dataset of homogeneous noise and estimate the statistics of the gathered data, prior to attempting target detection. Specifically, in the case of Gaussian noise with a mean of zero, the entire statistical information of the noise is encoded in its covariance matrix. In practice, however, the challenge is that the training samples do not purely contain homogeneous noise. In fact, some samples contain non-homogeneous outlier signals that do not have the same distribution as the noise samples. In this... 

    Waveform Design for Interference Mitigation in SAR Imaging and Sparse Image Recovery

    , M.Sc. Thesis Sharif University of Technology Keyhani, Erfan (Author) ; Karbasi, Mohammad (Supervisor)
    Abstract
    In this research, we design a compatible waveform for the purpose of high-quality synthetic aperture radar (SAR) imaging in conjunction with sparse recovery methods for image formation. The goal is to make the imaging system tolerable against the wide-band and narrowband electromagnetic interferences. Actually, we consider minimizing the mutual interference between our radar and coexisting licensed emitters and minimizing the jamming signal (unlicensed emitters) power while enforcing some constraints over the waveform features like peak-to-average-power ratio (PAPR). For the constrained optimization problem to design a proper waveform, we introduce a new constraint to the optimization... 

    Range-Doppler Map Generation in the Presence of Sparse Clutter for Multistatic Radar

    , M.Sc. Thesis Sharif University of Technology Haghighat, Soheil (Author) ; Karbasi, Mohammad (Supervisor)
    Abstract
    Multistatic radar has several advantages over monostatic radar (such as better detection), which are due to the use of different viewing angles and the difference in their clutter characteristics. Clutter in many applications (such as marine applications) has the property of being sparse in certain dictionaries. Therefore, the investigation of sparse clutter (such as sea clutter) is of particular importance. It is worth noting that the detection of targets in the vicinity of the sea faces difficulties due to the dynamics of the sea, which causes the Doppler spectrum to change with time and change in space. Considering the fact that the sea clutter is sparse clutter, one of the powerful... 

    Joint Range, Angle and Doppler Frequency Estimation in FDA-MIMO Radars via Atomic Norm Minimization

    , M.Sc. Thesis Sharif University of Technology Bagheri Jazi, Mohammad Reza (Author) ; Karbasi, Mohammad (Supervisor)
    Abstract
    In this thesis, we address the problem of joint Range, Angle and Doppler frequency estimation in FDA-MIMO radars. Target parameter estimation is among the most important areas of research in radar signal processing. In this field of research, there are numerous challenges to overcome, including low estimation accuracy, a scarcity of snapshots, and the high computational complexity of algorithms. For this reason, diverse methods have been developed to solve this problem. Each of these methods has its own features and capabilities, focusing on one or more of the existing challenges. The application of these methods also depends on various factors, including the signal structure, which is... 

    Design, Manufacturing and Testing a Micro Hydro Turbine Governor Using Commercial Controller

    , M.Sc. Thesis Sharif University of Technology Karbasi Zadeh, Morteza (Author) ; Durali, Mohammad (Supervisor)
    Abstract
    Lots of micro hydropower potentials are spread all around Iran country. They have the capability to produce electricity for workhouses, residential areas, agricultural and animal husbandries and recreational tourism centers. Hence, Sharif University of Technology and Iran Water and Power Resources Development Corporation decided to design and produce three types of hydro micro turbines to cover all range of micro hydropower potentials available in Iran. These three types of micro turbines are Pelton, Banki and Axial and they are designed in order to cover high, middle and low heads in sequence. Frequency control of micro hydropower plants is so vital due to plant off-grid. The most prevalent... 

    Design of Transmit Code & Receive Filter in Radar Via Manifold Optimization

    , M.Sc. Thesis Sharif University of Technology Attar Hamidi, Farzin (Author) ; Karbasi, Mohammad (Supervisor)
    Abstract
    The problem of joint design of transmit code and receive filter is considered in many application scenarios of multiple input multiple output (MIMO) radar systems. The performance of the joint design problem is evaluated with the signal-to-interference ratio (SINR) metric in the presence of noise. In such problems, the optimization problem is to maximize SINR on the receiver side with the constraints that are applied on the transmitted waveform. Our proposed method, RTR, is a manifold-based geometric method that performs better than the SDP method in terms of algorithm execution speed and calculation complexity. For simulation, we took sampled and real TIR with Target Aspect Angle (TAA) in a... 

    RIS-Aided DOA Estimation and Localization

    , M.Sc. Thesis Sharif University of Technology Shourezari, Ehsanollah (Author) ; Karbasi, Mohammad (Supervisor)
    Abstract
    Estimating the direction of arrival (DOA) of an impinging signal from an RF scattering object, and subsequently determining its location when there is no line-of-sight (LOS) channel between the object and receiver, is a challenging issue in sensing and wireless communication systems. This thesis presents a modified multiple signal classification (MUSIC) algorithm, utilizing a single-element receive antenna and harnessing the capabilities of reconfigurable intelligent surfaces (RIS) to tackle this issue. It is demonstrated that by leveraging massive low-cost passive elements able to reflect signals with different phase shifts in consecutive snapshots, RIS can generate linearly independent... 

    SAR Imaging Using the TomoSAR Technique to Resolve Multiple Scatterers

    , M.Sc. Thesis Sharif University of Technology Omati, Mohammad Mahdi (Author) ; Bastani, Mohammad Hassan (Supervisor) ; Karbasi, Mohammad (Co-Supervisor)
    Abstract
    During the last few years, the study of urban environment structures is considered as a research field of interest in remote sensing. In satellite observations of the earth's surface, continuous imaging in terms of time and space has caused the remote sensing technique to be proposed as a useful and efficient tool for the analysis of urban areas. Obtaining quantitative spatial information from the urban environment in fields such as determining the height of buildings plays an essential role in urban planning, monitoring damage to buildings, establishing communication bases and digital cities. During the last two decades, the use of Tomosar approach in order to reconstruct the structures of... 

    Complexity Reduction in AoA Estimation with root-MUSIC

    , M.Sc. Thesis Sharif University of Technology Eskandari, Meysam (Author) ; Bastani, Mohammad Hassan (Supervisor) ; Karbasi, Mohammad (Co-Supervisor)
    Abstract
    Localization of radio sources has various applications in civil and military communications. Some of important localization methods consist of Angle of Arrival (AoA) estimation of the illuminated signals from the interested sources. Between various Direction Finding (DF) methods, subspace based methods are of special importance, because of their high resolution and accuracy. And from beginning of invention, because of new challenges, have been focus of numerous researches. In this thesis, first we explain the most important subspace based DF methods called MUSIC and root-MUSIC. Then a novel modification for root-MUSIC is introduced. In our new method the computational complexity of... 

    Multi Dimensional Dictionary Based Sparse Coding in ISAR Image Reconstruction

    , Ph.D. Dissertation Sharif University of Technology Mehrpooya, Ali (Author) ; Nayebi, Mohammad Mahdi (Supervisor) ; Karbasi, Mohammad (Co-Supervisor)
    Abstract
    By generalizing dictionary learning (DL) algorithms to Multidimensional (MD) mode and using them in applications where signals are inherently multidimensional, such as in three-dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging, it is possible to achieve much higher speed and less computational complexity. In this thesis, the formulation of the Multidimensional Dictionary Learning (MDDL) problem is expressed and six algorithms are proposed to solve it. The first one is based on the method of optimum directions (MOD) algorithm for 1D dictionary learning (1DDL), which uses alternating minimization and gradient projection approach. As the MDDL problem is non-convex, the second... 

    Design and Development of Recommendation & Decision Maker Systems Based on Massive Traffic Data Analysis

    , M.Sc. Thesis Sharif University of Technology Safarpour, Alireza (Author) ; Gholampour, Iman (Supervisor) ; Karbasi, Mohammad (Supervisor)
    Abstract
    Data analysis is the science of extracting human-understandable knowledge from data. Massive data analysis is usually associated with the challenges of Volume, Velocity, Variety, Veracity and Value. These challenges are often referred to as 5V. With increasing processing power and reducing its cost in the last decade, massive data analysis methods have been used in various fields such as market, insurance and health, telecommunications and network, web search engines, intelligent transportation, etc. In this dissertation, we have tried to extract applied knowledge by using massive data analysis platforms and algorithms as well as traffic data. In this dissertation, a mathematical model for... 

    DOA Estimation in Communication Systems Using Deep Neural Networks

    , M.Sc. Thesis Sharif University of Technology Alikhani, Morteza (Author) ; Behrouzi, Hamid (Supervisor) ; Karbasi, Mohammad (Supervisor)
    Abstract
    The purpose of Direction-of-arrival(DOA) estimation in MIMO communication networks is to find the direction of signals that are mixed together and mixed with noise by using an array of multiple receiving antennas. DOA estimation is an important issue in array signal processing. The traditional methods for DOA estimation try to solve this problem with mathematical algorithms. For example, some of these methods are based on parameter estimation; such as DOA estimation methods using maximum likelihood estimation methods. Some of other traditional methods are based on subspace decomposition; Like the famous MUSIC method. The traditional methods of DOA estimation usually have a drawback, and that... 

    Waveform Design for Point-Like and Extended Targets in SISO and MIMO Radars

    , Ph.D. Dissertation Sharif University of Technology Karbasi, Mohammad (Author) ; Bastani, Mohammad Hassan (Supervisor) ; Naghsh, Mohammad Mahdi (Co-Advisor)
    Abstract
    Transmit waveform plays a significant role in active sensing systems. High speed and off-the-shelf processors, digital arbitrary waveform generators, and solid state transmitters have paved the way for an increased capability to adapt the transmit signal and the receive fil-ters with respect to (w.r.t.) the illuminated environment. In this thesis, we have considered the waveform design problem for different scenarios, in two parts. Firstly, we have con-sidered the problem for the extended target model in single-input single-output (SISO) radars. There, we have assumed known target impulse response (TIR) with an unknown reflection factor and found the waveform corresponding to the optimal... 

    Methylidyne (CH*)Chemiluminescence Measurement in Sooting Flames

    , M.Sc. Thesis Sharif University of Technology Karbasi Shargh, Kamyab (Author) ; Salehi, Mohammad Mahdi (Supervisor) ; Mardani, Amir (Supervisor)
    Abstract
    In this study, using a non-interference experimental method, the chemical luminosity of CH* in co-flow diffusion flames with soot and using natural gas hydrocarbon in a laminar flow regime has been investigated. In addition to experimental research, in current research, numerical simulation has been used to extract information such as temperature field, mass fraction of species, and flow structure and comparison and validation with experimental results. Numerical simulation has been used to simulate the mechanism of CH* species formation. Two different mechanisms have been used. In addition, the Soot simulation model from the Moss-Brookes model and radiation heat transfer mechanism... 

    Joint likelihood estimation and model order selection for outlier censoring

    , Article IET Radar, Sonar and Navigation ; Volume 15, Issue 6 , 2021 , Pages 561-573 ; 17518784 (ISSN) Karbasi, S. M ; Sharif University of Technology
    John Wiley and Sons Inc  2021
    Abstract
    This study deals with the problem of outlier censoring from the secondary data in a radar scenario, where the number of outliers is unknown. To this end, a procedure consisting of joint likelihood estimation and statistical model order selection (MOS) is proposed. Since the maximum likelihood (ML) estimation of the outlier subset requires to solve a combinatorial problem, an approximate ML (AML) method is employed to reduce the complexity. Therefore, to determine the number of outliers, different MOS criteria based on likelihood function are applied. At the analysis stage, the performance of the proposed methods is assessed based on simulated data. The results highlight that the devised... 

    Angle-incremental range estimation for FDA-MIMO radar via hybrid sparse learning

    , Article Digital Signal Processing: A Review Journal ; Volume 130 , 2022 ; 10512004 (ISSN) Karbasi, S. M ; Sharif University of Technology
    Elsevier Inc  2022
    Abstract
    In this paper, a target parameter estimation problem is addressed for the recently emerging frequency diverse array multiple-input multiple-output (FDA-MIMO) radar system, utilizing sparse learning. The scene is modeled as a two dimensional (2D) angle-incremental range grid. To solve the resulting sparse problem, the recently proposed user-parameter free algorithms including block sparse learning via iterative minimization (BSLIM), iterative adaptive approach (IAA), sparse iterative covariance-based estimation (SPICE), likelihood-based estimation of sparse parameters (LIKES), and orthogonal matching pursuit (OMP) are applied which achieve excellent parameter estimation performance. However,... 

    Distributed Optimal Control Based on an Efficient Method for Communication

    , M.Sc. Thesis Sharif University of Technology Karbasi, Ali (Author) ; Farhadi, Alireza (Supervisor)
    Abstract
    This thesis is concerned with an optimal trade-off between communication overhead and the number and size of neighborhoods in a distributed optimal control technique for large-scale systems that is based on the Jacobi iteration and two-layer architecture for communication. Although this method efficiently reduces computational overhead, continuous and proper data transfer between subsystems that are very often far from each other, is required to achieve an acceptable performance. However, limited transmission bandwidth and short communication range result in significant communication overhead. This causes significant time latency between measurement and applied calculated control actions,... 

    Combined Wrist and Forearm Movement Recognition using sEMG

    , M.Sc. Thesis Sharif University of Technology Karbasi, Hamed (Author) ; Jahed, Mehran (Supervisor)
    Abstract
    Physiotherapy is a major part of the rehabilitation process that is used to retrieve patients' physical ability. The recuperation feedback in the physiotherapy process has a twofold significance for both physiotherapists and patients. It helps patients regain their ability to recover more quickly, prevent recurrence of injury to the treated area and other areas, and motivate the patient to continue treatment. It also helps the physiotherapist to monitor the process of rehabilitation and ensure the correctness of the procedure. Meanwhile, many patients cannot maintain continuous workouts due to lack of access to the physiotherapist at home and inability to provide required feedback.In this... 

    Introducing a novel SEMG ANN-based regression approach for elbow motion interpolation

    , Article 4th IEEE International Conference on Computer and Communication Systems, ICCCS 2019, 23 February 2019 through 25 February 2019 ; 2019 , Pages 77-80 ; 9781728113227 (ISBN) Karbasi, H ; Jahed, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Abstract
    Surface electromyogram (sEMG) signals are extensively used for rehabilitation and control purposes. However due to their intrinsic complexities and intense sensor crosstalk, feature classification and pattern recognition of sEMG signals especially for motion analysis are quite challenging. This study proposes a versatile sEMG Artificial Neural Network based regression approach to evaluate a simple elbow motion with respect to a reference frame. The proposed approach attempts to appropriately interpolate intermediate position angles in an attempt to evaluate and substantiate a continuous motion of the forearm. Results show that based on the proposed algorithm, with a correlation of about 91%...