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    Design and Implementation of a Real-time Heuristic Algorithm for In-Motion Alignment of a Strapdown Inertial Navigation System

    , M.Sc. Thesis Sharif University of Technology Ashrafifar, Asghar (Author) ; Bahari, Hadi (Supervisor)
    Abstract
    In this work, the in-motion estimation of Euler angles of a strapdown inertial navigation system is studied using a speedometer, as an aided navigation sensor. First, the derivation of governing error equations has been done. Then, the most appropriate heuristic filters available in references have been chosen for this study. Among this filters, unscented particle filter, that has better performance and conditions for the real-time implementation, has been chosen. Then, an innovative solution for improving the performance of this filter is presented. The proposed filter uses particle swarm optimization to resample the particles. The proposed... 

    , M.Sc. Thesis Sharif University of Technology Sharifzadeh, Abdorrahman (Author) ; Behnia, Fereidoon (Supervisor)
    Abstract
    In this thesis, Particle Filter Methods are investigated in the context of moving targets tracking and the associated performance analysis. The application scope of this algorithm which is a particular case of Sequential Monte Carlo Method is far broader than tracking of moving targets. This algorithm can be used for mathematical calculations such as estimation of mathematical expectations, integrals, surface area of curves and many other mathematical calculations. In addition, it has applications in other branches of science like genetics. This algorithm is based on random sampling of a probability density function and resampling from the extracted samples. We change this algorithm in... 

    Distributed Tracking in Smart Camera Networks

    , M.Sc. Thesis Sharif University of Technology Rezaei Hosseinabadi, Fatemeh (Author) ; Hossein Khalaj, Babak (Supervisor)
    Abstract
    Human tracking is an essential step in many computer vision-based applications. As single view tracking may not be sufficiently robust and accurate, tracking based on multiple cameras has been widely considered in recent years. This thesis presents a distributed human tracking method in a smart camera network and introduces a particle filter design based on Histogram of Oriented Gradients (HOG) and color histogram. The proposed adaptive motion model also estimates the target speed from the history of its latest displacement and improves the robustness of the tracker by decreasing the probability of missing targets. In addition, a distributed data fusion method is proposed which fuses the... 

    Human Tracking by Probabilistic and Learning Methods

    , M.Sc. Thesis Sharif University of Technology Raziperchikolaei, Ramin (Author) ; Jamzad, Mansour (Supervisor)
    Abstract
    To overcome challenges such as object appearance changes and environment illumination variations in tracking methods, online algorithms are suggested to be used instead of offline ones. Online algorithms update the model by the information acquired in the last processed frame. The main challenge of using online algorithms is the accumulation of small errors after several steps of updating of the model (drift) which disturbs the model and causes tracking failure. Using the object information in the first frame in each update can be considered as a solution. The proposed online semi-supervised boosting algorithms can overcome the drift problem at the expense of decreasing their capabilities in... 

    Object Tracking Via Sparse Representation Model

    , M.Sc. Thesis Sharif University of Technology Zarezade, Ali (Author) ; Rabiee, Hamid Reza (Supervisor)
    Abstract
    Visual tracking is a classic problem, but is continuously an active area of research, in computer vision. Given a bounding box defining the object of interest (target) in the first frame of a video sequence, the goal of a general tracker is to determine the ob-ject’s bounding box in subsequent frames. Utilizing sparse representation, we propose a robust tracking algorithm to handle challenges such as illumination variation, pose change, and occlusion. Object appearance is modeled using a dictionary composed of target patch images contained in previous frames. In each frame, the target is found from a set of candidates via a likelihood measure that is proportional to the sum of the... 

    Development of Integrated Navigation Algorithm Based on the Integration of INS, GPS, Altimeter Using Particle Filters

    , M.Sc. Thesis Sharif University of Technology Kaviani, Samira (Author) ; Salarieh, Hassan (Supervisor) ; Alasti, Aria (Supervisor)
    Abstract
    Over the past years, several approaches have been used for navigation. Along with the advancement of technology, complicated navigation systems such as inertial navigation system and global positioning system have been employed. These navigation systems have their own strengths and weaknesses. To improve overall system performance they are integrated together as one system.
    Presented research aims at developing the integrated navigation system through particle filters. In this study, after a brief overview of the data integration techniques, several techniques which are more common and have better performance than others are examined. This contribution concentrates on filtering methods... 

    Pain Level Estimation Using Facial Expression

    , M.Sc. Thesis Sharif University of Technology Mohebbi Kalkhoran, Hamed (Author) ; Fatemizadeh, Emad (Supervisor)
    Abstract
    In this study pain level estimation using facial expression is investigated. To do this, there are two approaches, one approach is sequence level pain estimation and the other one is frame level pain estimation. In sequence level, after feature extraction from all frames of sequence, each sequence is represented by a fixed length feature vector, this feature vector is constructed by concatenating min, max and mean of frame features of that specific sequence, then KLPP is applied in order to reduce feature vector dimension and in the end a linear regression is implemented to predict the pain labels of the sequence. In the frame level, two approaches are introduced, the first one is based on... 

    Automatic Analysis and Tracking of Motile Cells in Video Microscopy

    , M.Sc. Thesis Sharif University of Technology Shayegh, Zahra (Author) ; Vosughi Vahdat, Bijan (Supervisor) ; Rabiei, Hamid Reza (Supervisor) ; Salman Yazdi, Reza (Co-Advisor)
    Abstract
    Analysis of semen and quality assessments of sperm cells is of great importance in scrutiny of male fertility. Several methods have been introduced for analyzing and identification of the sperm motility and morphology in a semen sample. Identifying and tracking of rapid and variant movements of multiple sperms, in a short duration of time, is somewhat difficult and complex for human, even for an expert. Then applying semi-automated or automated (un-supervised) methods, based on image analysis and computing, spread fast and computer aided semen analyzer systems, became wildly used in clinical and research laboratories.
    In this paper we propose an efficient multiple tracking methods to... 

    Stochastic Modeling of Online Advertising System

    , M.Sc. Thesis Sharif University of Technology Divsalar, Mohammad Reza (Author) ; Nobakhti, Amin (Supervisor) ; Babazadeh, Maryam (Supervisor)
    Abstract
    Television, radio, newspaper, magazines, and billboards are among the major channels that traditionally place ads, however, the advancement of the Internet enables users to seek information online. In display and mobile advertising, the most significant technical development in recent years is the growth of Real-Time Bidding (RTB), which facilitates a real-time auction for a display opportunity. Real-time means the auction is per impression and the process usually occurs less than 300 milliseconds before the ad is placed. RTB has fundamentally changed the landscape of the digital media market by scaling the buying process across a large number of available inventories among publishers in an... 

    Bayesian Modeling of Road Surface Roughness Characteristics for Fatigue Damage Assessment

    , M.Sc. Thesis Sharif University of Technology Mobassrfar, Yasin (Author) ; Adibnazari, Saeed (Supervisor) ; Shariyat, Mohammad (Supervisor)
    Abstract
    Road roughness-induced vibrations are the main source of fatigue damage accumulation in vehicles. Hence, road surface profile modeling and simulation are of high importance when it comes to vehicle fatigue damage assessment. This research focuses on uncovering the statistical distributions that describe the characteristics of road loads that affect fatigue damage accumulation. Particle filtering is deployed to estimate the log-volatility of the road surface profile by assuming a random walk behavior for the hidden log-volatility. The skew-normal distribution with three parameters is fitted to the estimated log-volatility. The inferred parameters are used for synthesizing artificial road... 

    Particle Filter and its Application in Tracking

    , M.Sc. Thesis Sharif University of Technology Amidzadeh, Mohsen (Author) ; Babaiezadeh, Massoud (Supervisor)
    Abstract
    The aim of tracking is localization and positioning of position-variant object through consecutive times. The essence of this object determines the application of tracking. For example this object can be the satellite, mobile, certain object in sequential movie or etc. The particle filter as an estimation filter is a method that provides us the solution of tracking Problem. Therefore this thesis is devoted to particle filter and its application in tracking. But tracking problem needs some prior information; one of them is access to measurements relating to object position. In situations that the measurement equation which is related to object position has ambiguity we need another mechanism... 

    Multitarget Tracking with Improved Particle Filter Eliminating Data Association Step

    , Ph.D. Dissertation Sharif University of Technology Raees Danaee, Meysam (Author) ; Behnia, Fereidoon (Supervisor)
    Abstract
    In general, multi-target tracking consists of estimation of the posterior density function of present targets at each scan in the observation area. These targets may have unknown and time varying number of targets. It is a tough job due to misdetections, false alarms, data association ambiguity, and nonlinear equations-non Gaussian noises. These all make it difficult to apply Kalman filter and its extensions such as extended Kalman filter and unscented Kalman filter. Monte Carlo methods, particularly particle filters, have recently aroused the interest of designers and enjoyed a lot of success to deal with multi-target tracking difficulties. In addition, they can handle nonthresholded data... 

    Integrated Orbit and Attitude Parameter Determination of a Satellite Using Hybrid Nonlinear Filters

    , Ph.D. Dissertation Sharif University of Technology Kiani, Maryam (Author) ; Pourtakdoust, Hossein (Supervisor)
    Abstract
    Rapid growth of space traffic and small satellite systems for current and future space missions have generated new enhanced performance requirements for navigation subsystem. As such, the navigation subsystem is considered as a vital part of all active satellite systems that effectively influences their successful missions. In this regard, the present work is dedicated on the subject of autonomous satellite navigation utilizing nonlinear filters, for which some laboratory experimentations have also been implemented. Generally, advanced orbit and attitude estimation algorithms can effectively compensate for the effect of low cost hardware and sensor packs utilized in microsatellites. In... 

    Dispersion and deposition of nanoparticles in microchannels with arrays of obstacles

    , Article Microfluidics and Nanofluidics ; Volume 21, Issue 4 , 2017 ; 16134982 (ISSN) Banihashemi Tehrani, S. M ; Moosavi, A ; Sadrhosseini, H ; Sharif University of Technology
    Springer Verlag  2017
    Abstract
    Air pollutants are among the hazardous materials for human health. Therefore, many scientists are interested in removing particles from the carrier gas. In this study, flow of air and airborne particles through the virtual multi-fibrous filters that consist of different fiber cross-sectional shapes and arrangements is simulated where particle deposition and filtration performance are studied. Regular and irregular arrangements of fibers with the circular, elliptical, and equilateral triangular cross sections have been considered. Effects of important parameters such as solid volume fraction, internal structure, and filter thickness on particle collection efficiency and pressure drop are... 

    Comparison of nonlinear filtering techniques for inertial sensors error identification in INS/GPS integration

    , Article Scientia Iranica ; Volume 25, Issue 3B , 2018 , Pages 1281-1295 ; 10263098 (ISSN) Kaviani, S ; Salarieh, H ; Alasty, A ; Abediny, M ; Sharif University of Technology
    Sharif University of Technology  2018
    Abstract
    Nonlinear filtering techniques are used to fuse the Global Positioning System (GPS) with Inertial Navigation System (INS) to provide a robust and reliable navigation system with a performance superior to that of either INS or GPS alone. Prominent nonlinear estimators in this field are Kalman Filters (KF) and Particle Filters (PF). The main objective of this research is the comparative study of the well-established filtering methods of EKF, UKF, and PF based on EKF and UKF in an INS-GPS integrated navigation system. Different features of INS-GPS integrated navigation methods in the state estimation, bias estimation, and bias/scale factor estimation are investigated using these four filtering... 

    A heuristic filter based on firefly algorithm for nonlinear state estimation

    , Article 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, 6 December 2016 through 9 December 2016 ; 2017 ; 9781509042401 (ISBN) Nobahari, H ; Raoufi, M ; Sharifi, A ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2017
    Abstract
    A new heuristic filter, called firefly filter, is proposed for state estimation of nonlinear stochastic systems. The new filter formulates the state estimation problem as a stochastic dynamic optimization and utilizes the firefly optimization algorithm to find and track the best estimation. The fireflies search the state space dynamically and are attracted to one other based on the perceived brightness. The performance of the proposed filter is evaluated for a set of benchmarks and the results are compared with the well-known filters like extended Kalman filter and particle filter, showing improvements in terms of estimation accuracy. © 2016 IEEE  

    Particle filtering-based low-elevation target tracking with multipath interference over the ocean surface

    , Article IEEE Transactions on Aerospace and Electronic Systems ; Volume 56, Issue 4 , 2020 , Pages 3044-3054 Shi, X ; Taheri, A ; Cecen, T ; Celik, N ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    Abstract
    As radar signals propagate above the ocean surface to determine the trajectory of a target, the signals that are reflected directly from the target arrive at the receiver along with indirect signals reflected from the ocean surface. These unwanted signals must be properly filtered; otherwise, their interference may mislead the signal receiver and significantly degrade the tracking performance of the radar. To this end, we propose a low-elevation target tracking mechanism considering the specular and diffuse reflection effects of multipath propagation over the ocean surface simultaneously. The proposed mechanism consists of a state-space model and a particle filtering algorithm and promises... 

    Adaptive square-root cubature-quadrature Kalman particle filter via KLD-sampling for orbit determination

    , Article Aerospace Science and Technology ; Volume 46 , October–November , 2015 , Pages 159-167 ; 12709638 (ISSN) Kiani, M ; Pourtakdoust, S. H ; Sharif University of Technology
    Elsevier Masson SAS  2015
    Abstract
    Orbit determination (OD) problem utilizing onboard sensors is a key requirement for many current and future space missions. Though there exists ample research and work on this subject, a novel algorithm is presented in this paper for the nonlinear problem of OD. In this regard, initially a new cubature-quadrature particle filter (CQPF) that uses the square-root cubature-quadrature Kalman filter (SR-CQKF) to generate the importance proposal distribution is developed. The developed CQPF scheme avoids the limitation of the standard particle filter (PF) concerning new measurements. Subsequently, CQPF is enhanced to take advantage of the relative entropy (Kullback-Leibler Distance) criterion to... 

    Online adaptive motion model-based target tracking using local search algorithm

    , Article Engineering Applications of Artificial Intelligence ; Volume 37 , January , 2015 , Pages 307-318 ; 09521976 (ISSN) Karami, A. H ; Hasanzadeh, M ; Kasaei, S ; Sharif University of Technology
    Elsevier Ltd  2015
    Abstract
    An adaptive tracker to address the problem of tracking objects which undergo abrupt and significant motion changes is introduced. Abrupt motion of objects is an issue which makes tracking a challenging task. To address this problem, a new adaptive motion model is proposed. The model is integrated into the sequential importance resampling particle filter (SIR PF), which is the most popular probabilistic tracking framework. In this model, in each time step, if necessary, the particles' configurations are updated by using feedback information from the observation likelihood. In order to overcome the local-trap problem, local search algorithm with best improvement strategy is used to update... 

    An algorithm to estimate parameters and states of a nonlinear maneuvering target

    , Article Cogent Engineering ; Volume 7, Issue 1 , 2020 Hosseini, S. N ; Haeri, M ; Khaloozadeh, H ; Sharif University of Technology
    Cogent OA  2020
    Abstract
    This paper investigates the problem of unknown input estimation such as acceleration, target class, and maneuvering target tracking using a hybrid algorithm. One of the challenges of unknown input estimation is that no effective method has been presented so far that could be applied to general cases. The available methods are ineffective when the range of variation of the unknown input parameter is large. Also, the issue of determining the system class could improve the performance of the tracking algorithms in many applications. Using the Bayesian theory, the posterior distribution functions of state and parameter could be obtained concurrently. In the proposed algorithm, Liu and West and...