Robotics and Computer Engineering - Master's theses

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Browsing Robotics and Computer Engineering - Master's theses by Author "Anbarjafari, Gholamreza, supervisor"

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    Deep Learning Based Automated Job Candidate Interview Screening
    (Tartu Ülikool, 2019) Aktas, Kadir; Anbarjafari, Gholamreza, supervisor; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
    Traditional way of recruitment process is challenging for both the candidate and the employer. To apply for a job, the candidate needs to prepare a CV. On the other hand, the employer needs to check all the submitted CVs and analyze the candidate data manually. These aspects can make the process very time consuming, especially when there are many candidates. Furthermore, the manual analysis of the candidate data is very open to human bias. The thesis proposes an automated video interview analysis system, which eliminates the problems mentioned above.
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    Imaging Simulator and Geometric Image Stitching for a Low Earth Orbit Satellite with High Spin Rates
    (Tartu Ülikool, 2019) Haamer, Rain Eric; Sünter, Indrek, supervisor; Anbarjafari, Gholamreza, supervisor; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
    ESTCube-2 is a low-earth orbit satellite with the main mission of deploying and testing an electrically charged tether and a secondary mission of photographing ground vegetation. The tether deployment and maintaining its separation from the satellite requires a very high spin rate, which poses too many challenges to the imaging system for it to be viable in its current state. In an attempt to resolve this issue, a novel image morphing and stitching algorithm was developed that uses geometric mapping for reconstruction. The proposed method was tested on a specifically made simulation environment that mimics predefined camera parameters. The stitching algorithm was verified to perform within acceptable margins even when expected high spin distortion models were applied.
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    Investigation and Comparison of Kinetostatic Performance Indices for Parallel Mechanisms
    (Tartu Ülikool, 2019) Sellis, Ott; Anbarjafari, Gholamreza, supervisor; Daneshmand, Morteza, supervisor; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
    For as long as we have used robots there has also been ongoing research to allow us to use and improve efficiency of automation in our daily lives. As our knowledge about robots has largely improved, so has the complexity of their structures. Thus, various methods and indices have been developed to help designers and engineers determine the best manipulator for a specific task. In addition, the interest towards parallel manipulators has seen growth in the last couple of years due to significantly better performance in various areas in comparison to serial mechanisms. However, no global performance index to evaluate accuracy and allow comparison in that perspective between parallel mechanisms has been developed. This thesis focuses on giving an overview on the developments towards finding a robust kinematic sensitivity index to measure accuracy performance of parallel manipulators.
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    Online Battery Cell State of Charge Estimation for use in Electric Vehicle Battery Management Systems
    (Tartu Ülikool, 2018) Dreija, Kristaps; Anbarjafari, Gholamreza, supervisor; Avots, Egils, supervisor; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Tehnoloogiainstituut
    The electric vehicle (EV) is a complex, safety-critical system, which must ensure the safety of the operator and the reliability and longevity of the device. The battery management system (BMS) of an EV is an embedded system, whose main responsibility is the protection and safety of the high-voltage battery pack. The BMS must ensure that the requirements for health, status and deliverable power are met by maintaining the battery pack within the defined operational conditions for the defined lifetime of the battery. The state of charge (SOC) of a cell describes the ratio of its current capacity (amount of charge stored) to the nominal capacity as defined by the manufacturer. SOC estimation is a crucial, but not trivial BMS task as it can not be measured directly, but must be estimated from known and measured parameters, such as the cell terminal voltage, current, temperature, and the static and dynamic behavior of the cell in different conditions. Many different SOC estimation methods exist, out of which (currently) the most practical methods for implementing on a real-time embedded system are adaptive methods, which adapt to different internal and external conditions. This thesis proposes the use of the sigma point Kalman filter (SPKF) for non-linear systems as an equivalentcircuit model-based state estimator to be used in one of the current series production EV projects developed by Rimac Automobili. The estimator has been implemented and validated to yield better results than the currently used SOC estimation method, and will be deployed on the BMS of a high-performance hybrid-electric vehicle.