Hallgatói dolgozatok (MK)
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Hallgatói dolgozatok (MK) Szerző szerinti böngészés "Almusawi , Husam Abdulkareem"
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Tétel Korlátozottan hozzáférhető Development of Face Following RobotZhang, Zheyuan; Almusawi , Husam Abdulkareem; DE--Műszaki KarThis face following robot project realises real time following of faces and human bodies by the robot in different environments through the integrated application of HOG (Histogram of Oriented Gradients) algorithms, video processing, servo control and sensor technologies. The project first establishes a clear workflow, including the steps of video head startup, image processing, servo control, and distance sensing. Through different and multiple experiments, the parameters of the HOG algorithm were adjusted, including the tolerance value, cell size and block size, these data improved the accuracy of detecting human targets and also improved the accuracy of the robot's work. Although the project achieved satisfactory results, there is still have some improvement. It is recommended to optimise the algorithms to make real time detection efficiency , and also increase flexibility of the robot in different surroundings, and to consider the introduction of more advanced hardware and machine learning techniques to improve system performance. Overall, the project provides useful experience in applying the HOG algorithm to real face following robot projects, and also suggests potential directions for future improvements.Tétel Korlátozottan hozzáférhető EMG SIGNALS BASED GESTURE RECOGNITION FOR COMPUTER INTERFACEDhaiban, Magd Saeed Dagham Mohammed; Almusawi , Husam Abdulkareem; DE--Műszaki KarThis paper presents an electromyogram (EMG) signals based hand gesture recognition for computer interface using an inside of the forearms-placed dual channel EMG sensor (shimmer 3). EMG signals were gathered from 10 participating subjects after filtration and normalization and to improve the data set used to train the machine -learning model, the system uses Time-Domain (TD) features and Frequency-Domain (FD) features of the signals that were collected. To develop a classification model of the signals produced by four different gestures performed with two hands, classification learner app in MATLAB was used along with an Ensemble Boosted Trees learning model with the chosen features, the Bayesian optimizer was then used and result with an accuracy of 84.1%. An RC car was used as a sample application so that it could be operated by the user's hands EMG signals rather than a remote control device.