Yazar "Alsaedi, Mustafa Oudah Hani" seçeneğine göre listele

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  • [ X ]
    Öğe
    Design a wideband class-j power amplifier
    (Institute of Electrical and Electronics Engineers Inc., 2023) Atilla, Çağdas Doğu; Alsaedi, Mustafa Oudah Hani; Alsaadi, Hajir Adil Jasim
    This paper presents the modern design and simulation of a broadband Class-J power amplifier (CJPA) tailored for enhancing military radar systems, especially those operating in the critical L-band spectrum. Motivated by the need for improved radar performance, our research aims to achieve higher efficiency and power output through the meticulous application of load pull and source pull techniques in the input and output matching circuit design to obtain the input and output impedance for active device at maximum power to match them with 50 Ω by design matching circuits using Smith chat utility. This study utilizes the GaN HEMT CGH40025F and focuses on designing and simulating a CJPA operating in the 1.6 to 2 GHz frequency range for the L-band. The test results demonstrate that the CJPA outputs around 40 dBm when the input power is 24 dBm, with a notable power added efficiency (PAE) of 63% and a transducer gain of 16 dB within the band. The simulation and testing were conducted using the Keysight ADS software. The significance of these findings lies in the enhanced capabilities they bring to military radar systems, with the potential for improved target detection, tracking, and overall system performance. Moreover, the optimized CJPA design may find applications beyond military settings, contributing to advancements in RF amplifier technology for diverse communication and sensing systems.
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    Öğe
    Design and implementation of an autonomous vehicle enhanced by advanced driver assistance systems (ADAS) using ML
    (Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü, 2024) Alsaedi, Mustafa Oudah Hani; Atilla, Doğu Çağdaş
    This thesis discusses the design and implementation of an autonomous vehicle enhanced with advance driver assistance systems (ADAS) using machine learning. This vehicle can be classified as an educational platform suitable for researchers and specialists in the field of autonomous vehicles. Its structure can be easily modified to meet the needs of researchers, and it can be reprogrammed with ease. The work details the construction of the vehicle, including the chassis structure, suspension system, steering system, brakes, and the anti-lock braking system (ABS). Control of the vehicle is achieved through a mobile phone using a control program developed with MIT App Inventor, allowing wireless Bluetooth communication for driving. The thesis also covers the vehicle's key tasks, such as path planning and navigation using a specialized algorithm for selecting the shortest path to the destination. The vehicle is equipped with ultrasonic sensors distributed around it to detect both stationary and moving obstacles. Additionally, a LIDAR sensor is used for obstacle detection. A machine learning model is created to sense obstacles, trained on data collected from various sensors and scenario, and used to implement autonomous driving in simulation and augmented reality. The results demonstrate the vehicle's ability to navigate obstacles during its journey. Finally, the vehicle can recognize different traffic signs, trained using machine learning on a dataset of over 50,000 samples of 43 classes of German traffic signs. The model is tested for visualization and through the vehicle's camera, enabling it to recognize and respond to all traffic signs appropriately.
  • [ X ]
    Öğe
    Design continues mode inverse class-f power amplifier
    (Institute of Electrical and Electronics Engineers Inc., 2023) Atilla, Doğu Çağdaş; Alsaedi, Mustafa Oudah Hani; Alsaadi, Hajir Adil Jasim
    This paper outlines the design, simulation, and testing of a continuous mode inverse class-F (CMICF) power amplifier (PA) specifically optimized for GSM applications. The comprehensive design methodology encompasses DC analysis, optimum load impedance calculation, stability circuit analysis, and input and output matching circuit design. The amplifier is built using Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT), and it introduces a novel technique for designing the output matching network (OMN). This technique involves extracting parasitic elements from the power device using a tuning method and employs a graphical approach with the Smith chart utility to obtain the second and third harmonics of the output signal. The resulting design achieves high efficiency with a reduced component count, simplifying the overall design process and reducing costs. The design work was facilitated using Keysight ADS software. Operating in the frequency range of 1.5 GHz to 2 GHz, the amplifier demonstrates a maximum output power of approximately 40 dBm and a power gain of 16 dB, with a power-added efficiency exceeding 70%.