publications

2022

1. 

   Autonomous UAV Navigation via Deep Reinforcement Learning Using PPO

   Bilal Kabas

   In 2022 30th Signal Processing and Communications Applications Conference (SIU), 2022

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   In this paper, a computer vision-based navigation system is proposed for autonomous unmanned aerial vehicles (UAV). The proposed navigation system is based on a deep reinforcement learning-based high-level controller. In this paper, proximal policy optimization (PPO), which is a deep reinforcement learning method, is used to train the artificial neural network in an end-to-end way using a continuous reward function. The proposed method has been tested on images obtained from different modalities (RGB and depth) in simulation environments that are created using Unreal Engine and Microsoft AirSim. For the navigation problem that this work is concerned with, a success rate of 96% has been obtained by using RGB cameras. Since RGB cameras are lighter than depth cameras and the trained artificial neural network has a parameter number less than 170.000, the proposed method is suitable to be deployed in micro aerial vehicles.

2. 

   Design optimization of a three-phase transformer using finite element analysis

   Ahmet Furkan Hacan, Bilal Kabas, and Samet Oguten

   arXiv preprint arXiv:2201.11769, 2022

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   Optimization of design parameters of a transformer is a crucial task to increase efficiency and lower the material cost. This research presents an approach to model a three-phase transformer and optimize design parameters to minimize the volume and loss. ANSYS Maxwell 2D is used to model the transformer and analyze it for different design parameters. The multi-objective differential evolution algorithm is used to find optimum design parameters that minimize the volume and loss. In this paper, we present the optimum design parameters for a 1 kVA transformer with a particular input and output voltage specification. The transformer with these optimum design parameters is then tested for different loading conditions and power factor values. The results show that the maximum efficiency is obtained for 75% loading condition with unity power factor. As the power factor decreases, the efficiency decreases as well.

2021

1. 

   PID Controller Optimization for Low-cost Line Follower Robots

   Samet Oguten, and Bilal Kabas

   arXiv preprint arXiv:2111.04149, 2021

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   In this paper, modification of the classical PID controller and development of open-loop control mechanisms to improve stability and robustness of a differential wheeled robot are discussed. To deploy the algorithm, a test platform has been constructed using low-cost and off-the-shelf components including a microcontroller, reflectance sensor, and motor driver. This paper describes the heuristic approach used in the identification of the system specifications as well as the optimization of the controller. The PID controller is analyzed in detail and the effect of each term is explained in the context of stability. Lastly, the challenges encountered during the development of controller and robot are discussed.

2. 

   Optimization of QPSK-Based Digital Communication Systems Using CAE Compression and CNN Denoising

   Bilal Kabas, and Samet Oguten

   2021

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   In this research, deep learning methods are used for optimization of QPSK-based communication systems. We propose a convolutional autoencoder based lossy image compression technique to increase data rate. We also propose a ResNet classifier to analyze the performance of the CAE compressor. This research also presents a CNN denoiser architecture for removing additive white Gaussian noise (AWGN) from images. In this research, we conducted performance analysis of filters by using PSNR (peak-signal-to-noise-ratio) and SSIM (structural similarity index measure) evaluation metrics. Experimental results demonstrate that our CNN denoiser model outperforms classical image filtering techniques which are Gaussian and Wiener filtering based on SSIM values. Our QPSK implementation includes root-raised-cosine filtering for pulse shaping and matched filtering to improve bit error ratio as well as to minimize intersymbol interference (ISI). It also has an adaptive equalizer filter to eliminate the ISI caused by the filtering effect of the communication channel. The code for this research is available on GitHub: github.com/bilalkabas/QPSK-with-CAE-Compressor-and-CNN-Denoiser.

2020

1. 

   A Comparative Analysis of an XOR-XOR Based 18-Transistor Full Adder Cell

   Bilal Kabas

   2020

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   This research presents a comparative analysis between an XOR-XOR based full adder cell and a fully complementary CMOS circuit. The two full adders have been reviewed and the design differences have been discussed. Subsequently, both circuits have been simulated in LTSpice by using model parameters for BS170 and BS250 MOS transistors. The aim of the research is to reveal the performances of the circuits through delay and power dissipation analyses. In order to get more precise results, LTSpice commands have been used to measure both propagation delays and power dissipations. The analysis has been conducted for 16 different input combinations in both circuits. The proposed full adder has been compared with the fully complementary CMOS circuit. The results show that the standard circuit has lower propagation delays and lower power dissipation levels than that of the proposed circuit. However, it has been found that the main reason of the unsatisfactory performance of the proposed circuit is the pull-down resistors that are added to the circuit as a part of necessary modifications.