Cilt:64 Sayı:01 (2022)

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https://dspace.ankara.edu.tr/handle/20.500.12575/86465

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Browsing Cilt:64 Sayı:01 (2022) by Author "Elektrik-Elektronik Mühendisliği"

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    A study on modeling growth model of Adana pigeons
    (Ankara Üniversitesi Mühendislik Fakültesi, 2022) Özbek, Levent; Elektrik-Elektronik Mühendisliği; Mühendislik Fakültesi
    The study aims to determine a mathematical model that can be used to describe the growth of the Adana pigeon. Since pigeons have only one breeding season, just one or two pairs of baby pigeons are raised per year. Hatchlings sometimes die before reaching adulthood. For this reason, measurements can be taken for 10, 15 and 60 days periods. Related with this issue, only 43-days measurements of 68 pigeons are used over a 6-year period. The study is modelled by taking the day-to-day average of the data (43 days) of 68 pigeons. The study was conducted on 68 Adana pigeons in the interval between the age of 1 and 43 days. The growth of pigeon cub was measured by daily live weight until 1 to 43 days. The estimation is carried out by writing the specific Matlab codes. Classical growth functions used in animals are in nonlinear form. Various numerical methods have been developed to estimate parameters in nonlinear functions. Special program routines have been developed to implement these methods. In these nonlinear models, there are more than one parameter to be estimated. Therefore, the number of mathematical operations in estimating the parameters is large. The most used models in the literature are Brody, Bertalanffy, Logistic, Generalized Logistic, Gompertz, Richards, Negative Exponential, Stevens, and Tanaka. However, as far as is known, there is no published article for Adana pigeons that uses all of these models and compares which one is better. These models are Brody, Bertalanffy, Logistic, Generalized Logistic, Gompertz, Richards, Negative Exponential, Stevens, and Tanaka. The best analysis was done by the Richards model in terms of both the Mean Squared Error (MSE), mean absolute percentage error (MAPE) and (Coefficient of Determination) R2 .
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    Determination of the surface topography in rill erosion by imaging techniques
    (Ankara Üniversitesi Mühendislik Fakültesi, 2022) Erdoğan, Kürşad; Elektrik-Elektronik Mühendisliği; Mühendislik Fakültesi
    Soil erosion, mainly occurring in agricultural areas, is an economic and ecological problem that can happen anywhere. Swelling and transport of soil particles reduce the productivity of agricultural lands. Soil surface analysis and soil-water interaction are essential topics in agricultural research and engineering as they affect the risk of soil erosion. Erosion affects the upper soil layers rich in organic matter. After the transport of this topsoil, the subsoil with a more compact structure emerges. In this case, the cultivation of the soil becomes complex, and agricultural productivity is adversely affected. Different techniques have been used to analyze the effects of erosion. In this study, we focused on rill erosion, one of the types. An electronic imaging system has been designed using the Microsoft Kinect Sensor and Raspberry Pi, which can be found quickly and at a low cost during operation. The software has been developed to extract the surface topography by analyzing the depth images of rill erosion obtained with this system. Measurements were taken using eight types of flow rates on four soil types. As a result of the experimental findings, it has been seen that volume changes of 1.3812 mm3 can be detected as a unit with the Kinect Sensor placed at a distance of 70 cm.
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    Evaluation of the impact of I/Q imbalance compensation on communication performance
    (Ankara Üniversitesi Mühendislik Fakültesi, 2022) Sağır, Gizem Eda; Elektrik-Elektronik Mühendisliği; Mühendislik Fakültesi
    Quadrature mixing is widely used in wireless communication receivers since it provides a solution for image signal problem with low-cost implementations. Image signal is caused by phase and amplitude mismatches between in-phase (I) and quadrature (Q) paths of the receiver. This problem is known as I/Q imbalance and degrades the communication performance if not compensated. In this study, the impact of I/Q imbalance compensation on wireless communication performance is evaluated through experiments and simulations. Simulation results demonstrate that significant communication performance improvement can be achieved in terms of bit error rate (BER) and symbol error rate (SER) by compensating the I/Q imbalance properly. In the experiments, compensation is applied to the signals captured using a software defined radio with zero-IF architecture. Experimental results demonstrate that wireless transmission success rate for the zero-IF receiver is increased by compensating I/Q imbalance.
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    The effects of DC offset in direct-conversion receivers for WLAN systems
    (Ankara Üniversitesi Mühendislik Fakültesi, 2022) Taşçıoğlu, Selçuk; Elektrik-Elektronik Mühendisliği; Mühendislik Fakültesi
    In this paper, the effects DC offset in direct-conversion receivers for WLAN systems are analyzed using both experimental and simulation data. DC offset estimation is performed by using data-aided methods which are based on the short training sequence of WLAN preamble. In the simulations, DC offset and frequency offset estimations are carried out on the signals affected by frequency selective Rayleigh fading channel and additive white Gaussian noise. Estimation performance of the methods is compared for different SNR levels and frequency offset values in terms of mean square error. Experimental data which is in the form of WLAN packets and transmitted through a wireless channel is captured by using a software defined radio. The experimental performance of the DC offset compensation methods is evaluated in terms of transmission success ratio.
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    Utilization of deep learning architectures for MIMO detection
    (Ankara Üniversitesi Mühendislik Fakültesi, 2022) Karahan, Sümeyye Nur; Elektrik-Elektronik Mühendisliği; Mühendislik Fakültesi
    Applications of deep learning in communications systems are becoming popular today with their powerful solutions to complex problems. This study considers the utilization of deep learning detectors for small-scale multiple-input multiple-output systems. Deep neural network, long short-term memory, and one-dimenisonal convolutional neural network architectures are discussed and the bit error rate performances of these deep learning based detectors are compared with the optimal maximum likelihood and sub-optimal minimum mean square error detectors. Simulation results show that the deep neural network architecture has the best detection performance among the discussed deep learning detectors and may outperform the sub-optimal minimum mean square error detector. For small-scale multiple-input multiple-output systems, the performance of the deep learning based detector is close to that of the optimal detector.