Demodulating the modulated signals used in digital communication on the receiver side is necessary in terms of communication. The currently used systems are systems with a variety of hardware. These systems are used separately for each type of communication signal. A single algorithm facilitates the classification and subsequent demodulation of signals without needing hardware instead of extra hardware cost and complex systems. This study, which aims to make modulation classification by using images of signals, provides this convenience. In this study, a classification and demodulation process is done by using images of digital modulation signals. Convolutional neural network (CNN), a deep learning algorithm, has been used for classification and recognition. Images of the signals of quadrate amplitude shift keying (QASK), quadrate frequency shift keying (QFSK), and quadrate phase shift keying (QPSK) digital modulation types at noise levels of 0 dB, 5 dB, 10 dB, and 15 dB were used. Thanks to this algorithm, which works without hardware, the success achieved is around 98%. Python programming language and libraries have been used in training and testing the algorithm. Demodulation processes of these signals have been performed for demodulation using the nonlinear autoregressive network with exogenous inputs (NARX) algorithm, an artificial neural network. As a result of using MATLAB, the NARX algorithm achieved approximately 94% success in obtaining the information signal. Thanks to the work done, it will be possible to classify and demodulate other communication signals without extra hardware.

In this study, a new reader has been designed to measure the amount of radiation dose detected by RadFET (pMOSFET) sensor. The designed reader calculates the voltage threshold voltage (Vth) shifts of the pMOSFET to determine radiation dose and display it on the Touch TFT LCD screen placed on the printed electronic circuit. It has been developed more in particular to be easily used in radiotherapy and other healthcare field which have radiation sources. The electronic board has also been developed to adjust and read the data for SiO2 and Er3O2 sensor structured RadFETs. The electronic card has been designed with STM32F103 series processor that has 12-bit ADC resolution. In addition, specific Bluetooth circuit has been designed for communication. Thus, dose measurements versus date graph, personal details (name, age etc.) can be sent to personal computers and devices such as smart phones and tablets. Dose measurements can be currently kept by micro SD card.

ÖzetEn önemli yenilenebilir enerji kaynaklarından birisi olan güneş enerjisinin elektrik enerjisine dönüştürülmesinde kullanılan Fotovoltaik(FV) modüller ile çevre dostu ve sınırsız enerjiye ulaşım sağlanmaktadır. FV modüllerin simülasyonları sayesinde enerji dönüşümünde ortaya çıkacak olan olumlu veya olumsuz etkilerde bulunan parametrelerin testleri yapılmaktadır. Yapılan bu çalışma ile hem gerçek ölçümler ile elde edilen veriler hem de datasheet' ten alınan verilerin Matlab/Simulink ortamında geliştirilen tek ve çift diyot modellerine uygulanarak simule edilmiştir. Karşılaştırılması yapılan tek ve çift diyot yapılarının akım -gerilim ve güçgerilim grafikleri incelenerek hava koşullarının güneş enerjisi üretimine yaptığı etkiler incelenmiştir. AbstractPhotovoltaic (PV) modules are used to convert solar energy into electrical energy, which is one of the most important renewable energy sources. Thanks to the simulations of PV modules, parameters that have positive or negative effects in energy conversion are tested. In this study, both the data obtained by real measurements and the data obtained from the datasheet were simulated by applying to single and double diode models developed in Matlab / Simulink environment. Current -voltage and powervoltage graphs of single and double diode structures were compared and effects of weather conditions on solar energy production were investigated.

The Pt-doped SnO2 thin film detector sensitivities for different gases including the propane, carbon dioxide, acetone, and oxygen have been investigated incorporating the structural evolution of the thin film. The crystallographic structure of the SnO2 layer significantly varied with increasing the Pt concentration and grain size of the film decrease with Pt content. The highest gas sensitivity of the films is exhibited for the oxygen gases. In addition, the oxygen sensitivity of the sensors increases with the Pt concentration up to a specific operating temperature. This variation may be due to the different contributions of the spillover and Fermi energy control mechanisms to sensor sensitivities. The present results have depicted that the sensor design should be carefully configured to promote the sensing responses of the gas sensors.