The use of unmanned aerial vehicles (UAV) is increasing today. UAVs can be divided into two parts, which are remote controlled and can travel automatically due to a certain battery problem. Recent research has also focused on the development and application of new algorithms to autonomously control these vehicles and determine the shortest flight paths. Together with these researches, UAVs are used in many civil activities such as weather forecasts, environmental studies and traffic control. Three-dimensional (3D) path planning is an important issue for autonomously moving UAVs. The shortest path for Unmanned Aerial Vehicles (UAV) is determined by using two-dimensional (2D) path planning algorithms using the obstacles in the environment, and allows UAVs to perform their environmental tasks as soon as possible. The purpose of this study is to determine the shortest path to the target point and avoiding obstacles for UAVs using the Dijkstra algorithm. It was developed to evaluate the arrival time of the UAVs in the path planning algorithm with the simulation performed in the MATLAB program. In this study, the obstacles were defined for the purpose of the building with different heights and different widths and 2D and 3D models were carried out, assuming that the UAV flies at certain heights. In addition, the flight of the UAVs in the route planning determined in the real applications was carried out and the data such as battery consumption, amount of battery spent, speed, amount of travel were examined.

In recent years, significant advancements in artificial intelligence, particularly in the field of deep learning, have increasingly been integrated into agricultural applications, including critical processes such as disease detection. Tomato, being one of the most widely consumed agricultural products globally and highly susceptible to a variety of fungal, bacterial, and viral pathogens, remains a prominent focus in disease detection research. In this study, we propose a deep learning-based approach for the detection of tomato diseases, a critical challenge in agriculture due to the crop’s vulnerability to fungal, bacterial, and viral pathogens. We constructed an original dataset comprising 6414 images captured under real production conditions, categorized into three image types: leaves, green tomatoes, and red tomatoes. The dataset includes five classes: healthy samples, late blight, early blight, gray mold, and bacterial cancer. Twenty-one deep learning models were evaluated, and the top five performers (EfficientNet-b0, NasNet-Large, ResNet-50, DenseNet-201, and Places365-GoogLeNet) were selected for feature extraction. From each model, 1000 deep features were extracted, and feature selection was conducted using MRMR, Chi-Square (Chi²), and ReliefF methods. The top 100 features from each selection technique were then used for reclassification with traditional machine learning classifiers under five-fold cross-validation. The highest test accuracy of 92.0% was achieved with EfficientNet-b0 features, Chi² selection, and the Fine KNN classifier. EfficientNet-b0 consistently outperformed other models, while the combination of NasNet-Large and Wide Neural Network yielded the lowest performance. These results demonstrate the effectiveness of combining deep learning-based feature extraction with traditional classifiers and feature selection techniques for robust detection of tomato diseases in real-world agricultural environments.

Transportation vehicles refer to the vehicles used to transport people's needs and goods from one area to another. Intelligent traffic signalling systems are needed due to the increase in the number of vehicles in traffic, the roads used, the insufficiency of the surrounding areas and the inability to widen these roads or construct upper / lower roads. Nowadays, there are situations where traffic control is insufficient in current density due to the traffic system mechanisms being dependent on fixed time. Therefore, some smart methods have been developed to prevent waste of time and economic problems. In this study, application of Petri Nets (PA) based on real data collected at certain times of the day for intelligent control of traffic lights at a four-way intersection. PA's performances have been compared with their classical (fixed time) performances. When the results obtained were evaluated in terms of both the number of passing vehicles and time, it was seen that the PA passed 849 vehicles in 755 seconds, 341 vehicles in 920 seconds in the classical method and a better result was obtained with PA.

In the present study, the inertia moment and friction coefficient of direct current (DC) and permanent magnet synchronous (PMS) motors were calculated. Retardation experiment was carried out to calculate these values, and current, voltage and rotation values of the motor were calculated with the help of sensors. The measurement results were read with the help of a card designed using dsPIC30F4013, a high-performance digital signal controller. Power and torque values were calculated using the data transferred into the computer. Graphs showing power-velocity and velocity-time were drawn through the use of the values that were measured and calculated. Power-time graph was obtained by using these two graphs. To be able to carry out numerical procedures using the power-time graph created, curve fitting method in MATLAB was used, and mathematical expression of the graph was obtained. The kinetic energy stored in the motor was calculated using the mathematical expression mentioned, and the inertia moment was found using this value. The friction coefficient was calculated using the torque equation for stationary running of the motor at rated speed. The results of the simulation carried out in MATLAB by using the calculated coefficients and those of the experiment were compared.

This study comprehensively addresses the design and modeling processes of autonomous underwater vehicles (AUVs). The research examines the fundamental design principles that affect underwater vehicle performance, focusing on factors such as hydrodynamic efficiency, structural durability, buoyancy control, and stability. Considering the harsh marine environment, the selection of materials was thoroughly discussed based on criteria such as mechanical properties, corrosion resistance, and weight optimization. The production stages, from 3D modeling to manufacturing, were explained step by step. Engineering challenges encountered during the design process—such as waterproofing, stability, and balance—were emphasized, and solution approaches to these problems were presented. Computational Fluid Dynamics (CFD) analyses were employed to evaluate the hydrodynamic performance of the vehicle and to optimize the body geometry. Finally, based on the analysis results and design iterations, solution strategies and development suggestions were proposed, aiming to guide future studies in the development of autonomous underwater vehicles.

Researchers and industry are seeking to manage energy better by increasing the efficiency of the energy system because of the increasing importance of energy in recent years. For this reason, a shift towards alternative energy sources to provide electricity has begun. As for alternative energy sources, interest in renewable energy sources is increasing day by day. Solar, wind and thermal sources are the most prominent renewable energy sources. Today we use of thermal sources in greenhouse, fish breeding, thermal facilities, city heating and electricity production. Nowadays, it is known that when we move thermal water from the source to the place to be used, it can be converted into electrical energy by utilizing the heat it emits around it. In this study, the effects of the type of the material and the placement of the thermoelectric generator (TEG) on the efficiency of two Thermoelectric Modules (TEM) were investigated by using thermal sources from renewable energy sources. Hot and cold water was passed through the sandwich blocks which were made from Copper, Aluminum and Brass materials, and the power produced in TEG was analyzed. As TEG, TEC1-12706 and TEC1-12710 materials were used. As a result of experimental studies, it is observed that the type of the material that conveys thermal water and the order of transmission of hot/cold water through sandwich blocks have an effect on the power produced by both TEC1-12706 and TEC1-12710