The people who use inner city public transportation vehicles want to get information about the current status of the public transportation vehicles and they want to know the travel time of the vehicles both while travelling and waiting at the bus stops.In this study, a smart bus stop-passenger information system was developed in order to enable administers effectively monitor the public transportation system and also enable the people who utilize this system simultaneously observe the information about the location and status of those vehicles. In the designed system, the embedded mini-computer based systems and digital monitors were used in order to instantly present the information related to the travel and transportation in the public transportation vehicles. The instant movement information of the vehicle was transferred to the central server through a GPS module which functions integrated to the embedded computer systems and web services. Moreover, the embedded mini-computer based systems and digital monitors were installed to the bus stops in order to present the information related to the movements of the public transportation vehicle and their approach to the related bus-stop. The mini-computers embedded on the bus stops provide communication with the central server through web services and the bus stops, public transportation vehicles and central server formed information network of the transportation. The software developed to manage the system provided the authorities the advantages of instant status observation, remote-informing and updating related to the management of the status and travel of the public transportation vehicles. Through this developed system, moreover, it was ensured that the position and travel information of the vehicles through the monitors both inside the public transportation vehicles and at the bus stops, increase the life qualities of the people who use the public transport vehicles and facilitate their urban life cycles.
In this study, the electromechanical control system of a photovoltaic (PV) panel tracking the sun on the axis it moves along according to its azimuthal angle was designed and implemented. In this system, Programmable Logic Controls (PLC) were used instead of photosensors, which are widely used for tracking the sun. The azimuthal angle of the sun from sunrise to sunset times was calculated for each day of the year at 37.6 degrees latitude in the Northern hemisphere, the location of the city where the experiment was conducted. According to this azimuth angle, the required analog signal was taken from the PLC analog module and sent to the actuator motor, which controlled the position of the panel to ensure that the rays fall vertically on the panel.After the mechanical control system of the system was started, the performance measurements of the solar panel were carried out. For this, the necessary measurements were implemented when the solar panel was in a fixed position. Afterwards, the panel was moved on a single axis according to the azimuthal angle and the necessary measurements were performed. The values obtained from the measurements were compared and the necessary evaluations were conducted.
Deep learning (DL) based localization and Simultaneous Localization and Mapping (SLAM) has recently gained considerable attention demonstrating remarkable results. Instead of constructing handcrafted algorithms through geometric theories, DL based solutions provide a data-driven solution to the problem. Taking advantage of large amounts of training data and computing capacity, these approaches are increasingly developing into a new field that offers accurate and robust localization systems. In this work, the problem of global localization for unmanned aerial vehicles (UAVs) is analyzed by proposing a sequential, end-to-end, and multimodal deep neural network based monocular visual-inertial localization framework. More specifically, the proposed neural network architecture is threefold ; a visual feature extractor convNet network, a small IMU integrator bi-directional long short-term memory (LSTM), and a global pose regressor bi-directional LSTM network for pose estimation. In addition, by fusing the traditional IMU filtering methods instead of LSTM with the convNet, a more time-efficient deep pose estimation framework is presented. It is worth pointing out that the focus in this study is to evaluate the precision and efficiency of visual-inertial (VI) based localization approaches concerning indoor scenarios. The proposed deep global localization is compared with the various state-of-the-art algorithms on indoor UAV datasets, simulation environments and real-world drone experiments in terms of accuracy and time-efficiency. In addition, the comparison of IMU-LSTM and IMU-Filter based pose estimators is also provided by a detailed analysis. Experimental results show that the proposed filter-based approach combined with a DL approach has promising performance in terms of accuracy and time efficiency in indoor localization of UAVs.
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