As the push for autonomous systems gains momentum, there has been a shift from automated to semi-autonomous systems, and current and future applications of artificial intelligence will revolve around the development of autonomous systems. At present, research is being performed to enable autonomous systems in trading, transportation, weapons, aircrafts, homes, drones, robots, and diagnostic imaging. Several companies such as Apple, Google, Microsoft and Tesla as well as NASA, the US Navy and the US military are investing in research on autonomous systems. Autonomous systems may involve the use of a large number of hardware and software components. While sensors are responsible for collecting data about the surroundings, machine learning and algorithms are used to generate useful information from available data and determine the action that should be taken. For most automatic and autonomous systems, it is important for the system to have the capability to detect hazardous situations and provide precise details, enabling either a human to take over or allowing the system itself to handle the situation. While an autonomous system must have sufficient knowledge or training to be able to achieve its ultimate goal, it must be capable of dynamically modifying the path to the goal based on the information obtained by fusing data from various sources or sensors. Besides, several autonomous systems, such as autonomous vehicles or autonomous trading, need fast real-time data processing in order to act accurately in real-time. Finally, the goal of all autonomous systems is to minimize human intervention. This dissertation contributes to the development of autonomous systems through applications that target different aspects of complex autonomous systems and belong to three distinct domains. It includes automatic segmentation of brain tumors, which is a step towards autonomous diagnostic imaging as well as an example of detection and exact identification of a hazardous condition. The automatic generation of high-resolution undistorted MRIs from distorted low-resolution MRIs reduces the amount of human intervention and develops a system that generates usable information from available data. On the other hand, drone self-localization, road segmentation, and extraction of road network graphs involve fusion of data from various types of sensors. Road graph extraction and drone self-localization play an import role in routing, and is crucial in the ability of an unmanned vehicle to reach its final goal. Besides, in the field of autonomous trading, we explore the use of time-series data to predict stock prices and make decisions about real-time stock transactions.
