Brain tumors diagnosis in children is a scientific concern due to rapid anatomical, metabolic, and functional changes arising in the brain and non-specific or conflicting imaging results. Pediatric brain tumors diagnosis is typically centralized in clinical practice on the basis of diagnostic clues such as, child age, tumor location and incidence, clinical history, and imaging (Magnetic resonance imaging MRI / computed tomography CT) findings. The implementation of deep learning has rapidly propagated in almost every field in recent years, particularly in the medical images’ evaluation. This review would only address critical deep learning issues specific to pediatric brain tumor imaging research in view of the vast spectrum of other applications of deep learning. The purpose of this review paper is to include a detailed summary by first providing a succinct guide to the types of pediatric brain tumors and pediatric brain tumor imaging techniques. Then, we will present the research carried out by summarizing the scientific contributions to the field of pediatric brain tumor imaging processing and analysis. Finally, to establish open research issues and guidance for potential study in this emerging area, the medical and technical limitations of the deep learning-based approach were included.

We present a realization of a didactic robot environment for robot PUMA 560 for educational and research purposes. Robot PUMA 560 is probably the mathematically best-described robot, and therefore it is frequently used for research and educational purposes. A developed control environment consists of a robot controller and teach pendant. The advantage of using a personally developed solution is its open structure, which allows various tests and measurements to be performed, and that is highly convenient for educational and research purposes. The motivation behind the design of this personal didactic robot control environment arose from a survey for students after the first Summer School on Mechatronic Systems. The student questionnaire revealed severe discrepancies between theory and practice in education. Even though the primary purpose of the new control environment for robot PUMA 560 was research, it was established that it is a viable lab resource that allows for the connection between theoretical and industrial robotics. It was used for the duration of four Summer Schools and university courses. Since then, it has been fully integrated into International Burch University’s Electrical and Electronics Engineering curriculum through several courses on the bachelor and master levels for multidisciplinary problem-based learning (PBL) projects.

In this paper we present two different, software and reconfigurable hardware, open architecture approaches to the PUMA 560 robot controller implementation, fully document them and provide the full design specification, software code and hardware description. Such solutions are necessary in today’s robotics and industry: deprecated old control units render robotic installations useless and allow no upgrades, advancements, or innovation in an inherently innovative ecosystem. For the sake of simplicity, just the first robot axis is considered. The first approach described is a PC solution with data acquisition I/O board (Humusoft MF634). This board is supported with Matlab Real-Time Windows Toolbox for real-time applications and thus whole controller was designed in Matlab environment. The second approach is a robot controller developed on field programmable gate arrays (FPGA) board. The complexity of FPGA design can be overcome by using a third party software package, such as self-developed Matlab FPGA Real Time Toolbox. In both cases, parameters of motion controller are calculated by using simulation of the PUMA 560 robot first axis motion. Simulations were conducted in Matlab/Simulink using Robotics Toolbox.