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.

Filtering of unwanted frequencies represents the main aspect of digital signal processing (DSP) in any modern communication system. The main role of the filter is to perform attenuation of certain frequencies and pass only frequencies of interest. In a DSP system, sampled or discrete-time signals are processed by digital filters using different mathematical operations. Digital filters are commonly categorized as Finite Impulse Response (FIR) and Infinite Impulse Response (IIR). This research focuses on the full VHDL implementation of digital second-order lowpass IIR filter for reducing the noisy frequencies on the FPGA board. The initial step is to determine, from continuous time domain function, the transfer function in the complex {s} domain, then map transfer function in complex {z} domain and finally calculate the difference equation in discrete-time domain of the system with adequate coefficients. Prior to the FPGA implementation, the IIR filter is tested in MATLAB using a signal with mixed frequencies and signal with randomly generated noise. The digital implementation is completed by using fixed-point binary vectors and clocked processes.

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.

Quality of measurement results and their accuracy in not neccessary to be high in conventional systems, approximate results are enough to know exactly what happens in the system. However, with increased penetration of renewable sources in the grid, need for high quality and precise measurements has risen. In order to have precise information about power quality, good knowledge of the behaviour of power quality analyzers is needed. Good knowledge can only be obtained by calibration of those meters. This paper describes start of development of a fully traceable power quality reference setup for the calibration of power quality analyzers based on digital sampling of voltage/current signals, and shown preliminary results.

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.

In this paper, the Incremental Conductance maximum power point tracking (MPPT) algorithm is evaluated using an experimental setup consisting of two 75W photovoltaic (PV) panels connected in series. Humusoft MF 634 board is used to obtain and produce signals. The model was tested under changing solar irradiance conditions, and the acquired results show that it is able to respond to these changes appropriately.

Over the course of the last decade, the subfield of artificial intelligence, called deep learning, becomes the main technology that provides breakthroughs in the computer vision area. Likewise, deep learning algorithms made a major impact in the automated driving domain. This research aims to apply and evaluate the performance of two pre-trained deep learning algorithms in order to recognize different street objects. Both RCNN, as well as YOLO algorithms, are used to recognize bikes, cars and pedestrians using the public GRAZ-02 dataset composed of 1476 raw images of street objects. Accuracy greater than 90% is achieved in recognizing all considered objects. The fine-tuning and training of both algorithms is established using databases named ImageNet and COCO, and afterwards, trained models are tried on the test data.

In this paper, two approaches are evaluated using the Full Error Detection and Correction (FEDC) method for a pipelined structure. The approaches are referred to as Full Duplication with Comparison...

Assessment of skeletal maturity is typical strategy applied in clinical pediatrics today. The main goal of a Bone Age Assessment (BAA) is to determine endocrinology and growth disorders by comparing the bone and chronological age of the patient. Several methods are developed to determine skeletal maturity, but Greulich-Pyle and Tanner-Whitehouse represent the two most common methods that involve left hand and wrist radiographs. However, these methods are extremely time-dependent and rely on an experienced radiologist, who further evaluates bone age using hand atlas as a reference. In this paper, VGG-16 and ResNet50 are two Deep Convolutional Neural Network (DCNN) models applied with ImageNet pre-trained weights in order to estimate correct bone age and achieve high accuracy of gender prediction using public RSNA dataset that includes 12611 radiographs. The experimental results show month discrepancy of approximately eight months and 82% accuracy during the process of gender classification.