Aircrew of a modern combat aircraft is exposed to hazardous effects of high G-forces and high angular velocities. In order to protect human lives and to avoid material losses, High-G training in human centrifuge is used to artificially increase accelerative force under controlled conditions. In this paper, centrifuge is modeled as 3 DoF revolute robot manipulator with planetary, roll and pitch axis. Pilot seat is controlled as last unit of robot manipulator. Control system is obtained by adding new functionalities for flight simulation in control unit for robot programming previously developed and by implementing solutions for the kinematics and dynamics of this robot. Standard open-loop flight simulations imply predefined profiles of absolute acceleration in the gondola of the centrifuge. It is necessary to develop algorithms for controlling this acceleration. An algorithm for calculating the angular velocity of main arm of human centrifuge which provides predefined profile of absolute acceleration in gondola of centrifuge, where the onset rate of absolute acceleration is constant, is developed. As a solution of nonlinear differential equation, Jacobi elliptic function appears. Results obtained by developed algorithm are compared with results obtained by simulation in Simulink.

Robot control systems are usually complex systems whose users must be well trained to use them. Also, control process is mainly carried out near the robot or by using wired connections. There is a need for a solution that can provide convenient and intuitive robot control with user’s location independence, easy adjustment and simultaneously monitoring of robot motion tasks. Android devices are powerful mobile devices with open architecture and permanently Internet connection that can be applied to resolving those issues. This paper presents a system for remote monitoring and control of industrial robots based on Android device and Wi-Fi communication which provides intuitive robot control at a great distance with simultaneously monitoring of robot motions by observing its 3D model movement or trajectory path. Simple definition of new motion tasks and their redefinition is provided through the so called “speed dial” mode and manual robot guiding is provided through the manual control mode. Proposed solution simplifies an interaction between the human and the industrial robot in the case of robot motion control and tracking at remote location.

Designing electrical circuits and devices is usually based on expertise in electronics and the thorough use of numerical software tools. This procedure can be time-consuming, and the designer has only one solution. This paper introduces a new approach focused on new concept design and optimization of specific circuits using symbolic expressions. The primary amplifier circuit, realized by a deep learning module, changes the value to reduce power dissipation. The control signal of the deep learning module is output from an amplifier that depends on the statistics of the input signal value.

In the process of defining commands and developing algorithms for movements of complex and massive devices, such as human centrifuge, it is unsafe and demanding to perform testing on the real machine. Getting overview of devices operations might be of great importance especially before the final realization. This paper presents a system for programming and simulation of the human centrifuge motion with integrated 3D motion simulator. The simulator is used for verification of newly defined motion commands and testing the new algorithms for the human centrifuge motion.

The smart visualization of medical images (SVMI) model is based on multi-detector computed tomography (MDCT) data sets and can provide a clearer view of changes in the brain, such as tumors (expansive changes), bleeding, and ischemia on native imaging (i.e., a non-contrast MDCT scan). The new SVMI method provides a more precise representation of the brain image by hiding pixels that are not carrying information and rescaling and coloring the range of pixels essential for detecting and visualizing the disease. In addition, SVMI can be used to avoid the additional exposure of patients to ionizing radiation, which can lead to the occurrence of allergic reactions due to the contrast media administration. Results of the SVMI model were compared with the final diagnosis of the disease after additional diagnostics and confirmation by neuroradiologists, who are highly trained physicians with many years of experience. The application of the realized and presented SVMI model can optimize the engagement of material, medical, and human resources and has the potential for general application in medical training, education, and clinical research.