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.

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, 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.

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.

Abstract The process of experimental identification refers to a challenging task since most of the closed-loop identification methods depend on some existing knowledge of the controlled object. Although various identification methods have been described and implemented in the past, the common issue refers to the use of nonlinear equipment such as relays that may cause an undesirable error and consequently affect the accuracy of the controlled object. The main aim of this research is to conduct experimental verification of a novel approach to linear system identification in the closed-loop feedback system. The novel approach represents an extension to the Ziegler-Nichols experiment performed in the closed-loop feedback system, but without using any additional equipment. One servomechanism composed of two motors is used as an object of identification where data acquisition is performed using a dSPACE embedded module allowing real-time reading of measurement results. The conducted experiment succeeds in locating a representative number of points in the Nyquist plot bringing only damped oscillations. Finally, the considered motor is identified as a first-order object due to the dynamic of available mechanical as well as electrical subsystems.

The regulation of functions such as respiratory or heart rate in human body as well as the control of motor movements are under the control of nervous system. As these actions and correlated tasks are directly influenced by the brain, the brain monitoring gives the possibility to differentiate the tasks, enabling at the same time the prediction of further actions. In this contribution, publicly available electroencephalography (EEG) datasets are analyzed with respect to the detection of epileptic seizure occurrence and BCI-related actions (here: cued motor imagery). For these purposes, timefrequency- based feature extraction alongside different classification methods is used. To perform the classification, Artificial Neural Network (ANN) and Support Vector Machine (SVM) are utilized and compared with previously obtained results. The feasibility of particular features for the detection of epileptic seizures and BCI-related tasks is discussed. Four different feature vectors per analyzed problem are identified. Acceptable accuracy of classification using ANN- and SVMbased classifiers is achieved using identified feature vectors.

Abstract In this paper, two methods are implemented and analyzed on a Field Programable Gate Array (FPGA) board for the design of fault-tolerant pipelined sequential and combinational circuits. Evaluated methods are named Error Detection and Partial Error Correction (EDPEC) and Full Error Detection and Correction (FEDC). The mentioned methods are based on Error Detection Logic (EDC) in the combinational circuit part combined with fault tolerant master-slave flip-flops with fault tolerant memory elements. Additional to the analysis and implementation of the methods, the enhancement to a method is proposed.

Abstract Purpose of this paper is to present the project that will use artificial intelligence and features of Altera FPGA board to imitate human. This problem was solved by using Cleverbot Google API and Altera FPGA board which has Linux system installed to run backend of our application. Frontend input and output peripherals will be used to read input from user and to present output to user. We present an implementation which has all the necessary features to be used for treating social anxiety disorder by simulating real human interaction by effectively employing such algorithms and system.