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Summary This paper presents implementation of a chaotic cellular neural network (CNN)‐based true random number generator on a field programmable gate array (FPGA) board. In this implementation, discrete time model of the chaotic CNN is used as the entropy source. Random number series are generated for three scenarios. Obtained number series are tested by using NIST 800.22 statistical test suite. Also, the scale index technique is carried out for these three scenarios to determine the degree of non‐periodicity for key stream. Copyright © 2017 John Wiley & Sons, Ltd.

COVID-19 is still a fatal disease, which has threatened all people by affecting the human lungs. Chest X-Ray or computed tomography imaging is commonly used to make a fast and reliable medical investigation to detect the COVID-19 virus. These medical images are remarkably challenging because it is a full-time job and prone to human errors. In this paper, a new normalization algorithm that consists of Mean–Variance-Softmax-Rescale (MVSR) processes respectively is proposed to provide facilitation pre-assessment and diagnosis Covid-19 disease. In order to show the effect of MVSR normalization technique, the algorithm of proposed method is applied to chest X-ray and Sars-Cov-2 computed tomography images dataset. The normalized X-ray images with MVSR are used to recognize Covid-19 virus via Convolutional Neural Network (CNN) model. At the implementation stage, the MVSR algorithm is executed on MATLAB environment, then all the arithmetic operations of the MVSR normalization are coded in VHDL with the help of fixed-point fractional number representation format on FPGA platform. The experimental platform consists of Zynq-7000 Development FPGA Board and VGA monitor to display the both original and MVSR normalized chest X-ray images. The CNN model is constructed and executed using Anaconda Navigator interface with python language. Based on the results of this study, infections of Covid-19 disease can be easily diagnosed with MVSR normalization technique. The proposed MVSR normalization technique increased the classification accuracy of the CNN model from 83.01, to 96.16% for binary class of chest X-ray images.

In this paper, success on cellular nonlinear network emulator core that generates active waves such as autowaves and traveling waves is intended. This wave computer core has 4 × 4 parallel processing units. Cellular nonlinear network in this study has 16, 384 nodes arranged in 128 × 128 normal grid form. The evolution algorithm of the network is executed by the hardware implemented on a Xilinx XC2VP30-FF896 FPGA chip using fixed-point arithmetic. The FPGA-based platform has an on-line monitor output in order to observe active wave evolution and a host computer in order to program the network. Using fixedpoint number format rather than floating-point number format has some advantages in the sense of speed and resource usage. The implementation in this study can be adapted to observe Doppler Effect on traveling waves and autowaves.

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This study aims to represent an FPGA (Field Programmable Gate Array) design of Artificial Neural Network (ANN) for Electroencephalography (EEG) signal processing in order to detect epileptic seizure. For analyzing brain's electrical activity, feedforward ANN model is used for classification of EEG signals. The designed ANN output layer makes a decision whether the person has epilepsy or not. In the proposed system, the ANN model is programmed and simulated on Xilinx ISE editor via computer and then, EEG signal data are transferred to FPGA-based ANN emulator core. The Core is trained on data which are patient's data and healthy person's data. After training, test data is loaded to ANN Emulator Core to detect any epileptic seizure of person's EEG signal. The main advantage of FPGA in the system is to improve speed and accuracy for epileptic seizure detection.