Barış Karakaya

Chaotic cellular neural network‐based true random number generator

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

Wave computer core using fixed-point arithmetic

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 fixed-point 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.

FPGA-based digital Filter Design for Biomedical Signal

Over the last years, much effort has been made toward developing computerized methods to detect seizures in patients by scientists. This paper is to present a Field Programmable Gate Array (FPGA)-based Fir Filter Design for Biomedical signal processing. The Electroencephalography (EEG) has been the most dependable tool used for biomedical signal processing. The digital filter is used to filter discrete time signals with the ability to modify the frequency response of the filter at any time and it is used many application such as data compression, biomedical signal processing, communication receivers, etc. The Finite Impulse Response (FIR) filter has many advantages on signal processing such that it has a simple structure, stationary response and adaptivity with embedded microprocessors. FIR filter is proposed due to facilitate structural characteristic and design properties on filtering EEG signal. The system has a computer where the design can be programmed and simulated on Xilinx FPGA ISE editor. The focus of this study is to compute FIR filter coefficients on MATLAB Filter Design Toolbox and then transfer them to embedded design on FPGA to filter EEG signal.