A chaos-based pseudo-random bit generator implemented in FPGA device

Dąbal, P.; Pełka, R.

doi:10.1109/ddecs.2011.5783069

Cited by 36 publications

(23 citation statements)

References 10 publications

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“…This is typically achieved by performing a singular value decomposition (SVD) of the targeted matrix, being this last the preferred method for the computation of the pseudoinverse of a generic matrix due to its accuracy and computational efficiency. However, different works have demonstrated that performing the SVD of a matrix inside a FPGA, although possible, consumes a very noticeable amount of its logic and memory resources even at a low processing speed [26], [27]. Hence, in this work, we have decided to estimate the noise at each originally sampled spectral band by following a different procedure than directly computing the pseudoinverse indicated at (2).…”

Section: A Implementation Of the Noise Estimationmentioning

confidence: 97%

FPGA Implementation of the HySime Algorithm for the Determination of the Number of Endmembers in Hyperspectral Data

González

López

Mozos

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. It amounts the identification of pure spectral signatures (endmembers) in the data, and the estimation of the abundance of each endmember in each (possibly mixed) pixel. A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. One of the most popular and widely used techniques for this purpose is the HySime algorithm but, due to the complexity and high dimensionality of hyperspectral scenes, this technique is computational expensive. Reconfigurable field-programmable gate arrays (FPGAs) are promising platforms that allow hardware/software codesign and the potential to provide powerful onboard computing capabilities and flexibility at the same time. In this paper, we present the first FPGA design for the HySime algorithm. Our system includes a direct memory access (DMA) module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-7 XC7VX690T FPGA and tested using real hyperspectral data collected by NASAs airborne visible infrared imaging spectrometer (AVIRIS) over the Cuprite mining district in Nevada and the World trade center (WTC) in New York.Experimental results demonstrate that our hardware version of the HySime algorithm can significantly outperform a software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing.Index Terms-Field-programmable gate arrays (FPGAs), hyperspectral imaging, HySime, number of endmembers estimation, reconfigurable hardware.

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Section: A Implementation Of the Noise Estimationmentioning

confidence: 97%

FPGA Implementation of the HySime Algorithm for the Determination of the Number of Endmembers in Hyperspectral Data

González

López

Mozos

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Logistic map [22] has been used in many pseudo-random number generators [10,11,16,20]. The basic description of this map is given by the iterative relationship: where 0 x n 1, 0 < r 4, n = 1, 2, 3 ….…”

Section: A Chaotic Pseudo-random Number Generator With the Logistic mentioning

confidence: 99%

“…Due to the well known disadvantages of TRNGs (relatively low output bit rate, complex design and vulnerability to external synchronization) very often true random sequences are replaced by pseudorandom numbers produced by pseudo-random number generators (PRNGs). Most common principles for generation of pseudo-random sequences use linear feedback shift registers (LFSR) [7], modulo m operations (linear congruential generators -LCG) [8], or nonlinear chaotic mappings [9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

An efficient post-processing method for pipelined pseudo-random number generator in SoC-FPGA

Dąbal

Pełka

2015

2015 22nd International Conference Mixed Design of Integrated Circuits &Amp; Systems (MIXDES)

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Pseudo-random number generators (PRNGs) are one of the common parts of digital systems used in cryptography, diagnostics, simulation and in many other areas of modern science and technology. Here we present a novel architecture of the PRNG based on the chaotic nonlinear model and pipelined data processing. A significant enhancement in terms of output throughput has been achieved by combining the advantages of pipelining with post-processing based on fast logical operations like bit shifting and XOR. The proposed method has been implemented using programmable SoC Zynq device from Xilinx and verified by standard statistical tests NIST SP800-22. For PRNGs based on the logistic chaotic map and frequency dependent negative resistance (FDNR) we obtained speed-up factors equal to 33% and 14%, respectively. We also present detailed comparison of the proposed post-processing method with the methods reported previously by the other authors. In particular, we compared the maximum output throughput and amount of total logical resources required by PRNG implementation in the programmable SoC device. The maximum output throughput of the proposed PRNG is equal to 38.44 Gbps and is significantly greater comparing to the chaotic PRNGs described so far.

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“…The distinct properties of chaos, such as diffusion and confusion can, in principle, ensure pseudo-random numbers of high-quality, mainly because of the sensitivity to initial conditions, ergodicity, mixing properties, complex numerical patterns, relatively simple equations and their determinism [3,5,39,28]. Therefore, significant progress has been reported, for instance chaos-based PRNGs were proposed using chaotic systems [35,10,7,33,19,34,13,38], cellular automata [47,25,46], quantum chaotic maps [1,2,53] and even mixing the physical chaotic sources, e.g. chaotic semiconductor lasers [48,21].…”

Section: Introductionmentioning

confidence: 99%

Improving the pseudo-randomness properties of chaotic maps using deep-zoom

Machicao

Bruno

2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

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A generalized method is proposed to compose new orbits from a given chaotic map. The method provides an approach to examine discrete-time chaotic maps in a "deep-zoom" manner by using k-digits to the right from the decimal separator of a given point from the underlying chaotic map. Interesting phenomena have been identified. Rapid randomization was observed, i.e., chaotic patterns tend to become indistinguishable when compared to the original orbits of the underlying chaotic map. Our results were presented using different graphical analyses (i.e., time-evolution, bifurcation diagram, Lyapunov exponent, Poincaré diagram, and frequency distribution). Moreover, taking advantage of this randomization improvement, we propose a Pseudo-Random Number Generator (PRNG) based on the k-logistic map. The pseudo-random qualities of the proposed PRNG passed both tests successfully, i.e., DIEHARD and NIST, and were comparable with other traditional PRNGs such as the Mersenne Twister. The results suggest that simple maps such as the logistic map can be considered as good PRNG methods.

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A chaos-based pseudo-random bit generator implemented in FPGA device

Cited by 36 publications

References 10 publications

FPGA Implementation of the HySime Algorithm for the Determination of the Number of Endmembers in Hyperspectral Data

FPGA Implementation of the HySime Algorithm for the Determination of the Number of Endmembers in Hyperspectral Data

An efficient post-processing method for pipelined pseudo-random number generator in SoC-FPGA

Improving the pseudo-randomness properties of chaotic maps using deep-zoom

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