Search

In this article, a convolution of second-order random measures on a locally compact abelian groups is considered. A random measure is an analog of a stochastic process. With a newly defined (random) convolution, we construct a ring and a normed algebra of second-order random measures.

This paper is devoted to a pseudo-random number generator that generates numbers according to a known probability density function. This function has its origins in a histogram that contains the observed properties of a monitored random variable X. Using the recorded PDF and an ON/OFF model we are able to generate output states of a random variable Y, which is nearly alike distributed as the random variable X. Because the used PDF has its origins in a normed histogram function, the resulting number of all possible output states of variable Y is the same as the number of classes that are used to form the histogram. We propose this model that generates practically an unlimited set of possible output states of the random variable Y. At first a cumulative distribution function is formed from the input density function. Then the ON/OFF model driven by a random number generator in (0, 1) is applied to address classes that are defined in the distribution function. To address these classes a search function is applied. Then when a class is chosen, we propose to use a second uniform random number generator that generates numbers within the range of the selected class. The resulting output state of the variable Y is the output of the second random number generator. In this paper we apply this model on a packet generator that generates IPTV inter-departure times of an MPEG2-TS H.264 VBR based video source.

In compressed sensing and sparse representation-based pattern recognition, random projection with a dense random transform matrix is widely used for information extraction. However, the complicated structure makes dense random matrices computationally expensive and difficult in hardware implementation. This paper considers the simplification of the random projection method. First, we propose a simple random method, random combination, for information extraction to address the issues of dense random methods. The theoretical analysis and the experimental results show that it can provide comparable performance to those of dense random methods. Second, we analyze another simple random method, random choosing, and give its applicable occasions. The comparative analysis and the experimental results show that it works well in dense cases but worse in sparse cases. Third, we propose a practical method for measuring the effectiveness of the feature transform matrix in sparse representation-based pattern recognition. A matrix satisfying the Representation Residual Restricted Isometry Property can provide good recognition results.

Real random values have wide range of application in different field of computer science such as cryptography, network security and communication, computer simulation, statistical sampling, etc. In purpose of generating real random values, need for a natural noisy source refers to the main challenge where a source of noise may be reliable for using in random number generator if and only if be derived from physical environment. In this work, we address this requirement by using DNA computing concepts where the molecular motion behavior of DNA molecular provides a pure source of physical noise that may be used for generating high quality real random values. Since one of the main factor for evaluating quality of real random values refer to expectation for generating approximately same amount of 0s and 1s, in this article we model a DNA-based random number generator in sticker mode with ability of generating equal numbers of 0 and 1. After using molecular motion behavior of DNA molecular as the natural source of noise into the proposed DNA-based random number generator, the generated value were subjected to frequency, run, and serial tests which are proposed by National Institute of Standards and Technology (NIST) for randomness evaluation. Obtained result from this evaluation shows that beside the achieving high scores in run and serial tests, the values generated by our DNA-based random number generator pass frequency test with 100% success.

Linear Feedback Shift Register based Unique Random Number Generator is an enhancement of Random Number generator with the additional property that any number generated by a unique random number generator can’t be duplicated. As per users demand for not duplicated random numbers in some applications like transferring a random number over the network on the behalf of actual character of the message for security point of view, existence of unique random number generators are very essential. In this paper LFSR [1] (Linear Feedback Shift Register) is used to implement the proposed concept of unique random number generator. Using LFSR is a faster approach for generating random sequences because it requires only X-OR operations and shift registers that’s why its implementation is very easy in software as well as in hardware [2, 3]. We can easily modify the LFSR and produce different random sequences. So it is the best option for using LFSR in unique random number generator.

A random number generator produces a periodic sequence of numbers on a computer. The starting point can be random, but after it is chosen, everything else is deterministic. A random number generator produces a periodic sequence of numbers on a computer. The starting point can be random, but after it is chosen, everything else is deterministic. This paper presents the entropy and p-value tests performed on classical and quantum random number generators, in order to check the randomness of the generated output data. Both generators have been integrated in software applications reproducing asymmetrical cryptosystems, their results contributing to the generation of key material within Diffie-Hellman protocol. Can regard these tests as a first step in determining if a generator is suitable for a particular cryptographic application. At the same time, the statistical tests cannot serve as a substitute for cryptanalysis.

International audience; Generating random numbers is essential in manycryptographic applications like key generation, cryptographicprotocols for example Transport Layer Security (TLS) protocol,nonce and also in Internet for choosing TCP sequence numbers.We need generators which are able to construct large amountsof secure random numbers. To this end, True Random NumberGenerators (TRNGs) which extract randomness from physicalprocesses are usually used. The sequences generated by TRNGscannot be reproduced. However, generating random numbersby this way is time-consuming and expensive. Another way togenerate random numbers is to use deterministic random numbergenerators in which the seed is reseeded many times during thegeneration of the sequence. In this paper, we propose a newpseudo-chaotic number generator (PCNG) that produces randomnumbers. The algorithm is refreshed many times by using entropysource from Linux kernel. Results of statistical properties (i.e.Nist test, auto and cross-correlation, histogram, chi2-test andsoftware security analysis) exhibit good performance thus demonstratingthat the proposed generator can be used confidently toproduce random numbers.

In this article, we show that combining random generators by group operation improves the statistical properties of the composite. It gives an effective way of finding random generators more and more close to the uniform. Moreover, we obtain an effective estimation of the speed of convergence to the uniform generator.

Some classes of applied problems from random field theory are discussed and methods to solve them are proposed. Examples from various fields of science and technology are considered.

The requirement of physical true random number generator and the difference between the true random number and pseudo-random number are presented. The recent experiements of true ran-dom number generator, expetically, the various schemes of true random number generator based on lasers, are summarized. Also, we report the progress of the thoretical and experimental research on physical true random number generator in our group.

True random number generators are in general more secure than pseudo random number generators. In this paper, we propose a novel true random number generator which generates a 256-bit random number by computer mouse movement. It is cheap, convenient and universal for personal computers. To eliminate the effect of similar movement patterns generated by the same user, three chaos-based approaches, namely, discretized 2D chaotic map permutation, spatiotemporal chaos and “MASK” algorithm, are adopted to post-process the captured mouse movements. Random bits generated by three users are tested using NIST statistical tests. Both the spatiotemporal chaos approach and the “MASK” algorithm pass the tests successfully. However, the latter has a better performance in terms of efficiency and effectiveness and so is more practical for common personal computer applications.

A novel, simple and efficient method for the generation of Tikhonov (a.k.a. von Mises) random variates is proposed. In the proposed method, circular variates of a prescribed Tikhonov distribution pT(x;alpha,xi) are generated via the transformation of variates selected randomly, on a one-for-one basis, from a bank of K distinct Cauchy and Gaussian generators. The mutually exclusive probabilities of sampling from each of the Cauchy or Gaussian generators, as well as the variance and half-width parameters that specify the latter, are derived directly from the Cauchy, Gaussian and Tikhonov characteristic functions, all of which are either known or given in closed form. The proposed random mixture technique is extremely efficient in that a single pair of uniform random numbers is consumed in the generation of each Tikhonov (or von Mises) sample, regardless of the prescribed concentration and centrality parameters (alpha, xi), all requiring neither the rejection of samples, nor the repetitive evaluation of computationally demanding functions. Additional attractive features of the method are as follows. By construction, the first (dominant) N circular moments of Tikhonov variates generated with the proposed random mixture technique are the ones that best approximate their corresponding theoretical values, with errors measured exactly. The exact distribution of generated Tikhonov variates is determined analytically, and its (Kullback-Leibler) divergence to the exact Tikhonov PDF is shown also analytically to be negligible. Finally, the technique establishes a connection between Tikhonov and Gaussian variates which can be exploited, e.g., in the generation of piecewise-continuous pseudo-random functions with Tikhonov-distributed outcomes.

Random number generators are widely used in different applications. However, it is difficult to obtain a good random number generator in low precision and resource constrained system, such as an eight-bit micro-controller system which is still commonly used in industrial and consumer markets. This paper provides a practical solution for this problem based on chaotic systems. By the use of a modified Chua's circuit, it is demonstrated that the sampled state, after post-processing by a high-dimensional chaotic map, can be used as a random source even in an eight-bit environment. The randomness of the generated sequence is testified and confirmed by different statistical tests and the up-to-date statistical suite.

The importance of random number generators has increased over the years. This follows from the fact that contemporary research methods rely more and more on simulation and the increased importance of encryption technology. The output of a random number generator is created by either an algorithm or a physical device. The most popular method for random number generation is through the use of an algorithm. This article presents a new category of physical random bit generator that is packaged by several manufacturers. A statistical analysis of the output from the generators is given.

Random numbers generators are widely used in different fields like cryptography, gaming development, artificial intelligence, etc. Being the engine of some security (cryptographic) protocols, the development of good and secure Random Number Generators aroused the attention of the research communities worldwide. Random Number Generators split into two categories: True Random Number Generators (based on physical non-deterministic processes, like the jitter of an oscillator) and Pseudo Random Number Generators (based on mathematical properties, where the output at the time t is based somehow on the output on time t-1). It is well known that the output distribution of a Random Number Generator (either True or Pseudo) output tends toward 50%. There are some particular cases where this probability must be different (some of these situations will be described here) and this paper will present a way of obtaining this. The novelty of this paper is based on a new approach on the classic Gollmann Cascade PRNG and starting from this, the development of a FPGA implementation to increase its security.

Generalized simulated annealing is an important method for finding the global minimum of a function. However, it requires the generation of Tsallis random numbers. Here we show that a D-dimensional Tsallis random variable is a multivariate t or Student distributed random variable. Using this finding we design an exact Tsallis random number generator and, finally, present some applications of this generator to generalized simulated annealing.

The acceptance/rejection approach is widely used in universal nonuniform random number generators. Its key part is an accurate approximation of a given probability density from above by a hat function. This article uses a piecewise constant hat function, whose values are overestimates of the density on the elements of the partition of the domain. It uses a sawtooth overestimate of Lipschitz continuous densities, and then examines all local maximizers of such an overestimate. The method is applicable to multivariate multimodal distributions. It exhibits relatively short preprocessing time and fast generation of random variates from a very large class of distributions.

Knuth [The Art of Computer Programming, vol. 2: Semi-Numerical Algorithms, 3rd ed., Addison-Wesley, Reading, MA, 1997] shows that iterations of a random function perform poorly on average as a random number generator. He proposes a generalization in which the next value depends on two or more previous values. This note demonstrates, via an analysis of the cycle length of a random k-ary map, the equally poor performance of a random instance in Knuth's more general model.

Pseudo Random Number Generator (PRNG) uses a deterministic system and an initial seed to generate random numbers. In order for the output sequence to be truly random, a truly random input seed is used. Most True Random Number Generators (TRNG), use noise in the form nuclear decay, atmospheric noise, electrical noise or Brownian motion as their initial seed. In order to reduce the computational complexity, we use a simple setup of a fish tank as the variable environment, capturing its images over time. The image data is then applied to a reduction algorithm and hash function to generate the initial seed. We propose a cost efficient method of extracting the true seed from the image data and applying it to a pseudo random generator, a Linear Congruential Generator (LCG) in our case to give true random numbers.

A one-way coupled chaotic map lattice is used for generating pseudo-random numbers. It is shown that with suitable cooperative applications of both chaotic and conventional approaches, the output of the spatiotemporally chaotic system can easily meet the practical requirements of random numbers, i.e., excellent random statistical properties, long periodicity of computer realizations, and fast speed of random number generations. This pseudo-random number generator system can be used as ideal synchronous and self-synchronizing stream cipher systems for secure communications.

Summary This paper investigates a new random contraction scheme which complements the length-biasing and convolution contraction schemes considered in the literature. A random power contraction is used with order statistics, leading to new and elegant characterizations of the power distribution. In view of Rossberg's counter-example of a non-exponential law with exponentially distributed spacings of order statistics, possibly the most appealing consequence of the result is a characterization of the exponential distribution via an independent exponential shift of order statistics.

Random unique number generator can be used for generating a series of unpredictable and unrepeatable numbers within limited ranges of data and numbers. These numbers are usually distributed equally, random, independent, unpredictable and unrepeatable. A good random number generator has to be effective for a long period and has good statistical distribution and efficient generating performance. This study proposes a computational methodology to generate pseudo-random numbers based on random base polynomial, which uses less memory but generates a great deal of unrepeated pseudo-random numbers. Then this method adopts the multi-thread parallelization to effectively get the benefits of multi-core processors to accelerate the generation of a huge amount of pseudo-random numbers.

A random number generation method based on a cross coupled chaotic oscillator is introduced. Numerical model for the proposed design has been developed where bootstrap method is utilized which allows us to estimate the statistical characteristics of underlying chaotic signal. Numerical results verifying the feasibility and correct operation of the random number generator are given such that numerically generated binary sequences fulfill FIPS-140-2 statistical test suites for randomness without post-processing. Presented random number generator features much higher and constant throughput rates and allows offset compensation.

Random number is widely used in cryptographic applications, which is mainly used as key. Because the security of key totally depends on the amount and randomness of itself, it's very important to produce random numbers for cryptographic applications. This paper presents a method to generate random numbers for cryptographic applications. NIST Statistical Test Suite which provides 15 statistical methods is used to test the randomness of the random number generated by this method. Because the tests focus on a variety of different types of non-randomness, not all tests are needed. The chosen statistical tests are Frequency (Monobit) Test, Frequency Test within a Block, The Cumulative Sums (Cusums) Test, The Runs Test, Test for the Longest Run of Ones in a Block, Discrete Fourier Transform (Specral) Test, Approximate Entropy Test and Serial Test. The result of tests shows that the random number generated by the random number generator is random. Therefore the conclusion is the random number generated is random enough for cryptographic applications.

Let F be a neutral to the right, random distribution function on [0,+∞[, with a stationary subordinator. We introduce new linear functionals of the increments of F . Each of them is distributed like the unique fixed point of a random affine system. For gamma subordinators, we prove, building on earlier work, that their densities involve linear combinations of hypergeometric functions. We apply these ideas to the random sampling of alternating renewal processes.

A number of applications in simulation and software testing require random number generation, both uniform and non-uniform. Although libraries are available for random number generation, there is no user-friendly tool to enable the user to use and build random number generators. This paper presents the RVGEN tool, developed at TRDDC. Using the tool, the software developer can design a random number generator specific to a particular pattern. The tool takes specifications at a high level and also partly in C++, and generates C++ code for a number of user-friendly functions. These include setting and getting of random variate parameters, GUIs for inputting parameters, validation of parameters, parameter input and output from a file, generation of random variate class declarations, generation of seeds for multiple streams, and testing of the random variate generator. The tool provides a number of classical, generalpurpose and empiric distributions by default.

A Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.

For the largest value of the control parameter, the logistic map is able to generate an infinite chaotic sequence of numbers. Here we describe a simple method for obtaining a random number generator based on this property of the logistic map. Comparing to usual congruential random generators, which are periodic, the logistic random number generator is infinite, aperiodic and not correlated. An aperiodic random number generator is a valuable tool for computer simulation methods.

A random number generator generates fair coin flips by processing deterministically an arbitrary source of nonideal randomness. An optimal random number generator generates asymptotically fair coin flips from a stationary ergodic source at a rate of bits per source symbol equal to the entropy rate of the source. Since optimal noiseless data compression codes produce incompressible outputs, it is natural to investigate their capabilities as optimal random number generators. We show under general conditions that optimal variable-length source codes asymptotically achieve optimal variable-length random bit generation in a rather strong sense. In particular, we show in what sense the Lempel-Ziv (1978) algorithm can be considered an optimal universal random bit generator from arbitrary stationary ergodic random sources with unknown distributions.

Exactly solvable iterative chaotic maps as a multiplicative congruential algorithm of random number generator is presented. Numerical techniques to generate well defined random numbers with high quality are also demonstrated.

In this paper, we present a random number generation method, which is one of the rejection methods. To accelerate ratio-of-uniform method, we use an efficiency variabler. After finding the optimal value of r with respect to interesting distribution with proportional density, random numbers can be generated in acceleration.

Fast and accurate selection of random pattern is needed for many scientific and commercial applications. One of the major applications is Online Examination system. In this paper, a sophisticated approach has been developed for the selection of uniform pseudo random pattern for Online Examination System. Three random integer generators have been compared for this purpose. Most commonly used procedural language based pseudo random number; PHP random generator and atmospheric noise based true random number generator have been considered for easy generation of random patterns. The test result shows a varying degree of improvement in the quality of randomness of the generated patterns. The randomness quality of the generated pseudo random pattern has been assured by diehard test suite. An experimental outcome for our recommended approach signifies that our approach selects a quality set of random pattern for Online Examination System.

In this paper, we investigate simple yet practical schemes to generate random variates from the characteristic function of any continuous distribution. We discuss the generation of non-uniform random variates from a uniform random number generator. The inverse of the cumulative distribution function is derived from its characteristic function via the fast Fourier transform. We conduct several numerical experiments to assess the accuracy and efficiency of the schemes.

The stochastic properties of the pseudo-random number generator [Formula: see text] are discussed, with emphasis on the average period. Within a factor 2 this turns out to be the root of the maximally possible period. The actual set of periods depends on minor details of the algorithm, and the system settles down in one of only a few different cycles. These features are in perfect agreement with absolute random motion in phase space, to the extent allowed by deterministic dynamics.

Random numbers are highly used in applications like computer simulation, gambling, statistical sampling and cryptography. Only a high-entropy random source can produce a better quality of random data. In this paper, we work on the generation of high-quality random numbers based on cellular automata (CA). We propose three-dimensional (3D) CA to generate a random sequence. We also propose various enhancements to CA-based pseudorandom number generator (CA-PRNG) and discuss its five variants by using two-dimensional (2D) and 3D CA. We analyze the switching between 2D and 3D CA for the same sequence to achieve high-quality randomness. We provide the architectures, CA rules and the algorithms for all the proposed variants with respect to 2D and 3D cell structures. We have done a software implementation of all the proposed variants and tested their produced sequences by using the internationally accepted standard for random numbers known as DIEHARD. The analysis of our results is made as per the recommendations of ANSI standards. A very similar criteria are applied to compare all the methods. Based on our results we are able to find the best CA-PRNG for producing high-quality random sequence.

This paper proposes a well-performing hybrid-type truly quantum random number generator based on the time interval between two independent single-photon detection signals, which is practical and intuitive, and generates the initial random number sources from a combination of multiple existing random number sources. A time-to-amplitude converter and multichannel analyzer are used for qualitative analysis to demonstrate that each and every step is random. Furthermore, a carefully designed data acquisition system is used to obtain a high-quality random sequence. Our scheme is simple and proves that the random number bit rate can be dramatically increased to satisfy practical requirements.

A random process is frequently defined entirely by outcomes measured from it. Generally an important goal is to be able to approximate the random process sufficiently accurately in some sense. Common reasons for doing so are to provide surrogate variates for performing tests of hypothesis, and also to provide a model of the random process computationally suitable for inputs to or as stochastic coefficients for a numerical model. In cases of concern here the context is not limited to univariate random process, nor to Gaussian processes. This paper examines an alternative approach for modeling an arbitrary random process known only through its source variates. In this new method the kernel density estimator is inverted to provide a functional in the space of standard normal random variables. This functional can be expanded into a series representation of the random process using Wiener expansions. Several benefits accrue to this method. First, the computation expense of evaluating a KDE (and computing its inverse) need only be done once. Secondly, the rate of convergence of the series representation yields information on the departure of the random process from a strictly Gaussian one.Copyright © 2012 by ASME

A universal generator for integer-valued square-integrable random variables is introduced. The generator relies on a rejection technique based on a generalization of the inversion formula for integer-valued random variables. The proposal gives rise to a simple algorithm which may be implemented in a few code lines and which may show good performance when the classical families of distributions - such as the Poisson and the Binomial - are considered. In addition, the method is suitable for the computer generation of integer-valued random variables which display closed-form characteristic functions, but do not possess a probability function expressible in a simple analytical way. As an example of such a framework, an application to the Poisson-Tweedie distribution is provided.

Pseudo random number generators designed for use in programs to be run serially do not function as intended when multiple instances of the programs are run in parallel. We discuss two techniques to address this issue. Further, we verify our techniques using a case study involving statistical analysis.

An important building block for many cryptographic systems is a random number generator. Random numbers are required in these systems, because they are unpredictable for potential attackers. These random numbers can either be generated by a truly random physical source (that is non-deterministic) or using a deterministic algorithm. In practical applications where relatively large amounts of random bits are needed, it is also possible to combine both of these generator types. A non-deterministic random number generator is used to provide a truly random seed, which is used as input for a deterministic algorithm that generates a larger amount of (pseudo-)random bits. In cryptographic systems where Physical Unclonable Functions (PUFs) are used for authentication or secure key storage, an interesting source of randomness is readily available. Therefore, we propose the construction of a FIPS 140-3 compliant random bit generator based on an SRAM PUF in this paper. These PUFs are a source of instant randomness, which is available when powering an IC. Based on large sets of measurements, we derive the min-entropy of noise on the start-up patterns of SRAM memories. The min-entropy determines the compression factor of a conditioning algorithm, which is used to extract a truly random (256 bits) seed from the memory. Using several randomness tests we prove that the conditioned seed has all the properties of a truly random string with full entropy. This truly random seed can be derived in a low cost and area efficient manner from the standard IC component SRAM. Furthermore, an efficient implementation of a deterministic algorithm for generating (pseudo-)random output bits will be proposed. Combining these two functions leads to an ideal way to generate large amounts of random data based on non-deterministic randomness.

This paper introduces a novel method for generating non-standard random distributions. These distributions are required when the system cannot be modelled accurately using conventional probabilistic distributions. In the proposed method, the desired random distribution is built by splitting the density function into smaller segments which are individually approximated with simple polynomial functions. Then, the inverse transformation method is used to form the final random distribution function. This paper presents also new criteria for expediting the selection of the correct segment. The complexity of the proposed process for segment selection is O(log M ), where M is the number of segments. An example of the application of the proposed method to simulation of disk access patterns for performance evaluation of computer systems is provided.

In modern cryptosystems, real random numbers play critical roles in most key management systems to make sure that the generated key is able to provide high level of security in order to protect the sensitive information and assets in highly secured information systems. One of the main challenges in generating random values is the needs for special noisy source such as nuclear decay, brownian motion, analog circuit and quantum mechanics for producing the random values. Beside the cost, complexity and difficulty of using these special noisy sources as the source of random number generator, there is also the need for additional hardware devices to communicate with these sources. In this paper, we proposed an inexpensive and less complicated method to generate true random values without any need for especial noisy sources. We explain how we use hash functions in combining two data extractors to transform normal image data and make it suitable as the source of random number generator.

Random numbers are used in a wide variety of applications from simulation and encryption to gambling and clinical trials. A good quality random number generator is an asset for applications like encryption, randomized designs and network and information security. Various mathematical models have been developed in the past to improve the quality of random numbers. It can be construed that in general to obtain random numbers of excellent quality, a complex mathematical model has to be used which can be a performance bottleneck. In this work, we propose a novel technique to implement a True Random Number Generator (TRNG) using sources of uncertainty found within Graphics Processing Units (GPUs) together with histogram equalization to obtain maximum entropy. We evaluate the random numbers generated by our approach using four tests. First, we measure the correlation values between two sequences of random numbers, second, we measure the entropy values, third, we use watermarking, an application used in network security and finally we use Monte Carlo analysis for pi-value calculation. Based on these quality measurements, our method has achieved better results than popular random number generators compared in this work. Furthermore, this approach is a massively scalable solution ideal for high performance computing implementations.

Computer simulation is a widely used powerful tool for solving real world problems. Pseudo random number plays an important role in computer simulation and modeling. It acts like the real random number. We discuss the asymptotic properties of pseudo random numbers as the period size of a random number generator approaches to infinity. Our results show that the pseudo random number converges to the true random number in mean, variance, skewness, kurtosis, moment, and distribution.

This paper proposes a chaos-based true random number generator using image as nondeterministic entropy sources. Logistic map is applied to permute and diffuse the image to produce a random sequence after the image is divided to bit-planes. The generated random sequence passes NIST 800-22 test suite with good performance.

Real random values play critical role in different application of computer science. One of the main challenges in generating real random values refers to the needs for physical noisy sources such as nuclear decay, brownian motion and quantum mechanics. Since usually these noisy sources are too costly and complicated, this research proposes an inexpensive and less complicated method for generating true random values by using normal image data as the source of generator and two cryptography hash functions as data extractor. The result of three levels of statistical tests on generated random sequences proves that the generated sequences achieved high level of randomness.

Security of a cryptographic system depends much on the randomness of random number used in the system. In this paper, a true random number generator based on the data of fingerprint (FPTRNG) is designed which extracts the random information from image data of fingerprint to generate random number. The random number generated by the generator is tested using the statistical test suite for random and pseudorandom number generators of NIST. Test result shows that the random data generated by this random number generator has perfect randomness. In fingerprint-based information security system, FPTRNG insures higher level of security.

Random number generators are necessary in every simulation which includes stochastic aspects. For High Performance Computing, there is an increasing interest in the distribution of parallel random number streams. Even if we have now at our disposal statistically sound random number generators according to very tough testing libraries, their parallelization can still be a delicate problem. A set of recent publications shows it still has to be mastered by the scientific community. In this paper, we discuss the different partitioning techniques currently in use to provide independent streams with their corresponding software. We try to give an up-to-date state of the art in the parallelization of random numbers for High Performance Computing from the point of view of a simulation practitioner.

The need for random numbers has long existed within the realm of empirical educational research, especially with regard to the random assignment of students to experimental conditions. Because of the growing popularity of simulation studies designed to investigate complex educational models, the quantity and quality of random numbers required by researchers have also increased dramatically. This article discusses the nature of random numbers, approaches to generating them with various distributional properties, tests of randomness, and other features that should be considered. Researchers are encouraged to thoroughly investigate and report on the random number generators they employ, and to avoid the substandard ones that are still widely available.