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About 10,000 papers for “Random sequence”

Random modulation cw lidar using new random sequence

Chikao Nagasawa, Makoto Abo, Hideki Yamamoto et al. · Applied Optics · 1990

New modulation codes are presented for a random modulation cw lidar. One characteristic of these modulation codes is that for very noisy background conditions, the signal-to-noise ratio is improved by using these new sequences, and is better than for the maximum-length sequence (the M-sequence) which is commonly used as the modulation code. Another characteristic of these modulation codes is that there is no correlation between them. This fact will be useful for the simultaneous multitransmitter of the differential absorption lidar. These two characteristics of the new modulation codes were confirmed experimentally.

Cited by 44

On generating a random sequence

F. K. Hwang, Shih-Wei Lin · Journal of Applied Probability · 1971

Suppose there are k kinds of balls identical except for their colours. The number of balls of the i th colour is r i ( i = 1, 2, ···, k ) and Put down the R balls in a sequence (a line), then an acceptable sequence is one which does not have two adjacent balls of the same colour. The problem is to generate a random sample of the set of acceptable sequences S ( r 1 , r 2 , ···, r k ).

Cited by 0

What is a random sequence?

B. P. Weiss · Advances in Applied Probability · 1977

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

Cited by 0

What Is a Random Sequence?

Sérgio B. Volchan · American Mathematical Monthly · 2002

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsSérgio B. VolchanSÉRGIO B. VOLCHAN earned an M.S. in physics from the Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Brazil. He then turned to mathematics, receiving his Ph.D. from the Courant Institute of Mathematical Sciences in 1995. After spending two years as a researcher at the Instituto de Matemática Pura e Aplicada (IMPA), he moved to the mathematics department of PUC-Rio, where he is assistant professor. His research interests are probability theory and its applications to mathematical physics.

Cited by 19

A Random Sequence of Reals

Kenshi Miyabe · 2010

We define a random sequence of reals as a random point on a computable topological space. This randomness has three equivalent simple characterizations, namely, by tests, by martingales and by complexity. We prove that members of a random sequence are relatively random. Conversely a relatively random sequence of reals has a random sequence such that each corresponding member is Turing equivalent. Furthermore strong law of large numbers and the law of the iterated logarithm hold for each random sequence.

Cited by 0

Detecting change in a random sequence

Cso¨rgo, Miklo´s, Horva´th, Lajos · RePEc: Research Papers in Economics

We propose a sequential procedure for detecting a possible changepoint in a random sequence of observations so that we can fix the probabilty of stopping at any level if there is no change, while otherwise we will stop with probabilty one in a specified length of time.

Cited by 0

The Random Sequence Closing Control System

Henri Joyaux · 2000

This thesis describes a digital control system used by the Network Analog Group of the Bonneville Power Administration. This system, the Random Sequence Closing Control System, provides automatic control for a special purpose analog computer used in the study of switching surge over voltages on power transmission lines. This system, which uses pseudorandom data, has made it feasible to analyze switching surge phenomena on a statistical basis.

Cited by 0Open access

On quasigroup pseudo random sequence generator

V. Dimitrova, J. Markovski · 2004

Abstract. Pseudo random sequence generators (PRSG) produce sequences of elements that imitate natural random behavior. They have extensive use in (1) scientific experiments as input sequences for different kinds of simulators, (2) cryptography for preparation of keys and establishing communication and (3) authentication for preparation of identification numbers, smart cards, serial numbers, etc. However, widely available PRSGs have limited periods (for example, 264) which means that the pseudo random sequences start repeating the same elements (after at most 264 elements). This makes them inappropriate for large scale scientific experiments, cryptography and authentication. In this paper we investigate the properties of a new type of PRSG which overcomes these difficulties. The PRSG is designed using quasigroup processing. We show that the quasigroup PRSG is highly scalable and with arbitrary large period. Also we present experimental results on some properties of the quasigroups which make them appropriate for implementation of PRSG. 1

Cited by 0

Oscillations in the Finite Random Sequence

J. P. Imhof · Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1976

Summary Oscillations are defined for a random sequence of finite length. The oscillation of maximal amplitude is preceded by oscillations of increasing amplitudes and followed by oscillations of decreasing amplitudes. The joint law of their numbers is determined. There is analogy with known results for upper and lower records. A sampling experiment gives some idea of the behaviour of oscillations in the presence of a trend in mean or in standard deviation.

Cited by 0

RANDNA: A Random DNA Sequence Generator

Francesco Piva, Giovanni Principato · In Silico Biology · 2006

Monte Carlo simulations are useful to verify the significance of data. Genomic regularities, such as the nucleotide correlations or the not uniform distribution of the motifs throughout genomic or mature mRNA sequences, exist and their significance can be checked by means of the Monte Carlo test. The test needs good quality random sequences in order to work, moreover they should have the same nucleotide distribution as the sequences in which the regularities have been found. Random DNA sequences are also useful to estimate the background score of an alignment, that is a threshold below which the resulting score is merely due to chance. We have developed RANDNA, a free software which allows to produce random DNA or RNA sequences setting both their length and the percentage of nucleotide composition. Sequences having the same nucleotide distribution of exonic, intronic or intergenic sequences can be generated. Its graphic interface makes it possible to easily set the parameters that characterize the sequences being produced and saved in a text format file. The pseudo-random number generator function of Borland Delphi 6 is used, since it guarantees a good randomness, a long cycle length and a high speed. We have checked the quality of sequences generated by the software, by means of well-known tests, both by themselves and versus genuine random sequences. We show the good quality of the generated sequences. The software, complete with examples and documentation, is freely available to users from: http://www.introni.it/en/software.

Cited by 15