Random Sequence Generation Algorithm for Multi-chaotic Systems

2018 ◽  
pp. 136-143
Author(s):  
Xiaodi Chen ◽  
Hong Wu
Keyword(s):  
Chaotic Systems ◽  
Random Sequence ◽  
Generation Algorithm ◽  
Sequence Generation ◽  
Random Sequence Generation

ON A CERTAIN MODEL OF CRYPTOGRAPHIC KEY MANAGEMENT

2020 ◽  
Vol 8 (4) ◽  
pp. 15-22
Author(s):  
S.E. Nyssanbayeva ◽  
◽  
N.A. Kapalova ◽  
A. Haumen
Keyword(s):  
Key Management ◽  
Random Sequence ◽  
Management Model ◽  
Generation Algorithm ◽  
Sequence Generation ◽  
Secure Storage ◽  
The World ◽  
Random Sequence Generation ◽  
Cryptographic Key

Cryptographic technologies that have become widespread in the world are inex- tricably linked to the issues of secure storage, use of keys, and key exchange. Often, insecure key management reduces the quality of even exceptionally good systems, since the security of the algorithm is concentrated mainly in the key. This paper proposes a key management model in cryptographic systems. The model is based on creating a unified key database for all users. This database is filled with keys of a certain length, which are generated using a pseudo-random sequence generation algorithm.

A pseudo-random sequence generation scheme based on RNS and permutation polynomials

2018 ◽  
Vol 61 (8) ◽  
Author(s):  
Shang Ma ◽  
Jianfeng Liu ◽  
Zeguo Yang ◽  
Yan Zhang ◽  
Jianhao Hu
Keyword(s):  
Random Sequence ◽  
Permutation Polynomials ◽  
Sequence Generation ◽  
Random Sequence Generation

Advances on Random Sequence Generation by Uniform Cellular Automata

2014 ◽  
pp. 56-70 ◽  
Author(s):  
Enrico Formenti ◽  
Katsunobu Imai ◽  
Bruno Martin ◽  
Jean-Baptiste Yunès
Keyword(s):  
Cellular Automata ◽  
Random Sequence ◽  
Sequence Generation ◽  
Random Sequence Generation

scholarly journals Risk of bias judgments for random sequence generation in Cochrane systematic reviews were frequently not in line with Cochrane Handbook

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Ognjen Barcot ◽  
Matija Boric ◽  
Tina Poklepovic Pericic ◽  
Marija Cavar ◽  
Svjetlana Dosenovic ◽  
...  
Keyword(s):  
Systematic Reviews ◽  
Random Sequence ◽  
Risk Of Bias ◽  
Sequence Generation ◽  
Random Sequence Generation

scholarly journals Judgments of risk of bias associated with random sequence generation in trials included in Cochrane systematic reviews are frequently erroneous

2018 ◽  
Author(s):  
Ognjen Barcot ◽  
Matija Boric ◽  
Tina Poklepovic Pericic ◽  
Marija Cavar ◽  
Svjetlana Dosenovic ◽  
...  
Keyword(s):  
Systematic Reviews ◽  
Random Number ◽  
Random Sequence ◽  
Computer Software ◽  
Risk Of Bias ◽  
Controlled Trials ◽  
Sequence Generation ◽  
Randomized Controlled ◽  
Random Sequence Generation ◽  
Random Number Table

AbstractBackgroundPurpose of this study was to analyze adequacy of judgments about risk of bias (RoB) for random sequence generation in Cochrane systematic reviews (CSRs) of randomized controlled trials (RCTs).MethodsInformation was extracted from RoB tables of CSRs using automated data scraping. We categorized all comments provided as supports for judgments for RoB related to randomization. We analyzed number and type of various supporting comments and assessed adequacy of RoB judgment for randomization in line with recommendations from the Cochrane Handbook.ResultsWe analyzed 10527 RCTs that were included in 729 CSRs. For 5682 RCTs randomization was not described; for the others it was indicated randomization was done using computer/software/internet (N=2886), random number table (N=888), mechanic method (N=366), or it was incomplete/inappropriate (N=303).Overall, 1194/10125 trials (12%) had erroneous RoB judgment about randomization. The highest proportion of errors was found for trials with high RoB (28%), followed by those with low (19%), or unclear (3%). Therefore, one in eight judgments for the analyzed domain in CSRs was erroneous, and one in three if the judgment was “high risk”.ConclusionCochrane systematic reviews cannot be necessarily trusted when it comes to judgments for risk of bias related to randomized sequence generation.

Deconfounding recognition and recall in random sequence generation

2009 ◽  
Author(s):  
James Heyman ◽  
James Zelubowski
Keyword(s):  
Random Sequence ◽  
Sequence Generation ◽  
Random Sequence Generation

Chaotic-laser-based true random sequence generation for spread-spectrum communications

2012 ◽  
Vol 39 (10) ◽  
pp. 1005006
Author(s):  
Zhaoxia Zhang Zhaoxia Zhang ◽  
Junjie Zhou Junjie Zhou ◽  
Dongze Zhang Dongze Zhang ◽  
Zheng Fu Zheng Fu ◽  
Jianzhong Zhang and Jianzhong Zhang
Keyword(s):  
Spread Spectrum ◽  
Random Sequence ◽  
Sequence Generation ◽  
Chaotic Laser ◽  
Spread Spectrum Communications ◽  
Random Sequence Generation
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