Results 31 to 40 of about 115,275 (294)

PACE: Redundancy Engineering in RLNC for Low-Latency Communication

IEEE Access, 2017
Random linear network coding (RLNC) is attractive for data transfer as well as data storage and retrieval in complex and unreliable settings. The existing systematic RLNC approach first sends all source symbols in a generation without encoding followed ...
Sreekrishna Pandi, Frank Gabriel, Juan A. Cabrera, Simon Wunderlich, Martin Reisslein, Frank H. P. Fitzek   +5 more
doaj   +1 more source

Uniform Generation in Trace Monoids [PDF]

, 2015
International audienceWe consider the problem of random uniform generation of traces (the elements of a free partially commutative monoid) in light of the uniform measure on the boundary at infinity of the associated monoid.
Jean Mairesse, Samy Abbes, Mairesse, Jean, Abbes, Samy   +3 more
core   +1 more source

Certification of the efficient random number generation technique based on single‐photon detector arrays and time‐to‐digital converters

IET Quantum Communication, 2021
True random number generators (TRNGs) allow the generation of true random bit sequences, guaranteeing the unpredictability and perfect balancing of the generated values.
Andrea Stanco, Davide G. Marangon, Giuseppe Vallone, Samuel Burri, Edoardo Charbon, Paolo Villoresi   +5 more
doaj   +1 more source

A Method for Generating True Random Numbers With Multiple Distribution Characteristics

IEEE Access, 2023
In this paper, a method of generating true random numbers obeying multiple distribution characteristics is proposed. First, two resistance-capacitance (RC) self-excited oscillation circuits are used to generate two jittered, periodically unstable square ...
Gang Su, Changchun Ding, Sida Li, Zijian Liu, Zheng Gao, Junfeng Song, Shuxu Guo, Min Tao   +7 more
doaj   +1 more source

A General Formula for Uniform Common Randomness Capacity

2022 IEEE Information Theory Workshop (ITW), 2022
arXiv admin note: text overlap with arXiv:2105 ...
Rami Ezzine, Moritz Wiese, Christian Deppe, Holger Boche   +3 more
openaire   +2 more sources

Automatic March tests generation for static and dynamic faults in SRAMs [PDF]

, 2005
New memory production modern technologies introduce new classes of faults usually referred to as dynamic memory faults. Although some hand-made March tests to deal with these new faults have been published, the problem of automatically generate March ...
Di Natale, Giorgio, Bosio, Alberto, A. Benso, P. Prinetto, A. Bosio, G. Di Natale, Benso, Alfredo, Prinetto, Paolo Ernesto, S. Di Carlo, Di Carlo, Stefano   +9 more
core   +1 more source

Random generation of RNA secondary structures according to native distributions

Algorithms for Molecular Biology, 2011
Background Random biological sequences are a topic of great interest in genome analysis since, according to a powerful paradigm, they represent the background noise from which the actual biological information must differentiate.
Nebel Markus E, Scheid Anika, Weinberg Frank   +2 more
doaj   +1 more source

A Gaussian-Distributed Quantum Random Number Generator Using Vacuum Shot Noise

Entropy, 2020
Among all the methods of extracting randomness, quantum random number generators are promising for their genuine randomness. However, existing quantum random number generator schemes aim at generating sequences with a uniform distribution, which may not ...
Min Huang, Ziyang Chen, Yichen Zhang, Hong Guo   +3 more
doaj   +1 more source

Using porous random fields to predict the elastic modulus of unoxidized and oxidized superfine graphite

Materials & Design, 2022
Nuclear graphite is a candidate material for Generation IV nuclear power plants. Porous materials such as graphite can contain complex networks of pores that influence the material's mechanical and irradiation response.
José David Arregui-Mena, D.V. Griffiths, Robert N. Worth, Christa E. Torrence, Aaron Selby, Cristian Contescu, Nidia Gallego, Philip D. Edmondson, Paul M. Mummery, Lee Margetts   +9 more
doaj   +1 more source

Algorithm 1: Random BBA generation—Uniform sampling from all focal elements. [PDF]

, 2016
Algorithm 1: Random BBA generation—Uniform sampling from all focal elements.
Yi Yang (116183), Yuanli Liu (36282)
core   +1 more source