Search

Your search keyword '"Tsimring, Lev S."' showing total 2 results
Start Over Author "Tsimring, Lev S." Topic algorithms
2 results on '"Tsimring, Lev S."'

1. Additive Mixing Modulation for Public Key Encryption Based on Distributed Dynamics.

Author

Tenny, Roy and Tsimring, Lev S.

Subjects
*PUBLIC key cryptography, *ELECTRONIC modulation, *MODULATION theory, *ELECTRONICS, *ALGORITHMS, *ALGEBRA, *LATTICE theory
Abstract

We introduce a public key encryption scheme that is based on additive mixing of a message with chaotic nonlinear dynamics. A high-dimensional dissipative nonlinear dynamical system is distributed between transmitter and receiver. The transmitter dynamics is public (known to all) and the receiver dynamics is private (known only to the authorized receiver). Bidirectional signals that couple transmitter and receiver are transmitted over a public channel. Once the chaotic dynamics which is initialized with a randornstate converges to the attractor, a message is mixed with the chaotic dynamics at the transmitter. The authorized receiver who knows the entire dynamics can use a simple algorithm to decode the message. An unauthorized receiver does not know the receiver dynamics and needs to use computationally unfeasible algorithms in order to decode the message. Security is maintained by altering the private receiver dynamics during transmission. We show that using additive mixing modulation is more efficient than the attractor position modulation distributed dynamics encryption scheme. We demonstrate the concept of this new scheme by simulating a simple coupled map lattice [ABSTRACT FROM AUTHOR]

Published
2005
Full Text
View/download PDF

2. Fast stochastic algorithm for simulating evolutionary population dynamics.

Author

Mather, William H., Hasty, Jeff, and Tsimring, Lev S.

Subjects
*ALGORITHMS, *POPULATION dynamics, *SIMULATION methods & models, *BIOLOGICAL evolution, *REPRODUCTION, *GENETIC mutation, *BIRTH rate, *DEATH rate
Abstract

Motivation: Many important aspects of evolutionary dynamics can only be addressed through simulations. However, accurate simulations of realistically large populations over long periods of time needed for evolution to proceed are computationally expensive. Mutants can be present in very small numbers and yet (if they are more fit than others) be the key part of the evolutionary process. This leads to significant stochasticity that needs to be accounted for. Different evolutionary events occur at very different time scales: mutations are typically much rarer than reproduction and deaths.Results: We introduce a new exact algorithm for fast fully stochastic simulations of evolutionary dynamics that include birth, death and mutation events. It produces a significant speedup compared to direct stochastic simulations in a typical case when the population size is large and the mutation rates are much smaller than birth and death rates. The algorithm performance is illustrated by several examples that include evolution on a smooth and rugged fitness landscape. We also show how this algorithm can be adapted for approximate simulations of more complex evolutionary problems and illustrate it by simulations of a stochastic competitive growth model.Contact: ltsimring@ucsd.eduSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Published
2012
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results