Errors and their mitigation at the kirchhoff-law-johnson-noise secure key exchange.

PLoS One

Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, United States of America.

Published: October 2014

A method to quantify the error probability at the Kirchhoff-law-Johnson-noise (KLJN) secure key exchange is introduced. The types of errors due to statistical inaccuracies in noise voltage measurements are classified and the error probability is calculated. The most interesting finding is that the error probability decays exponentially with the duration of the time window of single bit exchange. The results indicate that it is feasible to have so small error probabilities of the exchanged bits that error correction algorithms are not required. The results are demonstrated with practical considerations.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3841199PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0081103PLOS

Publication Analysis

Top Keywords

error probability
12
secure key
8
key exchange
8
error
5
errors mitigation
4
mitigation kirchhoff-law-johnson-noise
4
kirchhoff-law-johnson-noise secure
4
exchange method
4
method quantify
4
quantify error
4

Similar Publications

Decoding uncertainty for clinical decision-making.

Philos Trans A Math Phys Eng Sci

March 2025

Injury, Recovery and Inflammation Sciences Academic Unit, School of Medicine and National Institute for Health and Care Research, Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK.

In this opinion piece, we examine the pivotal role that uncertainty quantification (UQ) plays in informing clinical decision-making processes. We explore challenges associated with healthcare data and the potential barriers to the widespread adoption of UQ methodologies. In doing so, we highlight how these techniques can improve the precision and reliability of medical evaluations.

View Article and Find Full Text PDF

Parameter inference for stochastic reaction models of ion channel gating from whole-cell voltage-clamp data.

Philos Trans A Math Phys Eng Sci

March 2025

Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK.

Mathematical models of ion channel gating describe the changes in ion channel configurations due to the electrical activity of the cell membrane. Experimental findings suggest that ion channels behave randomly, and therefore stochastic models of ion channel gating should be more realistic than deterministic counterparts. Whole-cell voltage-clamp data allow us to calibrate the parameters of ion channel models.

View Article and Find Full Text PDF

Ability and utility of the Physician Well-Being Index to identify distress among Chinese physicians.

Ann Med

December 2025

Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China.

Background: Despite the high prevalence of mental stress among physicians, reliable screening tools are scarce. This study aimed to evaluate the capability of the Physician Well-Being Index (PWBI) in identifying distress and adverse consequences among Chinese physicians.

Methods: This cross-sectional online survey recruited 2803 physicians from Southern Mainland China snowball sampling between October and December 2020.

View Article and Find Full Text PDF

Borrowing external controls to augment the concurrent control arm is a popular topic in clinical trials. Bayesian dynamic borrowing methods adaptively discount external controls according to prior-data conflict. For the Gaussian endpoint, parameter-specific information borrowing enables differential discounting between the population mean and variance.

View Article and Find Full Text PDF

Neuromodulation with low-intensity focused ultrasound (LIFUS) holds significant promise for noninvasive treatment of neurological disorders, but its success relies heavily on accurately targeting specific brain regions. Computational model predictions can be used to optimize LIFUS, but uncertain acoustic tissue properties can affect prediction accuracy. The Monte Carlo method is often used to quantify the impact of uncertainties, but many iterations are generally needed for accurate estimates.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!