A Data Concealing Technique with Random Noise Disturbance and A Restoring Technique for the Concealed Data by Stochastic Process Estimation

10/08/2019
by   Tomohiro Fujii, et al.
0

We propose a technique to conceal data on a physical layer by disturbing them with some random noises, and moreover, a technique to restore the concealed data to the original ones by using the stochastic process estimation. Our concealing-restoring system manages the data on the physical layer from the data link layer. In addition to these proposals, we show the simulation result and some applications of our concealing-restoring technique.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 12

page 13

page 15

page 16

page 17

05/24/2020

Networks with pixels embedding: a method to improve noise resistance in images classification

In the task of images classification, usually, the network is sensitive ...
03/10/2021

Anti-Counterfeiting for Polymer Banknotes Based on Polymer Substrate Fingerprinting

Polymer banknotes are the trend for printed currency and have been adopt...
02/16/2022

Gaussian Process-Driven History Matching for Physical Layer Parameter Estimation in Optical Fiber Communication Networks

We present a methodology for the estimation of optical network physical ...
05/31/2021

A New Transmit Antenna Selection Technique for Physical Layer Security with Strong Eavesdropping

We propose a new transmit antenna selection (TAS) technique that can be ...
02/02/2018

Stochastic Kriging for Inadequate Simulation Models

Stochastic kriging is a popular metamodeling technique for representing ...
07/10/2019

A Range Matching CAM for Hierarchical Defect Tolerance Technique in NRAM Structures

Due to the small size of nanoscale devices, they are highly prone to pro...
08/07/2018

Learning-Aided Physical Layer Authentication as an Intelligent Process

Performance of the existing physical layer authentication schemes could ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.