Efficient error correction technique improving the efficiency of image transmission over a mobile Bluetooth networks

2012 ◽  
Author(s):  
M. A. M. El-Bendary ◽  
M. El-Tokhy ◽  
A. E. AbouElazm ◽  
N. A. El-Fishawy
Keyword(s):  
Error Correction ◽  
Image Transmission ◽  
Correction Technique

scholarly journals Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2009
Author(s):  
Fatemeh Najafi ◽  
Masoud Kaveh ◽  
Diego Martín ◽  
Mohammad Reza Mosavi
Keyword(s):  
Error Correction ◽  
Random Access ◽  
Error Correction Codes ◽  
Access Memory ◽  
Deep Convolutional Neural Networks ◽  
Device Identification ◽  
Physical Unclonable Functions ◽  
Server Side ◽  
Correction Technique ◽  
Reduction Methods

Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.

A new error correction technique for BD systems

2010 ◽  
Vol 56 (2) ◽  
pp. 663-668 ◽  
Author(s):  
Jun Lee ◽  
Seong-Hun Lee
Keyword(s):  
Error Correction ◽  
Correction Technique

Digital error correction technique for binary decision successive approximation ADCs

2009 ◽  
Vol 45 (8) ◽  
pp. 395 ◽  
Author(s):  
S.-H. Cho ◽  
C.-K. Lee ◽  
B.-R.-S. Sung ◽  
S.-T. Ryu
Keyword(s):  
Error Correction ◽  
Successive Approximation ◽  
Binary Decision ◽  
Correction Technique
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