scholarly journals Device-independent randomness extraction for arbitrarily weak min-entropy source

2014 ◽  
Author(s):  
Jan Bouda ◽  
Marcin Pawłowski ◽  
Matej Pivoluska ◽  
Martin Plesch
Keyword(s):  
Randomness Extraction ◽  
Entropy Source

scholarly journals Device-independent randomness extraction from an arbitrarily weak min-entropy source

2014 ◽  
Vol 90 (3) ◽  
Author(s):  
Jan Bouda ◽  
Marcin Pawłowski ◽  
Matej Pivoluska ◽  
Martin Plesch
Keyword(s):  
Randomness Extraction ◽  
Entropy Source

scholarly journals Forest Fire Detection via Feature Entropy Guided Neural Network

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 128
Author(s):  
Zhenwei Guan ◽  
Feng Min ◽  
Wei He ◽  
Wenhua Fang ◽  
Tao Lu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Forest Fire ◽  
State Of The Art ◽  
Fire Detection ◽  
High Entropy ◽  
Training Samples ◽  
Feature Information ◽  
Forest Fire Detection ◽  
Entropy Source

Forest fire detection from videos or images is vital to forest firefighting. Most deep learning based approaches rely on converging image loss, which ignores the content from different fire scenes. In fact, complex content of images always has higher entropy. From this perspective, we propose a novel feature entropy guided neural network for forest fire detection, which is used to balance the content complexity of different training samples. Specifically, a larger weight is given to the feature of the sample with a high entropy source when calculating the classification loss. In addition, we also propose a color attention neural network, which mainly consists of several repeated multiple-blocks of color-attention modules (MCM). Each MCM module can extract the color feature information of fire adequately. The experimental results show that the performance of our proposed method outperforms the state-of-the-art methods.

Enhancing the Security of FPGA-SoCs via the Usage of ARM TrustZone and a Hybrid-TPM

2022 ◽  
Vol 15 (1) ◽  
pp. 1-26
Author(s):  
Mathieu Gross ◽  
Konrad Hohentanner ◽  
Stefan Wiehler ◽  
Georg Sigl
Keyword(s):  
Software Design ◽  
Hardware Accelerators ◽  
Biometric Authentication ◽  
Trusted Platform Module ◽  
Start Up ◽  
Execution Environment ◽  
On Chip ◽  
Trusted Platform ◽  
Trusted Execution Environment ◽  
Entropy Source

Isolated execution is a concept commonly used for increasing the security of a computer system. In the embedded world, ARM TrustZone technology enables this goal and is currently used on mobile devices for applications such as secure payment or biometric authentication. In this work, we investigate the security benefits achievable through the usage of ARM TrustZone on FPGA-SoCs. We first adapt Microsoft’s implementation of a firmware Trusted Platform Module (fTPM) running inside ARM TrustZone for the Zynq UltraScale+ platform. This adaptation consists in integrating hardware accelerators available on the device to fTPM’s implementation and to enhance fTPM with an entropy source derived from on-chip SRAM start-up patterns. With our approach, we transform a software implementation of a TPM into a hybrid hardware/software design that could address some of the security drawbacks of the original implementation while keeping its flexibility. To demonstrate the security gains obtained via the usage of ARM TrustZone and our hybrid-TPM on FPGA-SoCs, we propose a framework that combines them for enabling a secure remote bitstream loading. The approach consists in preventing the insecure usages of a bitstream reconfiguration interface that are made possible by the manufacturer and to integrate the interface inside a Trusted Execution Environment.

scholarly journals Randomness Extraction from Bell Violation with Continuous Parametric Down-Conversion

2018 ◽  
Vol 121 (15) ◽  
Author(s):  
Lijiong Shen ◽  
Jianwei Lee ◽  
Le Phuc Thinh ◽  
Jean-Daniel Bancal ◽  
Alessandro Cerè ◽  
...  
Keyword(s):  
Bell Violation ◽  
Parametric Down Conversion ◽  
Randomness Extraction ◽  
Down Conversion
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