با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Are the Interpulse Intervals of an ECG signal a good source of entropy? An in-depth entropy analysis based on NIST 800-90B recommendation
آیا فاصله های interpulse از یک سیگنال ECG منبع خوبی برای آنتروپی است؟ تجزیه و تحلیل آنتروپی عمیق بر اساس توصیه NIST 800-90B-2019
In recent years many authors have explored the use of biological signals for security issues. In the context of cardiac signals, the use of Inter-Pulse Interval (IPI) values as a source of entropy is one of the most widely used solutions in the literature. To date, there is a broad consensus that the four least significant bits of each IPI are highly entropic and can be used, for instance, in the generation of a cryptographic key. In this article, we demonstrate that the choice of the IPI bits used to date may not be the most correct (e.g., the combination of bits 2638 are much better that the common assumed 5678). To come to our conclusions, we have done a rigorous and in-depth study, analyzing cardiac signals from more than 160,000 files from 19 databases of the Physionet public repository and basing our analysis on the NIST 800-90B recommendation.
Keywords: Entropy| NIST 800-90B| Security | Privacy
Automatic driver stress level classification using multimodal deep learning
Automatic driver stress level classification using multimodal deep learning-2019
Stress has been identified as one of the contributing factors to vehicle crashes which create a significant cost in terms of loss of life and productivity for governments and societies. Motivated by the need to ad- dress the significant costs of driver stress, it is essential to build a practical system that can detect drivers’ stress levels in real time with high accuracy. A driver stress detection model often requires data from dif- ferent modalities, including ECG signals, vehicle data (e.g., steering wheel, brake pedal) and contextual data (e.g., weather conditions and other ambient factors). Most of the current works use traditional ma- chine learning techniques to fuse multimodal data at different levels (e.g., feature level) to classify drivers’ stress levels. Although traditional multimodal fusion models are beneficial for driver stress detection, they inherently have some critical limitations (e.g., ignore non-linear correlation across modalities) that may hinder the development of a reliable and accurate model. To overcome the limitations of traditional mul- timodal fusion, this paper proposes a framework based on adopting deep learning techniques for driver stress classification captured by multimodal data. Specifically, we propose a multimodal fusion model based on convolutional neural networks (CNN) and long short-term memory (LSTM) to fuse the ECG, vehicle data and contextual data to jointly learn the highly correlated representation across modalities, after learning each modality, with a single deep network. To validate the effectiveness of the proposed model, we perform experiments on our dataset collected using an advanced driving simulator. In this pa- per, we present a multi-modal system based on the adoption of deep learning techniques to improve the performance of driver stress classification. The results show that the proposed model outperforms model built using the traditional machine learning techniques based on handcrafted features (average accuracy: 92.8%, sensitivity: 94.13%, specificity: 97.37% and precision: 95.00%).
Keywords: Deep learning | Driver stress detection | Convolutional neural network | Long short term memory | ECG signal | Vehicle data