Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks

2017 ◽  
Author(s):  
Bhargava Reddy ◽  
Ye-Hoon Kim ◽  
Sojung Yun ◽  
Chanwon Seo ◽  
Junik Jang
Keyword(s):  
Neural Networks ◽  
Embedded System ◽  
Real Time ◽  
Deep Neural Networks ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Model Compression

scholarly journals Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques

2018 ◽  
Vol 130 ◽  
pp. 400-407 ◽  
Author(s):  
Rateb Jabbar ◽  
Khalifa Al-Khalifa ◽  
Mohamed Kharbeche ◽  
Wael Alhajyaseen ◽  
Mohsen Jafari ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Deep Neural Networks ◽  
Android Application ◽  
Drowsiness Detection ◽  
Driver Drowsiness

Real-time strawberry detection using deep neural networks on embedded system (rtsd-net): An edge AI application

2022 ◽  
Vol 192 ◽  
pp. 106586
Author(s):  
Yanchao Zhang ◽  
Jiya Yu ◽  
Yang Chen ◽  
Wen Yang ◽  
Wenbo Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Embedded System ◽  
Real Time ◽  
Deep Neural Networks

scholarly journals Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

2021 ◽  
Vol 35 (2) ◽  
pp. 251-264
Author(s):  
Dimitrios Boursinos ◽  
Xenofon Koutsoukos
Keyword(s):  
Neural Networks ◽  
Distance Learning ◽  
Real Time ◽  
Speaker Recognition ◽  
Deep Neural Networks ◽  
Cyber Physical Systems ◽  
Error Rates ◽  
Traffic Sign ◽  
Conformal Prediction ◽  
Physical Systems

AbstractMachine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

scholarly journals Diagnostic data integration using deep neural networks for real-time plasma analysis

2021 ◽  
pp. 1-1
Author(s):  
A. Rigoni Garola ◽  
R. Cavazzana ◽  
M. Gobbin ◽  
R.S. Delogu ◽  
G. Manduchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Data Integration ◽  
Real Time ◽  
Deep Neural Networks ◽  
Plasma Analysis ◽  
Diagnostic Data
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