A Practical Real Time Svd Machine With Multi-Level Fault Tolerance

1986 ◽  
Author(s):  
David E. Schimmel ◽  
Franklin T. Luk
Keyword(s):  
Fault Tolerance ◽  
Real Time ◽  
Multi Level

scholarly journals Deeper Siamese network with multi‐level feature fusion for real‐time visual tracking

2019 ◽  
Vol 55 (13) ◽  
pp. 742-745 ◽  
Author(s):  
Kang Yang ◽  
Huihui Song ◽  
Kaihua Zhang ◽  
Jiaqing Fan
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Feature Fusion ◽  
Siamese Network ◽  
Multi Level

A Design for Fault-Tolerant Communication Middleware Based on Time-Triggered

2014 ◽  
Vol 548-549 ◽  
pp. 1326-1329
Author(s):  
Juan Jin ◽  
Qing Fan Gu
Keyword(s):  
Fault Tolerance ◽  
Real Time ◽  
Fault Location ◽  
Fault Tolerant ◽  
Application Software ◽  
Tolerance Mechanisms ◽  
The Real ◽  
Communication Middleware ◽  
Fault Mechanism ◽  
Health Diagnosis

Against to the unsustainable problems of health diagnosis, fault location and fault tolerance mechanisms that existing in the current avionics applications, we proposed a fault-tolerant communication middleware which is based on time-triggered in this paper. This middleware is designed to provide a support platform for applications of the real-time based on communication middleware. From the communication middleware level and also combined with time-triggered mechanism and fault-tolerant strategy, it diagnoses the general faults first, and then routes them to the appropriate fault mechanism to process it. So the middleware completely separates fault-tolerant process from the application software functions.

Driver inattention monitoring system based on multimodal fusion with visual cues to improve driving safety

2016 ◽  
Vol 40 (3) ◽  
pp. 885-895 ◽  
Author(s):  
Xuanpeng Li ◽  
Emmanuel Seignez
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Visual Cues ◽  
Real Life ◽  
Multimodal Fusion ◽  
Driving Safety ◽  
Driver Drowsiness ◽  
Driver Monitoring ◽  
Driver Inattention ◽  
Multi Level

Driver inattention, either driver drowsiness or distraction, is a major contributor to serious traffic crashes. In general, most research on this topic studies driver drowsiness and distraction separately, and is often conducted in a well-controlled, simulated environment. By considering the reliability and flexibility of real-time driver monitoring systems, it is possible to evaluate driver inattention by the fusion of multiple selected cues in real life scenarios. This paper presents a real-time, visual-cue-based driver monitoring system, which can track both multi-level driver drowsiness and distraction simultaneously. A set of visual cues are adopted via analysis of drivers’ physical behaviour and driving performance. Driver drowsiness is evaluated using a multi-level scale, by applying evidence theory. Additionally, a general framework of extensive hierarchical combinations is used to generate a probabilistic evaluation of driving risk in real time. This driver inattention monitoring system with multimodal fusion has been proven to improve the accuracy of risk evaluation and reduce the rate of false alarms, and acceptance of the system is recommended.

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