On-line Monitoring of Real Time Applications for Early Error Detection

2008 ◽  
Author(s):  
Thomas Robert ◽  
Jean-Charles Fabre ◽  
Matthieu Roy
Keyword(s):  
Real Time ◽  
Error Detection ◽  
Early Error ◽  
On Line ◽  
Real Time Applications

A constraint-based solution for on-line testing of processors embedded in real-time applications

2005 ◽  
Author(s):  
Marcelo Moraes ◽  
Érika Cota ◽  
Luigi Carro ◽  
Flávio Wagner ◽  
Marcelo Lubaszewski
Keyword(s):  
Real Time ◽  
On Line ◽  
Real Time Applications

Speeding Up On-Line Route Scheduling for an Autonomous Robot Through Pre-Built Paths

Robotica ◽  
2020 ◽  
pp. 1-13
Author(s):  
Raul Alves ◽  
Josué Silva de Morais ◽  
Keiji Yamanaka
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Indoor Environments ◽  
On Line ◽  
Real Time Applications ◽  
The Traveling Salesman Problem ◽  
Route Scheduling

SUMMARY Today, robots can be found helping humans with their daily tasks. Some tasks require the robot to visit a set of locations in the environment efficiently, like in the Traveling Salesman Problem. As indoor environments are maze-like areas, feasible paths connecting locations must be computed beforehand, so they can be combined during the scheduling, which can be impracticable for real-time applications. This work presents an on-line Route Scheduling supported by a Fast Path Planning Method able to adjust pre-built paths. Experiments were carried out with virtual and real robots to evaluate time and quality of tours.

Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant
Keyword(s):  
Computer Architecture ◽  
Real Time ◽  
Data Abstraction ◽  
Monitoring Strategy ◽  
Intelligent Monitoring ◽  
Patient Status ◽  
On Line ◽  
Main Components ◽  
And Control ◽  
Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

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