scholarly journals Data Analysis and Results of the Radiation-Tolerant Collaborative Computer On-Board OPTOS CubeSat

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Alberto Martín-Ortega ◽  
Santiago Rodríguez ◽  
José R. de Mingo ◽  
Sergio Ibarmia ◽  
Joaquín Rivas ◽  
...  
Keyword(s):  
Data Analysis ◽  
Power Consumption ◽  
Real Time ◽  
Space Missions ◽  
Small Satellites ◽  
Unit Level ◽  
Aerospace Technology ◽  
Computer Based ◽  
Commercial Off The Shelf ◽  
Radiation Tolerant

The current evolution of the space missions demands to increase the computing capacities of the on-board computer while reducing its power consumption. This requirement evolves faster than the ability of the manufacturers to develop better space-qualified processors. To meet the strong requirements, the National Institute of Aerospace Technology has developed a distributed on-board computer based on commercial off-the-shelf (COTS). This computer, named OPTOS, provides enhanced computational capacities with respect to what computers of other small satellites typically provide. To maintain the reliability needed to perform typical critical activities such as real-time maintenance or current surveillance, authors have conceived a set of collaborative hardening techniques, taking advantage of the distributed architecture of the OPTOS On-Board Computer. The 3-year mission data analysis shows the feasibility of the collaborative hardening techniques implemented, despite using SEU sensitive devices. The authors describe the processes and tools used to analyse the data and clearly expose the functional errors found at unit level, while the system remains unfaulty and reliable thanks to the collaborative techniques.

Teaching Computer Based Historical Data Analysis

1994 ◽  
Vol 6 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Charles Anderson ◽  
Robert J. Morris
Keyword(s):  
Data Analysis ◽  
Social History ◽  
Historical Data ◽  
Good Practice ◽  
Computer Assisted ◽  
Historical Data Analysis ◽  
University Of Edinburgh ◽  
Computer Based ◽  
The University

A case study ofa third year course in the Department of Economic and Social History in the University of Edinburgh isusedto considerandhighlightaspects of good practice in the teaching of computer-assisted historical data analysis.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

P17.23 A Real Time Computer Based Nosocomial Infection Surveillance System

2006 ◽  
Vol 64 ◽  
pp. S88-S89
Author(s):  
J.A. Alava ◽  
C. Ezpeleta ◽  
I. Atutxa ◽  
C. Busto ◽  
E. Gómez ◽  
...  
Keyword(s):  
Nosocomial Infection ◽  
Real Time ◽  
Surveillance System ◽  
Infection Surveillance ◽  
Computer Based

scholarly journals Goals and measures for analyzing power consumption data in manufacturing enterprises

2021 ◽  
Vol 3 (1) ◽  
pp. 65-82
Author(s):  
Sören Henning ◽  
Wilhelm Hasselbring ◽  
Heinz Burmester ◽  
Armin Möbius ◽  
Maik Wojcieszak
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Manufacturing Industry ◽  
Fog Computing ◽  
Electrical Power ◽  
Time Data ◽  
Manufacturing Enterprises ◽  
Computing Paradigm ◽  
Consumption Data ◽  
Analyzing Power

AbstractThe Internet of Things adoption in the manufacturing industry allows enterprises to monitor their electrical power consumption in real time and at machine level. In this paper, we follow up on such emerging opportunities for data acquisition and show that analyzing power consumption in manufacturing enterprises can serve a variety of purposes. In two industrial pilot cases, we discuss how analyzing power consumption data can serve the goals reporting, optimization, fault detection, and predictive maintenance. Accompanied by a literature review, we propose to implement the measures real-time data processing, multi-level monitoring, temporal aggregation, correlation, anomaly detection, forecasting, visualization, and alerting in software to tackle these goals. In a pilot implementation of a power consumption analytics platform, we show how our proposed measures can be implemented with a microservice-based architecture, stream processing techniques, and the fog computing paradigm. We provide the implementations as open source as well as a public show case allowing to reproduce and extend our research.

User-centered Requirements for Patient Fluid Intake Monitoring Support

2020 ◽  
Vol 64 (1) ◽  
pp. 736-740
Author(s):  
Ellen J. Bass ◽  
Andrew J. Abbate ◽  
Yaman Noaiseh ◽  
Rose Ann DiMaria-Ghalili
Keyword(s):  
Data Analysis ◽  
Focus Group ◽  
Focus Groups ◽  
Real Time ◽  
Information Needs ◽  
Fluid Intake ◽  
Fluid Consumption ◽  
Registered Dietitians ◽  
Group Data ◽  
Focus Group Data

There is a need to support patients with monitoring liquid intake. This work addresses development of requirements for real-time and historical displays and reports with respect to fluid consumption as well as alerts based on critical clinical thresholds. We conducted focus groups with registered nurses and registered dietitians in order to identify the information needs and alerting criteria to support fluid consumption measurement. This paper presents results of the focus group data analysis and the related requirements resulting from the analysis.

scholarly journals Efficient Prediction of Network Traffic for Real-Time Applications

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Faisal Iqbal ◽  
Muhammad Zahid ◽  
Durdana Habib ◽  
Lizy Kurian John
Keyword(s):  
Power Consumption ◽  
Real Time ◽  
Power Management ◽  
Dynamic Resource Allocation ◽  
Computation Complexity ◽  
Real Time Traffic ◽  
Double Exponential ◽  
Efficient Prediction ◽  
Dynamic Resource ◽  
Double Exponential Smoothing

Accurate real-time traffic prediction is required in many networking applications like dynamic resource allocation and power management. This paper explores a number of predictors and searches for a predictor which has high accuracy and low computation complexity and power consumption. Many predictors from three different classes, including classic time series, artificial neural networks, and wavelet transform-based predictors, are compared. These predictors are evaluated using real network traces. Comparison of accuracy and cost, both in terms of computation complexity and power consumption, is presented. It is observed that a double exponential smoothing predictor provides a reasonable tradeoff between performance and cost overhead.

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