scholarly journals Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
E. Bertino ◽  
M. R. Jahanshahi ◽  
A. Singla ◽  
R.-T. Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
The Times ◽  
Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

scholarly journals Intelligent IoT Systems for Civil Infrastructure Monitoring: A Research Roadmap

2020 ◽  
Author(s):  
Elisa Bertino ◽  
Mohammad Jahanshahi ◽  
Ankush Singla ◽  
Rih-Teng Wu
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Original Data ◽  
Local Analysis ◽  
Civil Infrastructure ◽  
Infrastructure Monitoring ◽  
Time Requirements ◽  
Iot Devices ◽  
Learning Reinforcement ◽  
Acquisition Processes

Abstract This paper address the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discusses approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices

scholarly journals ABB robot data collection based on dynamic link library

2021 ◽  
Vol 2113 (1) ◽  
pp. 012077
Author(s):  
Zhiyong Wu ◽  
Hongping Wang ◽  
Yiwen Zhang ◽  
Minglu Ai
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Production Capacity ◽  
Labor Cost ◽  
Dynamic Link Library ◽  
The Times ◽  
Very High ◽  
Dynamic Link

Abstract At present, with the development of the times and technology, in order to meet the needs of industrial production, robots have begun to be widely used in factories. Although the production efficiency of the robot is very high and relatively cheap compared to the labor cost, when an industrial robot fails, it will have a serious impact on the production capacity of the entire production line. Therefore, it is necessary to monitor various data of the robot. If some abnormal data can be found, it can perform effective predictive maintenance on the robot. This article mainly proposes a method for various data acquisition of ABB robots. The data acquisition interface is developed using C# and the dynamic link library is called. The data of ABB robots can be read in real time. It can realize real-time monitoring of each data of the robot.

PROTOTYPE ALAT PEMANTAU SALURAN AIR BAWAH TANAH DENGAN MENGGUNAKAN WEBCAM C170 BERBASIS RASPBERRY PI

2015 ◽  
Vol 1 (1) ◽  
pp. 37-45
Author(s):  
Irwansyah Irwansyah ◽  
Hendra Kusumah ◽  
Muhammad Syarif
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Public Awareness ◽  
Underground Water ◽  
Raspberry Pi ◽  
Laptop Computer ◽  
Web Browser ◽  
Disaster Monitoring ◽  
The Times ◽  
Human Desire

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.

scholarly journals The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Antonio Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Real Time ◽  
Failure Detection ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Time Data ◽  
Physical Systems ◽  
The Real ◽  
Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

A real time, wireless and low cost data acquisition system for residential PV modules

2020 ◽  
Author(s):  
Cheyma BARKA ◽  
Hanen MESSAOUDI-ABID ◽  
Houda BEN ATTIA SETTHOM ◽  
Afef BENNANI-BEN ABDELGHANI ◽  
Ilhem SLAMA-BELKHODJA ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Low Cost ◽  
Data Acquisition System ◽  
Cost Data ◽  
Acquisition System ◽  
Pv Modules

scholarly journals Development of the Cloud-Based Magnetic Data Acquisition System for Real Time Geomagnetic Monitoring

2021 ◽  
Vol 1768 (1) ◽  
pp. 012017
Author(s):  
K Burhanudin ◽  
M H Jusoh ◽  
Z I Abdul Latiff ◽  
M S Suaimi ◽  
Z Ibrahim ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Data Acquisition System ◽  
Acquisition System ◽  
Magnetic Data

Real-time, High-resolution Depth Upsampling on Embedded Accelerators

2021 ◽  
Vol 20 (3) ◽  
pp. 1-22
Author(s):  
David Langerman ◽  
Alan George
Keyword(s):  
High Resolution ◽  
Low Power ◽  
Real Time ◽  
Mixed Reality ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Reconfigurable Logic ◽  
Depth Sensors ◽  
Time Requirements ◽  
Graphics Processing

High-resolution, low-latency apps in computer vision are ubiquitous in today’s world of mixed-reality devices. These innovations provide a platform that can leverage the improving technology of depth sensors and embedded accelerators to enable higher-resolution, lower-latency processing for 3D scenes using depth-upsampling algorithms. This research demonstrates that filter-based upsampling algorithms are feasible for mixed-reality apps using low-power hardware accelerators. The authors parallelized and evaluated a depth-upsampling algorithm on two different devices: a reconfigurable-logic FPGA embedded within a low-power SoC; and a fixed-logic embedded graphics processing unit. We demonstrate that both accelerators can meet the real-time requirements of 11 ms latency for mixed-reality apps. 1

scholarly journals A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm

2021 ◽  
Vol 11 (11) ◽  
pp. 4874
Author(s):  
Milan Brankovic ◽  
Eduardo Gildin ◽  
Richard L. Gibson ◽  
Mark E. Everett
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Matrix Decomposition ◽  
Original Data ◽  
Velocity Estimation ◽  
Singular Vectors ◽  
Well Stimulation ◽  
Marine Seismic ◽  
Value Decomposition ◽  
Seismic Data Compression

Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distributed acoustic sensing (DAS) has seen increasing application in microseimic monitoring. Given large volumes of data to be analyzed in real-time and impractical memory and storage requirements, fast compression and accurate interpretation methods are necessary for real-time monitoring campaigns using DAS. In response to the developments in data acquisition, we have created shifted-matrix decomposition (SMD) to compress seismic data by storing it into pairs of singular vectors coupled with shift vectors. This is achieved by shifting the columns of a matrix of seismic data before applying singular value decomposition (SVD) to it to extract a pair of singular vectors. The purpose of SMD is data denoising as well as compression, as reconstructing seismic data from its compressed form creates a denoised version of the original data. By analyzing the data in its compressed form, we can also run signal detection and velocity estimation analysis. Therefore, the developed algorithm can simultaneously compress and denoise seismic data while also analyzing compressed data to estimate signal presence and wave velocities. To show its efficiency, we compare SMD to local SVD and structure-oriented SVD, which are similar SVD-based methods used only for denoising seismic data. While the development of SMD is motivated by the increasing use of DAS, SMD can be applied to any seismic data obtained from a large number of receivers. For example, here we present initial applications of SMD to readily available marine seismic data.

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