scholarly journals AIRBORNE CAMERA SYSTEM FOR REAL-TIME APPLICATIONS – SUPPORT OF A NATIONAL CIVIL PROTECTION EXERCISE

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1189-1194 ◽  
Author(s):  
V. Gstaiger ◽  
H. Römer ◽  
D. Rosenbaum ◽  
F. Henkel
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Data Transfer ◽  
Task Force ◽  
Aerial Imagery ◽  
Traffic Information ◽  
Civil Protection ◽  
Camera System ◽  
Real Time Processing ◽  
Emergency Situations

In the VABENE++ project of the German Aerospace Center (DLR), powerful tools are being developed to aid public authorities and organizations with security responsibilities as well as traffic authorities when dealing with disasters and large public events. One focus lies on the acquisition of high resolution aerial imagery, its fully automatic processing, analysis and near real-time provision to decision makers in emergency situations. For this purpose a camera system was developed to be operated from a helicopter with light-weight processing units and microwave link for fast data transfer. In order to meet end-users’ requirements DLR works close together with the German Federal Office of Civil Protection and Disaster Assistance (BBK) within this project. One task of BBK is to establish, maintain and train the German Medical Task Force (MTF), which gets deployed nationwide in case of large-scale disasters. In October 2014, several units of the MTF were deployed for the first time in the framework of a national civil protection exercise in Brandenburg. The VABENE++ team joined the exercise and provided near real-time aerial imagery, videos and derived traffic information to support the direction of the MTF and to identify needs for further improvements and developments. <br><br> In this contribution the authors introduce the new airborne camera system together with its near real-time processing components and share experiences gained during the national civil protection exercise.

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data

Collaborative Systems for Decision Making for Disaster Preparedness and Response, Department of Information Systems

2010 ◽  
pp. 41-57
Author(s):  
Deidre Hahn ◽  
Jessica Block ◽  
Mark Keith ◽  
Ajay Vinze
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Disaster Preparedness ◽  
Large Scale ◽  
Emergency Situation ◽  
Group Processes ◽  
Group Decision Support Systems ◽  
Emergency Situations ◽  
Collaborative Technologies ◽  
Disaster Preparedness And Response

Real time collaboration solutions are critical during a large scale emergency situation and necessitate the coordination of multiple disparate groups. Collaborative technologies may be valuable in the planning and execution of disaster preparedness and response. Yet, research suggests that specific collaborative technologies, such as group decision support systems, are not often leveraged for decision-making during real time emergency situations in the United States. In this chapter, we propose a theoretical model of the impact of disaster immediacy and collaboration systems on group processes and outcomes. Using a 3D model of the dimensions of space, time, and situation, we explore media richness and group polarization within the context of collaboration technologies and disaster situations. We also present the next generation of collaboration technology extensions in order to address the need for more contemporary decisional settings. This set of principles and theories suggest how collaborative technologies may be positioned to better manage future disasters.

scholarly journals AN INTEGRATED RAPID MAPPING SYSTEM FOR DISASTER MANAGEMENT

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 499-504 ◽  
Author(s):  
D. Hein ◽  
S. Bayer ◽  
R. Berger ◽  
T. Kraft ◽  
D. Lesmeister
Keyword(s):  
Real Time ◽  
Disaster Management ◽  
Large Scale ◽  
Software Tool ◽  
Aerial Images ◽  
Sensor Data ◽  
Camera System ◽  
Rapid Mapping ◽  
Mapping System ◽  
Effective Assessment

Natural disasters as well as major man made incidents are an increasingly serious threat for civil society. Effective, fast and coordinated disaster management crucially depends on the availability of a real-time situation picture of the affected area. However, in situ situation assessment from the ground is usually time-consuming and of limited effect, especially when dealing with large or inaccessible areas. A rapid mapping system based on aerial images can enable fast and effective assessment and analysis of medium to large scale disaster situations. This paper presents an integrated rapid mapping system that is particularly designed for real-time applications, where comparatively large areas have to be recorded in short time. The system includes a lightweight camera system suitable for UAV applications and a software tool for generating aerial maps from recorded sensor data within minutes after landing. The paper describes in particular which sensors are applied and how they are operated. Furthermore it outlines the procedure, how the aerial map is generated from image and additional gathered sensor data.

scholarly journals Measuring Traffic in Cities Through a Large-Scale Online Platform

2019 ◽  
Vol 1 (2-3) ◽  
pp. 161-173 ◽  
Author(s):  
Vilhelm Verendel ◽  
Sonia Yeh
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Data Availability ◽  
Traffic Information ◽  
Traffic Data ◽  
Online Platform ◽  
Real Time Traffic ◽  
Data Source ◽  
Road Segments

Abstract Online real-time traffic data services could effectively deliver traffic information to people all over the world and provide large benefits to the society and research about cities. Yet, city-wide road network traffic data are often hard to come by on a large scale over a longer period of time. We collect, describe, and analyze traffic data for 45 cities from HERE, a major online real-time traffic information provider. We sampled the online platform for city traffic data every 5 min during 1 year, in total more than 5 million samples covering more than 300 thousand road segments. Our aim is to describe some of the practical issues surrounding the data that we experienced in working with this type of data source, as well as to explore the data patterns and see how this data source provides information to study traffic in cities. We focus on data availability to characterize how traffic information is available for different cities; it measures the share of road segments with real-time traffic information at a given time for a given city. We describe the patterns of real-time data availability, and evaluate methods to handle filling in missing speed data for road segments when real-time information was not available. We conduct a validation case study based on Swedish traffic sensor data and point out challenges for future validation. Our findings include (i) a case study of validating the HERE data against ground truth available for roads and lanes in a Swedish city, showing that real-time traffic data tends to follow dips in travel speed but miss instantaneous higher speed measured in some sensors, typically at times when there are fewer vehicles on the road; (ii) using time series clustering, we identify four clusters of cities with different types of measurement patterns; and (iii) a k-nearest neighbor-based method consistently outperforms other methods to fill in missing real-time traffic speeds. We illustrate how to work with this kind of traffic data source that is increasingly available to researchers, travellers, and city planners. Future work is needed to broaden the scope of validation, and to apply these methods to use online data for improving our knowledge of traffic in cities.

Research and Implementation of the Secure Database-Update Mechanism

2014 ◽  
Vol 513-517 ◽  
pp. 1752-1755 ◽  
Author(s):  
Chun Liu ◽  
Kun Tan
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Data Transfer ◽  
Time Data ◽  
Safety Critical ◽  
Large Scale Data ◽  
Data Transfer Protocol ◽  
Safety And Reliability ◽  
Computer Based ◽  
Short Time

For a safety critical computer, large-scale data like database which has to be transferred in an instant time cannot be voted directly. This paper proposes a database update algorithm for safety critical computer based on status vote,which is to vote the database status instead of database itself. This algorithm can solve the problem of voting too much data in a short time, and compare versions of database of different modules in real time. A Markov model is built to calculate the safety and reliability of this algorithm. The results show that this algorithm meets the update requirement of safety critical computer. 1. Communication protocol for database update 1.1 TFTP protocol TFTP is a simple protocol for transporting document. It usually uses the UDP protocol to realize but the TFTP does not require the specific agreement of implementation and can implement with TCP in special occasions. [This agreement is designed for small file transferring, so it doesn't have function many FTP usually does; it can only acquire or write the file from the server and not able tot list directory, not authenticate. It transfers 8 bits of data with three models: netascii, the eight-bit ASCII form; octet, the eight-bit source data type; mail, no longer supported, it returns the data back directly to the user rather than saved as a file. 1.2 SRTP Ethernet security real-time data transfer protocol

scholarly journals Log Data Real Time Analysis Using Big Data Analytic Framework with Storm and Hadoop

2018 ◽  
Vol 246 ◽  
pp. 03009
Author(s):  
Jia-Ke Lv ◽  
Yang Li ◽  
Xuan Wang
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Knowledge Extraction ◽  
Streaming Data ◽  
Small Scale ◽  
Global Knowledge ◽  
Data Set ◽  
Log Data ◽  
Real Time Processing ◽  
Real Time Analysis

The log data real-time processing platform which is built using Storm On YARN integrated MapReduce and Storm that use MapReduce to complete large-scale off-line data global knowledge extraction, sudden knowledge extraction of small-scale data in Kafka buffers through Storm, and continuous real-time calculation of streaming data in combination with global knowledge. We tested our technique with the well-known KDD99 CUP data set. The experimentation results prove the system to be effective and efficient.

scholarly journals Real-time cortical simulation on neuromorphic hardware

2019 ◽  
Vol 378 (2164) ◽  
pp. 20190160 ◽  
Author(s):  
Oliver Rhodes ◽  
Luca Peres ◽  
Andrew G. D. Rowley ◽  
Andrew Gait ◽  
Luis A. Plana ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Large Scale ◽  
Spiking Neural Network ◽  
Real Time Processing ◽  
Real Time Simulation ◽  
Statistical Measures ◽  
Time Simulation ◽  
Neuromorphic Hardware ◽  
Cortical Microcircuit

Real-time simulation of a large-scale biologically representative spiking neural network is presented, through the use of a heterogeneous parallelization scheme and SpiNNaker neuromorphic hardware. A published cortical microcircuit model is used as a benchmark test case, representing ≈1 mm 2 of early sensory cortex, containing 77 k neurons and 0.3 billion synapses. This is the first hard real-time simulation of this model, with 10 s of biological simulation time executed in 10 s wall-clock time. This surpasses best-published efforts on HPC neural simulators (3 × slowdown) and GPUs running optimized spiking neural network (SNN) libraries (2 × slowdown). Furthermore, the presented approach indicates that real-time processing can be maintained with increasing SNN size, breaking the communication barrier incurred by traditional computing machinery. Model results are compared to an established HPC simulator baseline to verify simulation correctness, comparing well across a range of statistical measures. Energy to solution and energy per synaptic event are also reported, demonstrating that the relatively low-tech SpiNNaker processors achieve a 10 × reduction in energy relative to modern HPC systems, and comparable energy consumption to modern GPUs. Finally, system robustness is demonstrated through multiple 12 h simulations of the cortical microcircuit, each simulating 12 h of biological time, and demonstrating the potential of neuromorphic hardware as a neuroscience research tool for studying complex spiking neural networks over extended time periods. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.

scholarly journals FUSION OF MULTI-VIEW AND MULTI-SCALE AERIAL IMAGERY FOR REAL-TIME SITUATION AWARENESS APPLICATIONS

2015 ◽  
Vol XL-1/W4 ◽  
pp. 201-206 ◽  
Author(s):  
X. Zhuo ◽  
F. Kurz ◽  
P. Reinartz
Keyword(s):  
Real Time ◽  
Reference Frame ◽  
Situation Awareness ◽  
Large Scale ◽  
Bundle Adjustment ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Aerial Image ◽  
Main Concern ◽  
Multi Scale

Manned aircraft has long been used for capturing large-scale aerial images, yet the high costs and weather dependence restrict its availability in emergency situations. In recent years, MAV (Micro Aerial Vehicle) emerged as a novel modality for aerial image acquisition. Its maneuverability and flexibility enable a rapid awareness of the scene of interest. Since these two platforms deliver scene information from different scale and different view, it makes sense to fuse these two types of complimentary imagery to achieve a quick, accurate and detailed description of the scene, which is the main concern of real-time situation awareness. This paper proposes a method to fuse multi-view and multi-scale aerial imagery by establishing a common reference frame. In particular, common features among MAV images and geo-referenced airplane images can be extracted by a scale invariant feature detector like SIFT. From the tie point of geo-referenced images we derive the coordinate of corresponding ground points, which are then utilized as ground control points in global bundle adjustment of MAV images. In this way, the MAV block is aligned to the reference frame. Experiment results show that this method can achieve fully automatic geo-referencing of MAV images even if GPS/IMU acquisition has dropouts, and the orientation accuracy is improved compared to the GPS/IMU based georeferencing. The concept for a subsequent 3D classification method is also described in this paper.

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