scholarly journals Guarantee of Information Delivery in the Spacecraft Onboard Network Based on the SpaceWire Interface

2020 ◽  
Vol 7 (4) ◽  
pp. 83-89
Author(s):  
A. Yu. Koshelev ◽  
◽  
A. M. Dianova ◽  
D. O. Petukhov ◽  
◽  
...  
Keyword(s):  
Real Time ◽  
Data Transmission ◽  
Information Delivery ◽  
Protocol Stack ◽  
Software Model ◽  
Time Requirements

Nowadays SpaceWire is regarded as a perspective aerospace data transmission interface standard. This paper gives an analysis of a guaranteed information delivery of SpaceWire networks. The result of the analysis showed key problems that did not allow using this standard in real-time networks. A method was developed and proposed to guarantee the information delivery in the SpaceWire network based on scheduled routing. Tests using a software model of the modified network were performed. The results obtained confirmed the efficiency of the method used to ensure the guarantee of information delivery in the network. It is shown that a network with a modified protocol stack meets real-time requirements.

scholarly journals Remote Video Surveillance System Based on Direct Show and Image Compression

2013 ◽  
Vol 756-759 ◽  
pp. 439-442
Author(s):  
Shang Fu Gong ◽  
Li Gang Wu ◽  
Yan Jun Wang
Keyword(s):  
Image Compression ◽  
Real Time ◽  
Video Surveillance ◽  
Data Transmission ◽  
Video Transmission ◽  
Massive Data ◽  
Time Requirements ◽  
Rich Information ◽  
Precautionary Measures ◽  
Application Requirements

Video surveillance, convenience and rich information, has been widely used in security, protection, monitoring and other occasions, and has already been one of the most important precautionary measures in commercial, residential and transportation areas. However, considering the massive data transmission needs and higher real-time requirements for video surveillance, a remote video surveillance plan has been put forward on the analysis basis of Microsoft DirectShow and Image Compression. This plan adopts the C/S structure, adapts to the requirements of real-time video transmission, with a better fluency. In addition, the picture clarity satisfies the application requirements.

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.

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 On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data

2020 ◽  
Vol 13 (1) ◽  
pp. 89
Author(s):  
Manuel Carranza-García ◽  
Jesús Torres-Mateo ◽  
Pedro Lara-Benítez ◽  
Jorge García-Gutiérrez
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Remote Sensing Data ◽  
Autonomous Driving ◽  
Two Stage ◽  
Detection Systems ◽  
One Stage ◽  
Time Requirements ◽  
Speed Accuracy

Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance on their suitability when using them in a particular scenario such as autonomous driving. In this work, we aim to assess the performance of existing 2D detection systems on a multi-class problem (vehicles, pedestrians, and cyclists) with images obtained from the on-board camera sensors of a car. We evaluate several one-stage (RetinaNet, FCOS, and YOLOv3) and two-stage (Faster R-CNN) deep learning meta-architectures under different image resolutions and feature extractors (ResNet, ResNeXt, Res2Net, DarkNet, and MobileNet). These models are trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context. For the experimental study, we use the Waymo Open Dataset, which is the largest existing benchmark. Despite the rising popularity of one-stage detectors, our findings show that two-stage detectors still provide the most robust performance. Faster R-CNN models outperform one-stage detectors in accuracy, being also more reliable in the detection of minority classes. Faster R-CNN Res2Net-101 achieves the best speed/accuracy tradeoff but needs lower resolution images to reach real-time speed. Furthermore, the anchor-free FCOS detector is a slightly faster alternative to RetinaNet, with similar precision and lower memory usage.

The Design of ZigBee Wireless Network for the Detection Platform of the Cylinders

2012 ◽  
Vol 241-244 ◽  
pp. 2504-2509
Author(s):  
Yan Li ◽  
Qiao Xiang Gu
Keyword(s):  
Real Time ◽  
Wireless Network ◽  
Environmental Conditions ◽  
Protocol Stack ◽  
The Real ◽  
The Core ◽  
Core Components ◽  
Real Time Detection ◽  
Cable Connection

The equipment, called detection platform of the cylinders, is used for detecting cylinders so that cylinders can be at ease use. In order to transmit the real-time detection data to PC for further processing, the platform should be connected with PC. Cable connection, in some production and environmental conditions, is limited. Under the circumstance, building wireless network is the better choice. Through comparative studying, ZigBee is chosen to be the technology for building wireless network. ZigBee chip and ZigBee2006 protocol stack are the core components in the ZigBee nodes.

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