scholarly journals A real-time visual processing system using a general-purpose vision chip

2003 ◽  
Author(s):  
S. Kagami ◽  
T. Komuro ◽  
I. Ishii ◽  
M. Ishikawa
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Processing System ◽  
General Purpose ◽  
Vision Chip

scholarly journals XSTREAM: A Highly Efficient High Speed Real-time Satellite Data Acquisition and Processing System using Heterogeneous Computing

2014 ◽  
Vol XL-8 ◽  
pp. 1171-1176
Author(s):  
K. Pramod Kumar ◽  
P. Mahendra ◽  
V. Ramakrishna rReddy ◽  
T. Tirupathi ◽  
A. Akilan ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Satellite Data ◽  
High Speed ◽  
Heterogeneous Computing ◽  
Processing System ◽  
Cost Effective ◽  
General Purpose ◽  
Quick Response ◽  
Computing Platform

In the last decade, the remote sensing community has observed a significant growth in number of satellites, sensors and their resolutions, thereby increasing the volume of data to be processed each day. Satellite data processing is a complex and time consuming activity. It consists of various tasks, such as decode, decrypt, decompress, radiometric normalization, stagger corrections, ephemeris data processing for geometric corrections etc., and finally writing of the product in the form of an image file. Each task in the processing chain is sequential in nature and has different computing needs. Conventionally the processes are cascaded in a well organized workflow to produce the data products, which are executed on general purpose high-end servers / workstations in an offline mode. Hence, these systems are considered to be ineffective for real-time applications that require quick response and just-intime decision making such as disaster management, home land security and so on. <br><br> This paper discusses anovel approach to processthe data online (as the data is being acquired) using a heterogeneous computing platform namely XSTREAM which has COTS hardware of CPUs, GPUs and FPGA. This paper focuses on the process architecture, re-engineering aspects and mapping of tasks to the right computing devicewithin the XSTREAM system, which makes it an ideal cost-effective platform for acquiring, processing satellite payload data in real-time and displaying the products in original resolution for quick response. The system has been tested for IRS CARTOSAT and RESOURCESAT series of satellites which have maximum data downlink speed of 210 Mbps.

General-purpose vision chip architecture for real-time machine vision

1997 ◽  
Vol 12 (6) ◽  
pp. 619-627 ◽  
Author(s):  
Takashi Komuro ◽  
Idaku Ish ◽  
Masatoshi Ishikawa
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
General Purpose ◽  
Time Machine ◽  
Vision Chip

scholarly journals A Real Time Processing system for big data in astronomy: Applications to HERA

2021 ◽  
pp. 100489
Author(s):  
Paul La Plante ◽  
P.K.G. Williams ◽  
M. Kolopanis ◽  
J.S. Dillon ◽  
A.P. Beardsley ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Processing System ◽  
Real Time Processing ◽  
Time Processing

Design of a real-time video processing system with FPGA

2002 ◽  
Author(s):  
Wei Liu ◽  
Zeying Chi ◽  
Wenjian Chen
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Processing System

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

scholarly journals Evaluating Latency in Multiprocessing Embedded Systems for the Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3322
Author(s):  
Sara Alonso ◽  
Jesús Lázaro ◽  
Jaime Jiménez ◽  
Unai Bidarte ◽  
Leire Muguira
Keyword(s):  
Operating System ◽  
Smart Grid ◽  
Real Time ◽  
Processing System ◽  
The Other ◽  
Programmable Logic ◽  
Detailed Characterization ◽  
Timing Constraint ◽  
Time Part

Smart grid endpoints need to use two environments within a processing system (PS), one with a Linux-type operating system (OS) using the Arm Cortex-A53 cores for management tasks, and the other with a standalone execution or a real-time OS using the Arm Cortex-R5 cores. The Xen hypervisor and the OpenAMP framework allow this, but they may introduce a delay in the system, and some messages in the smart grid need a latency lower than 3 ms. In this paper, the Linux thread latencies are characterized by the Cyclictest tool. It is shown that when Xen hypervisor is used, this scenario is not suitable for the smart grid as it does not meet the 3 ms timing constraint. Then, standalone execution as the real-time part is evaluated, measuring the delay to handle an interrupt created in programmable logic (PL). The standalone application was run in A53 and R5 cores, with Xen hypervisor and OpenAMP framework. These scenarios all met the 3 ms constraint. The main contribution of the present work is the detailed characterization of each real-time execution, in order to facilitate selecting the most suitable one for each application.

Design and implementation of a virtual ARINC 653 simulation platform

SIMULATION ◽  
2021 ◽  
pp. 003754972199601
Author(s):  
Jinchao Chen ◽  
Keke Chen ◽  
Chenglie Du ◽  
Yifan Liu
Keyword(s):  
Real Time ◽  
System Development ◽  
General Purpose ◽  
Simulation Platform ◽  
Communication Management ◽  
Management Communication ◽  
Real Time Simulation ◽  
Testing And Debugging ◽  
Time Simulation ◽  
Efficiency And Effectiveness

The ARINC 653 operation system is currently widely adopted in the avionics industry, and has become the mainstream architecture in avionics applications because of its strong agility and reliability. Although ARINC 653 can efficiently reduce the weight and energy consumption, it results in a serious development and verification problem for avionics systems. As ARINC 653 is non-open source software and lacks effective support for software testing and debugging, it is of great significance to build a real-time simulation platform for ARINC 653 on general-purpose operating systems, improving the efficiency and effectiveness of system development and implementation. In this paper, a virtual ARINC 653 platform is designed and realized by using real-time simulation technology. The proposed platform is composed of partition management, communication management, and health monitoring management, provides the same operation interfaces as the ARINC 653 system, and allows dynamic debugging of avionics applications without requiring the actual presence of real devices. Experimental results show that the platform not only simulates the functionalities of ARINC 653, but also meets the real-time requirements of avionics applications.

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