Frame rate computing and aggregation measurement toward QoS/QoE in Video-SAR systems for UAV-borne real-time remote sensing

2021 ◽  
Author(s):  
Mohammad R. Khosravi ◽  
Sadegh Samadi
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Frame Rate

scholarly journals Real-time FPGA-based implementation of the AKAZE algorithm with nonlinear scale space generation using image partitioning

2021 ◽  
Author(s):  
Parastoo Soleimani ◽  
David W. Capson ◽  
Kin Fun Li
Keyword(s):  
Real Time ◽  
Memory Management ◽  
Scale Space ◽  
Image Resolution ◽  
Hardware Architecture ◽  
Frame Rate ◽  
Time Sharing ◽  
Scale Invariant ◽  
Nonlinear Scale Space ◽  
Nonlinear Scale

AbstractThe first step in a scale invariant image matching system is scale space generation. Nonlinear scale space generation algorithms such as AKAZE, reduce noise and distortion in different scales while retaining the borders and key-points of the image. An FPGA-based hardware architecture for AKAZE nonlinear scale space generation is proposed to speed up this algorithm for real-time applications. The three contributions of this work are (1) mapping the two passes of the AKAZE algorithm onto a hardware architecture that realizes parallel processing of multiple sections, (2) multi-scale line buffers which can be used for different scales, and (3) a time-sharing mechanism in the memory management unit to process multiple sections of the image in parallel. We propose a time-sharing mechanism for memory management to prevent artifacts as a result of separating the process of image partitioning. We also use approximations in the algorithm to make hardware implementation more efficient while maintaining the repeatability of the detection. A frame rate of 304 frames per second for a $$1280 \times 768$$ 1280 × 768 image resolution is achieved which is favorably faster in comparison with other work.

scholarly journals Real-Time Semantic Segmentation of Remote Sensing Images Based on Bilateral Attention Refined Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 28349-28360
Author(s):  
Jiali Cai ◽  
Chunjuan Liu ◽  
Haowen Yan ◽  
Xiaosuo Wu ◽  
Wanzhen Lu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Semantic Segmentation ◽  
Remote Sensing Images

scholarly journals Distributed X-ray photon correlation spectroscopy data reduction using Hadoop MapReduce

2018 ◽  
Vol 25 (4) ◽  
pp. 1135-1143 ◽  
Author(s):  
Faisal Khan ◽  
Suresh Narayanan ◽  
Roger Sersted ◽  
Nicholas Schwarz ◽  
Alec Sandy
Keyword(s):  
Real Time ◽  
Photon Correlation Spectroscopy ◽  
Correlation Spectroscopy ◽  
Frame Rate ◽  
Computational Techniques ◽  
Complex Materials ◽  
Photon Correlation ◽  
X Ray ◽  
Wide Range ◽  
Recent Developments

Multi-speckle X-ray photon correlation spectroscopy (XPCS) is a powerful technique for characterizing the dynamic nature of complex materials over a range of time scales. XPCS has been successfully applied to study a wide range of systems. Recent developments in higher-frame-rate detectors, while aiding in the study of faster dynamical processes, creates large amounts of data that require parallel computational techniques to process in near real-time. Here, an implementation of the multi-tau and two-time autocorrelation algorithms using the Hadoop MapReduce framework for distributed computing is presented. The system scales well with regard to the increase in the data size, and has been serving the users of beamline 8-ID-I at the Advanced Photon Source for near real-time autocorrelations for the past five years.

Resource management of GEO relays for real-time remote sensing

2021 ◽  
Author(s):  
Xiaobin Xu ◽  
Hui Zhao ◽  
Chang Liu ◽  
Qi Wang ◽  
Shangguang Wang
Keyword(s):  
Remote Sensing ◽  
Resource Management ◽  
Real Time

YOLOrs-lite: A Lightweight CNN For Real-Time Object Detection in Remote-Sensing

2021 ◽  
Author(s):  
Manish Sharma ◽  
Panos P. Markopoulos ◽  
Eli Saber
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Real Time

Real time monitoring fire detection Using Remote Sensing

2021 ◽  
Author(s):  
Muhammad Iqbal Habibie ◽  
Nety Nurda ◽  
Harun Idham Akbar ◽  
Oni Bibin Bintoro ◽  
Robby Arifandri ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Fire Detection ◽  
Real Time Monitoring

Use of geographic information systems and remote sensing as automated tool for variable rate irrigation prescriptions

2019 ◽  
Author(s):  
◽  
Anh Thi Tuan Nguyen
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Regression Models ◽  
Field Data ◽  
Irrigation Management ◽  
Irrigation Scheduling ◽  
Crop Growth ◽  
Variable Rate ◽  
Water Application ◽  
Center Pivot

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Economic as well as water shortage pressure on agricultural use of water has placed added emphasis on efficient irrigation management. Center pivot technology has made great improvement with variable rate irrigation (VRI) technology to vary water application spatially and temporally to maximize the economic and environmental return. Proper management of VRI systems depends on correctly matching the pivot application to specific field temporal and areal conditions. There is need for a tool to accurately and inexpensively define dynamic management zones, to sense within-field variability in real time, and control variable rate water application so that producers are more willing to adopt and utilize the advantages of VRI systems. This study included tests of the center pivot system uniformity performance in 2014 at Delta Research Center in Portageville, MO. The goal of this research was to develop MOPivot software with an algorithm to determine unique management areas under center pivot systems based on system design and limitations. The MOPivot tool automates prescriptions for VRI center pivot based on non-uniform water needs while avoiding potential runoff and deep percolation. The software was validated for use in real-time irrigation management in 2018 for VRI control system of a Valley 8000 center pivot planted to corn. The water balance model was used to manage irrigation scheduling. Field data, together with soil moisture sensor measurement of soil water content, were used to develop the regression model of remote sensing-based crop coefficient (Kc). Remote sensing vegetation index in conjunction with GDD and crop growth stages in regression models showed high correlation with Kc. Validation of those regression models was done using Centralia, MO, field data in 2016. The MOPivot successfully created prescriptions to match system capacity of the management zone based on system limitations for center pivot management. Along with GIS data sources, MOPivot effectively provides readily available graphical prescription maps, which can be edited and directly uploaded to a center pivot control panel. The modeled Kc compared well with FAO Kc. By combining GDD and crop growth in the models, these models would account for local weather conditions and stage of crop during growing season as time index in estimating Kc. These models with Fraction of growth (FrG) and cumulative growing degree days (cGDD) had a higher coefficient of efficiency, higher Nash-Sutcliffe coefficient of efficiency and higher Willmott index of agreement. Future work should include improvement in the MOPivot software with different crops and aerial remote sensing imagery to generate dynamic prescriptions during the season to support irrigation scheduling for real-time monitoring of field conditions.

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