scholarly journals Note: Quasi-real-time analysis of dynamic near field scattering data using a graphics processing unit

2012 ◽  
Vol 83 (10) ◽  
pp. 106101 ◽  
Author(s):  
G. Cerchiari ◽  
F. Croccolo ◽  
F. Cardinaux ◽  
F. Scheffold
Keyword(s):  
Real Time ◽  
Graphics Processing Unit ◽  
Near Field ◽  
Scattering Data ◽  
Processing Unit ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Graphics Processing

Development of a Parallel Fast Fourier Transform-Based Algorithm for Digital Hologram Reconstruction

2014 ◽  
Vol 1040 ◽  
pp. 949-953 ◽  
Author(s):  
T.R. Vuyets ◽  
Vladislav A. Ovchinnikov
Keyword(s):  
Digital Holography ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Time Analysis ◽  
Digital Hologram ◽  
Central Processing ◽  
Numerical Reconstruction ◽  
Real Time Analysis ◽  
Observation Method ◽  
Graphics Processing

Digital holography is a comparatively new observation method for micron-sized particles. It is based on numerical reconstruction of recorded interference fringe. Calculation processes for reconstruction are both time- and memory-intensive. The aim of this study was to develop a faster, more efficient algorithm for digital hologram reconstruction. To this purpose Central Processing Unit (CPU) and Graphics Processing Unit (GPU) programming were implemented. For the problem solving the algorithms’ run-time for both configurations was measured. The results showed that the algorithm using a GPU board is faster and more suitable for reconstruction processes. Thus, it makes possible the accomplishment of real-time analysis.

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 Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

2020 ◽  
Vol 32 ◽  
pp. 03054
Author(s):  
Akshata Parab ◽  
Rashmi Nagare ◽  
Omkar Kolambekar ◽  
Parag Patil
Keyword(s):  
Real Time ◽  
Visually Impaired ◽  
Graphics Processing Unit ◽  
Human Perception ◽  
Processing Unit ◽  
Surrounding Environment ◽  
Visually Impaired People ◽  
Impaired People ◽  
Definition Of ◽  
Graphics Processing

Vision is one of the very essential human senses and it plays a major role in human perception about surrounding environment. But for people with visual impairment their definition of vision is different. Visually impaired people are often unaware of dangers in front of them, even in familiar environment. This study proposes a real time guiding system for visually impaired people for solving their navigation problem and to travel without any difficulty. This system will help the visually impaired people by detecting the objects and giving necessary information about that object. This information may include what the object is, its location, its precision, distance from the visually impaired etc. All these information will be conveyed to the person through audio commands so that they can navigate freely anywhere anytime with no or minimal assistance. Object detection is done using You Only Look Once (YOLO) algorithm. As the process of capturing the video/images and sending it to the main module has to be carried at greater speed, Graphics Processing Unit (GPU) is used. This will help in enhancing the overall speed of the system and will help the visually Impaired to get the maximum necessary instructions as quickly as possible. The process starts from capturing the real time video, sending it for analysis and processing and get the calculated results. The results obtained from analysis are conveyed to user by means of hearing aid. As a result by this system the blind or the visually impaired people can visualize the surrounding environment and travel freely from source to destination on their own.

scholarly journals Real-time speckle variance swept-source optical coherence tomography using a graphics processing unit

2012 ◽  
Vol 3 (7) ◽  
pp. 1557 ◽  
Author(s):  
Kenneth K. C. Lee ◽  
Adrian Mariampillai ◽  
Joe X. Z. Yu ◽  
David W. Cadotte ◽  
Brian C. Wilson ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Real Time ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Optical Coherence ◽  
Swept Source ◽  
Graphics Processing

scholarly journals Significance of Parallel Computing on the Performance of Digital Image Correlation Algorithms in MATLAB

Designs ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 15
Author(s):  
Andreas Thoma ◽  
Abhijith Moni ◽  
Sridhar Ravi
Keyword(s):  
Digital Image Correlation ◽  
Real Time ◽  
Digital Image ◽  
Correct Choice ◽  
Image Correlation ◽  
Particle Swarm Algorithm ◽  
Processing Unit ◽  
Time Analysis ◽  
The Real ◽  
Real Time Analysis

Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one from the undeformed reference states of the sample and the other from the deformed target state, the relative displacement between the two states is determined. DIC is well-known and often used for post-processing analysis of in-plane displacements and deformation of the specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and expand the scope of this method. Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether the real-time analysis is possible with these methods. The effects of computing with different hardware settings were also analyzed and discussed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm, such that it becomes practically slower than a sub-optimal algorithm. The Newton–Raphson algorithm in combination with a modified particle swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss–Newton algorithm is superior. As expected, the brute force search algorithm is the least efficient method. We also found that the correct choice of parallelization tasks is critical in attaining improvements in computing speed. A poorly chosen parallelization approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode, the correct choice of combinations of integer-pixel and sub-pixel search algorithms is critical for efficient analysis. The real-time analysis using DIC will be difficult on computers with standard computing capabilities, even if parallelization is implemented, so the suggested solution would be to use graphics processing unit (GPU) acceleration.

A Real-Time Photogrammetric System for Acquisition and Monitoring of Three-Dimensional Human Body Kinematics

2021 ◽  
Vol 87 (5) ◽  
pp. 363-373
Author(s):  
Long Chen ◽  
Bo Wu ◽  
Yao Zhao ◽  
Yuan Li
Keyword(s):  
Real Time ◽  
Human Body ◽  
Graphics Processing Unit ◽  
Three Dimensional ◽  
Stereo Pair ◽  
Processing Unit ◽  
Detection Distance ◽  
Human Kinematics ◽  
Graphics Processing ◽  
Time Acquisition

Real-time acquisition and analysis of three-dimensional (3D) human body kinematics are essential in many applications. In this paper, we present a real-time photogrammetric system consisting of a stereo pair of red-green-blue (RGB) cameras. The system incorporates a multi-threaded and graphics processing unit (GPU)-accelerated solution for real-time extraction of 3D human kinematics. A deep learning approach is adopted to automatically extract two-dimensional (2D) human body features, which are then converted to 3D features based on photogrammetric processing, including dense image matching and triangulation. The multi-threading scheme and GPU-acceleration enable real-time acquisition and monitoring of 3D human body kinematics. Experimental analysis verified that the system processing rate reached ∼18 frames per second. The effective detection distance reached 15 m, with a geometric accuracy of better than 1% of the distance within a range of 12 m. The real-time measurement accuracy for human body kinematics ranged from 0.8% to 7.5%. The results suggest that the proposed system is capable of real-time acquisition and monitoring of 3D human kinematics with favorable performance, showing great potential for various applications.

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