Raspberry Pi Performance Analysis in Real-Time Applications with the RT-Preempt Patch

Author(s):  
Alan Carvalho ◽  
Claudio Machado ◽  
Fabiano Moraes
Computers ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 64
Author(s):  
George K. Adam

This research performs real-time measurements of Linux kernels with real-time support provided by the PREEMPT_RT patch on embedded development devices such as BeagleBoard and Raspberry Pi. The experimental measurements of the Linux real-time performance on these devices are based on real-time software modules developed specifically for the purposes of this research. Taking in consideration the constraints of the specific hardware platforms under investigation, new measurements software was developed. The measurement algorithms are designed upon response and periodic task models. Measurements investigate latencies of real-time applications at user and kernel space. An outcome of this research is that the proposed performance measurements approach and evaluation methodology could be applied and deployed on other Linux-based boards and platforms. Furthermore, the results demonstrate that the PREEMPT_RT patch overall improves the Linux kernel real-time performance compared to the standard one. The reduced worst-case latencies on such devices running Linux with real-time support could make them potentially more suitable for real-time applications as long as a latency value of about 160 μs, as an upper bound, is an acceptable safety margin.


Author(s):  
Alexander Cameron ◽  
Markus Stumptner ◽  
Nanda Nandagopal ◽  
Wolfgang Mayer ◽  
Todd Mansell

Author(s):  
Delia Velasco-Montero ◽  
Jorge Fernández-Berni ◽  
Ricardo Carmona-Galán ◽  
Ángel Rodríguez-Vázquez

2018 ◽  
Author(s):  
Smitha Lingadahalli Ravi

Face Recognition gives a more straightforward, agreeable and helpful ID strategy and it is more satisfactory to clients when contrasted with other biometric strategies. On account of variety in confront posture edge, brightening, appearance and impediment there are numerous difficulties in confront recognition. Face appearance changes radically with change in facial posture in view of misalignment and in addition stowing away of numerous facial highlights and consequently recognition of countenances under stance varieties has turned out to be a troublesome issue. Face recognition under posture varieties alludes to perceiving face pictures of various stances. The main objective is to design an efficient face recognition that is invariant to occlusion In this approach the moment based feature extraction technique (Hu’s) is implemented on different poses of the face. Extracted feature are classified by kNN classifier and is implemented on the image captured by Pi Camera using Raspberry Pi for the real-time applications. .


1995 ◽  
Vol 18 (12) ◽  
pp. 871-879
Author(s):  
G Anastasi ◽  
M Conti ◽  
E Gregori ◽  
L Lenzini

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