scholarly journals Development of Multi-Quadrotor Simulator Based on Real-Time Hypervisor Systems

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 59
Author(s):  
Muhammad-Faris Fathoni ◽  
Seo-Nah Lee ◽  
Yoon-Soo Kim ◽  
Ki-Il Kim ◽  
Kyong-Hoon Kim
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Response Times ◽  
Computation Time ◽  
System Stability ◽  
The Real ◽  
Time Extension ◽  
Time Requirements ◽  
And Performance ◽  
Quadrotor System

Today, simulator technology has been widely used as an important part of quadrotor development such as validation and testing. A good quadrotor simulator can simulate the quadrotor system as closely as possible to the real one. Therefore, in case of multi-quadrotor simulator, the simulator should not only can simulate a multi-quadrotor system, but also every quadrotor should be able to leverage their own resources. To solve this issues, in this paper, we present a hypervisor-based multi-quadrotor simulator. We used RT-Xen as hypervisor, a real-time Xen hypervisor. To ensure every quadrotor runs in real-time manner, we implemented quadrotor simulator in Litmus-RT which is a real-time extension of Linux. In this paper, we conducted some testing and performance evaluation for particular cases on our multi-quadrotor simulator: step-input responses, computation time, and response times. Based on the performance evaluation, our hypervisor-based multi-quadrotor simulator environment is proven to meet the real-time requirements. The results show that three important tasks in quadrotor system: Stability Controllability Augmented System (SCAS), Equation of Motion (EOM), and waypoint following task, are finished before their deadlines; in fact, 20 ms, 10 ms, and 40 ms before the deadlines for SCAS, EOM, and waypoint following, respectively.

A Survey of Real-Time Industrial Wireless LAN Research

2015 ◽  
Vol 738-739 ◽  
pp. 1105-1110 ◽  
Author(s):  
Yuan Qing Qin ◽  
Ying Jie Cheng ◽  
Chun Jie Zhou
Keyword(s):  
Real Time ◽  
Future Development ◽  
Wireless Lan ◽  
State Of The Art ◽  
The State ◽  
Local Networks ◽  
The Real ◽  
Time Requirements ◽  
And Performance ◽  
Fundamental Mechanism

This paper mainly surveys the state-of-the-art on real-time communicaton in industrial wireless local networks(WLANs), and also identifys the suitable approaches to deal with the real-time requirements in future. Firstly, this paper summarizes the features of industrial WLANs and the challenges it encounters. Then according to the real-time problems of industrial WLAN, the fundamental mechanism of each recent representative resolution is analyzed in detail. Meanwhile, the characteristics and performance of these resolutions are adequately compared. Finally, this paper concludes the current of the research and discusses the future development of industrial WLANs.

scholarly journals Performance Evaluation of the Real-Q Cytomegalovirus (CMV) Quantification Kit Using Two Real-Time PCR Systems for Quantifying CMV DNA in Whole Blood

2016 ◽  
Vol 36 (6) ◽  
pp. 603-606 ◽  
Author(s):  
Jong Eun Park ◽  
Ji-Youn Kim ◽  
Sun Ae Yun ◽  
Myoung-Keun Lee ◽  
Hee Jae Huh ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Real Time Pcr ◽  
Whole Blood ◽  
The Real

Real-time engine model development based on time complexity analysis

2021 ◽  
pp. 146808742110397
Author(s):  
Haotian Chen ◽  
Kun Zhang ◽  
Kangyao Deng ◽  
Yi Cui
Keyword(s):  
Real Time ◽  
Time Complexity ◽  
Complexity Analysis ◽  
High Accuracy ◽  
The Real ◽  
Cycle Simulation ◽  
Crank Angle ◽  
Time Requirements ◽  
Improved Model ◽  
Simulation Time

Real-time simulation models play an important role in the development of engine control systems. The mean value model (MVM) meets real-time requirements but has limited accuracy. By contrast, a crank-angle resolved model, such as the filling -and-empty model, can be used to simulate engine performance with high accuracy but cannot meet real-time requirements. Time complexity analysis is used to develop a real-time crank-angle resolved model with high accuracy in this study. A method used in computer science, program static analysis, is used to theoretically determine the computational time for a multicylinder engine filling-and-empty (crank-angle resolved) model. Then, a prediction formula for the engine cycle simulation time is obtained and verified by a program run test. The influence of the time step, program structure, algorithm and hardware on the cycle simulation time are analyzed systematically. The multicylinder phase shift method and a fast calculation method for the turbocharger characteristics are used to improve the crank-angle resolved filling-and-empty model to meet real-time requirements. The improved model meets the real-time requirement, and the real-time factor is improved by 3.04 times. A performance simulation for a high-power medium-speed diesel engine shows that the improved model has a max error of 5.76% and a real-time factor of 3.93, which meets the requirement for a hardware-in-the-loop (HIL) simulation during control system development.

scholarly journals A new algorithm for considering green communication and excellent sensing performance in cognitive radio networks

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772093313 ◽  
Author(s):  
Tangsen Huang ◽  
Xiangdong Yin ◽  
Qingjiao Cao
Keyword(s):  
Real Time ◽  
Spectrum Sensing ◽  
Computation Time ◽  
Time Spectrum ◽  
Fusion Center ◽  
Local Data ◽  
Green Communication ◽  
Processing Efficiency ◽  
The Real ◽  
Sensing Performance

Multi-node cooperative sensing can effectively improve the performance of spectrum sensing. Multi-node cooperation will generate a large number of local data, and each node will send its own sensing data to the fusion center. The fusion center will fuse the local sensing results and make a global decision. Therefore, the more nodes, the more data, when the number of nodes is large, the global decision will be delayed. In order to achieve the real-time spectrum sensing, the fusion center needs to quickly fuse the data of each node. In this article, a fast algorithm of big data fusion is proposed to improve the real-time performance of the global decision. The algorithm improves the computing speed by reducing repeated computation. The reinforcement learning mechanism is used to mark the processed data. When the same environment parameter appears, the fusion center can directly call the nodes under the parameter environment, without having to conduct the sensing operation again. This greatly reduces the amount of data processed and improves the data processing efficiency of the fusion center. Experimental results show that the algorithm in this article can reduce the computation time while improving the sensing performance.

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