RECET-a real-time cache evaluation tool

Real-Time Internet of Things (RT-IoT) is a newer technology paradigm envisioned as a global inter-networking of devices and physical things enabling real-time communication over the Internet. The research in Edge Computing and 5G technology is making way for the realisation of future IoT applications. In RT-IoT tasks will be performed in real-time for the remotely controlling and automating of various jobs and therefore, missing their deadline may lead to hazardous situations in many cases. For instance, in the case of safety-critical and mission-critical IoT systems, a missed task could lead to a human loss. Consequently, these systems must be simulated, as a result, and tasks should only be deployed in a real scenario if the deadline is guaranteed to be met. Numerous simulation tools are proposed for traditional real-time systems using desktop technologies, but these relatively older tools do not adapt to the new constraints imposed by the IoT paradigm. In this paper, we design and implement a cloud-based novel architecture for the formal verification of IoT jobs and provide a simulation environment for a typical RT-IoT application where the feasibility of real-time remote tasks is perceived. The proposed tool, to the best of our knowledge, is the first of its kind effort to support not only the feasibility analysis of real-time tasks but also to provide a real environment in which it formally monitors and evaluates different IoT tasks from anywhere. Furthermore, it will also act as a centralised server for evaluating and tracking the real-time scheduled jobs in a smart space. The novelty of the platform is purported by a comparative analysis with the state-of-art solutions against attributes which is vital for any open-source tools in general and IoT in specifics.

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Use of mobile devices in medical education

South Florida Journal of Development ◽

10.46932/sfjdv2n2-008 ◽

2021 ◽

Vol 2 (2) ◽

pp. 1230-1240

Author(s):

Raquel Morales ◽

Isabel Legaz Pérez

Keyword(s):

University Students ◽

Real Time ◽

Mobile Devices ◽

Audience Participation ◽

Evaluation Tool ◽

Digital Evaluation ◽

Degree Of Acceptance ◽

The Subject ◽

Design And Methods ◽

High Degree

Background: The evaluation in real-time allows the teacher to improve the deficiencies and propose immediate improvement actions that allow the student's correct acquisition of knowledge. Purpose: The objective was to implement in university students conduct real-time theoretical questionnaires in the classroom using mobile devices with real-time resolution and a later critical analysis of the results to improve the evaluation and learning process. Sample: A total of 300 university students belonging to a Medicine degree were analyzed in this study. All students have participated in this study voluntarily. Design and methods: A real-time questionnaire about Legal Medicine was conducted in the classroom using an online platform (“Live Interactive Audience Participation | Poll Everywhere” 2019). The students responded using Twitter. The students' different answers to the questionnaire's questions are visualized in real-time in the teaching classroom. A questionnaire with 13 items was developed to assess the students' opinions regarding this evaluation tool's use and capabilities. Results: Our results show that 86.5% of students considered and were very useful in using this teaching resource, which increased students' interest in the subject taught (75.3%). The use of real-time questionnaires in class showed a high degree of acceptance (77.4%) in the group of delighted students and only 37% in the group of students not very satisfied with the experience (P <0.001). A 43.8% of the student considered that the use of digital evaluation should increase in the classroom. Conclusions: This digital evaluation tool is an appropriate didactic resource to develop in the classroom since it increases student motivation and promotes the students' natural and active participation.

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$$360^{\circ }$$ real-time and power-efficient 3D DAMOT for autonomous driving applications

Multimedia Tools and Applications ◽

10.1007/s11042-021-11624-2 ◽

2022 ◽

Author(s):

Carlos Gómez-Huélamo ◽

Javier Del Egido ◽

Luis Miguel Bergasa ◽

Rafael Barea ◽

Elena López-Guillén ◽

...

Keyword(s):

Embedded System ◽

Object Detection ◽

Real Time ◽

Driving Simulator ◽

Autonomous Driving ◽

Intelligent Vehicles ◽

Evaluation Tool ◽

Automated Driving ◽

Safe Driving ◽

Power Efficient

AbstractAutonomous Driving (AD) promises an efficient, comfortable and safe driving experience. Nevertheless, fatalities involving vehicles equipped with Automated Driving Systems (ADSs) are on the rise, especially those related to the perception module of the vehicle. This paper presents a real-time and power-efficient 3D Multi-Object Detection and Tracking (DAMOT) method proposed for Intelligent Vehicles (IV) applications, allowing the vehicle to track $$360^{\circ }$$ 360 ∘ surrounding objects as a preliminary stage to perform trajectory forecasting to prevent collisions and anticipate the ego-vehicle to future traffic scenarios. First, we present our DAMOT pipeline based on Fast Encoders for object detection and a combination of a 3D Kalman Filter and Hungarian Algorithm, used for state estimation and data association respectively. We extend our previous work ellaborating a preliminary version of sensor fusion based DAMOT, merging the extracted features by a Convolutional Neural Network (CNN) using camera information for long-term re-identification and obstacles retrieved by the 3D object detector. Both pipelines exploit the concepts of lightweight Linux containers using the Docker approach to provide the system with isolation, flexibility and portability, and standard communication in robotics using the Robot Operating System (ROS). Second, both pipelines are validated using the recently proposed KITTI-3DMOT evaluation tool that demonstrates the full strength of 3D localization and tracking of a MOT system. Finally, the most efficient architecture is validated in some interesting traffic scenarios implemented in the CARLA (Car Learning to Act) open-source driving simulator and in our real-world autonomous electric car using the NVIDIA AGX Xavier, an AI embedded system for autonomous machines, studying its performance in a controlled but realistic urban environment with real-time execution (results).

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A real-time performability evaluation tool

10.2514/6.1995-989 ◽

1995 ◽

Author(s):

Krishna Kavi ◽

Hee Youn

Keyword(s):

Real Time ◽

Evaluation Tool

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Real-Time Evaluation of City–University Partnerships for Sustainability and Resilience

Sustainability ◽

10.3390/su12218796 ◽

2020 ◽

Vol 12 (21) ◽

pp. 8796

Author(s):

Liliana Caughman ◽

Lauren Withycombe Keeler ◽

Fletcher Beaudoin

Keyword(s):

Focus Groups ◽

Real Time ◽

Formative Evaluation ◽

Urban Environments ◽

Structural Barriers ◽

Evaluation Tool ◽

University Partnerships ◽

Collaborative Efforts ◽

Time Evaluation

Cities face many challenges in their efforts to create more sustainable and resilient urban environments for their residents. Among these challenges is the structure of city administrations themselves. Partnerships between cities and universities are one way that cities can address some of the internal structural barriers to transformation. However, city–university partnerships do not necessarily generate transformative outcomes, and relationships between cities and universities are complicated by history, politics, and the structures the partnerships are attempting to overcome. In this paper, focus groups and trial evaluations from five city–university partnerships in three countries are used to develop a formative evaluation tool for city–university partnerships working on challenges of urban sustainability and resilience. The result is an evaluative tool that can be used in real-time by city–university partnerships in various stages of maturity to inform and improve collaborative efforts. The paper concludes with recommendations for creating partnerships between cities and universities capable of contributing to long-term sustainability transformations in cities.

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