The following topics are dealt with: mobile robots; robot vision; medical robotics; feature extraction; path planning; motion control; stability; control engineering computing; learning (artificial intelligence); obje...
The following topics are dealt with: mobile robots; robot vision; medical robotics; feature extraction; path planning; motion control; stability; control engineering computing; learning (artificial intelligence); object detection.
Mobile Edge computing (MEC) places computing and storage resources on the network's edge, which opens the door for implementing an entirely new set of delay-sensitive and compute-intensive applications in constrai...
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ISBN:
(纸本)9798350366457;9798350366440
Mobile Edge computing (MEC) places computing and storage resources on the network's edge, which opens the door for implementing an entirely new set of delay-sensitive and compute-intensive applications in constrained IoT devices. Nevertheless, from the perspective of an IoT device, using MEC introduces several challenges that need to be addressed. One is the proper execution and resource utilization of device-native and edge-dependent tasks, especially when considering multi-task edge applications. Current real-time schedulers do not contemplate factors inherent to the nature of MEC, such as shared resource utilization, dynamic execution site migrations, the influence of remote computing, etc. Therefore, this paper introduces an agent embedded in IoT devices to provide real-time schedulers with mobile edge computing awareness. Moreover, the performance of the proposed solution is assessed by running two multi-task edge applications in the smart vehicles domain, depicting a considerable task throughput enhancement when compared to existing real-time schedulers.
In the dynamically evolving of Massive Open Online Courses (MOOCs), the imperative for real-time and the efficient evaluation of learner engagement has never been more pronounced. Traditional methodologies, while prov...
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ISBN:
(纸本)9798350362060;9798350362053
In the dynamically evolving of Massive Open Online Courses (MOOCs), the imperative for real-time and the efficient evaluation of learner engagement has never been more pronounced. Traditional methodologies, while providing foundational insights, often fall short in terms of objectivity and immediacy. This paper introduces an innovative system that uses advanced computer vision and machine learning algorithms to dynamically detect and analyze learner emotions and engagement levels during MOOC sessions. Additionally, this system facilitates the identification of areas for improvement and supports the design of personalized and engaging learning experiences, particularly for learners with disabilities. Our findings reveal that this system not only monitors the duration and intensity of learner engagement but also actively identifies moments of peak engagement and discerns learning patterns. This information enables the personalization of educational paths to suit individual learning styles, significantly enhancing engagement and overall MOOC effectiveness. Powered by affective computing, this technology seeks to make a difference in the field of education technology, transforming MOOCs into personalized learning environments that match the specific interests, goals and needs of each user.
This paper presents a novel approach for creating real-time, secure and interactable Digital Twins of industrial robots. This approach diverges from previous approaches that relied on Digital Twin Definition Language ...
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ISBN:
(纸本)9798350352511;9798350352504
This paper presents a novel approach for creating real-time, secure and interactable Digital Twins of industrial robots. This approach diverges from previous approaches that relied on Digital Twin Definition Language (DTDL). The proposed method uses robotics Operating Systems (ROS) and the Unity Game Engine to build the Digital Twins. The prototype built to showcase this approach comprises 4 sub-systems namely Data Acquisition sub-system, Cloud sub-system, Digital Client subsystem, and Remote-Control sub-system. The methodology focuses on real-time data synchronization using cloud-based communication services like IoT servers. There are also multiple security measures employed to secure the data- like passing data through proxy and end-to-end encryption of data. The prototype made using Niryo Ned 2 Robot and a Raspberry Pi 3b+ showcases accurate physical-Digital Twin and effective remote control. Key findings highlight the system's ability to overcome latency, security and data accuracy, which in turn helps improve human-robot collaboration and interactions. This research contributes to the Digital Twin field by providing a modular and scalable framework that can be adapted for various robots and systems. This paper not only provides the general idea for implementation and problems faced during development but also suggests pathways for future work, like - optimization of message rates, integration of machine learning for maintenance and other purposes.
To enhance the performance of autonomous driving, recent studies have been incorporating various tasks that require increasingly more computation. As computational demands increase, it is often difficult to achieve ti...
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ISBN:
(纸本)9798350387964;9798350387957
To enhance the performance of autonomous driving, recent studies have been incorporating various tasks that require increasingly more computation. As computational demands increase, it is often difficult to achieve timely execution with the limited performance of onboard computing units alone. To address this issue, Vehicle Edge computing (VEC), which offloads computational workloads to the edge and retrieves the results back to the vehicle, is gaining significant attention. To achieve efficient offloaded analytics via VEC, it is crucial to comprehensively consider both of the computing and network conditions of the V2X systems, as well as the vehicle energy consumption and timely execution. However, current studies have not sufficiently addressed the comprehensive modeling of computational and network loads in these V2X systems. To deal with this, we propose a Cooperative Network-Computation Load Balancing Simulator for VEC.
The Internet of Bio-Nano Things (IoBNT) is an innovative field of research located at the intersection of nanotechnology, biotechnology and information and communication technologies. It aims to enable the seamless in...
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Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
In recent years, serverless computing has become increasingly popular in the domain of microservices. Compared to serverful computing, serverless computing significantly reduces developers' expenses due to its res...
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ISBN:
(纸本)9798350368567;9798350368550
In recent years, serverless computing has become increasingly popular in the domain of microservices. Compared to serverful computing, serverless computing significantly reduces developers' expenses due to its resource elasticity and on-demand allocation features. However, serverless computing suffers from long cold start time and high function invocation latency, leading to suboptimal service performance. Due to the dynamic workload, microservices exhibit varying demands for different run-time types over time, which is overlooked by existing approaches. Therefore, we propose the Adaptive Selecting Algorithm for Run-time Types of microservices, which optimizes resource usage in cloud service providers (CSPs) and ensures efficient execution of developers' applications. Specifically, the algorithm dynamically switches microservices to the optimal runtime type by analyzing the workload patterns, resource requirements, and execution efficiency of microservices. We conducted experiments on real clusters, demonstrating that algorithm enhances the service quality of microservices while improving their cost efficiency.
This paper proposes an elderly fall detection system based on distributed edge computing and machine learning. The system can identify individual fall events from multiple camera sources and trigger real-time alerts, ...
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ISBN:
(纸本)9798350386851;9798350386844
This paper proposes an elderly fall detection system based on distributed edge computing and machine learning. The system can identify individual fall events from multiple camera sources and trigger real-time alerts, achieving 97% accuracy experimentally.
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