5G networks are being designed to support ultra reliable and low latency communication (URLLC) services in many real-time industrial applications. The conventional grant-based dynamic scheduling can hardly fulfill the...
5G networks are being designed to support ultra reliable and low latency communication (URLLC) services in many real-time industrial applications. The conventional grant-based dynamic scheduling can hardly fulfill the URLLC requirements due to the non-negligible transmission delays introduced during the spectrum resource grant process. To address this problem, 5G defines a grant-free transmission scheme, namely configured grant (CG) scheduling, for uplink (UL) traffic to pre-allocate spectrum resource to user equipments (UEs). This paper studies CG scheduling for periodic URLLC traffic with real-time and collision-free guarantees. An exact solution based on Satisfiability Modulo Theory (SMT) is first proposed to generate a feasible CG configuration for a given traffic set. To enhance scalability, we further develop an efficient graph-based heuristic consisting of an offset selection method and a multicoloring algorithm for spectrum resource allocation. Extensive experiments are conducted using 3GPP industrial use cases to show that both approaches can satisfy the real-time and collision-free requirements, and the heuristic can achieve comparable schedulability ratio with the SMT-based approach but require significantly lower running time.
The threat of credit card fraud is high and so there should be efficient ways to combat it. The research is being employed to focus on how to enhance credit card fraud detection, machine learning methodologies can be ...
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A combat error control in wireless networks, this proposed protocol is an extension of the Aggressive Packet Combining scheme (APC). We will be discussing packets with a set payload size in this paper. We have Packet ...
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Watermarking is a very useful technique to ensure the integrity of a document image. But when too many bits are embedded into image, it reduces quality of the document. When limited number of watermark bits are used, ...
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Simulation of conflict situations for autonomous driving research is crucial for understanding and managing interactions between Automated Vehicles (AVs) and human drivers. This paper presents a set of exemplary confl...
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The prediction of occupancy is an ongoing research area, using various methods and data sources to enhance the accuracy of predictions and improve energy efficiency in buildings. Accurate occupancy prediction is key f...
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With the rapid advancement in the next-generation Internet of Things (IoT) and the ever-expanding virtual representation of devices, novel techniques and definitions are key to accommodate emerging paradigms to transf...
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Simulation of conflict situations for autonomous driving research is crucial for understanding and managing interactions between Automated Vehicles (AVs) and human drivers. This paper presents a set of exemplary confl...
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ISBN:
(数字)9798350378931
ISBN:
(纸本)9798350378948
Simulation of conflict situations for autonomous driving research is crucial for understanding and managing interactions between Automated Vehicles (AVs) and human drivers. This paper presents a set of exemplary conflict scenarios in CARLA that arise in shared autonomy settings, where both AVs and human drivers must navigate complex traffic environments. We explore various conflict situations, focusing on the impact of driver behavior and decision-making processes on overall traffic safety and efficiency. We build a simple extendable toolkit for situation awareness research, in which the implemented conflicts can be demonstrated.
Managing attendance in educational institutions is often a time-consuming and error-prone task, with traditional methods like roll calls or sign-in sheets being inefficient and susceptible to proxy attendance. This pa...
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ISBN:
(数字)9798331518578
ISBN:
(纸本)9798331518585
Managing attendance in educational institutions is often a time-consuming and error-prone task, with traditional methods like roll calls or sign-in sheets being inefficient and susceptible to proxy attendance. This paper presents a Smart Attendance Management System that leverages facial recognition technology to automate the attendance process. By using cameras to capture images of students, the system recognizes their faces and marks their attendance in real time. Developed in Python, the system employs the K-Nearest Neighbours (KNN) algorithm for accurate face recognition. Once attendance is recorded, it is stored in a CSV file and can be visualized using the flask web application. The proposed system significantly reduces manual effort, ensures accuracy, and operates efficiently in real-time environments.
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