In analyzing and recognizing wrist pulse signals, it isn’t easy to mine the nonlinear information of wrist pulse signals using analysis methods such as time and frequency. Traditional machine learning methods require...
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The growth of industrialization and urbanization has stimulated a surge in population and vehicles, causing severe traffic congestion and impeding the timely movement of emergency vehicles, including loss of lives and...
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The ever-increasing volume of data has exacerbated data breach problems, thus promoting advancements in data security and privacy technology. This study employs statistical and text mining methods to provide a detaile...
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Bearing only target motion analysis (BOTMA) is essential for tracking and locating underwater targets, particularly in situations when conventional approaches such as range-based observations are not feasible or acces...
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Distributed systems based on the Internet allow resources widely dispersed throughout the network to be utilized cooperatively and shared. This allows the end-user to obtain massive computational power to perform a co...
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Machine learning (ML) and Blockchain play fundamental roles in cryptocurrencies, and both are essential technology. By solving the intrinsic shortcomings in data security even with encryption, machine learning (ML) in...
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In the current challenging environment with the highly speed development that the companies' world faced, finding and keeping up with the auxiliary tools and systems has become a requirement and a necessity to dea...
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Using natural language processing technology and Python software, word frequency statistics were performed on the relevant policy texts of the collaborative innovation of science and technology in Beijing-Tianjin-Hebe...
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In recent years, with the increase in the number of high-end parks, the importance of blind spot security has become increasingly high, which means whether confidential points within the park can be protected for priv...
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Integrated Sensing and Communication (ISAC) technology has attracted extensive attention and been widely studied in automatic drive. However, most of existing works related to automatic drive focused on the single-veh...
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ISBN:
(纸本)9798350304060;9798350304053
Integrated Sensing and Communication (ISAC) technology has attracted extensive attention and been widely studied in automatic drive. However, most of existing works related to automatic drive focused on the single-vehicle intelligence, i.e., each vehicle processes its radar sensing data independently. With the single-vehicle intelligence, the sensing results may not satisfy the accuracy requirements in application scenarios such as obstacle detection and congestion detection. In this work, we consider information fusion within a vehicular mobile edge computing system. Multiple vehicles transmit their unique sensing data to an edge server, which fuses and processes the data to enhance sensing accuracy. Then, we formulate a joint resource allocation and beamforming design problem to minimize the system delay with heterogeneous tasks while satisfying the sensing quality requirements. Due to the non-convexity of the problem, we decouple the problem into two sub-problems and propose an efficient method based on alternate optimization and successive convex approximation to approximate the optimal solution. Simulation results demonstrate that the proposed method improves the performance by 38.1%, 43.8% and 65.7% in system delay compared with the existing algorithms, i.e., rate maximization, MEC computing and equal allocation.
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