图像和视觉计算(Image and Vision Computing)期刊的主要目标是为高质量的理论和应用研究成果提供有效的交流媒介,这些研究成果是图像解释和计算机视觉各个方面的基础。该杂志发表了一些工作,提出了新的图像解释和计算机视觉方法,或者讨论了这些方法在现实世界场景中的应用。它试图通过鼓励对所提议的方法进行定量比较和绩效评估来加强对这一学科的更深层次的理解。覆盖范围包括:图像判读、场景建模、对象识别和跟踪、形状分析、监测和监督,主动视觉和机器人系统,生物计算机视觉、运动分析、立体视觉、文档图像理解和手写文本识别,脸和手势识别,生物识别技术,人机交互,建立人类活动和行为的理解,来自多个传感器的数据融合输入、图像数据库。 官网地址:http://dblp.uni-trier.de/db/journals/ivc/

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We investigate the performance of a dual-hop intervehicular communications (IVC) system with relay selection strategy. We assume a generalized fading channel model, known as cascaded Rayleigh (also called n*Rayleigh), which involves the product of n independent Rayleigh random variables. This channel model provides a realistic description of IVC, in contrast to the conventional Rayleigh fading assumption, which is more suitable for cellular networks. Unlike existing works, which mainly consider double-Rayleigh fading channels (i.e, n = 2); our system model considers the general cascading order n, for which we derive an approximate analytic solution for the outage probability under the considered scenario. Also, in this study we propose a machine learning-based power allocation scheme to improve the link reliability in IVC. The analytical and simulation results show that both selective decode-and-forward (S-DF) and amplify-and-forward (S-AF) relaying schemes have the same diversity order in the high signal-to-noise ratio regime. In addition, our results indicate that machine learning algorithms can play a central role in selecting the best relay and allocation of transmission power.

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