Vehicular ad hoc networks (VANETs) employ multichannel to provide a variety of safety and non-safety applications. Safety applications require reliable and timely transmission, while non-safety applications need high network throughput. IEEE 802.11p and IEEE 1609.4 protocol divide the bandwidth into seven channels. One control channel (CCH) is to serve safety applications and the other six service channels (SCHs) to serve non-safety applications. The IEEE 1609.4 protocol specifies an alternating scheme to allow vehicles to switch between two types of applications. However, the IEEE 1609.4 multichannel media access control (MAC) protocol has limitations on its capability of supporting either delay-or throughput-sensitive applications. In this paper, we propose an adaptive multi-priority distributed multichannel (APDM) MAC protocol for VANETs. Considering that in realistic VANETs, the queue of MAC layer is far from saturated. We assume that generated packets with different priorities arrive at the MAC layer in a Poisson manner. A Markov analytical model is conducted to optimize the packet transmission probabilities and adjust the ratio between CCH interval and SCH interval dynamically according to the real-time traffic in a distributed way. An M/M/1 queue model is then adopted to analyze the time performance. Extensive simulation results show that the APDM MAC protocol can ensure prioritized transmission of safety packets, reduce the transmission delay of packets and enhance the unsaturated and saturated throughput of SCHs.
A novel method for the real-time globally optimal path planning of mobile robots is proposed based on the ant colony system (ACS) algorithm. This method includes three steps: the first step is utilizing the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is utilizing the Dijkstra algorithm to find a sub-optimal collision-free path, and the third step is utilizing the ACS algorithm to optimize the location of the sub-optimal path so as to generate the globally optimal path. The result of computer simulation experiment shows that the proposed method is effective and can be used in the real-time path planning of mobile robots. It has been verified that the proposed method has better performance in convergence speed, solution variation, dynamic convergence behavior, and computational efficiency than the path planning method based on the genetic algorithm with elitist model.
Abstract:Preprocessing is one of the main components in a conventional document categorization (DC) framework. This paper aims to highlight the effect of preprocessing tasks on the efficiency of the Arabic DC system. In this study, three classification techniques are used, namely, naive Bayes (NB), k-nearest neighbor (KNN), and support vector machine (SVM). Experimental analysis on Arabic datasets reveals that preprocessing techniques have a significant impact on the classification accuracy, especially with complicated morphological structure of the Arabic language. Choosing appropriate combinations of preprocessing tasks provides significant improvement on the accuracy of document categorization depending on the feature size and classification techniques. Findings of this study show that the SVM technique has outperformed the KNN and NB techniques. The SVM technique achieved 96.74% micro-F1 value by using the combination of normalization and stemming as preprocessing tasks.
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