Mobile Ad hoc NETworks (MANETs) are becoming more essential to wireless communications due to growing popularity of mobile devices. Many researchers have committed effort to enhance the Multimedia (video) transmission...
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Mobile Ad hoc NETworks (MANETs) are becoming more essential to wireless communications due to growing popularity of mobile devices. Many researchers have committed effort to enhance the Multimedia (video) transmission over MANETs. Various algorithms and mechanisms concerning the optimization of multimedia transmission have been presented. In this work we evaluate the effect of using multiple interfaces and multiple channels per node in the performance of already existing MANET routing protocols during video transmission. The evaluation shows that all routing protocols benefit from using multiple interfaces and multiple channels per node, and the video transmission over MANETs is improved.
We propose the direct production of 3D CSG models from sketches as a way of relieving the user from having to input detailed 3D CAD models. This shortens the CAD/CAM process and simplifies it, allowing non-expert end-...
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This paper addresses the problem of leader election in virtual traffic lights. A virtual traffic light (VTL) is a self-organizing traffic control system that allows road vehicles equipped with vehicle-to-vehicle commu...
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ISBN:
(纸本)9781479901807
This paper addresses the problem of leader election in virtual traffic lights. A virtual traffic light (VTL) is a self-organizing traffic control system that allows road vehicles equipped with vehicle-to-vehicle communication facilities to implement the function of a traffic light without the support of a roadside installation. Previous research has shown that it is impossible to construct a leader election protocol that guarantees agreement among the participating vehicles in the presence of massive communication failures. The paper addresses the problem of calculating the probability of disagreement in situations where a large number of protocol messages are lost due to communication interference, so-called communication grey-outs. To this end, we present a probabilistic analysis of a family of simple round-based consensus algorithms that solve the 1-of-n selection problem. We propose to use these algorithms for the core logic of a VTL leader election protocol (LEP). Our analysis shows that the probability of disagreement depends on: i) the number of vehicles involved in the leader election, ii) the number of rounds of message exchange, iii) the probability of message loss, and iv) the decision criterion used by the LEP. We propose an optimistic and a pessimistic decision criterion for the proposed 1-of-n selection algorithms. The analysis encompasses two probabilistic failure models, one for symmetric communication failures and one for asymmetric communication failures.
We describe a three-stage model of computing instruction beginning with a simple, highly scaffolded programming en-vironment (Kodu) and progressing to more challenging frame-works (Alice and Lego NXT-G). In moving bet...
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This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vecto...
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ISBN:
(纸本)9781479913282
This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.
In this paper, we evaluate the effectiveness of Generalized Procrustes Analysis into an Iterative Closest Point(GPA-ICP) for 3D surface reconstruction of underwater coral reef imagesinstead of using original ICP we us...
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In this paper, we evaluate the effectiveness of Generalized Procrustes Analysis into an Iterative Closest Point(GPA-ICP) for 3D surface reconstruction of underwater coral reef imagesinstead of using original ICP we use GPA-ICP for final alignment to build 3D modelIn land 3D reconstruction, GPA-ICP is preferable for efficiency and robustnessThis is challenging task to implement GPA-ICP for 3D reconstruction of underwater imagesThis paper generates 3D point cloud of underwater coral reef images from dataset acquired by stereo camera systemThe performance of GPA-ICP is measured by computation time of registration process, visual appearance and Mean Square Error between the point and their closest neighbor compared to the original ICPThe result shows that GPA-ICP is more accurate and faster than the original ICP for 3D surface reconstruction of seafloor images.
Due to the sudden eruption of activity in the social networking domain, analysts, social media as well as general public are drawn to Sentiment Analysis domain to gain invaluable information. In this paper, we go beyo...
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Due to the sudden eruption of activity in the social networking domain, analysts, social media as well as general public are drawn to Sentiment Analysis domain to gain invaluable information. In this paper, we go beyond basic sentiment classification (positive, negative and neutral) and target deeper emotion classification of Twitter data. We have focused on emotion identification into Ekman's six basic emotions i.e. JOY, SURPRISE, ANGER, DISGUST, FEAR and SADNESS. We have employed two diverse machine learning algorithms with three varied datasets and analyzed their outcomes. We show how equal distribution of emotions in training tweets results in better learning accuracies and hence better performance in the classification task.
Image exams are a fundamental tool in health care for decision making. A challenge in Content-based image retrieval (CBIR) is to provide a timely answer that complies with the specialist's expectation. There are d...
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In this paper we present a solution to the problem of positioning a team of Micro Aerial Vehicles for a surveillance task in an environment of arbitrary and unknown morphology. The problem is addressed taking into acc...
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ISBN:
(纸本)9781479909964
In this paper we present a solution to the problem of positioning a team of Micro Aerial Vehicles for a surveillance task in an environment of arbitrary and unknown morphology. The problem is addressed taking into account physical and environmental constraints like limited sensor capabilities and obstacle avoidance. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. The proposed method is a distributed extension of our previous work based on the Cognitive Adaptive Optimization (CAO) algorithm. This distributed and scalable approach allows us to obtain coordinated and safe trajectories to accomplish the task in 3D environments. The different formulation of the problem considered in this paper allows also dealing with communication constraints. We provide extensive experimental results using data collected by a team of aerial robots and compare the efficiency of the distributed and centralized approach.
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