The interaction between electromagnetic radiation and infinite plane shield having finite thickness d, placed in free space in the plane waves region depends on material/shield macroscopic parameters (e, fl, a), its t...
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The primary objective of cooperation in Cognitive Radio (CR) networks is to increase the efficiency and improve the network performance. However, CR users may act destructively and decrease both their own and others&#...
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The primary objective of cooperation in Cognitive Radio (CR) networks is to increase the efficiency and improve the network performance. However, CR users may act destructively and decrease both their own and others'' performances. This can be due to Byzantine adversaries or unintentional erroneous conduct in cooperation. This work presents an autonomous cooperation solution for each CR user, i.e., each CR user decides with whom to cooperate. The objective of the proposed solution is to increase the spectrum access in cooperative CR networks. To realize this, a Reinforcement Learning (RL) algorithm is utilized to determine the suitability of the available cooperators and select the appropriate set of cooperators. In addition, the proposed so-lution determines the most appropriate number of cooperators to achieve the highest efficiency for spectrum access. Accordingly, the control communication overhead is reduced. The simulation results demonstrate the learning capabilities of the proposed to achieve reliable behavior under highly unreliable conditions.
Previous studies have reported that common dense linear algebra operations do not achieve speed up by using multiple geographical sites of a computational grid. Because such operations are the building blocks of most ...
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ISBN:
(纸本)9781424464425
Previous studies have reported that common dense linear algebra operations do not achieve speed up by using multiple geographical sites of a computational grid. Because such operations are the building blocks of most scientific applications, conventional supercomputers are still strongly predominant in high-performance computing and the use of grids for speeding up large-scale scientific problems is limited to applications exhibiting parallelism at a higher level. We have identified two performance bottlenecks in the distributed memory algorithms implemented in ScaLAPACK, a state-of-the-art dense linear algebra library. First, because ScaLA-PACK assumes a homogeneous communication network, the implementations of ScaLAPACK algorithms lack locality in their communication pattern. Second, the number of messages sent in the ScaLAPACK algorithms is significantly greater than other algorithms that trade flops for communication. In this paper, we present a new approach for computing a QR factorization - one of the main dense linear algebra kernels - of tall and skinny matrices in a grid computing environment that overcomes these two bottlenecks. Our contribution is to articulate a recently proposed algorithm (Communication Avoiding QR) with a topology-aware middleware (QCG-OMPI) in order to confine intensive communications (ScaLAPACK calls) within the different geographical sites. An experimental study conducted on the Grid'5000 platform shows that the resulting performance increases linearly with the number of geographical sites on large-scale problems (and is in particular consistently higher than ScaLAPACK's).
Graphical models have proved to be very efficient models for labeling image data. In this paper, the use of graphical models based on Decomposable Triangulated Graphs are applied for several still image databases land...
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In this work, we showcase a set of implemented multiplayer games and interactive installations based on Fun in Numbers (FinN). FinN allows the quick prototyping of applications that utilize input from multiple physica...
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ISBN:
(纸本)9781605588643
In this work, we showcase a set of implemented multiplayer games and interactive installations based on Fun in Numbers (FinN). FinN allows the quick prototyping of applications that utilize input from multiple physical sources (sensors and other means of interfacing), by offering a set of programming templates and services, such as proximity, localization and synchronization, that hide the underlying complexity.
The paper describes a set of experiments involving the application of three state-of-the-art part-of-speech taggers to Ethiopian Amharic, using three different tagsets. The taggers showed worse performance than previo...
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Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively ...
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This paper presents a new memory-based approach to Collaborative Filtering where the neighbors of the active user will be selected taking into account their predictive capability. Our hypothesis is that if a user was ...
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ISBN:
(纸本)9781605584355
This paper presents a new memory-based approach to Collaborative Filtering where the neighbors of the active user will be selected taking into account their predictive capability. Our hypothesis is that if a user was good at predicting the past ratings, then his/her predictions will be also helpful to recommend ratings in the future. The predictive capability of a user will be measured using two different criteria: The first one which is based on the likelihood of the active user's rating and the second one tries to minimize the error obtained using his/her predictions. We show our experimental results using standard data sets. Copyright 2009 ACM.
Graphical models have proved to be very efficient models for labeling image data. In particular, they have been used to label data samples from human body images. In this paper, the use of graphical models is studied ...
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ISBN:
(纸本)9789898111692
Graphical models have proved to be very efficient models for labeling image data. In particular, they have been used to label data samples from human body images. In this paper, the use of graphical models is studied for human-body landmark localization. Here a new algorithm based on the Branch & Bound methodology, improving the state of the art, is presented. The initialization stage is defined as a local optimum labeling of the sample data. An iterative improvement is given on the labeling space in order to reach new graphs with a lower cost than the current best one. Two branch prune strategies are suggested under a B&B approach in order to speed up the search: a) the use of heuristics;and b) the use of a node dominance criterion. Experimental results on human motion databases show that our proposed algorithm behaves better than the classical Dynamic Programming based approach.
The user observed latency of retrieving Web documents is one of limiting factors while using the Internet as an information data source. Prefetching became important technique to reduce the average Web access latency....
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The user observed latency of retrieving Web documents is one of limiting factors while using the Internet as an information data source. Prefetching became important technique to reduce the average Web access latency. Existing prefetching methods are based predominantly on URL graphs. They use the graphical nature of HTTP links to determine the possible paths through a hypertext system. Although the URL graph-based approaches are effective in the prefetching of frequently accessed documents, few of them can pre-fetch those URLs that are rarely visited. In our paper we aim to propose a new prefetching algorithm that would increase the efficiency of Web prefetching and that will embody the new demands for Web personalisation and Web search assistance. The aim of the research is to design a system for web page prefetching. The system should use user's link path history in combination with the semantic path history. To enable this, semantically annotated web pages are necessary. We cannot rely on the web documents' creators thus one part of the work must be the design and implementation of simple annotator based on WordNet just for purposes of our research.
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