This thesis addresses two closely related problems. The first, translation alignment, con- sists of identifying bilingual document pairs that are translations of each other within multilingual document collections (do...
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This thesis addresses two closely related problems. The first, translation alignment, con- sists of identifying bilingual document pairs that are translations of each other within multilingual document collections (document alignment); identifying sentences, titles, etc, that are translations of each other within bilingual document pairs (sentence align- ment); and identifying corresponding word and phrase translations within bilingual sentence pairs (phrase alignment). The second is extraction of bilingual pairs of equiva- lent word and multi-word expressions, which we call translation equivalents (TEs), from sentence- and phrase-aligned parallel corpora. While these same problems have been investigated by other authors, their focus has been on fully unsupervised methods based mostly or exclusively on parallel corpora. Bilingual lexica, which are basically lists of TEs, have not been considered or given enough importance as resources in the treatment of these problems. Human validation of TEs, which consists of manually classifying TEs as correct or incorrect translations, has also not been considered in the context of alignment and extraction. Validation strengthens the importance of infrequent TEs (most of the entries of a validated lexicon) that otherwise would be statistically unimportant. The main goal of this thesis is to revisit the alignment and extraction problems in the context of a lexica-centered iterative workflow that includes human validation. There- fore, the methods proposed in this thesis were designed to take advantage of knowledge accumulated in human-validated bilingual lexica and translation tables obtained by un- supervised methods. Phrase-level alignment is a stepping stone for several applications, including the extraction of new TEs, the creation of statistical machine translation sys- tems, and the creation of bilingual concordances. Therefore, for phrase-level alignment, the higher accuracy of human-validated bilingual lexica is crucial f
Maximizing target coverage with minimum number of sensors is an elementary problem in the area of wireless sensor networks which has many applications in monitoring, tracking, security, and surveillance systems. The r...
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This paper develops an effective approach for the 3D deployment of a heterogeneous set of unmanned aerial vehicles (UAVs) acting as aerial base stations that provide maximum wireless coverage for ground users in a giv...
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This paper develops an effective approach for the 3D deployment of a heterogeneous set of unmanned aerial vehicles (UAVs) acting as aerial base stations that provide maximum wireless coverage for ground users in a given geographical area. This problem is addressed in two steps. First, in order to maximize the utilization of each UAV, its optimal flight altitude is found based on the UAV's transmit power which provides maximum coverage radius on the ground. The UAVs are classified into separate groups based on their transmit powers and optimal flight altitudes. Next, given a repository of UAVs belonging to different classes, the proposed technique finds an optimal subset of the available UAVs along with their optimal 3D placement to provide the maximum network coverage for a given area on the ground with the minimum power consumption. This optimization problem is proved to be NP-hard, for which a novel algorithm is proposed to solve the problem. Simulation results demonstrate the effectiveness of the proposed solution and provide valuable insights into the performance of the Heterogeneous UAV-supported small cell networks.
Deployment of sensor nodes is one of the crucial factors in mobile wireless sensor networks for improving the performance of the network. The network's lifetime primarily depends on the consumed energy and area co...
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Deployment of sensor nodes is one of the crucial factors in mobile wireless sensor networks for improving the performance of the network. The network's lifetime primarily depends on the consumed energy and area coverage by the sensor nodes. The efficiency of mobile wireless sensor networks increases by the efficient deployment of the sensors. coverage and energy consumption mainly depends on the effective deployment schemes of sensors. This article presents an energy-efficient coverage optimization technique with the help of the Voronoi-Glowworm Swarm Optimization-K-means algorithm. In this approach, Glowworm Swarm Optimization, K-means algorithm, and Voronoi cell structure enhance the coverage area with a minimum number of active nodes. This approach considers optimum sensing radius calculation for efficient sensor deployment. Further-more, the proposed method improves the lifetime of the deployed network by decreasing the consumed energy by the deployed sensor nodes using multi-hop transmission and the sleep-wake mechanism. The simulation result shows that area coverage is achieved by the proposed method up to 99.99% with the optimum number of active sensor nodes.
The distribution of sensor nodes is the key factor in mobile wireless sensor networks (WSNs) to enhance the functionality of the network. The effectiveness of a wireless sensor network can be increased by the efficien...
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The distribution of sensor nodes is the key factor in mobile wireless sensor networks (WSNs) to enhance the functionality of the network. The effectiveness of a wireless sensor network can be increased by the efficient distribution of several sensor nodes. Previously researchers considered existing algorithms or their hybrid for the effective distribution of sensor nodes. Those only optimize energy or enhance network coverage none of them considered both aspects with the minimum cost of equipment. This piece of work proposes a novel bio-inspired ruminant algorithm. The algorithm is developed as a result of the in-spiration of the efficient digestive system of ruminant animals. That takes a large amount of raw food as input and produces an optimal value of food that is full of energy. The proposed algorithm is validated on several benchmark functions. By keeping inspiration in mind, the proposed algorithm not only applied to enhance network coverage of WSNs with optimized energy and sensor nodes distribution, which increases the lifetime of the equipment. Furthermore, the results of the algo-rithm show that if network coverage enhances more optimized value of energy is achieved with no increase in the number of deployed sensors. That proves the no increase in the cost of the equipment with more network coverage on enhanced lifetime.
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