Life expectancy at older ages has seen an increment in the past 100 years and factors influencing longevity are since being studied. Public health sectors (PHS) across the globe are trying hard to understand longevity...
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Wireless Sensor Networks (WSNs) are made up of tiny sensor nodes which sense the data and communicate to the base station via other nodes. These sensor nodes are inexpensive portable devices with limited processing po...
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Wireless Sensor Networks (WSNs) are made up of tiny sensor nodes which sense the data and communicate to the base station via other nodes. These sensor nodes are inexpensive portable devices with limited processing power and energy resources which make them in need of smart clustering protocols. Many clustering and routing protocols were proposed in the literature to serve large networks of such tiny devices. In this paper, we have implemented and analyzed different clustering protocols, namely LEACH, LEACH-C, LEACH-1R, and HEED using MATLAB environment. These clustering protocols are compared in different terms such as residual energy, data delivery to the base station, maximum number of rounds and the number of live nodes. Experimental results showed a better performance of the LEACH protocols when compared to the different versions of HEED. Moreover, LEACH-1R proved to be efficient in terms of network lifetime.
Recent studies have shown the promise of direct data processing on hierarchically-compressed text documents. By removing the need for decompressing data, the direct data processing technique brings large savings in bo...
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ISBN:
(数字)9781728129037
ISBN:
(纸本)9781728129044
Recent studies have shown the promise of direct data processing on hierarchically-compressed text documents. By removing the need for decompressing data, the direct data processing technique brings large savings in both time and space. However, its benefits have been limited to data traversal operations; for random accesses, direct data processing is several times slower than the state-of-the-art baselines. This paper presents a set of techniques that successfully eliminate the limitation, and for the first time, establishes the feasibility of effectively handling both data traversal operations and random data accesses on hierarchically-compressed data. The work yields a new library, which achieves 3.1× speedup over the state-of-the-art on random data accesses to compressed data, while preserving the capability of supporting traversal operations efficiently and providing large (3.9×) space savings.
A new concept Graded Finite Poset is proposed in this paper. Through discussing some basic properties of it, we come to that the direct product of graded finite posets is connected if and only if every graded finite p...
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A new concept Graded Finite Poset is proposed in this paper. Through discussing some basic properties of it, we come to that the direct product of graded finite posets is connected if and only if every graded finite poset is connected. The graded function of a graded finite poset is unique if and only if the graded finite poset is connected.
The pulsar classification represents a major issue in the astrophysical area. The Bagging Algorithm is an ensemble method widely used to improve the performance of classification algorithms, especially in the case of ...
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The pulsar classification represents a major issue in the astrophysical area. The Bagging Algorithm is an ensemble method widely used to improve the performance of classification algorithms, especially in the case of pulsar search. In this way, our paper tries to prove how the Bagging Method can improve the performance of pulsar candidate detection in connection with four basic classifiers: Core Vector Machines (CVM), the K-Nearest-Neighbors (KNN), the Artificial Neural Network (ANN), and Cart Decision Tree (CDT). The Error Rate, Area Under the Curve (AUC), and Computation Time (CT) are measured to compare the performance of different classifiers. The High Time Resolution Universe (HTRU2) dataset, collected from the UCI Machine Learning Repository, is used in the experimentation phase.
Ordinal real-world data such as concept hierarchies, ontologies, genealogies, or task dependencies in scheduling often has the property to not only contain pairwise comparable, but also incomparable elements. Order di...
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Many of the famous single-player games, commonly called puzzles, can be shown to be NP-Complete. Indeed, this class of complexity contains hundreds of puzzles, since people particularly appreciate completing an intrac...
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Existing deep learning-based pan-sharpening methods mainly learn spatial information from a high-resolution (HR) panchromatic (PAN) image for each spectral channel. However, due to the own characteristics of remote se...
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ISBN:
(数字)9781728163956
ISBN:
(纸本)9781728163963
Existing deep learning-based pan-sharpening methods mainly learn spatial information from a high-resolution (HR) panchromatic (PAN) image for each spectral channel. However, due to the own characteristics of remote sensing image data, the spatial information of PAN image often shows weak correlation with some spectral channel, especially for channels non-overlapped by PAN channel. In this paper, we propose a parallel pyramid network (PPN) for pan-sharpening. First, a three-branch parallel structure is proposed for dealing with PAN image detail, multispectral (MS) images detail and spectral property respectively. Second, pyramid network structure is introduced in two detail branches to solve the problem of weak correlation due to scale difference. Third, the feature level fusion in two detail branches is implemented, which utilizes redundancy between channels to solve detail representation of channels non-overlapped by PAN channel. The qualitative and quantitative experimental results on various data sets demonstrate the superiority of our proposed method over the state-of-the-art methods.
Phylogenetic trees and networks are leaf-labelled graphs used to model evolution. Display graphs are created by identifying common leaf labels in two or more phylogenetic trees or networks. The treewidth of such graph...
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