For the polarization sensitive array considered in the most current literature, the physical length of the electric-dipole is less than 0.1 of a wavelength, and the radiation efficiency of this "short" elect...
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For the polarization sensitive array considered in the most current literature, the physical length of the electric-dipole is less than 0.1 of a wavelength, and the radiation efficiency of this "short" electric-dipole is too low. Therefore, it is of great practical significance to study the array of "long" electric-dipole (the physical length lies within the range of [0.1, 1] of a wavelength). This paper constructs a sparse uniform planar array composed of the orthogonally oriented and spatially separated "long" tri-electric-dipole and proposes an algorithm to solve the problem of the 2-D direction-of-arrival (2-D-DOA) estimation with the proposed array. It is worth mentioning that the proposed algorithm does not require the prior knowledge of polarization parameters. Moreover, the spatially separated structure of the vector sensor is beneficial to engineering practice. Simulations are conducted to verify the effectiveness of the proposed algorithm.
This paper exploits a unique 2003-2011 large dataset, indexed by Thomson Reuters, consisting of 17.2 million disambiguated authors classified into 30 broad scientific fields, as well as the 48.2 million articles resul...
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This paper exploits a unique 2003-2011 large dataset, indexed by Thomson Reuters, consisting of 17.2 million disambiguated authors classified into 30 broad scientific fields, as well as the 48.2 million articles resulting from a multiplying strategy in which any article coauthored by two or more persons is wholly assigned as many times as necessary to each of them. The dataset is characterized by a large proportion of authors who have their oeuvre in several fields. We measure individual productivity in two ways that are uncorrelated: as the number of articles per person and as the mean citation per article per person in the 2003-2011 period. We analyze the shape of the two types of individual productivity distributions in each field using size-and scale-independent indicators. To assess the skewness of productivity distributions we use a robust index of skewness, as well as the Characteristic Scores and Scales approach. For productivity inequality, we use the coefficient of variation. In each field, we study two samples: the entire population, and what we call "successful authors", namely, the subset of scientists whose productivity is above their field average. The main result is that, in spite of wide differences in production and citation practices across fields, the shape of field productivity distributions is very similar across fields. The parallelism of the results for the population as a whole and for the subset of successful authors, when productivity is measured as mean citation per article per person, reveals the fractal nature of the skewness of scientific productivity in this case. These results are essentially maintained when any article co-authored by two or more persons is fractionally assigned to each of them. (C) 2014 Elsevier Ltd. All rights reserved.
Many researchers have devoted their efforts on optimizing the character arrangement for the singlecharacter keyboard (SCK), however, the SCK market is still dominated by the QWERTY keyboard because people are used to ...
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Many researchers have devoted their efforts on optimizing the character arrangement for the singlecharacter keyboard (SCK), however, the SCK market is still dominated by the QWERTY keyboard because people are used to the typology and hesitate to learn a new one even the QWERTY keyboard is ill-designed. As the use of hand-held entry devices is growing, the optimal arrangement of multi-character keyboard (MCK) is increasingly important. This also apples to customized keyboard design for motor-impaired users who are usually not able to do the typing on many keys. In this paper, we propose a unified mathematical model that integrates varying objectives. An enhanced particle swarm algorithm is developed by embedding a character-swapping optimizer and a bounding technique. Experimental results manifest that the proposed method outperforms competing ones by taking into account the cooccurrence frequency of characters in words, typing ergonomics, and word-disambiguation effectiveness.
This paper presents context-group discrimination, a disambiguation algorithm based on clustering, Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses a...
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This paper presents context-group discrimination, a disambiguation algorithm based on clustering, Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a high-dimensional, real-valued space in which closeness corresponds to semantic similarity. Similarity in Word Space is based on second-order co-occurrence: two tokens (or contexts) of the ambiguous worn are assigned to the same sense cluster if the words they co-occur with in turn occur with similar words in a training corpus. The algorithm is automatic and unsupervised in both training and application: senses are induced from re corpus without labeled training instances or other external knowledge sources. The paper demonstrates good performance of context-group discrimination for a sample of natural and artificial ambiguous words.
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