No abstract

In this paper, the two-dimensional (2-D) directionof-arrival (DOA) estimation problem for a mixture of circular and non-circular sources is considered. In particular, we focus on a 2-D array structure consisting of two parallel uniform linear arrays (ULAs) and build a general array model with mixed circular and non-circular sources. The received array data and its conjugate counterparts are combined together to form a new data vector, based on which a series of 2-D DOA estimators are derived. Compared to existing methods, the proposed one has three main advantages. Firstly, it can give a more accurate estimation in situations where the number of sources is within the traditional limit of high resolution methods; secondly, it can still work effectively when the number of mixed signals is larger than that of the array elements; thirdly, the paired 2-D DOAs of the proposed method can be obtained automatically without the complicated 2-D spectrum peak search and therefore has a much lower computational complexity.

In this paper, a two-dimensional (2-D) direction-of-arrival (DOA) estimation method for a mixture of circular and strictly noncircular signals is presented based on a uniform rectangular array (URA). We first formulate a new 2-D array model for such a mixture of signals, and then utilize the observed data coupled with its conjugate counterparts to construct a new data vector and its associated covariance matrix for DOA estimation. By exploiting the second-order non-circularity of incoming signals, a computationally effective ESPRIT-like method is adopted to estimate the 2-D DOAs of mixed sources which are automatically paired by joint diagonalization of two direc

No abstract

Owing to the complex structure of Chinese characters and the huge number of Chinese characters, it is very challenging and time consuming for artists to design a new font of Chinese characters. Therefore, the generation of Chinese characters and the transformation of font styles have become research hotspots. At present, most of the models on Chinese character transformation cannot generate multiple fonts, and they are not doing well in faking fonts. In this article, the authors propose a novel method of Chinese character fonts transformation and generation based on generative adversarial networks. The authors’ model is able to generate multiple fonts at once through font style‐specifying mechanism and it can generate a new font at the same time if the authors combine the characteristics of existing fonts.

The use of amelogenin locus typing as a gender marker incorporated in short tandem repeat (STR) multiplexes is a common practice in sex typing. Mutations in the X or Y homologue of the amelogenin gene can be misleading and result in serious mistakes in forensic applications and prenatal diagnosis. In these present studies, the amelogenin gene of 8,087 unrelated male individuals from Chinese Han population was genotyped with Powerplex(®)16 system. The samples that showed discordant results were taken for frequency calculation and further validated by re-amplification with different primer sets, Y-STR typing, and sequencing. Our results describe six amelogenin X-allele (AMELX) or amelogenin Y-allele (AMELY) null cases in these studied subjects with an overall prevalence of 0.074%. Further validation revealed point mutations in the amelogenin-priming sites associated with AMELX nulls (three cases, 0.037%) and deletions on the Y chromosome encompassing the AMELY and other Y-STR loci with three AMELY nulls (0.037%). These mutations and failure of the amplification of the AMELX and AMELY alleles have not been reported for the Chinese population. These and previous findings suggest that mutations in the amelogenin gene may result in amplification failure of the AMELX or AMELY allele, and an additional gender test for unambiguous sex determination may be needed.

No abstract

No abstract