Classifying ships by their acoustic signals with a cross-bispectrum algorithm and a radial basis function neural network

An algorithm for estimating the cross-bispectrum of an acoustic vector signal was formulated. Composed features of sound pressure and acoustic vector signals are extracted by the proposed algorithm and other estimating algorithms for secondary and higher order spectra. Its effectiveness was tested with lake and sea trial data. These features can be used to construct an input vector set for a radial basis function neural network. The classification of vessels can then be made based on the extracted features. It was shown that the composed features of acoustic vector signals are more easily divided into categories than those of pressure signals. When using the composed features of acoustic vector signals, the recognition rate of underwater acoustic targets improves.

莫里森的文化立场阐释

由于深受两种文化传统的影响,莫里森的作品明显具有“双声特征”, 她在继承非裔美国文学传统的同时,积极借鉴美国自人文学成果,她从黑人、女性的角度反思白人文化并成功地超越了种族与性别身份,丰富了“美国文化”的内涵,扩大了“美国文学”版图。