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We propose a fully convolutional neural network (FCNN) model for ice concentration estimation from dual-polarized SAR images. Our network contains 5 convolutional layers. Tested in the Gulf of Saint Lawrence during freeze-up, the proposed model is demonstrated to generate improved ice concentration estimates compared to a CNNs with similar structure.
Using only one training symbol, a novel cross ambiguity function (CAF) based integer frequency offset (IFO) estimator for OFDM systems is proposed. From the energy distribution characteristics of the ideal CAF, an energy-detection based metric is obtained. By designing a training symbol whose ambiguity function (AF) is a valid approximation of the ideal thumbtack-type AF, a high-accuracy and full-range...
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