A pair of binary sequences is generalized from the concept of a two-level autocorrelation function of a single binary sequence. In this paper, new families of binary sequence pairs with period , where gcd , and optimal correlation values are constructed, as well as new families of binary sequence pairs with three-level correlation and period . For the new binary sequence pairs with optimal correlation values, their corresponding new difference set pairs and almost difference set pairs are also derived.Index Terms-Almost difference set pair (ADSP), binary sequence pair, difference set pair (DSP), optimal correlation value.
With the introduction of effective and general deep learning network frameworks, deep learning based methods have achieved remarkable success in various visual tasks. However, there are still tough challenges in applying them to convolutional neural networks due to the lack of a potential rule structure of point clouds. Therefore, by taking the original point clouds as the input data, this paper proposes an orientation-encoding (OE) convolutional module and designs a convolutional neural network for effectively extracting local geometric features of point sets. By searching for the same number of points in 8 directions and arranging them in order in 8 directions, the OE convolution is then carried out according to the number of points in the direction, which realizes the effective feature learning of the local structure of the point sets. Further experiments on diverse datasets show that the proposed method has competitive performance on classification and segmentation tasks of point sets.
Perfect sequences have wide application fields, including spread spectrum communication, synchronization techniques, channel estimation, and cell search. In this letter, two kinds of (almost) perfect Gaussian integer sequences of length N = 2(mod4) and N = 0(mod4) are proposed and constructed by complex transformations and interleaving techniques, using the perfect Gaussian integer sequences of odd length as the base sequences.
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