Linear Discriminant Analysis (LDA) is one of the most popular dimensionality reduction techniques in existing handwritten Chinese character (HCC) recognition systems. However, when used for unconstrained handwritten Chinese character recognition, the traditional LDA algorithm is prone to two problems, namely, the class separation problem and multimodal sample distributions. To deal with these problems,we propose a new locally linear discriminant analysis (LLDA) method for handwritten Chinese character recognition.Our algorithm operates as follows. (1) Using the clustering algorithm, find clusters for the samples of each class. (2) Find the nearest neighboring clusters from the remaining classes for each cluster of one class. Then, use the corresponding cluster means to compute the between-class scatter matrix in LDA while keeping the within-class scatter matrix unchanged. (3) Finally, apply feature vector normalization to further improve the class separation problem. A series of experiments on both the HCL2000 and CASIA Chinese character handwriting databases show that our method can effectively improve recognition performance, with a reduction in error rate of 28.7% (HCL2000) and 16.7% (CASIA) compared with the traditional LDA method.Our algorithm also outperforms DLA (Discriminative Locality Alignment,one of the representative manifold learning-based dimensionality reduction algorithms proposed recently). Large-set handwritten Chinese character recognition experiments also verified the effectiveness of our proposed approach.

This paper is focused on constructing even-length binary Z-complementary pairs (EB-ZCPs) with new length. Inspired by a recent work of Adhikary et al., we give a construction of EB-ZCPs with length 8N+4 (where N=2α 10β 26γ and α, β, γ are nonnegative integers) and zero correlation zone (ZCZ) width 5N+2. The maximum aperiodic autocorrelation sums (AACS) magnitude of the proposed sequences outside the ZCZ region is 8. It turns out that the generated sequences have low PAPR.

Codebooks with small maximal cross-correlation amplitudes have important applications in code division multiple access (CDMA) communication, coding theory and compressed sensing. In this letter, we design a new codebook based on a construction of Ramanujan graphs over finite abelian groups. We prove that the new codebook with length K=q+1 and size N=q2+2q+2 is asymptotically optimal with nearly achieving the Levenshtein bound when n=3, where q is a prime power. The parameters of the new codebook are new.

The auto-correlation property of Huffman sequence makes it a good candidate for its application in radar and communication systems. However, high peak-to-average power ratio (PAPR) of Huffman sequence severely limits its application value. In this paper, we propose a novel algorithm to construct Huffman sequences with low PAPR. We have used the roots of the polynomials corresponding to Huffman sequences of length M + 1 to construct Huffman sequences of length 2M + 1, with low PAPR.