Novel Power-Imbalanced Dense Codebooks for Reliable Multiplexing in Nakagami Channels

This paper studies enhanced dense code multiple access (DCMA) system design for downlink transmission over the Nakagami-$m$ fading channels. By studying the DCMA pairwise error probability (PEP) in a Nakagami-$m$ channel, a novel design metric called minimum logarithmic sum distance (MLSD) is first derived. With respect to the proposed MLSD, we introduce a new family of power-imbalanced dense codebooks by deleting certain rows of a special non-unimodular circulant matrix. Simulation results demonstrate that our proposed dense codebooks lead to both larger minimum Euclidean distance and MLSD, thus yielding significant improvements of error performance over the existing sparse code multiple access and conventional unimodular DCMA schemes in Nakagami-$m$ fading channels under different overloading factors.

Asymptotically Optimal Quasi-Complementary Code Sets from Multivariate Functions

Owing to the more significant set size properties as compared to the set of complete complementary codes (CCCs), quasi-complementary code sets (QCCSs) are more convenient to support a large number of users in multicarrier code-division multiple-access (MC-CDMA) system over CCCs. Besides set size, it is also desirable to have a low maximum aperiodic correlation magnitude and small alphabet size. This paper aims to construct asymptotically optimal and near-optimal aperiodic QCCSs having a small alphabet size and low maximum correlation magnitude. Using multivariate functions and its associated graph, we propose a family of QCCSs consisting of multiple sets of CCCs and determine the parameters of the proposed QCCSs. Unlike the existing constructions of aperiodic QCCSs, the proposed construction can maintain a small alphabet size irrespective of the increasing sequence length and large set size.

Defect detection for patterned fabric images based on GHOG and low-rank decomposition

In order to accurately detect defects in patterned fabric images, a novel detection algorithm based on Gabor-HOG (GHOG) and low-rank decomposition is proposed in this paper. Defect-free pattern fabric images have the specified direction, while defects damage their regularity of direction. Therefore, a direction-aware descriptor is designed, denoted as GHOG, a combination of Gabor and HOG, which is extremely valuable for localizing the defect region. Upon devising a powerful directional descriptor, an efficient low-rank decomposition model is constructed to divide the matrix generated by the directional feature extracted from image blocks into a low-rank matrix (background information) and a sparse matrix (defect information). A nonconvex log det(.) as a smooth surrogate function for the rank instead of the nuclear norm is also exploited to improve the efficiency of the low-rank model. Moreover, the computational efficiency is further improved by utilizing the alternative direction method of multipliers (ADMM). Thereafter, the saliency map generated by the sparse matrix is segmented via the optimal threshold algorithm to locate the defect regions. Experimental results show that the proposed method can effectively detect patterned fabric defects and outperform the state-of-the-art methods.