The virtual array generation process based on typical sparse arrays is studied for a mixture of circular and non-circular impinging signals. It consists of two sub-arrays: one is the traditional difference co-array and the other one is the new sum co-array. The number of consecutive virtual array sensors is analysed for the nested array case, but it is difficult to give a closed-form result for a general sparse array. Based on the extended covariance matrix of the physical array, two classes of direction of arrival (DOA) estimation algorithms are then developed, with one based on the subspace method and one based on sparse representation or the compressive sensing (CS) concept. Both the consecutive and non-consecutive parts of the virtual array can be exploited by the CS-based method, while only the consecutive part can be exploited by the subspace-based one. As a result, the CS-based solution can have a better performance than the subspace-based one, though at the cost of significantly increased computational complexity. The two classes of algorithms can also deal with the special case when all the signals are noncircular. Simulation results are provided to verify the performance of the proposed algorithms.

Time-sync video tagging aims to automatically generate tags for each video shot. It can improve the user's experience in previewing a video's timeline structure compared to traditional schemes that tag an entire video clip. In this paper, we propose a new application which extracts time-sync video tags by automatically exploiting crowdsourced comments from video websites such as Nico Nico Douga, where videos are commented on by online crowd users in a time-sync manner. The challenge of the proposed application is that users with bias interact with one another frequently and bring noise into the data, while the comments are too sparse to compensate for the noise. Previous techniques are unable to handle this task well as they consider video semantics independently, which may overfit the sparse comments in each shot and thus fail to provide accurate modeling. To resolve these issues, we propose a novel temporal and personalized topic model that jointly considers temporal dependencies between video semantics, users' interaction in commenting, and users' preferences as prior knowledge. Our proposed model shares knowledge across video shots via users to enrich the short comments, and peels off user interaction and user bias to solve the noisy-comment problem. Log-likelihood analyses and user studies on large datasets show that the proposed model outperforms several state-of-the-art baselines in video tagging quality. Case studies also demonstrate our model's capability of extracting tags from the crowdsourced short and noisy comments.

Recently, compressive hyperspectral imaging (CHI) has received increasing interests, which can recover a large range of scenes with a small number of sensors via compressed sensing (CS) theory. However, most of the available CHI methods separate and vectorize hyperspectral cubes into spatial and spectral vectors, which will result in heavy computational and storage burden in the recovery. Moreover, the complexity of real scene makes the sparsifying difficult and thus requires more measurements to achieve accurate recovery. In this paper, these two issues are addressed, and a new CHI approach via sparse tensors and nonlinear CS (NCS) is advanced for accurate maintenance of image structure with limited number of sensors. Based on a multidimensional multiplexing (MDMP) CS scheme, the observed measurements are denoted as tensors and a nonlinear sparse tensor coding is adopted, to develop a new tensor-NCS (T-NCS) algorithm for noniterative recovery of hyperspectral images. Moreover, two recovery schemes are advanced for T-NCS, including example-aided and self-learning CHI approaches. Finally, some experiments are performed on three real hyperspectral data sets to investigate the performance of T-NCS, and the results demonstrate its efficiency and superiority to the counterparts.Index Terms-Compressive hyperspectral imaging (CHI), joint spatial-spectral, multidimensional multiplexing (MDMP), nonlinear compressed sensing (NCS), sparse tensor.

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