The proposed sparse-array is different from the sparse-array in [10, 11], where the array is composed of six-component electromagnetic vector-sensors.
Here, the embellishment is densely clustered for a richness that gives a very different feel from the sparse, night-market look of the cheap stuff.
Different from the classic sparse signal recovery based on Lemma 3.1 in [19], CCSS aims to exactly obtain Γ=Λ since that (Lambda - Gamma neq varnothing ) indicates the existence of false alarm.
Note that the sparsity model used in this paper is different from the conventional joint sparse model (JSM) [35], in that our node source signals or messages are scalar random variables, without correlation over time in each node.
We use different priors on the sparse coefficient vector and the dictionary atoms, which lead to various sparse representation models.
Here, the sparse matrix can be non-squared, i.e., the number of rows can be different from the number of columns in the sparse matrix.
Different from the former parallel algorithms in sparse grid techniques, which have troubles in decomposing the domain equally and keeping load balance among the processors, our parallel algorithm can achieve load balance easily among the threads for any sparse grid configurations.
As a consequence, y is K-sparse with regard to Kronecker dictionary D=(D N ⊗D N−1⊗…⊗D 1), where K=K 1 K 2…K N. It should be noted that the definition of block sparsehere is different from the conventional definition of block sparse representation for one-dimensional signal.
Different from existing sparse phase retrieval approaches in[23 30], we propose an iterative projection approach with phase sparse constraint for semi-sparse wave field.
Different from traditional sparse representation-based tracking algorithms, our model not only exploits convolutional features to improve the robustness for describing the object appearance but also uses the trivial templates to model both reconstruction errors caused by sparse representation and the eigen-subspace representation.
Different from traditional sparse representation based tracking methods, our model not only exploits convolutional features to improve the robustness for describing the object appearance but also uses the trivial templates to model both reconstruction errors caused by sparse representation and the eigen-subspace representation.
