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Abstract-In this paper, we consider the dictionary learning problem for the sparse analysis model. A novel algorithm is proposed by adapting the simultaneous codeword optimization (SimCO) algorithm, based on the sparse synthesis model, to the sparse analysis model. This algorithm assumes that the analysis dictionary contains unit ℓ2-norm atoms and learns the dictionary by optimization on manifolds. This framework allows multiple dictionary atoms to be updated simultaneously in each iteration. However, similar to several existing analysis dictionary learning algorithms, dictionaries learned by the proposed algorithm may contain similar atoms, leading to a degenerate (coherent) dictionary. To address this problem, we also consider restricting the coherence of the learned dictionary and propose Incoherent Analysis SimCO by introducing an atom decorrelation step following the update of the dictionary. We demonstrate the competitive performance of the proposed algorithms using experiments with synthetic data and image denoising as compared with existing algorithms.

A commonly used measurement model for locating a mobile source is time-difference-of-arrival (TDOA). As each TDOA measurement defines a hyperbola, it is not straightforward to compute the mobile source position due to the nonlinear relationship in the measurements. This brief exploits the Lagrange programming neural network (LPNN), which provides a general framework to solve nonlinear constrained optimization problems, for the TDOA-based localization. The local stability of the proposed LPNN solution is also analyzed. Simulation results are included to evaluate the localization accuracy of the LPNN scheme by comparing with the state-of-the-art methods and the optimality benchmark of Cramér-Rao lower bound.

The chemical weathering intensity and element migration features of the Xiashu loess profile in Zhenjiang are studied in this paper. (1) The Xiashu loess profile underwent moderate chemical weathering. It seems that the precipitation is a more important factor than the temperature in controlling the process of the chemical weathering. (2) The major elements such as Si, K, Na, Ca, Mg, Mn and P were migrated and leached, while the elements Fe and Ti were slightly enriched. The migration features of the major elements reveal that the Xiashu loess finished the primary process of chemical weathering characterized by leaching of Ca and Na, and almost reached the secondary process characterized by leaching of K. Except the elements Sr and Ga, other trace elements such as Th, Ba, Cu, Zn, Co, Ni, Cr and V were enriched. It might be caused by both the biogeochemical process and the adsorption of trace elements by clay mineral and organic materials. (3) The difference of element migration down the Xiashu loess profile reveals that the climate was warm and wet at the early-middle stage of the middle Pleistocene. At the end of the middle Pleistocene, it became dry and cool. At the early stage of the Late Pleistocene, the paleoclimate became warm and wet again. As a whole, the paleoclimate generally became drier and cooler in this region from the beginning of the middle Pleistocene.