With the proliferation of mobile devices, recent years have witnessed an emerging potential to integrate mobile visual search techniques into digital library. Such a mobile application scenario in digital library has posed significant and unique challenges in document image search. The mobile photograph makes it tough to extract discriminative features from the landmark regions of documents, like line drawings, as well as text layouts. In addition, both search scalability and query delivery latency remain challenging issues in mobile document search. The former relies on an effective yet memory-light indexing structure to accomplish fast online search, while the latter puts a bit budget constraint of query images over the wireless link. In this paper, we propose a novel mobile document image retrieval framework, consisting of a robust Local Inner-distance Shape Context (LISC) descriptor of line drawings, a Hamming distance KD-Tree for scalable and memory-light document indexing, as well as a JBIG2 based query compression scheme, together with a Retinex based enhancement and an OTSU based binarization, to reduce the latency of delivering query while maintaining query quality in terms of search performance. We have extensively validated the key techniques in this framework by quantitative comparison to alternative approaches.
Macrocycle-1 molecules can self-assemble into glassy state networks via van der Waals force and form many triangular nanopores in networks. The nanopores can be expressed by triangular tilings, which lead to a particularly rich range of arrangements. Moreover an interesting molecular rotation phenomenon was observed in the glassy networks.
Due to its compatibility with the well-developed Si-based semiconductor industry, silicene has attracted considerable attention. Using density functional theory we show for the first time that the recently synthesized superhalogen MnCl3 can be used to tune the electronic and magnetic properties of silicene, from semi-metallic to semiconducting with a wide range of band gaps, as well as from nonmagnetic to ferromagnetic (or antiferromagnetic) by changing the coverage of the superhalogen molecules. The electronic properties can be further modulated when a superhalogen and a halogen are used synergistically. The present study indicates that because of the large electron affinity and rich structural diversity superhalogen molecules have advantages over the conventional halogen atoms in modulating the material properties of silicene.
Surface-plasmon-polariton (SPP) launchers, which can couple the free space light to the SPPs on the metal surface, are among the key elements for the plasmonic devices and nano-photonic systems. Downscaling the SPP launchers below the diffraction limit and directly delivering the SPPs to the desired subwavelength plasmonic waveguides are of importance for high-integration plasmonic circuits. By designing a submicron double-slit structure with different slit widths, an ultra-broadband (>330 nm) unidirectional SPP launcher is realized theoretically and experimentally based on the different phase delays of SPPs propagating along the metal surface and the near-field interfering effect. More importantly, the broadband and unidirectional properties of the SPP launcher are still maintained when the slit length is reduced to a subwavelength scale. This can make the launcher occupy only a very small area of
While water diversion and dilution are often proposed and implemented for lake eutrophication management, their effectiveness and efficiency in achieving water quality goals is often questionable. Although water quality modeling (WQM) has been applied to quantify lake responses to water diversion and dilution in practice, it is necessary to improve the existing analysis approaches with an uncertainty-based decision-support framework to address the situation of severe data limitation that exists in many realworld cases. This study implemented an enhanced multiple-pattern inverse water quality modeling (MPIWQM) approach in a water diversion study for a terminal plateau lake in southwestern China to address the difficulty of developing robust water-diversion decision support under data limitation and model uncertainty. A two-dimensional, longitudinal and vertical hydrodynamic and water quality model was developed to simulate water circulation and nutrient fate and transport in the lake. To overcome a severe limitation of data, this study employed a multiple-pattern load-parameter estimation (MPLE) method that couples the numerical model with a Genetic Algorithm (GA) and a cluster algorithm to construct an uncertainty-based decision support system. Execution of the MPLE approach resulted in 27 load-parameter patterns for the case study to represent all possible combinations of loading-parameter patterns conditioned on the available water quality data in the lake. The uncertainty-based decision-support framework was then applied to evaluate three realistic water diversion scenarios proposed by local management authorities, and the system was able to predict a range of possibilities given a specific water diversion condition. The scenario analysis results showed that (a) within the range of uncertainties represented by the 27 load-parameter patterns, the model consistently predict that the water diversions would unlikely cause significant water quality improvement in the lake; (b) the water quality response to water diversion demonstrates clear spatial variability, temporal variability, and the effect is in general cumulative over time; (c) different water quality constituents respond to the diversions differently, where the chemical oxygen demand (COD) demonstrates the strongest response, while the total phosphorus (TP) the weakest; and (d) none of the proposed water diversion scenarios is able to reverse, or significantly mitigate the water quality deterioration trend in the lake. (C) 2014 Elsevier B.V. All rights reserved.
