A Markov Chain Random Field Cosimulation-Based Approach for Land Cover Post-classification and Urban Growth Detection

The recently proposed Markov chain random field (MCRF) approach has great potential to significantly improve land cover classification accuracy when used as a post-classification method by taking advantage of expert-interpreted data and pre-classified image data. This doctoral dissertation explores the effectiveness of the MCRF cosimulation (coMCRF) model in land cover post-classification and further improves it for land cover post-classification and urban growth detection. The intellectual merits of this research include the following aspects: First, by examining the coMCRF method in different conditions, this study provides land cover classification researchers with a solid reference regarding the performance of the coMCRF method for land cover post-classification. Second, this study provides a creative idea to reduce the smoothing effect in land cover post-classification by incorporating spectral similarity into the coMCRF method, which should be also applicable to other geostatistical models. Third, developing an integrated framework by integrating multisource data, spatial statistical models, and morphological operator reasoning for large area urban vertical and horizontal growth detection from medium resolution remotely sensed images enables us to detect and study the footprint of vertical and horizontal urbanization so that we can understand global urbanization from a new angle. Such a new technology can be transformative to urban growth study. The broader impacts of this research are concentrated on several points: The first point is that the coMCRF method and the integrated approach will be turned into open access user-friendly software with a graphical user interface (GUI) and an ArcGIS tool. Researchers and other users will be able to use them to produce high-quality land cover maps or improve the quality of existing land cover maps. The second point is that these research results will lead to a better insight of urban growth in terms of horizontal and vertical dimensions, as well as the spatial and temporal relationships between urban horizontal and vertical growth and changes in socioeconomic variables. The third point is that all products will be archived and shared on the Internet

A Rapid Prototyping Language Workbench for Textual DSLs based on Xtext: Vision and Progress

Metamodel-based DSL development in language workbenches like Xtext allows language engineers to focus more on metamodels and domain concepts rather than grammar details. However, the grammar generated from metamodels often requires manual modification, which can be tedious and time-consuming. Especially when it comes to rapid prototyping and language evolution, the grammar will be generated repeatedly, this means that language engineers need to repeat such manual modification back and forth. Previous work introduced GrammarOptimizer, which automatically improves the generated grammar using optimization rules. However, the optimization rules need to be configured manually, which lacks user-friendliness and convenience. In this paper, we present our vision for and current progress towards a language workbench that integrates GrammarOptimizer's grammar optimization rules to support rapid prototyping and evolution of metamodel-based languages. It provides a visual configuration of optimization rules and a real-time preview of the effects of grammar optimization to address the limitations of GrammarOptimizer. Furthermore, it supports the inference of a grammar based on examples from model instances and offers a selection of language styles. These features aim to enhance the automation level of metamodel-based DSL development with Xtext and assist language engineers in iterative development and rapid prototyping. Our paper discusses the potential and applications of this language workbench, as well as how it fills the gaps in existing language workbenches.Comment: 6 pages, 3 figure

A Hop-by-Hop Relay Selection Strategy in Multi-Hop Cognitive Relay Networks

In this paper, a hop-by-hop relay selection strategy for multi-hop underlay cognitive relay networks (CRNs) is proposed. In each stage, relays that successfully decode the message from previous hop form a decoding set. Taking both maximum transmit power and maximum interference constraints into consideration, the relay in the decoding set which has the largest number of channels with an acceptable signal-to-noise ratio (SNR) level to the relays in the next stage is selected for retransmission. Therefore, relay selection in each stage only relies on channel state information (CSI) of the channels in that stage and does not require the CSI of any other stage. We analyze the performance of the proposed strategy in terms of endto-end outage probability and throughput, and show that the results match those obtained from simulation closely. Moreover, we derive the asymptotic end-to-end outage probability of the proposed strategy when there is no upper bound on transmitters’ power. We compare this strategy to other hop-by-hop strategies that have appeared recently in the literature and show that this strategy has the best performance in terms of outage probability and throughput. Finally it is shown that the outage probability and throughput of the proposed strategy are very close to that of exhaustive strategy which provides a lower bound for outage probability and an upper bound for throughput of all path selection strategies

Effects of Preparation Conditions on the Yield and Embedding Ratio of Vinyl Silicone Oil Microcapsules

Self-healing materials could repair themselves without external influences when they are damaged. In this paper, microcapsules are prepared by in-situ polymerization method, utilizing vinyl silicone oil as core material, polyurea formaldehyde as wall material and polyvinyl alcohol as dispersant. The morphology and structure of the microcapsules are tested with scanning electron microscopy, optical microscopy and laser particle analyzer. Effect of the reaction temperature, stirring speed and polyvinyl alcohol concentration on the yield, embedding ratio, particle size and its distribution are studied. Results show that the microcapsules can be successfully prepared by in-situ polymerization method. Under the reaction condition of temperature 60 °C, stirring speed 1000 r/min, dispersant concentration 0.1 wt.%, the yield and embedding ratio of the microcapsule are found to be 52.5 % and 50.1 %, respectively. The prepared microcapsules have smooth surface, good dispersibility, narrow particle size distribution and the average particle size is 13 μm