PSPACE-completeness of Pulling Blocks to Reach a Goal

We prove PSPACE-completeness of all but one problem in a large space of pulling-block problems where the goal is for the agent to reach a target destination. The problems are parameterized by whether pulling is optional, the number of blocks which can be pulled simultaneously, whether there are fixed blocks or thin walls, and whether there is gravity. We show NP-hardness for the remaining problem, Pull?-1FG (optional pulling, strength 1, fixed blocks, with gravity).Comment: Full version of JCDCGGG2019 paper, 22 pages, 25 figure

Annual Research Report, 2010-2011

Annual report of collaborative research projects of Old Dominion University faculty and students in partnership with business, industry and government.https://digitalcommons.odu.edu/or_researchreports/1000/thumbnail.jp

Modelling of River Flows, Sediment and Contaminants Transport

This book presents five articles that are also part of a Special Issue titled: Modelling of River flows, Sediment and Contaminants Transport published in the Water Journal under the section: Water Erosion and Sediment Transport. It covers a wide range of topics, such as predicting the impacts of wildfires on sediment transport and water quality in a mountainous region and estimating the sediment erosion due to release of ice-jams in cold region rivers

Geodetic and Remote-Sensing Sensors for Dam Deformation Monitoring

In recent years, the measurement of dam displacements has benefited from a great improvement of existing technology, which has allowed a higher degree of automation. This has led to data collection with an improved temporal and spatial resolution. Robotic total stations and GNSS (Global Navigation Satellite System) techniques, often in an integrated manner, may provide efficient solutions for measuring 3D displacements on precise locations on the outer surfaces of dams. On the other hand, remote-sensing techniques, such as terrestrial laser scanning, ground-based SAR (synthetic aperture radar) and satellite differential interferometric SAR offer the chance to extend the observed region to a large portion of a structure and its surrounding areas, integrating the information that is usually provided in a limited number of in-situ control points. The design and implementation of integrated monitoring systems have been revealed as a strategic solution to analyze different situations in a spatial and temporal context. Research devoted to the optimization of data processing tools has evolved with the aim of improving the accuracy and reliability of the measured deformations. The analysis of the observed data for the interpretation and prediction of dam deformations under external loads has been largely investigated on the basis of purely statistical or deterministic methods. The latter may integrate observation from geodetic, remote-sensing and geotechnical/structural sensors with mechanical models of the dam structure. In this paper, a review of the available technologies for dam deformation monitoring is provided, including those sensors that are already applied in routinary operations and some experimental solutions. The aim was to support people who are working in this field to have a complete view of existing solutions, as well as to understand future directions and trends

Polarimetric Synthetic Aperture Radar (SAR) Application for Geological Mapping and Resource Exploration in the Canadian Arctic

The role of remote sensing in geological mapping has been rapidly growing by providing predictive maps in advance of field surveys. Remote predictive maps with broad spatial coverage have been produced for northern Canada and the Canadian Arctic which are typically very difficult to access. Multi and hyperspectral airborne and spaceborne sensors are widely used for geological mapping as spectral characteristics are able to constrain the minerals and rocks that are present in a target region. Rock surfaces in the Canadian Arctic are altered by extensive glacial activity and freeze-thaw weathering, and form different surface roughnesses depending on rock type. Different physical surface properties, such as surface roughness and soil moisture, can be revealed by distinct radar backscattering signatures at different polarizations. This thesis aims to provide a multidisciplinary approach for remote predictive mapping that integrates the lithological and physical surface properties of target rocks. This work investigates the physical surface properties of geological units in the Tunnunik and Haughton impact structures in the Canadian Arctic characterized by polarimetric synthetic aperture radar (SAR). It relates the radar scattering mechanisms of target surfaces to their lithological compositions from multispectral analysis for remote predictive geological mapping in the Canadian Arctic. This work quantitatively estimates the surface roughness relative to the transmitted radar wavelength and volumetric soil moisture by radar scattering model inversion. The SAR polarization signatures of different geological units were also characterized, which showed a significant correlation with their surface roughness. This work presents a modified radar scattering model for weathered rock surfaces. More broadly, it presents an integrative remote predictive mapping algorithm by combining multispectral and polarimetric SAR parameters

Earth resources: A continuing bibliography with indexes, issue 22, July 1979

This bibliography lists 390 reports, articles, and other documents introduced into the NASA scientific and technical information system between 1 April 1979 and 30 June 1979. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

System Nonlinear Performance of Low-Rise Buildings under Database-Assisted Hurricane Loads

Light-frame wood buildings account for over 95% of all residential structures in the U.S, of which the majority are designed as low-rise buildings. These low-rise residential buildings in the U.S. have performed unsatisfactorily and are the largest source of the damage and fatality during the past extreme wind events. To deepen the understanding and reduce the vulnerability of the infrastructures, the accurate prediction of the hurricane loss has been an urgent need, and the hurricane catastrophe models are developed in response. However, the current hurricane catastrophe models are focused on the economy loss estimation rather than investigating the root causes of structural failures that only little or empirical structural analysis is involved. Thus, these models cannot reveal the realistic load paths nor the stage-wise damage propagation. This dissertation aims to develop a validated finite-element (FE) modeling frame work for predicting the system nonlinear performance of low-rise buildings under the spatiotemporally varying wind loads with the reasonable accuracy. This framework would serve for the successive damage prediction as a part of the risk assessment of low-rise buildings under extreme wind events. To reach the final objective, a refined 3D modeling methodology is proposed first. This modeling methodology contributes to combine the strengths of each involved disciplines to achieve a desired resolution, i.e., the dynamic form of wind loads, the full-scale scope of modeling, and the extensive nonlinear representative of the critical components. It is validated by a large-scale wind test from the linear to the nonlinear range including the successive failure stages. This modeling methodology provides the foundation for the future research. Secondly, a progressive failure prediction methodology aiming at finding the quantitative relationship between the wind intensity and the damage state of the building is well developed with an explicit explanation on the failure mode, the failure location, and the failure criteria. This methodology is also validated in the building scale and the individual connection scale by a corresponding destructive wind test with the agreement on the failure mode and sequence. Meanwhile, the database-assisted design (DAD) technique is extended from its original application on the linear prediction on the frames of the metal structure to the nonlinear modeling on the envelope of the wood structure in the current study. This framework that consists of the building modeling and the failure prediction provides a guideline on the three crucial steps for a more accurate performance prediction: directly using the aerodynamic database derived from wind tests, applying the loads onto a refined building model considering the nonlinear behaviors of critical components, and conducting the analysis on the progressive failure process. Then, some attempts are made on the application of this framework, e.g., the effect of the geometric and loading forms on the load paths and structural failure is discussed, and the adequacy of the wind design by using the ASCE 7-10 wind provisions on residential buildings is evaluated

Modelling the temporal variation of the ionosphere in a network-RTK environment

The Global Positioning System (GPS) has been widely used for precise positioning applications throughout the world. However, there are still some limiting factors that affect the performance of satellite-based positioning techniques, including the ionosphere. The GPS Network-RTK (NRTK) concept has been developed in an attempt to remove the ionospheric bias from user observations within the network. This technique involves the establishment of a series of GNSS reference stations, spread over a wide geographical region. Real time data from each reference station is collected and transferred to a computing facility where the various spatial and temporal errors affecting the GNSS satellite observations are estimated. These corrections are then transmitted to users observations in the field. As part of a Victorian state government initiative to implement a cm-level real time position ing service state-wide, GPSnet is undergoing extensive infrastructure upgrades to meet high user demand. Due to the sparse (+100km) configuration of GPSnet's reference stations, the precise modelling of Victoria's ionosphere will play a key role in providing this service. This thesis aims is to develop a temporal model for the ionospheric bias within a Victorian NRTK scenario. This research has analysed the temporal variability of the ionosphere over Victoria. It is important to quantify the variability of the ionosphere as it is essential that NRTK corrections are delivered sufficiently often with a small enough latency so that they adequately model variations in the ionospheric bias. This will promote the efficient transmission of correctional data to the rover whilst still achieving cm-level accuracy. Temporal analysis of the ionosphere revealed that, during stable ionospheric conditions, Victoria's double differenced ionospheric (DDI) bias remains correlated to within +5cm out to approximately two minutes over baselines of approximately 100km. However, the data revealed that during more disturbed ionospheric conditions this may decrease to one minute. As a preliminary investigation, four global empirical ionospheric models were tested to assess their ability to estimate the DDI bias. Further, three temporal predictive modelling schemes were tested to assess their suitability for providing ionospheric corrections in a NRTK environment. The analysis took place over four seasonal periods during the previous solar maximum in 2001 and 2002. It was found that due to the global nature of their coefficients, the four global empirical models were unable to provide ionospheric corrections to a level sufficient for precise ambiguity resolution within a NRTK environment. Three temporal ionospheric predictive schemes were developed and tested. These included a moving average model, a linear model and an ARIMA (Auto-Regressive Integrated Moving Average) time series analysis. The moving average and ARIMA approaches gave similar performance and out-performed the linear modelling scheme. Both of these approaches were able to predict the DDI to +5cm within a 99% confidence interval, out to an average of approximately two minutes, on average 90% of the time when compared to the actual decorrelation rates of the ionosphere. These results suggest that the moving average scheme, could enhance the implementation of next generation NRTK systems by predicting the DDI bias to latencies that would enable cm-level positioning