Field Data Acquisition Technologies for Iowa Transportation Agencies, HR-366 August 1994

This report describes the results of the research project investigating the use of advanced field data acquisition technologies for lowa transponation agencies. The objectives of the research project were to (1) research and evaluate current data acquisition technologies for field data collection, manipulation, and reporting; (2) identify the current field data collection approach and the interest level in applying current technologies within Iowa transportation agencies; and (3) summarize findings, prioritize technology needs, and provide recommendations regarding suitable applications for future development. A steering committee consisting oretate, city, and county transportation officials provided guidance during this project. Technologies considered in this study included (1) data storage (bar coding, radio frequency identification, touch buttons, magnetic stripes, and video logging); (2) data recognition (voice recognition and optical character recognition); (3) field referencing systems (global positioning systems [GPS] and geographic information systems [GIs]); (4) data transmission (radio frequency data communications and electronic data interchange); and (5) portable computers (pen-based computers). The literature review revealed that many of these technologies could have useful applications in the transponation industry. A survey was developed to explain current data collection methods and identify the interest in using advanced field data collection technologies. Surveys were sent out to county and city engineers and state representatives responsible for certain programs (e.g., maintenance management and construction management). Results showed that almost all field data are collected using manual approaches and are hand-carried to the office where they are either entered into a computer or manually stored. A lack of standardization was apparent for the type of software applications used by each agency--even the types of forms used to manually collect data differed by agency. Furthermore, interest in using advanced field data collection technologies depended upon the technology, program (e.g.. pavement or sign management), and agency type (e.g., state, city, or county). The state and larger cities and counties seemed to be interested in using several of the technologies, whereas smaller agencies appeared to have very little interest in using advanced techniques to capture data. A more thorough analysis of the survey results is provided in the report. Recommendations are made to enhance the use of advanced field data acquisition technologies in Iowa transportation agencies: (1) Appoint a statewide task group to coordinate the effort to automate field data collection and reporting within the Iowa transportation agencies. Subgroups representing the cities, counties, and state should be formed with oversight provided by the statewide task group. (2) Educate employees so that they become familiar with the various field data acquisition technologies.

Stringless Portland Cement Concrete Paving, TR-490, 2004

This report describes results from a study evaluating the use of stringless paving using a combination of global positioning and laser technologies. CMI and Geologic Computer Systems developed this technology and successfully implemented it on construction earthmoving and grading projects. Concrete paving is a new area for considering this technology. Fred Carlson Co. agreed to test the stringless paving technology on two challenging concrete paving projects located in Washington County, Iowa. The evaluation was conducted on two paving projects in Washington County, Iowa, during the summer of 2003. The research team from Iowa State University monitored the guidance and elevation conformance to the original design. They employed a combination of physical depth checks, surface location and elevation surveys, concrete yield checks, and physical survey of the control stakes and string line elevations. A final check on profile of the pavement surface was accomplished by the use of the Iowa Department of Transportation Light Weight Surface Analyzer (LISA). Due to the speed of paving and the rapid changes in terrain, the laser technology was abandoned for this project. Total control of the guidance and elevation controls on the slip-form paver were moved from string line to global positioning systems (GPS). The evaluation was a success, and the results indicate that GPS control is feasible and approaching the desired goals of guidance and profile control with the use of three dimensional design models. Further enhancements are needed in the physical features of the slipform paver oil system controls and in the computer program for controlling elevation.

Grade and Cross Slope Estimation from LIDAR-based Surface Models, 2003

Many transportation agencies maintain grade as an attribute in roadway inventory databases; however, the information is often in an aggregated format. Cross slope is rarely included in large roadway inventories. Accurate methods available to collect grade and cross slope include global positioning systems, traditional surveying, and mobile mapping systems. However, most agencies do not have the resources to utilize these methods to collect grade and cross slope on a large scale. This report discusses the use of LIDAR to extract roadway grade and cross slope for large-scale inventories. Current data collection methods and their advantages and disadvantages are discussed. A pilot study to extract grade and cross slope from a LIDAR data set, including methodology, results, and conclusions, is presented. This report describes the regression methodology used to extract and evaluate the accuracy of grade and cross slope from three dimensional surfaces created from LIDAR data. The use of LIDAR data to extract grade and cross slope on tangent highway segments was evaluated and compared against grade and cross slope collected using an automatic level for 10 test segments along Iowa Highway 1. Grade and cross slope were measured from a surface model created from LIDAR data points collected for the study area. While grade could be estimated to within 1%, study results indicate that cross slope cannot practically be estimated using a LIDAR derived surface model.

GPS to LRM: Integration of Spatial Point Features with Linear Referencing Methods, 2001

Global positioning systems (GPS) offer a cost-effective and efficient method to input and update transportation data. The spatial location of objects provided by GPS is easily integrated into geographic information systems (GIS). The storage, manipulation, and analysis of spatial data are also relatively simple in a GIS. However, many data storage and reporting methods at transportation agencies rely on linear referencing methods (LRMs); consequently, GPS data must be able to link with linear referencing. Unfortunately, the two systems are fundamentally incompatible in the way data are collected, integrated, and manipulated. In order for the spatial data collected using GPS to be integrated into a linear referencing system or shared among LRMs, a number of issues need to be addressed. This report documents and evaluates several of those issues and offers recommendations. In order to evaluate the issues associated with integrating GPS data with a LRM, a pilot study was created. To perform the pilot study, point features, a linear datum, and a spatial representation of a LRM were created for six test roadway segments that were located within the boundaries of the pilot study conducted by the Iowa Department of Transportation linear referencing system project team. Various issues in integrating point features with a LRM or between LRMs are discussed and recommendations provided. The accuracy of the GPS is discussed, including issues such as point features mapping to the wrong segment. Another topic is the loss of spatial information that occurs when a three-dimensional or two-dimensional spatial point feature is converted to a one-dimensional representation on a LRM. Recommendations such as storing point features as spatial objects if necessary or preserving information such as coordinates and elevation are suggested. The lack of spatial accuracy characteristic of most cartography, on which LRM are often based, is another topic discussed. The associated issues include linear and horizontal offset error. The final topic discussed is some of the issues in transferring point feature data between LRMs.

Review. Precision viticulture. Research topics, challenges and opportunities in site-specific vineyard management

Precision Viticulture (PV) is a concept that is beginning to have an impact on the wine-growing sector. Its practical implementation is dependant on various technological developments: crop sensors and yield monitors, local and remote sensors, Global Positioning Systems (GPS), VRA (Variable-Rate Application) equipment and machinery, Geographic Information Systems (GIS) and systems for data analysis and interpretation. This paper reviews a number of research lines related to PV. These areas of research have focused on four very specific fields: 1) quantification and evaluation of within-field variability, 2) delineation of zones of differential treatment at parcel level, based on the analysis and interpretation of this variability, 3) development of Variable-Rate Technologies (VRT) and, finally, 4) evaluation of the opportunities for site-specific vineyard management. Research in these fields should allow winegrowers and enologists to know and understand why yield variability exists within the same parcel, what the causes of this variability are, how the yield and its quality are interrelated and, if spatial variability exists, whether site-specific vineyard management is justifiable on a technical and economic basis.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Delineation of within-site terroir effects using soil and vine water measurements in Riesling vineyards within the Niagara Peninsula

The major focus of this dissertation was to explain terroir effects that impact wine varietal character and to elucidate potential determinants of terroir by testing vine water status (VWS) as the major factor of the terroir effect. It was hypothesized that consistent water status zones could be identified within vineyard sites, and, that differences in vine performance, fruit composition and wine sensory attributes could be related to VWS. To test this hypothesis, ten commercial Riesling vineyards representative of each Vintners Quality Alliance sub-appellation were selected. Vineyards were delineated using global positioning systems and 75 to 80 sentinel vines per vineyard were geo-referenced for data collection. During the 2005 to 2007 growing seasons, VWS measurements [midday leaf water potential ('l')] were collected from a subset of these sentinel vines. Data were collected on soil texture and composition, soil moisture, vine performance (yield components, vine size) and fruit composition. These variables were mapped using global information system (GIS) software and relationships between them were elucidated. Vines were categorized into "low" and "high" water status regions within each vineyard block and replicate wines were made from each. Many geospatial patterns and relationships were spatially and temporally stable within vineyards. Leaf'l' was temporally stable within vineyards despite different weather conditions during each growing season. Generally, spatial relationships between 'l', soil moisture, vine size, berry weight and yield were stable from year to year. Leaf", impacted fruit composition in several vineyards. Through sorting tasks and multidimensional scaling, wines of similar VWS had similar sensory properties. Descriptive analysis further indicated that VWS impacted wine sensory profiles, with similar attributes being different for wines from different water status zones. Vineyard designation had an effect on wine profiles, with certain sensory and chemical attributes being associated from different subappellations. However, wines were generally grouped in terms of their regional designation ('Lakeshore', 'Bench', 'Plains') within the Niagara Peninsula. Through multivariate analyses, specific sensory attributes, viticulture and chemical variables were associated with wines of different VWS. Vine water status was a major contributor to the terroir effect, as it had a major impact on vine size, berry weight and wine sensory characteristics.

Técnicas de geotecnologia na avaliação na área erodida na Bacia Hidrográfica Dois Córregos, Selviria-MS

Pós-graduação em Agronomia - FEIS

A General Framework for the Analysis of Animal Resource Selection from Telemetry Data

We propose a general framework for the analysis of animal telemetry data through the use of weighted distributions. It is shown that several interpretations of resource selection functions arise when constructed from the ratio of a use and availability distribution. Through the proposed general framework, several popular resource selection models are shown to be special cases of the general model by making assumptions about animal movement and behavior. The weighted distribution framework is shown to be easily extended to readily account for telemetry data that are highly auto-correlated; as is typical with use of new technology such as global positioning systems animal relocations. An analysis of simulated data using several models constructed within the proposed framework is also presented to illustrate the possible gains from the flexible modeling framework. The proposed model is applied to a brown bear data set from southeast Alaska.

Management and routing algorithms for ad-hoc and sensor networks

Large scale wireless adhoc networks of computers, sensors, PDAs etc. (i.e. nodes) are revolutionizing connectivity and leading to a paradigm shift from centralized systems to highly distributed and dynamic environments. An example of adhoc networks are sensor networks, which are usually composed by small units able to sense and transmit to a sink elementary data which are successively processed by an external machine. Recent improvements in the memory and computational power of sensors, together with the reduction of energy consumptions, are rapidly changing the potential of such systems, moving the attention towards datacentric sensor networks. A plethora of routing and data management algorithms have been proposed for the network path discovery ranging from broadcasting/floodingbased approaches to those using global positioning systems (GPS). We studied WGrid, a novel decentralized infrastructure that organizes wireless devices in an adhoc manner, where each node has one or more virtual coordinates through which both message routing and data management occur without reliance on either flooding/broadcasting operations or GPS. The resulting adhoc network does not suffer from the deadend problem, which happens in geographicbased routing when a node is unable to locate a neighbor closer to the destination than itself. WGrid allow multidimensional data management capability since nodes' virtual coordinates can act as a distributed database without needing neither special implementation or reorganization. Any kind of data (both single and multidimensional) can be distributed, stored and managed. We will show how a location service can be easily implemented so that any search is reduced to a simple query, like for any other data type. WGrid has then been extended by adopting a replication methodology. We called the resulting algorithm WRGrid. Just like WGrid, WRGrid acts as a distributed database without needing neither special implementation nor reorganization and any kind of data can be distributed, stored and managed. We have evaluated the benefits of replication on data management, finding out, from experimental results, that it can halve the average number of hops in the network. The direct consequence of this fact are a significant improvement on energy consumption and a workload balancing among sensors (number of messages routed by each node). Finally, thanks to the replications, whose number can be arbitrarily chosen, the resulting sensor network can face sensors disconnections/connections, due to failures of sensors, without data loss. Another extension to {WGrid} is {W*Grid} which extends it by strongly improving network recovery performance from link and/or device failures that may happen due to crashes or battery exhaustion of devices or to temporary obstacles. W*Grid guarantees, by construction, at least two disjoint paths between each couple of nodes. This implies that the recovery in W*Grid occurs without broadcasting transmissions and guaranteeing robustness while drastically reducing the energy consumption. An extensive number of simulations shows the efficiency, robustness and traffic road of resulting networks under several scenarios of device density and of number of coordinates. Performance analysis have been compared to existent algorithms in order to validate the results.

Human Mobility, Exposure to the Built Environment, and Health

Thesis (Ph.D.)--University of Washington, 2016-06

Feral goat eradications on islands

Introduced mammals are major drivers of extinction. Feral goats (Capra hircus) are particularly devastating to island ecosystems, causing direct and indirect impacts through overgrazing, which often results in ecosystem degradation and biodiversity loss. Removing goat populations from islands is a powerful conservation tool to prevent extinctions and restore ecosystems. Goats have been eradicated successfully from 120 islands worldwide. With newly developed technology and techniques, island size is perhaps no longer a limiting factor in the successful removal of introduced goat populations. Furthermore,. the use of global positioning systems, geographic information systems, aerial hunting by helicopter specialized bunting dogs, and Judas goats has dramatically increased efficiency and significantly reduced the duration of eradication campaigns. Intensive monitoring programs are also critical for successful eradications. Because of the presence of humans with domestic goat populations on large islands, future island conservation actions will require eradication programs that involve local island inhabitants in a collaborative approach with biologists, sociologists, and educators. Given the clear biodiversity benefits, introduced goat populations should be routinely removed from islands.

The use of computing technology in highway construction contractor companies as a Total Jobsite Management Tool (TJMT)

The integration of automation (specifically Global Positioning Systems (GPS)) and Information and Communications Technology (ICT) through the creation of a Total Jobsite Management Tool (TJMT) in construction contractor companies can revolutionize the way contractors do business. The key to this integration is the collection and processing of real-time GPS data that is produced on the jobsite for use in project management applications. This research study established the need for an effective planning and implementation framework to assist construction contractor companies in navigating the terrain of GPS and ICT use. An Implementation Framework was developed using the Action Research approach. The framework consists of three components, as follows: (i) ICT Infrastructure Model, (ii) Organizational Restructuring Model, and (iii) Cost/Benefit Analysis. The conceptual ICT infrastructure model was developed for the purpose of showing decision makers within highway construction companies how to collect, process, and use GPS data for project management applications. The organizational restructuring model was developed to assist companies in the analysis and redesign of business processes, data flows, core job responsibilities, and their organizational structure in order to obtain the maximum benefit at the least cost in implementing GPS as a TJMT. A cost-benefit analysis which identifies and quantifies the cost and benefits (both direct and indirect) was performed in the study to clearly demonstrate the advantages of using GPS as a TJMT. Finally, the study revealed that in order to successfully implement a program to utilize GPS data as a TJMT, it is important for construction companies to understand the various implementation and transitioning issues that arise when implementing this new technology and business strategy. In the study, Factors for Success were identified and ranked to allow a construction company to understand the factors that may contribute to or detract from the prospect for success during implementation. The Implementation Framework developed as a result of this study will serve to guide highway construction companies in the successful integration of GPS and ICT technologies for use as a TJMT.

Caracterização 3D de movimentos de massa em terrenos com permafrost nas Ilhas Shetland do Sul (1956 - 2010) através de detecção remota, na banda do visível

Dissertação de Mestrado, Geomática, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Dynamic Match Analysis

Recent technological development has enabled research- ers to gather data from different performance scenarios while considering players positioning and action events within a specific time frame. This technology varies from global positioning systems to radio frequency devices and computer vision tracking, to name the most common, and aims to collect players’ time motion data and enable the dynamical analysis of performance. Team sports—and in particular, invasion games—present a complex dynamic by nature based on the interaction between 2 opposing sides trying to outperform 1 another. During match and training situations, players’ actions are coupled to their performance context at different interaction levels. As expected, ball, teammates’, and opponents’ positioning play an important role in this interaction process. But other factors, such as final score, teams’ development level, and players’ expertise, seem to affect the match dynamics. In this symposium, we will focus on how different constraints affect invasion games dynamics during both match and training situations. This relation will be established while underpinning the importance of these effects to game teaching and performance optimization. Regarding the match, different performance indicators based on spatial-temporal relations between players and teams will be presented to reveal the interaction processes that form the crucial component of game analysis. Considering the training, this symposium will address the relationship of small-sided games with full- sized matches and will present how players’ dynamical interaction affects different performance indicators.