gRAID: A Geospatial Real-Time Aerial Image Display for a Low-Cost Autonomous Multispectral Remote Sensing Platform (AggieAir)

Remote sensing helps many applications like precision irrigation, habitat mapping, and traffic monitoring. However, due to shortcomings of current remote sensing platforms - like high cost, low spatial, and temporal resolution - many applications do not have access to useful remote sensing data. A team at the Center for Self-Organizing and Intelligent Systems (CSOIS) together with the Utah Water Research Laboratory (UWRL) at Utah State University has been developing a new remote sensing platform to deal with these shortcomings in order to give more applications access to remote sensing data. This platform (AggieAir) is low cost, fully autonomous, easy to use, independent of a runway, has a fast turnover time, and a high spatial resolution. A program called the Geospatial Real-Time Aerial Image Display (gRAID) has also been developed to process the images taken from AggieAir. gRAID is able to correct the camera lens distortion, georeference, and display the images on a 3D globe, and export them in a conventional Geographic Information System (GIS) format for further processing. AggieAir and gRAID prove to be innovative and useful tools for remote sensing

Innovative Payloads for Small Unmanned Aerial System-Based Personal Remote Sensing and Applications

Remote sensing enables the acquisition of large amounts of data, over a small period of time, in support of many ecological applications (i.e. precision agriculture, vegetation mapping, etc.) commonly from satellite or manned aircraft platforms. This dissertation focuses on using small unmanned aerial systems (UAS) as a remote sensing platform to collect aerial imagery from commercial-grade cameras and as a radio localization platform to track radio-tagged sh. The small, low-cost nature of small UAS enables remotely sensed data to be captured at a lower cost, higher spatial and temporal resolution, and in a more timely manner than conventional platforms. However, these same attributes limit the types of cameras and sensors that can be used on small UAS and introduce challenges in calibrating the imagery and converting it into actionable information for end users. A major contribution of this dissertation addresses this issue and includes a complete description on how to calibrate imagery from commercial-grade visual, near-infrared, and thermal cameras. This includes the presentation of novel surface temperature sampling methods, which can be used during the ight, to help calibrate thermal imagery. Landsat imagery is used to help evaluate these methods for accuracy; one of the methods performs very well and is logistically feasible for regular use. Another major contribution of this dissertation includes novel, simple methods to estimate the location of radio-tagged fish using multiple unmanned aircraft (UA). A simulation is created to test these methods, and Monte Carlo analysis is used to predict their performance in real-world scenarios. This analysis shows that the methods are able to locate the radio-tagged fish with good accuracy. When multiple UAs are used, the accuracy does not improve; however the fish is located much quicker than when one UA is used

AggieAir: Towards Low-cost Cooperative Multispectral Remote Sensing Using Small Unmanned Aircraft Systems

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Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks

Many crop production management decisions can be informed using data from high-resolution aerial images that provide information about crop health as influenced by soil fertility and moisture. Surface soil moisture is a key component of soil water balance, which addresses water and energy exchanges at the surface/atmosphere interface; however, high-resolution remotely sensed data is rarely used to acquire soil moisture values. In this study, an artificial neural network (ANN) model was developed to quantify the effectiveness of using spectral images to estimate surface soil moisture. The model produces acceptable estimations of surface soil moisture (root mean square error (RMSE) = 2.0, mean absolute error (MAE) = 1.8, coefficient of correlation (r) = 0.88, coefficient of performance (e) = 0.75 and coefficient of determination (R2) = 0.77) by combining field measurements with inexpensive and readily available remotely sensed inputs. The spatial data (visual spectrum, near infrared, infrared/thermal) are produced by the AggieAir™ platform, which includes an unmanned aerial vehicle (UAV) that enables users to gather aerial imagery at a low price and high spatial and temporal resolutions. This study reports the development of an ANN model that translates AggieAir™ imagery into estimates of surface soil moisture for a large field irrigated by a center pivot sprinkler system

Topsoil Moisture Estimation for Precision Agriculture Using Unmanned Aerial Vehicle Multispectral Imagery

There is an increasing trend in crop production management decisions in precision agriculture based on observation of high resolution aerial images from unmanned aerial vehicles (UAV). Nevertheless, there are still limitations in terms of relating the spectral imagery information to the agricultural targets. AggieAir™ is a small, autonomous unmanned aircraft which carries multispectral cameras to capture aerial imagery during pre-programmed flights. AggieAir enables users to gather imagery at greater spatial and temporal resolution than most manned aircraft and satellite sources. The platform has been successfully used in support of a wide variety of water and natural resources management areas. This paper presents results of an on-going research in the application of the imagery from AggieAir in the remote sensing of top soil moisture estimations for a large field served by a center pivot sprinkler irrigation system

Suppression of core polarization in halo nuclei

We present a microscopic study of halo nuclei, starting from the Paris and Bonn potentials and employing a two-frequency shell model approach. It is found that the core-polarization effect is dramatically suppressed in such nuclei. Consequently the effective interaction for halo nucleons is almost entirely given by the bare G-matrix alone, which presently can be evaluated with a high degree of accuracy. The experimental pairing energies between the two halo neutrons in $^6$He and $^{11}$Li nuclei are satisfactorily reproduced by our calculation. It is suggested that the fundamental nucleon-nucleon interaction can be probed in a clearer and more direct way in halo nuclei than in ordinary nuclei.Comment: 11 pages, RevTex, 2 postscript figures; major revisions, matches version to appear in Phys. Rev. Letter

Universal Correlations of Coulomb Blockade Conductance Peaks and the Rotation Scaling in Quantum Dots

We show that the parametric correlations of the conductance peak amplitudes of a chaotic or weakly disordered quantum dot in the Coulomb blockade regime become universal upon an appropriate scaling of the parameter. We compute the universal forms of this correlator for both cases of conserved and broken time reversal symmetry. For a symmetric dot the correlator is independent of the details in each lead such as the number of channels and their correlation. We derive a new scaling, which we call the rotation scaling, that can be computed directly from the dot's eigenfunction rotation rate or alternatively from the conductance peak heights, and therefore does not require knowledge of the spectrum of the dot. The relation of the rotation scaling to the level velocity scaling is discussed. The exact analytic form of the conductance peak correlator is derived at short distances. We also calculate the universal distributions of the average level width velocity for various values of the scaled parameter. The universality is illustrated in an Anderson model of a disordered dot.Comment: 35 pages, RevTex, 6 Postscript figure

ESPEN guidelines on definitions and terminology of clinical nutrition

BACKGROUND: A lack of agreement on definitions and terminology used for nutrition-related concepts and procedures limits the development of clinical nutrition practice and research. OBJECTIVE: This initiative aimed to reach a consensus for terminology for core nutritional concepts and procedures. METHODS: The European Society of Clinical Nutrition and Metabolism (ESPEN) appointed a consensus group of clinical scientists to perform a modified Delphi process that encompassed e-mail communication, face-to-face meetings, in-group ballots and an electronic ESPEN membership Delphi round. RESULTS: Five key areas related to clinical nutrition were identified: concepts; procedures; organisation; delivery; and products. One core concept of clinical nutrition is malnutrition/undernutrition, which includes disease-related malnutrition (DRM) with (eq. cachexia) and without inflammation, and malnutrition/undernutrition without disease, e.g. hunger-related malnutrition. Over-nutrition (overweight and obesity) is another core concept. Sarcopenia and frailty were agreed to be separate conditions often associated with malnutrition. Examples of nutritional procedures identified include screening for subjects at nutritional risk followed by a complete nutritional assessment. Hospital and care facility catering are the basic organizational forms for providing nutrition. Oral nutritional supplementation is the preferred way of nutrition therapy but if inadequate then other forms of medical nutrition therapy, i.e. enteral tube feeding and parenteral (intravenous) nutrition, becomes the major way of nutrient delivery. CONCLUSION: An agreement of basic nutritional terminology to be used in clinical practice, research, and the ESPEN guideline developments has been established. This terminology consensus may help to support future global consensus efforts and updates of classification systems such as the International Classification of Disease (ICD). The continuous growth of knowledge in all areas addressed in this statement will provide the foundation for future revisions

Standalone vertex finding in the ATLAS muon spectrometer

A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bbar b final states, and pp collision data at √s = 7 TeV collected with the ATLAS detector at the LHC during 2011