Use of Cone-Beam Computed Tomography in the Diagnosis and Treatment of an Unusual Canine Abnormality

Diagnosis and treatment planning are important for successful endodontic treatment. We report a 24-year old male who presented to the Government Dental College in Kozhikode, Kerala, India, in 2015 with pain in his right upper canine. A digital periapical radiograph indicated the presence of a supernumerary tooth superimposing the root of the canine. However, cone-beam computed tomography (CBCT) confirmed that the supernumerary tooth was an illusion and that the canine root had a sharp invagination involving the labial and pulpal dentin surfaces, with evidence of periapical bone destruction. A blunt resection was performed at the level of the invagination and the resected end was filled with a dentin substitute. At a one-year follow-up, the patient was asymptomatic and the periapical region appeared to be healing well. This report highlights the importance of CBCT in visualising abnormal canine morphology, thus allowing appropriate endodontic treatment

Antenna Beam Steering For Wireless Sensors Using Real Time Phase Shifter

As part of the increasing demand for accurate, secure and robust short range wireless sensors for Smart Grid systems, we present the design and the simulation of phased array transmitter with variable delay based phase shifters. Multiple antennas are used to achieve beam steering using active beamforming technique. Our design exploits the multiple signal paths. In addition, the transmitter will provide feasible directional point-to-point communication networks via transmitting the signal to the preferred receiver with the desired coverage. The sensitivity and the accuracy of the system are enhanced in terms of object identification and location, respectively. This wireless sensor appears well suited for use in Smart Grid technologies operating at 2.4GHz ISM band with 250kbps data rate capacity where minimum cost and high integration are valued

Modeling the isotopic evolution of snowpack and snowmelt : Testing a spatially distributed parsimonious approach

This work was funded by the NERC/JPI SIWA project (NE/M019896/1) and the European Research Council ERC (project GA 335910 VeWa). The Krycklan part of this study was supported by grants from the Knut and Alice Wallenberg Foundation (Branch-points), Swedish Research Council (SITES), SKB and Kempe foundation. The data and model code is available upon request. Authors declare that they have no conflict of interest. We would like to thank the three anonymous reviewers for their constructive comments that improved the manuscript.Peer reviewedPublisher PD

An open source Bayesian Monte Carlo isotope mixing model with applications in Earth surface processes

The implementation of isotopic tracers as constraints on source contributions has become increasingly relevant to understanding Earth surface processes. Interpretation of these isotopic tracers has become more accessible with the development of Bayesian Monte Carlo (BMC) mixing models, which allow uncertainty in mixing end‐members and provide methodology for systems with multicomponent mixing. This study presents an open source multiple isotope BMC mixing model that is applicable to Earth surface environments with sources exhibiting distinct end‐member isotopic signatures. Our model is first applied to new δ18O and δD measurements from the Athabasca Glacier, which showed expected seasonal melt evolution trends and vigorously assessed the statistical relevance of the resulting fraction estimations. To highlight the broad applicability of our model to a variety of Earth surface environments and relevant isotopic systems, we expand our model to two additional case studies: deriving melt sources from δ18O, δD, and 222Rn measurements of Greenland Ice Sheet bulk water samples and assessing nutrient sources from ɛNd and 87Sr/86Sr measurements of Hawaiian soil cores. The model produces results for the Greenland Ice Sheet and Hawaiian soil data sets that are consistent with the originally published fractional contribution estimates. The advantage of this method is that it quantifies the error induced by variability in the end‐member compositions, unrealized by the models previously applied to the above case studies. Results from all three case studies demonstrate the broad applicability of this statistical BMC isotopic mixing model for estimating source contribution fractions in a variety of Earth surface systems.Key Points:Open source BMC model determines source contributions in Earth surface systemsEffectively applied to stable and radiogenic isotope systems in various settingsModel able to encompass end‐member uncertainties and multiple isotopic systemsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111937/1/ggge20708.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/111937/2/ggge20708-sup-0001-2014GC005683-ts01.pd

Testing a spatially distributed tracer-aided runoff model in a snow-influenced catchment : effects of multi-criteria calibration on streamwater ages

This work was funded by the European Research Council (project GA 335910 VeWa). Dr. Huaxia Yao, Chris McConnell and Tim Field were instrumental to the collection of isotope samples and for many aspects of hydrological data collection.Peer reviewedPostprin

A VECTOR SPACE APPROACH TO SPATIAL SPECTRUM ESTIMATION (SIGNAL PROCESSING, GEOPHYSICAL APPLICATIONS, RADAR)

Array processing for spatial spectrum estimation is reexamined from the vector space viewpoint with the objective of finding a common framework within which the various known superresolution estimators may be compared. Based on the experience with eigenstructure methods, which are ideal in the sense that they asymptotically yield unbiased estimates and have infinite resolving power for point sources, a generic form for an ideal spectrum estimator is proposed. Within this context it is shown that the MUltiple SIgnal Classification (MUSIC) method is an exact realization and the well known superresolution estimators, such as the Maximum Likelihood Method (MLM) of Capon and the Linear Prediction Method (LPM), are approximate realizations of this form. Further, this formulation is shown to suggest ways to modify both MLM and LPM so as to achieve asymptotically ideal performance for point sources. In the case of estimated covariance matrices the compensation for the improved performance is shown to be the requirement of larger number of samples compared to the eigenstructure based methods. The question of how to deploy the array elements for improved performance, in terms of the ability of the array to detect and resolve a larger number of sources than conventionally possible, is addressed. A study related to the statistical properties of the estimator of the unknown angles of arrival is reported. This includes a general result based on Cramer-Rao bound, and specific analyses for various superresolution techniques. A test based on higher powers of the eigenvalues of the sample covariance matrix is derived to estimate the number of point sources present in the data. This test is found to be useful even when the number of array elements is less than the number of point sources and is applied to the augmentation technique where negative eigenvalues are encountered. Other results include determination of the sensitivity of the eigenstructure based techniques on element position uncertainty, and a new spectral estimator combining the properties of the Maximum Likelihood Method and the MUSIC estimator. The latter is potentially useful for dealing with distributed sources. (Abstract shortened with permission of author.

A VECTOR SPACE APPROACH TO SPATIAL SPECTRUM ESTIMATION (SIGNAL PROCESSING, GEOPHYSICAL APPLICATIONS, RADAR)

Array processing for spatial spectrum estimation is reexamined from the vector space viewpoint with the objective of finding a common framework within which the various known superresolution estimators may be compared. Based on the experience with eigenstructure methods, which are ideal in the sense that they asymptotically yield unbiased estimates and have infinite resolving power for point sources, a generic form for an ideal spectrum estimator is proposed. Within this context it is shown that the MUltiple SIgnal Classification (MUSIC) method is an exact realization and the well known superresolution estimators, such as the Maximum Likelihood Method (MLM) of Capon and the Linear Prediction Method (LPM), are approximate realizations of this form. Further, this formulation is shown to suggest ways to modify both MLM and LPM so as to achieve asymptotically ideal performance for point sources. In the case of estimated covariance matrices the compensation for the improved performance is shown to be the requirement of larger number of samples compared to the eigenstructure based methods. The question of how to deploy the array elements for improved performance, in terms of the ability of the array to detect and resolve a larger number of sources than conventionally possible, is addressed. A study related to the statistical properties of the estimator of the unknown angles of arrival is reported. This includes a general result based on Cramer-Rao bound, and specific analyses for various superresolution techniques. A test based on higher powers of the eigenvalues of the sample covariance matrix is derived to estimate the number of point sources present in the data. This test is found to be useful even when the number of array elements is less than the number of point sources and is applied to the augmentation technique where negative eigenvalues are encountered. Other results include determination of the sensitivity of the eigenstructure based techniques on element position uncertainty, and a new spectral estimator combining the properties of the Maximum Likelihood Method and the MUSIC estimator. The latter is potentially useful for dealing with distributed sources. (Abstract shortened with permission of author.