Delineation of Management Zones in Hedgerow Almond Orchards Based on Vegetation Indices from UAV Images Validated by LiDAR-Derived Canopy Parameters

One of the challenges in orchard management, in particular of hedgerow tree plantations, is the delineation of management zones on the bases of high-precision data. Along this line, the present study analyses the applicability of vegetation indices derived from UAV images to estimate the key structural and geometric canopy parameters of an almond orchard. In addition, the classes created on the basis of the vegetation indices were assessed to delineate potential management zones. The structural and geometric orchard parameters (width, height, cross-sectional area and porosity) were characterized by means of a LiDAR sensor, and the vegetation indices were derived from a UAV-acquired multispectral image. Both datasets summarized every 0.5 m along the almond tree rows and were used to interpolate continuous representations of the variables by means of geostatistical analysis. Linear and canonical correlation analyses were carried out to select the best performing vegetation index to estimate the structural and geometric orchard parameters in each cross-section of the tree rows. The results showed that NDVI averaged in each cross-section and normalized by its projected area achieved the highest correlations and served to define potential management zones. These findings expand the possibilities of using multispectral images in orchard management, particularly in hedgerow plantations.

MODERN ALGORITHMS AND SOFTWARE FOR INTERPRETATION OF RESISTIVITY LOGGING DATA

The electrodynamics of geological media investigates the interrelations of resistivity logging signals and properties of fluid-containing rocks and creates innovative well logging technologies. Its development is inextricably linked with modern techniques for mathematical modeling and quantitative interpretation of high-precision data. In order to increase the information content of galvanic and electromagnetic logging, we have developed algorithms and software for numerical simulation and inversion of field data. In our study of the Cretaceous and Jurassic deposits of West Siberia, a quantitative interpretation of high-frequency electromagnetic and lateral logging signals was carried out. To create geoelectric models, we interpreted the field resistivity logging data by an unconventional quantitative technique based on their joint numerical inversion and estimations of the vertical resistivity of permeable deposits. Another line of our research was aimed at a scientific substantiation of a new technology for mapping and spatial tracking of lateral heterogeneities and oil-promising zones in the Bazhenov Formation. The aim was achieved by using the TEM sounding data on a spatially distributed system of directional and horizontal wells.

Study of LEAP® 5000 Deadtime and Precision via Silicon Pre-Sharpened-Microtip™ Standard Specimens

Atom probe tomography (APT) is a technique that has expanded significantly in terms of adoption, dataset size, and quality during the past 15 years. The sophistication used to ensure ultimate analysis precision has not kept pace. The earliest APT datasets were small enough that deadtime and background considerations for processing mass spectrum peaks were secondary. Today, datasets can reach beyond a billion atoms so that high precision data processing procedures and corrections need to be considered to attain reliable accuracy at the parts-per-million level. This paper considers options for mass spectrum ranging, deadtime corrections, and error propagation as applied to an extrinsic-silicon standard specimen to attain agreement for silicon isotopic fraction measurements across multiple instruments, instrument types, and acquisition conditions. Precision consistent with those predicted by counting statistics is attained showing agreement in silicon isotope fraction measurements across multiple instruments, instrument platforms, and analysis conditions.

Resurgence in the O(4) sigma model

We analyze the free energy of the integrable two dimensional O(4) sigma model in a magnetic field. We use Volin’s method to extract high number (2000) of perturbative coefficients with very high precision. The factorial growth of these coefficients are regulated by switching to the Borel transform, where we perform several asymptotic analysis. High precision data allowed to identify Stokes constants and alien derivatives with exact expressions. These reveal a nice resurgence structure which enables to formulate the first few terms of the ambiguity free trans-series. We check these results against the direct numerical solution of the exact integral equation and find complete agreement.

Deep learning-aided high-precision data detection for massive MU-MIMO systems

The data detector for future wireless system needs to achieve high throughput and low bit error rate (BER) with low computational complexity. In this paper, we propose a deep neural networks (DNNs) learning aided iterative detection algorithm. We first propose a convex optimization-based method for calculating the efficient detection of iterative soft output data, and then propose a method for adjusting the iteration parameters using the powerful data driven by DNNs, which achieves fast convergence and strong robustness. The results show that the proposed method can achieve the same performance as the known algorithm at a lower computation complexity cost.