MIDAS, prototype Multivariate Interactive Digital Analysis System for large area earth resources surveys. Volume 1: System description

A third-generation, fast, low cost, multispectral recognition system (MIDAS) able to keep pace with the large quantity and high rates of data acquisition from large regions with present and projected sensots is described. The program can process a complete ERTS frame in forty seconds and provide a color map of sixteen constituent categories in a few minutes. A principle objective of the MIDAS program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughput. The hardware and software generated in the overall program is described. The system contains a midi-computer to control the various high speed processing elements in the data path, a preprocessor to condition data, and a classifier which implements an all digital prototype multivariate Gaussian maximum likelihood or a Bayesian decision algorithm. Sufficient software was developed to perform signature extraction, control the preprocessor, compute classifier coefficients, control the classifier operation, operate the color display and printer, and diagnose operation

MIDAS, prototype Multivariate Interactive Digital Analysis System, Phase 1. Volume 2: Diagnostic system

The MIDAS System is a third-generation, fast, multispectral recognition system able to keep pace with the large quantity and high rates of data acquisition from present and projected sensors. A principal objective of the MIDAS Program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughout. The hardware and software generated in Phase I of the over-all program are described. The system contains a mini-computer to control the various high-speed processing elements in the data path and a classifier which implements an all-digital prototype multivariate-Gaussian maximum likelihood decision algorithm operating 2 x 105 pixels/sec. Sufficient hardware was developed to perform signature extraction from computer-compatible tapes, compute classifier coefficients, control the classifier operation, and diagnose operation. Diagnostic programs used to test MIDAS' operations are presented

Anomalous change in leakage and displacement currents after electrical poling on lead-free ferroelectric ceramics

We report the polarization, displacement current and leakage current behavior of a trivalent nonpolar cation Al cation substituted lead free ferroelectric NBT-BT electroceramics with tetragonal phase and P4mm space group symmetry. Nearly three orders of magnitude decrease in leakage current were observed under electrical poling, which significantly improves microstructure, polarization, and displacement current. Effective poling neutralizes the domain pinning, traps charges at grain boundaries and fills oxygen vacancies with free charge carriers in matrix, thus saturated macroscopic polarization in contrast to that in upoled samples. E-poling changes bananas type polarization loops to real ferroelectric loops.Comment: 18 pages, 5 figure

Bipolar-Driven Large Magnetoresistance in Silicon

Large linear magnetoresistance (MR) in electron-injected p-type silicon at very low magnetic field is observed experimentally at room temperature. The large linear MR is induced in electron-dominated space-charge transport regime, where the magnetic field modulation of electron-to-hole density ratio controls the MR, as indicated by the magnetic field dependence of Hall coefficient in the silicon device. Contrary to the space-charge-induced MR effect in unipolar silicon device, where the large linear MR is inhomogeneity-induced, our results provide a different insight into the mechanism of large linear MR in non-magnetic semiconductors that is not based on the inhomogeneity model. This approach enables homogeneous semiconductors to exhibit large linear MR at low magnetic fields that until now has only been appearing in semiconductors with strong inhomogeneities.Comment: 23 pages, 4 figures (main text), 6 figures (supplemental material

Circle talks as situated experiential learning: Context, identity, and knowledgeability in \u27learning from reflection\u27

This article presents research that used ethnographic and sociolinguistic methods to study ways participants learn through reflection when carried out as a “circle talk.” The data indicate that participants in the event (a) invoked different contextual frames that (b) implicated them in various identity positions, which (c) affected how they could express their knowledge. These features worked together to generate socially shared meanings that enabled participants to jointly achieve conceptualization—the ideational role “reflection” is presumed to play in the experiential learning process. The analysis supports the claim that participants generate new knowledge in reflection, but challenges individualistic and cognitive assumptions regarding how this occurs. The article builds on situated views of experiential learning by showing how knowledge can be understood as socially shared and how learning and identity formation are mutually entailing processes

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas

Theory of shot noise in space-charge limited diffusive conduction regime

As is well known, the fluctuations from a stable stationary nonequilibrium state are described by a linearized nonhomogeneous Boltzmann-Langevin equation. The stationary state itself may be described by a nonlinear Boltzmann equation. The ways of its linearization sometimes seem to be not unique. We argue that there is actually a unique way to obtain a linear equation for the fluctuations. In the present paper we treat as an example an analytical theory of nonequilibrium shot noise in a diffusive conductor under the space charge limited regime. Our approach is compared with that of Schomerus, Mishchenko and Beenakker [Phys. Rev. B 60, 5839 (1999)]. We find some difference between the present theory and the approach of their paper and discuss a possible origin of the difference. We believe that it is related to the fundamentals of the theory of fluctuation phenomena in a nonequilibrium electron gas.Comment: 17 pages, no figure

Charge injection instability in perfect insulators

We show that in a macroscopic perfect insulator, charge injection at a field-enhancing defect is associated with an instability of the insulating state or with bistability of the insulating and the charged state. The effect of a nonlinear carrier mobility is emphasized. The formation of the charged state is governed by two different processes with clearly separated time scales. First, due to a fast growth of a charge-injection mode, a localized charge cloud forms near the injecting defect (or contact). Charge injection stops when the field enhancement is screened below criticality. Secondly, the charge slowly redistributes in the bulk. The linear instability mechanism and the final charged steady state are discussed for a simple model and for cylindrical and spherical geometries. The theory explains an experimentally observed increase of the critical electric field with decreasing size of the injecting contact. Numerical results are presented for dc and ac biased insulators.Comment: Revtex, 7pages, 4 ps figure