Art Neural Networks for Remote Sensing: Vegetation Classification from Landsat TM and Terrain Data

A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on the fuzzy ARTMAP neural network, is developed. System capabilities are tested on a challenging remote sensing classification problem, using spectral and terrain features for vegetation classification in the Cleveland National Forest. After training at the pixel level, system performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, as well as back propagation neural networks and K Nearest Neighbor algorithms. ARTMAP dynamics are fast, stable, and scalable, overcoming common limitations of back propagation, which did not give satisfactory performance. Best results are obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. A prototype remote sensing example introduces each aspect of data processing and fuzzy ARTMAP classification. The example shows how the network automatically constructs a minimal number of recognition categories to meet accuracy criteria. A voting strategy improves prediction and assigns confidence estimates by training the system several times on different orderings of an input set.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-l-0409, N00014-95-0657

ART and ARTMAP Neural Networks for Applications: Self-Organizing Learning, Recognition, and Prediction

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems. Applications include parts design retrieval at the Boeing Company, automatic mapping from remote sensing satellite measurements, medical database prediction, and robot vision. This chapter features a self-contained introduction to ART and ARTMAP dynamics and a complete algorithm for applications. Computational properties of these networks are illustrated by means of remote sensing and medical database examples. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, that allows the network to encode important rare cases but that may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. In medical database prediction problems, which often feature inconsistent training input predictions, the ARTMAP-IC network further improves ARTMAP performance with distributed prediction, category instance counting, and a new search algorithm. A recently developed family of ART models (dART and dARTMAP) retains stable coding, recognition, and prediction, but allows arbitrarily distributed category representation during learning as well as performance.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-1-0409, N00014-95-0657

Dual mode nanoparticles: CdS coated iron nanoparticles

Reverse micelles can be used in a sequential fashion to make core-shell nanoparticles. Using this technique it is possible to make a magnetic quantum dot, by coating an iron core with a cadmium sulfide shell. Transmission electron microscopy indicated core-shell morphology and narrow size distribution of the obtained particles. Collectively, x-ray powder diffraction and x-ray photoelectron spectroscopy verified the presence of cadmium sulfide on the surface of the nanoparticles. Optical properties of the coated particles were demonstrated using fluorescence spectroscopy. A vibrating sample magnetometer was used to determine magnetic properties. Dual mode cadmium sulfide coatediron core-shell nanoparticles make unique candidates for the use in biomedical applications

TB30: A Critical Evaluation of Results from Spectographic Analysis of Plan Tissue

For this study, samples of wheat, corn, timothy, orchardgrass, alfalfa, Bermuda grass, and tomato were analyzed for eleven elements. A statistical study of these data was undertaken to determine the precision of the spectrographic analyses and to determine the precision that could be expected from analyses by this method.https://digitalcommons.library.umaine.edu/aes_techbulletin/1169/thumbnail.jp

TB20: Preliminary Tables of Some Chemical Elements in Seven Tree Species in Maine

These tables show the amount in grams for each of twelve elements for the complete tree and the merchantable bole, for seven tree species (red spruce, balsam fir, hemlock, white pine, white birch, red maple, aspen) in terms of five height classes and ten diameter classes.https://digitalcommons.library.umaine.edu/aes_techbulletin/1180/thumbnail.jp

Incomplete Punishment Networks in Public Goods Games: Experimental Evidence

Abundant evidence suggests that high levels of contributions to public goods can be sustained through self-governed monitoring and sanctioning. This experimental study investigates the effectiveness of decentralized sanctioning institutions in alternative punishment networks. Our results show that the structure of punishment network significantly affects allocations to the public good. In addition, we observe that network configurations are more important than punishment capacities for the levels of public good provision, imposed sanctions and economic efficiency. Lastly, we show that targeted revenge is a major driver of anti-social punishment

Sequence of phase transitions induced in an array of Josephson junctions by their crossover to pi-state

We show that the transition of Josephson junctions between the conventional and pi states caused by the decrease in temperature induces in a regular two-dimensional array of such junctions not just a single phase transition between two phases with different ordering but a sequence of two, three or four phase transitions. The corresponding phase diagrams are constructed for the cases of bipartite (square or honeycomb) and triangular lattices.Comment: 5 pages, v2: as published in EP

RXTE Observations of 1A 1744-361: Correlated Spectral and Timing Behavior

We analyze Rossi X-ray Timing Explorer (RXTE) Proportional Counter Array (PCA) data of the transient low mass X-ray binary (LMXB) system 1A 1744-361. We explore the X-ray intensity and spectral evolution of the source, perform timing analysis, and find that 1A 1744-361 shows `atoll' behavior during the outbursts. The color-color diagram indicates that this LMXB was observed in a low intensity spectrally hard (low-hard) state and in a high intensity `banana' state. The low-hard state shows a horizontal pattern in the color-color diagram, and the previously reported `dipper QPO' appears only during this state. We also perform energy spectral analyses, and report the first detection of broad iron emission line and iron absorption edge from 1A 1744-361.Comment: 20 pages, 4 tables, 4 figures, accepted for publication in Ap

Dirac Gauginos, Negative Supertraces and Gauge Mediation

In an attempt to maximize General Gauge Mediated parameter space, I propose simple models in which gauginos and scalars are generated from disconnected mechanisms. In my models Dirac gauginos are generated through the supersoft mechanism, while independent R-symmetric scalar masses are generated through operators involving non-zero messenger supertrace. I propose several new methods for generating negative messenger supertraces which result in viable positive mass squareds for MSSM scalars. The resultant spectra are novel, compressed and may contain light fermionic SM adjoint fields.Comment: 16 pages 3 figure

Enhanced magnetic anisotropy in cobalt-carbide nanoparticles

An outstanding problem in nano-magnetism is to stabilize the magnetic order in nanoparticles at room temperatures. For ordinary ferromagnetic materials, reduction in size leads to a decrease in the magnetic anisotropy resulting in superparamagnetic relaxations at nanoscopic sizes. In this work, we demonstrate that using wet chemical synthesis, it is possible to stabilize cobalt carbide nanoparticles which have blocking temperatures exceeding 570 K even for particles with magnetic domains of 8 nm. First principles theoretical investigations show that the observed behavior is rooted in the giant magnetocrystalline anisotropies due to controlled mixing between C p- and Co d-states