Electron spin resonance in cupric acid fluoride

The electron spin resonance in undiluted single crystals of cupric acid fluoride has been investigated at room temperature with microwaves of frequency 9625 Mc/s. The anisotropy in the g value has been measured in three orthogonal planes. The principal g values gave gshort parallel = 2.410 ± 0.010, gperpendicular = 2.090 ± 0.010. The linewidth shows anisotropy with orientation. The exchange frequency has been estimated to be approximately 0.08 cm-1.The powdered specimen shows asymmetry in the line shape.

Studies in sesquiterpenes-XVIII. The proton magnetic resonance spectra of some sesquiterpenes and the structure of humulene

The nuclear magnetic resonance spectra of longifolene, zerumbone, humulene, and their hydroderivatives have been studied in order to gauge the potentialities of this new tool in the field of sesquiterpenes. On the basis of present study, it has been possible to unequivocally fix the positions of the ethylene linkages in humulene and thus provide a straightforward solution of this hitherto unsolved problem.

Stabilizing hadron resonance gas models

We examine the stability of hadron resonance gas models by extending them to include undiscovered resonances through the Hagedorn formula. We find that the influence of unknown resonances on thermodynamics is large but bounded. We model the decays of resonances and investigate the ratios of particle yields in heavy-ion collisions. We find that observables such as hydrodynamics and hadron yield ratios change little upon extending the model. As a result, heavy-ion collisions at the RHIC and LHC are insensitive to a possible exponential rise in the hadronic density of states, thus increasing the stability of the predictions of hadron resonance gas models in this context. Hadron resonance gases are internally consistent up to a temperature higher than the crossover temperature in QCD, but by examining quark number susceptibilities we find that their region of applicability ends below the QCD crossover.

Pressure dependence of chlorine nuclear quadrupole resonance in sodium chlorate

The pressure dependence of the chlorine NQR frequency in NaClo3 has been investigated up to 20 k bar hydrostatic pressure. A distinct break in slope in the pressure dependence of the resonance frequency is observed near 11 k bar. This is attributed to a phase transition reported earlier by Bridgman in this pressure region.

Performance analysis of a suprathreshold, stochastic resonance based nonlinear detector

In this paper we introduce a nonlinear detector based on the phenomenon of suprathreshold stochastic resonance (SSR). We first present a model (an array of 1-bit quantizers) that demonstrates the SSR phenomenon. We then use this as a pre-processor to the conventional matched filter. We employ the Neyman-Pearson(NP) detection strategy and compare the performances of the matched filter, the SSR-based detector and the optimal detector. Although the proposed detector is non-optimal, for non-Gaussian noises with heavy tails (leptokurtic) it shows better performance than the matched filter. In situations where the noise is known to be leptokurtic without the availability of the exact knowledge of its distribution, the proposed detector turns out to be a better choice than the matched filter.

A suprathreshold stochastic resonance based pre-processor for direction-of-arrival estimation

In this paper we propose a nonlinear preprocessor for enhancing the performance of processors used for direction-of-arrival (DOA) estimation in heavy-tailed non-Gaussian noise. The preprocessor based on the phenomenon of suprathreshold stochastic resonance (SSR), provides SNR gain. The preprocessed data is used for DOA estimation by the MUSIC algorithm. Simulation results are presented to show that the SSR preprocessor provides a significant improvement in the performance of MUSIC in heavy-tailed noise at low SNR.

Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.

An electron paramagnetic resonance study of Pr0.6Ca0.4MnO3 across the charge-ordering transition

We report the first electron paramagnetic resonance studies of single crystals and powders of Pr0.6Ca0.4MnO3 in the 300-4.2 K range, covering the charge-ordering transition (Tco) at ~240 K and antiferromagnetic transition (TN) at ~170 K. The asymmetry parameter for the Dysonian single-crystal spectra shows an anomalous increase at Tco. Below Tco the g-value increases continuously, suggesting a gradual strengthening of the orbital ordering. The linewidth undergoes a sudden increase at Tco and continues to increase down to TN. The intensity increases as the temperature is decreased until Tco is reached, due to the renormalization of the magnetic susceptibility arising from the build-up of ferromagnetic correlations.