Nonlinear averaging theorems, and the determination of parameter convergence rates in adaptive control

The paper presents nonlinear averaging theorems for two-time scale systems, where the dynamics of the fast system are allowed to vary with the slow system. The results are applied to the Narendra-Valavani adaptive control algorithm, and estimates of the parameter convergence rates are obtained which do not rely on a linearization of the system around the equilibrium, and therefore are valid in a larger region in the parameter space.     

A microcomputer-based system for processing 31-phosphorus nuclear magnetic resonance spectra from studies of cardiac metabolism in immature hearts

We designed an interactive microcomputer-based digital data processing system for analysis of 31-phosphorus nuclear magnetic resonance (31P-NMR) spectra from studies of cardiac metabolism in immature and neonatal hearts. This system included a digitizing tablet (Kurta Series Two), a microcomputer (IBM PC XT) and a graphics plotter (Hewlett-Packard 7470A) used in conjunction with a Nicolet 1280 NMR signal processing computer. We obtained 31P spectra from isolated perfused rabbit hearts with a Nicolet NT-200 4.7 Tesla superconducting NMR spectrometer operated in the pulsed Fourier transform mode. The small size of the hearts resulted in increased noise in spectra and demanded comparison of methods used to quantitate changes in inorganic phosphorus, phosphocreatine and ATP during ischemic stress. We performed microcomputer operations and interfacing functions with a software package written in BASIC. This system simplified documentation, data filing and statistical data processing. Our microcomputer system displayed and made hard copies of digitized spectra and results of analyses. Errors in data entry were rectified directly with this program. Consistent data reduction improved the precision of the physiological results and reduced the influence of noise on 31P spectra from neonatal hearts weighing about 0.5 g. The system flexibility extends its application to NMR spectra analysis for other in vivo organ systems, and signal processing in other biological research.     

Electromagnetic Devices Applications of EBG for Systems Communications

This paper presents the study and applications of EBG (Electromagnetic Band Gap)/PBG (Photonic Band Gap) substrates, of planar microstrip structures, including quadrature directional couplers, rat-race (ring coupler) and impedance transformers. Through specific programs developed in FORTRAN PowerStation, the frequencies and couplings for each structure are obtained. Also are used the PACMO—Computer Aided Design in Microwave program developed by H.C.C. Fernandes. Results of the dimensions and coupling of these devices, varying the frequency band (cellular communication and Wimax systems) and permittivity of the substrate, are obtained, comparing the results of conventional materials with EBG/PBG materials in the s and p polarizations.     

FFT regression and cross-noise reduction for comparing images in remote sensing

In many remote sensing studies it is desired to quantify the functional relationship between images of a given target that were acquired by different sensors. Such comparisons are problematic because when the pixel values of one image are plotted versus the other, the 'cross-noise' is quite high. Typically, the correlation coefficient is quite low, even when the compared images look alike. Nevertheless, we can try to quantify the functional relationship between two images by a suitable regression model function Y = f ( X ), while choosing one of them as 'the reference' Y and using the other one as a 'predictor' X . The underlying assumption of classical regression is that Y is absolutely correct while X is erroneous. Thus, the objective is to fit X to Y by choosing the parameters of Y = f ( X ), which minimize the 'residuals' ( “ - Y ). When comparing images in remote sensing this objective is not valid because Y itself is error prone. The alternative FFT regression method presented herein comprises a two-stage sensor fusion approach, whereby the initially low correlation between X and Y is increased and the residuals are dramatically decreased. First, pairwise image transforms are applied to X and Y whereby the correlation coefficient is increased, e.g. from roughly 0.4 to about 0.8-0.85. A predicted image Y fft is then derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. In the second stage, there are two options: For one time predictions, the phase matrix of Y is combined with the amplitude matrix of Y fft, whereby an improved predicted image Y plock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Y fft versus Y . For long term predictions, the phase matrix of a 'field mask' is combined with the amplitude matrices of the reference image Y and the predicted image Y fft . The field mask is a binary image of a pre-selected region of interest in X and Y . The resultant images Y pref and Y pred are modified versions of Y and Y fft respectively. The residuals of Y pred versus Y pref are even lower than the residuals of Y plock versus Y . Images Y pref and Y pred represent a close consensus of two independent imaging methods which view the same target. The practical utility of FFT regression is demonstrated by examples wherein remotely sensed NDVI images X are used for predicting yield distributions in agricultural fields. Reference yield maps Y were derived by yield monitors which measure the flow rate of the crop while it is being harvested. The 2D FFT transforms, as well as other mathematical operations in this paper were performed in the 'MATLAB' environment.     

Comparison of calibration curve fitting methods in the determination of H2S mass fractions in natural gas by GC-SCD

This paper discusses metrologically the best practice regarding the calibration curves applied to H₂S mass fractions determination in natural gas by gas chromatography with sulfur chemiluminescence detection (GC-SCD). Three calibration curves were constructed by performing GC-SCD analysis of different H₂S gas standard concentrations (from 3 mg kg^?1 up to 500 mg kg^?1). These experimental curves are better fitted by an unweighted quadratic calibration curve considering ANOVA approach compared to ASTM D5504-12. Despite this, the obtained results show that these two different calibration curve approaches (ASTM and ANOVA) lead to comparable results. Hence, there are no significant statistical differences between these two approaches based on the hypothesis test applied. However, the quadratic calibration curve presents measurement uncertainties of H₂S mass fractions much lower than the ASTM approach.     

Multigigabit transmission through rain in a dual polarizationfrequency reuse system: an experimental study

An experimental terrestrial link was set up for the purpose of evaluating very-high-data-rate communications in dual-polarization frequency reuse systems. Possible applications are in the field of satellite communications. Two 3.2-Gb/s signals using staggered quadriphase shift keying (SQPSK) were transmitted on opposite senses of circular polarization over a 7-km path located in Crane rain region D3. Performance degradation of the demodulator as a function of cross-polarization discrimination (XPD) was evaluated and found to closely match simulation predictions. However, the relationship between XPD and rain attenuation was found to be more complex than thought previously, displaying a relatively slowly varying hysteresis effect as well as significant interactions with characteristics of the receive antenna. The latter effect suggests that careful control of antenna characteristics may be required if such high-data-rate techniques are to be employed in space-ground links     

Criteria for local myocardial electrical activation: effects ofelectrogram characteristics

The ability of several variables to distinguish unipolar deflections due to local activation from those due to nonlocal activity is studied. A model of polyphasic deflections based on atrial recordings during reentrant tachycardia was used to facilitate distinction of local and distant activity by methods independent of the test variables. The performances of variables were assessed by comparing areas under receiver operating characteristic curves. Optimal thresholds of test variables were identified by maximizing statistics which corrected for the pretest probability of local activation. It was found that the greatest negative first derivative of the unipolar potential discriminated between local and distant ventricular signals, but performed less well than the ratio of the first derivative to the potential for distinguishing between local atrial signals and distant ventricular signals. A linear combination of the potential and the ratio of the first derivative and the potential performed well for all groups of signals studied. Optimal criteria for detecting local activation are discussed     

Multivariate autoregressive models for classification ofspontaneous electroencephalographic signals during mental tasks

This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram (EEG) with which mental tasks can be discriminated. This is part of a larger project to investigate the feasibility of using EEG to allow paralyzed persons to control a device such as a wheelchair. EEG signals from four subjects were recorded while they performed two mental tasks. Quarter-second windows of six-channel EEG were transformed into four different representations: scalar AR model coefficients, multivariate AR coefficients, eigenvalues of a correlation matrix, and the Karhunen-Loeve transform of the multivariate AR coefficients. Feature vectors defined by these representations were classified with a standard, feedforward neural network trained via the error backpropagation algorithm. The four representations produced similar results, with the multivariate AR coefficients performing slightly better and more consistently with an average classification accuracy of 91.4% on novel, untrained, EEG signals     

Reliability life-testing and failure-analysis of GaAs monolithicKu-band driver amplifiers

`Gallium-arsenide monolithic microwave integrated-circuit (MMIC) Ku-band driver amplifiers were life tested under accelerated high temperature, DC and RF conditions until failure. These MMIC are used in various applications such as radar and satellite communication systems. The failure mechanisms controlling their reliability must be understood in order to improve the lifetime for these and other applications. This paper discusses the experimental procedures, statistical evaluation of the data and failure analysis of the devices. To the authors' knowledge, this is the first report of RF life testing of dual-gate driver amplifiers. The majority of the devices failed catastrophically due to high drain current, while others failed parametrically due to low output power. Failure analysis indicates that degradation of the Si₃N₄ dielectric layer to be the main failure mechanism in these MMIC. Statistical analysis revealed an activation energy of 0.87 eV and a median lifetime of 5.8·10⁴ hours at 140°C channel temperature, which is consistent with surface-phenomena failure mechanisms