Data-driven Identification of Parametric Governing Equations of Dynamical Systems Using the Signed Cumulative Distribution Transform

This paper presents a novel data-driven approach to identify partial differential equation (PDE) parameters of a dynamical system. Specifically, we adopt a mathematical "transport" model for the solution of the dynamical system at specific spatial locations that allows us to accurately estimate the model parameters, including those associated with structural damage. This is accomplished by means of a newly-developed mathematical transform, the signed cumulative distribution transform (SCDT), which is shown to convert the general nonlinear parameter estimation problem into a simple linear regression. This approach has the additional practical advantage of requiring no a priori knowledge of the source of the excitation (or, alternatively, the initial conditions). By using training data, we devise a coarse regression procedure to recover different PDE parameters from the PDE solution measured at a single location. Numerical experiments show that the proposed regression procedure is capable of detecting and estimating PDE parameters with superior accuracy compared to a number of recently developed machine learning methods. Furthermore, a damage identification experiment conducted on a publicly available dataset provides strong evidence of the proposed method's effectiveness in structural health monitoring (SHM) applications. The Python implementation of the proposed system identification technique is integrated as a part of the software package PyTransKit [1].

Transport model for the propagation of partially coherent, partially polarized, polarization-gradient vector beams

Recently we predicted and experimentally validated a new physical mechanism for altering the propagation path of a monochromatic beam [Opt. Express30, 38907 (2022)OPEXFF1094-408710.1364/OE.467678]. Specifically, we showed that by properly tailoring the spatial distribution of the linear state of polarization transverse to the direction of propagation, the beam followed a curved trajectory in free space. Here we extend the model to the partially coherent and partially polarized polychromatic case by redefining the beam amplitude, phase, and polarization angle as appropriate statistical quantities. In particular, the definition of polarization angle represents a fundamentally new quantity in modeling beam propagation and is shown to be consistent with recent works on energy and momentum flow. In the new model, the beam curvature matches that of our previous work in the fully coherent case but is predicted to vanish for an unpolarized, spatially incoherent beam. Simulated beam trajectories are shown for varying levels of initial partial coherence and for different polarization profiles. A new class of non-diffracting beams is also suggested by way of example.

An evidence-based approach to assessing the effectiveness of training regimen on athlete performance: Youth soccer as a case study

Athletic performance data are modeled in an effort to better understand the relationship between both hours spent training and a measurement of “commitment” to that training, and improvements in performance. Both increased training time and greater commitment were predicted to produce larger increases in performance improvement, and commitment was predicted to be the more important determinant of improvement. The performance of 108 soccer players (ages 9–18) was quantified over a 10-week training program. Hours spent training ranged from 16 to 90 during the course of the study, while commitment scores ranged from 0.55 to 2.00, based on a scale from 0.00 to 2.40. A model selection approach was used to discriminate among models specifying relationships between training hours and improvement, and commitment and improvement. Despite considerable variability in the data, results provided strong evidence for an increase in performance improvement with both training hours and commitment score. The best models for hours and commitment were directly compared by computing an evidence ratio of 5799, indicating much stronger evidence favoring the model based on commitment. Results of analyses such as these go beyond anecdotal experience in an effort to establish a formal evidentiary basis for athletic training programs.

Vector beam bending via a polarization gradient

We propose, analyze and demonstrate experimentally an entirely new optical effect in which the centroid of a coherent optical beam can be designed to propagate along a curved trajectory in free space by tailoring the spatial distribution of linear polarization across the transverse beam profile. Specifically, a non-zero spatial gradient of second order or higher in the linear state of polarization is shown to cause the beam centroid to "accelerate" in the direction transverse to the direction of propagation. The effect is confirmed experimentally using spatial light modulation to create the distribution in linear polarization and then measuring the transverse location of the beam profile at varying propagation distances. The observed displacement of the beam centroid is shown to closely match the theory out to 34m propagation distance.

Radon Cumulative Distribution Transform Subspace Modeling for Image Classification

We present a new supervised image classification method applicable to a broad class of image deformation models. The method makes use of the previously described Radon Cumulative Distribution Transform (R-CDT) for image data, whose mathematical properties are exploited to express the image data in a form that is more suitable for machine learning. While certain operations such as translation, scaling, and higher-order transformations are challenging to model in native image space, we show the R-CDT can capture some of these variations and thus render the associated image classification problems easier to solve. The method - utilizing a nearest-subspace algorithm in the R-CDT space - is simple to implement, non-iterative, has no hyper-parameters to tune, is computationally efficient, label efficient, and provides competitive accuracies to state-of-the-art neural networks for many types of classification problems. In addition to the test accuracy performances, we show improvements (with respect to neural network-based methods) in terms of computational efficiency (it can be implemented without the use of GPUs), number of training samples needed for training, as well as out-of-distribution generalization. The Python code for reproducing our results is available at [1].

Transport-based pattern recognition versus deep neural networks in underwater OAM communications

Comparisons between machine learning and optimal transport-based approaches in classifying images are made in underwater orbital angular momentum (OAM) communications. A model is derived that justifies optimal transport for use in attenuated water environments. OAM pattern demultiplexing is performed using optimal transport and deep neural networks and compared to each other. Additionally, some of the complications introduced by signal attenuation are highlighted. The Radon cumulative distribution transform (R-CDT) is applied to OAM patterns to transform them to a linear subspace. The original OAM images and the R-CDT transformed patterns are used in several classification algorithms, and results are compared. The selected classification algorithms are the nearest subspace algorithm, a shallow convolutional neural network (CNN), and a deep neural network. It is shown that the R-CDT transformed images are more accurate than the original OAM images in pattern classification. Also, the nearest subspace algorithm performs better than the selected CNNs in OAM pattern classification in underwater environments.

Learned linear models for detecting watercraft in a maritime environment

This work provides a new, to the best of our knowledge, approach to constructing linear models for object detection in a scene. Specifically, we use representative training data in order to estimate the parameters describing a generalized wavelet model for the express purpose of detecting the presence of maritime targets in a scene. The parameter estimates are taken as those that maximize the probability of detecting the targets for a fixed probability of false alarm. The approach is then demonstrated on a database of short-wave infrared imagery containing various watercraft. Results are then compared to some of the more standard wavelet bases used in detection applications.

Parametric Signal Estimation Using the Cumulative Distribution Transform

We present a new method for estimating signal model parameters using the Cumulative Distribution Transform (CDT). Our approach minimizes the Wasserstein distance between measured and model signals. We derive some useful properties of the CDT and show that the resulting estimation problem, while nonlinear in the original signal domain, becomes a linear least squares problem in the transform domain. Furthermore, we discuss the properties of the estimator in the presence of noise and present a novel approach for mitigating the impact of the noise on the estimates. The proposed estimation approach is evaluated by applying it to a source localization problem and comparing its performance against traditional approaches.

Linear transfer function estimation using the photodiode impulse response

In this Letter, we implement a model-based auto-regressive estimator of the frequency response for a high-speed photodiode (PD). A transfer function is developed, and the associated coefficients representing the PD model and frequency response are solved for using a two-stage least-squares approach. The model is implemented for a modified uni-traveling carrier PD and experimentally compared to measured impulse response data.

Transport-based model for turbulence-corrupted imagery

A new model for turbulence-corrupted imagery is proposed based on the theory of optimal mass transport. By describing the relationship between photon density and the phase of the traveling wave, and combining it with a least action principle, the model suggests a new class of methods for approximately recovering the solution of the photon density flow created by a turbulent atmosphere. Both coherent and incoherent imagery are used to validate and compare the model to other methods typically used to describe this type of data. Given its superior performance in describing experimental data, the new model suggests new algorithms for a variety of atmospheric imaging and wave propagation applications.

De-multiplexing vortex modes in optical communications using transport-based pattern recognition

Free space optical communications utilizing orbital angular momentum beams have recently emerged as a new technique for communications with potential for increased channel capacity. Turbulence due to changes in the index of refraction emanating from temperature, humidity, and air flow patterns, however, add nonlinear effects to the received patterns, thus making the demultiplexing task more difficult. Deep learning techniques have been previously been applied to solve the demultiplexing problem as an image classification task. Here we make use of a newly developed theory suggesting a link between image turbulence and photon transport through the continuity equation to describe a method that utilizes a "shallow" learning method instead. The decoding technique is tested and compared against previous approaches using deep convolutional neural networks. Results show that the new method can obtain similar classification accuracies (bit error ratio) at a small fraction (1/90) of the computational cost, thus enabling higher bit rates.

Distribution of splice loss in single mode optical fiber

This work investigates a probabilistic model for splice loss in single mode optical fibers. We derive the probability density function for loss values as a function of lateral and angular misalignment. We then use observed data to estimate these model parameters; both Bayesian and maximum likelihood estimation procedures are described. These estimates can then be used to provide an indication of the relative importance of various loss mechanisms. Alternatively, if one is given values for maximum lateral and angular misalignment, our results allow for predictions of expected distribution of loss values. An overall goal of this paper is to demonstrate that, beyond the mean and variance of splice loss, there is significant information in the shape of the distribution of values. A second goal is to understand the trade-off between the number of splice loss measurements and the confidence in estimates of parameters in the splice loss model.

Experimental infrared point-source detection using an iterative generalized likelihood ratio test algorithm

This work documents the performance of a recently proposed generalized likelihood ratio test (GLRT) algorithm in detecting thermal point-source targets against a sky background. A calibrated source is placed above the horizon at various ranges and then imaged using a mid-wave infrared camera. The proposed algorithm combines a so-called "shrinkage" estimator of the background covariance matrix and an iterative maximum likelihood estimator of the point-source parameters to produce the GLRT statistic. It is clearly shown that the proposed approach results in better detection performance than either standard energy detection or previous implementations of the GLRT detector.

Bayesian approach to decompression sickness model parameter estimation

We examine both maximum likelihood and Bayesian approaches for estimating probabilistic decompression sickness model parameters. Maximum likelihood estimation treats parameters as fixed values and determines the best estimate through repeated trials, whereas the Bayesian approach treats parameters as random variables and determines the parameter probability distributions. We would ultimately like to know the probability that a parameter lies in a certain range rather than simply make statements about the repeatability of our estimator. Although both represent powerful methods of inference, for models with complex or multi-peaked likelihoods, maximum likelihood parameter estimates can prove more difficult to interpret than the estimates of the parameter distributions provided by the Bayesian approach. For models of decompression sickness, we show that while these two estimation methods are complementary, the credible intervals generated by the Bayesian approach are more naturally suited to quantifying uncertainty in the model parameters.

Analytical expression for pixel-averaged point sources as observed by a focal plane array

A closed-form expression is derived for pixel-averaged point source signals as observed by an imaging array. Obtaining this solution requires the probability distribution of squared Euclidean distances in two dimensions when the end points are confined to a uniformly spaced square domain. The derivation and associated expression are provided.

Range performance of the DARPA AWARE wide field-of-view visible imager

In a prior paper, we described a new imaging architecture that addresses the need for wide field-of-view imaging combined with the resolution required to identify targets at long range. Over the last two years substantive improvements have been made to the system, both in terms of the size, weight, and power of the camera as well as to the optics and data management software. The result is an overall improvement in system performance, which we demonstrate via a maritime target identification experiment.

A study of molecular adsorption of a cationic surfactant on complex surfaces with atomic force microscopy

The study of molecular adsorption on solid surfaces is of broad interest. However, so far the study has been restricted to idealized flat smooth rigid surfaces which are rarely the case in real world applications. Here we describe a study of molecular adsorption on a complex surface of the submicron fibers of a fibrous membrane of regenerated cellulose in aqueous media. We use a cationic surfactant, cetyltrimethylammonium chloride (CTAC), as the adsorbing molecule. We study the equilibrium adsorption of CTAC molecules on the same area of the fibers by sequentially immersing the membrane in pure water, 1 mM and then a 20 mM solution of CTAC. Atomic force microscopy (AFM) is applied to study the adsorption. The force-volume mode is used to record the force-deformation curves of the adsorbed molecules on the fiber surface. We suggest a model to separate the forces due to the adsorbed molecules from the elastic deformation of the fiber. Interestingly, knowledge of the surface geometry is not required in this model provided the surface is made of elastically homogeneous material. Different models are investigated to estimate the amount of the adsorbed molecules based on the obtained force curves. The exponential steric repulsion model fits the force data the best. The amount of the adsorbed surfactant molecules and its dependence on the concentration are found to be reasonable compared to the data previously measured by means of Raman scattering done on a flat surface of silica.

"Store-operated" cAMP signaling contributes to Ca2+-activated Cl- secretion in T84 colonic cells

Apical cAMP-dependent CFTR Cl(-) channels are essential for efficient vectorial movement of ions and fluid into the lumen of the colon. It is well known that Ca(2+)-mobilizing agonists also stimulate colonic anion secretion. However, CFTR is apparently not activated directly by Ca(2+), and the existence of apical Ca(2+)-dependent Cl(-) channels in the native colonic epithelium is controversial, leaving the identity of the Ca(2+)-activated component unresolved. We recently showed that decreasing free Ca(2+) concentration ([Ca(2+)]) within the endoplasmic reticulum (ER) lumen elicits a rise in intracellular cAMP. This process, which we termed "store-operated cAMP signaling" (SOcAMPS), requires the luminal ER Ca(2+) sensor STIM1 and does not depend on changes in cytosolic Ca(2+). Here we assessed the degree to which SOcAMPS participates in Ca(2+)-activated Cl(-) transport as measured by transepithelial short-circuit current (Isc) in polarized T84 monolayers in parallel with imaging of cAMP and PKA activity using fluorescence resonance energy transfer (FRET)-based reporters in single cells. In Ca(2+)-free conditions, the Ca(2+)-releasing agonist carbachol and Ca(2+) ionophore increased Isc, cAMP, and PKA activity. These responses persisted in cells loaded with the Ca(2+) chelator BAPTA-AM. The effect on Isc was enhanced in the presence of the phosphodiesterase (PDE) inhibitor 3-isobutyl-1-methylxanthine (IBMX), inhibited by the CFTR inhibitor CFTRinh-172 and the PKA inhibitor H-89, and unaffected by Ba(2+) or flufenamic acid. We propose that a discrete component of the "Ca(2+)-dependent" secretory activity in the colon derives from cAMP generated through SOcAMPS. This alternative mode of cAMP production could contribute to the actions of diverse xenobiotic agents that disrupt ER Ca(2+) homeostasis, leading to diarrhea.

Moving off the grid in an experimental, compressively sampled photonic link

Perhaps the largest obstacle to practical compressive sampling is an inability to accurately, and sparsely describe the data one seeks to recover due to poor choice of signal model parameters. In such cases the recovery process will yield artifacts, or in many cases, fail completely. This work represents the first demonstration of a solution to this so-called "off-grid" problem in an experimental, compressively sampled system. Specifically, we show that an Alternating Convex Search algorithm is able to significantly reduce these data model errors in harmonic signal recovery.

Analytical expression for the average ensquared energy

We derive an expression for the average area of intersection between a blur spot of radius R and a square pixel, where the center of the blur is uniformly chosen from the pixel interior. Implications of the result are then discussed in the context of a point source detection problem.

Characterization of the AWARE 10 two-gigapixel wide-field-of-view visible imager

System requirements for many military electro-optic and IR camera systems reflect the need for both wide-field-of-view situational awareness as well as high-resolution imaging for target identification. In this work we present a new imaging system architecture designed to perform both functions simultaneously and the AWARE 10 camera as an example at visible wavelengths. We first describe the basic system architecture and user interface followed by a laboratory characterization of the system optical performance. We then describe a field experiment in which the camera was used to identify several maritime targets at varying range. The experimental results indicate that users of the system are able to correctly identify ~10 m targets at between 4 and 6 km with 70% accuracy.

Estimating detection and identification probabilities in maritime target acquisition

This work describes several approaches to the estimation of target detection and identification probabilities as a function of target range. A Bayesian approach to estimation is adopted, whereby the posterior probability distributions associated with these probabilities are analytically derived. The parameter posteriors are then used to develop credible intervals quantifying the degree of uncertainty in the parameter estimates. In our first approach we simply show how these credible intervals evolve as a function of range. A second approach, also following the Bayesian philosophy, attempts to directly estimate the parameterized performance curves. This second approach makes efficient use of the available data and yields a distribution of probability versus range curves. Finally, we demonstrate both approaches using experimental data collected from wide field-of-view imagers focused on maritime targets.

Characterization of a compressively sampled photonic link

The emerging field of compressive sampling has potentially powerful implications for the design of analog-to-digital sampling systems. In particular, the mathematics of compressive sampling suggests that one can recover a signal at a smaller sampling interval than is dictated by the rate at which the samples are digitized. In a recent work the authors presented an all-photonic implementation of such a system and experimentally demonstrated the basic operating principles. This paper offers a more in-depth study of the system, including a more detailed description of the hardware, issues involved in real-time implementation, and how choice of signal model and model fidelity can influence the reconstruction.

Denoising infrared maritime imagery using tailored dictionaries via modified K-SVD algorithm

Recent work has shown that tailored overcomplete dictionaries can provide a better image model than standard basis functions for a variety of image processing tasks. Here we propose a modified K-SVD dictionary learning algorithm designed to maintain the advantages of the original approach but with a focus on improved convergence. We then use the learned model to denoise infrared maritime imagery and compare the performance to the original K-SVD algorithm, several overcomplete "fixed" dictionaries, and a standard wavelet denoising algorithm. Results indicate the superiority of overcomplete representations and show that our tailored approach provides similar peak signal-to-noise ratios as the traditional K-SVD at roughly half the computational cost.

Signal design using nonlinear oscillators and evolutionary algorithms: application to phase-locked loop disruption

This work describes an approach for efficiently shaping the response characteristics of a fixed dynamical system by forcing with a designed input. We obtain improved inputs by using an evolutionary algorithm to search a space of possible waveforms generated by a set of nonlinear, ordinary differential equations (ODEs). Good solutions are those that result in a desired system response subject to some input efficiency constraint, such as signal power. In particular, we seek to find inputs that best disrupt a phase-locked loop (PLL). Three sets of nonlinear ODEs are investigated and found to have different disruption capabilities against a model PLL. These differences are explored and implications for their use as input signal models are discussed. The PLL was chosen here as an archetypal example but the approach has broad applicability to any input∕output system for which a desired input cannot be obtained analytically.

Beating Nyquist with light: a compressively sampled photonic link

We report the successful demonstration of a compressively sampled photonic link. The system takes advantage of recent theoretical developments in compressive sampling to enable signal recovery beyond the Nyquist limit of the digitizer. This rather remarkable result requires that (1) the signal being recovered has a sparse (low-dimensional) representation and (2) the digitized samples be incoherent with this representation. We describe an all-photonic system architecture that meets these requirements and then show that 1 GHz harmonic signals can be faithfully reconstructed even when digitizing at 500 MS/s, well below the Nyquist rate.

Bayesian estimation of weak material dispersion: theory and experiment

This work considers the estimation of dispersion in materials via an interferometric technique. At its core, the problem involves extracting the quadratic variation in phase over a range of wavelengths based on measured optical intensity. The estimation problem becomes extremely difficult for weakly dispersive materials where the quadratic nonlinearity is very small relative to the uncertainty inherent in experiment. This work provides a means of estimating dispersion in the face of such uncertainty. Specifically, we use a Markov Chain Monte Carlo implementation of Bayesian analysis to provide both the dispersion estimate and the associated confidence interval. The interplay between various system parameters and the size of the resulting confidence interval is discussed. The approach is then applied to several different experimental samples.

Modeling and detecting localized nonlinearity in continuum systems with a multistage transform

A general method is presented for modeling spatially extended systems that may contain a localized source of nonlinearity. It has direct applications to structural health monitoring (SHM) where physical damage may cause such nonlinearity and also communications channels which may exhibit localized nonlinearity due to bad electrical contacts or component nonlinearity. The method uses a multistage nonlinear transform in order to model the system dynamics. We discuss the application to SHM and provide a preliminary test of the method with experimental data from a randomly shaken beam with loose bolts. We discuss the application to telecommunications, provide an experimental observation of symmetric nonlinearity in a "bad" electrical contact, and provide a preliminary test of using this method to remove nonlinear echo (and thereby improve data rate) on a telephone line used for data transmission.

Chaotic signal detection and estimation based on attractor sets: applications to secure communications

We consider the problem of detection and estimation of chaotic signals in the presence of white Gaussian noise. Traditionally this has been a difficult problem since generalized likelihood ratio tests are difficult to implement due to the chaotic nature of the signals of interest. Based on Poincare's recurrence theorem we derive an algorithm for approximating a chaotic time series with unknown initial conditions. The algorithm approximates signals using elements carefully chosen from a dictionary constructed based on the chaotic signal's attractor. We derive a detection approach based on the signal estimation algorithm and show, with simulated data, that the new approach can outperform other methods for chaotic signal detection. Finally, we describe how the attractor based detection scheme can be used in a secure binary digital communications protocol.

Mapping the information landscape: discerning peaks and valleys for ecological monitoring

We investigate previously unreported phenomena that have a potentially significant impact on the design of surveillance monitoring programs for ecological systems. Ecological monitoring practitioners have long recognized that different species are differentially informative of a system's dynamics, as codified in the well-known concepts of indicator or keystone species. Using a novel combination of analysis techniques from nonlinear dynamics, we describe marked variation among spatial sites in information content with respect to system dynamics in the entire region. We first observed these phenomena in a spatially extended predator-prey model, but we observed strikingly similar features in verified water-level data from a NOAA/NOS Great Lakes monitoring program. We suggest that these features may be widespread and the design of surveillance monitoring programs should reflect knowledge of their existence.

Bragg grating-based fibre optic sensors in structural health monitoring

This work first considers a review of the dominant current methods for fibre Bragg grating wavelength interrogation. These methods include WDM interferometry, tunable filter (both Fabry-Perot and acousto-optic) demultiplexing, CCD/prism technique and a newer hybrid method utilizing Fabry-Perot and interferometric techniques. Two applications using these techniques are described: hull loads monitoring on an all-composite fast patrol boat and bolt pre-load loss monitoring in a composite beam in conjunction with a state-space modelling data analysis technique.

Detecting nonlinearity in structural systems using the transfer entropy

The transfer entropy was recently proposed as a means of exploring coupling in dynamical systems. Transfer entropy is an information theoretic that quantifies the degree to which one dynamical process affects the transition probabilities (dynamics) of another. Here we demonstrate how this metric may be utilized to detect the presence of nonlinearity in a system. Using the method of surrogate data, the transfer entropy computed at various lag times are compared to values computed from linearized surrogates. The transfer entropy is shown to be a more sensitive indicator of nonlinearity than is the mutual information for both simulated and experimental data. This technique is particularly applicable to the field of structural health monitoring, where damage is often equated with the presence of a nonlinearity in an otherwise linear system.

Inferences about information flow and dispersal for spatially extended population systems using time-series data

This work explores an information-theoretic approach to drawing inferences about coupling of spatially extended ecological populations based solely on time-series of abundances. The efficacy of the approach, time-delayed mutual information, was explored using a spatially extended predator-prey model system in which populations at different patches were coupled via diffusive movement. The approach identified the relative magnitude and direction of information flow resulting from animal movement between populations, the change in information flow as a function of distance separating populations, and the diffusive nature of the information flow. In addition, when the diffusive movement was eliminated from the model, mutual information correctly provided no evidence of information flow, even when population synchrony was generated by a common environmental driving function. Thus, for this model system, time-delayed mutual information was useful in discriminating between the Moran effect and animal movement as causes of population synchrony, as well as in characterizing dispersal in terms of direction, relative speed and diffusive nature.

Assessing spatial coupling in complex population dynamics using mutual prediction and continuity statistics

A number of important questions in ecology involve the possibility of interactions or "coupling" among potential components of ecological systems. The basic question of whether two components are coupled (exhibit dynamical interdependence) is relevant to investigations of movement of animals over space, population regulation, food webs and trophic interactions, and is also useful in the design of monitoring programs. For example, in spatially extended systems, coupling among populations in different locations implies the existence of redundant information in the system and the possibility of exploiting this redundancy in the development of spatial sampling designs. One approach to the identification of coupling involves study of the purported mechanisms linking system components. Another approach is based on time series of two potential components of the same system and, in previous ecological work, has relied on linear cross-correlation analysis. Here we present two different attractor-based approaches, continuity and mutual prediction, for determining the degree to which two population time series (e.g., at different spatial locations) are coupled. Both approaches are demonstrated on a one-dimensional predator-prey model system exhibiting complex dynamics. Of particular interest is the spatial asymmetry introduced into the model as linearly declining resource for the prey over the domain of the spatial coordinate. Results from these approaches are then compared to the more standard cross-correlation analysis. In contrast to cross-correlation, both continuity and mutual prediction are clearly able to discern the asymmetry in the flow of information through this system.

Using chaotic interrogation and attractor nonlinear cross-prediction error to detect fastener preload loss in an aluminum frame

Structural health monitoring is an important field concerned with assessing the current state (or "health") of a structural system or component with regard to its ability to perform its intended function appropriately. One approach to this problem is identifying appropriate features obtained from time series vibration responses of the structure that change as structural degradation occurs. In this work, we present a novel technique adapted from the nonlinear time series prediction community whereby the structure is excited by an applied chaotic waveform, and predictive maps built between structural response attractors are used as the feature space. The structural response is measured at several points on the structure, and pairs of attractors are used to predict each other. As the dynamics of the structure change due to damage, the prediction error rises. This approach is applied to detecting the preload loss in a bolted joint in an aluminum frame structure.

Controlling system dimension: a class of real systems that obey the Kaplan-Yorke conjecture

The Kaplan-Yorke conjecture suggests a simple relationship between the fractal dimension of a system and its Lyapunov spectrum. This relationship has important consequences in the broad field of nonlinear dynamics where dimension and Lyapunov exponents are frequently used descriptors of system dynamics. We develop an experimental system with controllable dimension by making use of the Kaplan-Yorke conjecture. A rectangular steel plate is driven with the output of a chaotic oscillator. We controlled the Lyapunov exponents of the driving and then computed the fractal dimension of the plate's response. The Kaplan-Yorke relationship predicted the system's dimension extremely well. This finding strongly suggests that other driven linear systems will behave similarly. The ability to control the dimension of a structure's vibrational response is important in the field of vibration-based structural health monitoring for the robust extraction of damage-sensitive features.

Use of chaotic excitation and attractor property analysis in structural health monitoring

This work explores the utility of attractor-based approaches in the field of vibration-based structural health monitoring. The technique utilizes the unique properties of chaotic signals by driving the structure directly with the output of a chaotic oscillator. Using the Kaplan-Yorke conjecture, the Lyapunov exponents of the driving signal may be tuned to the dominant eigenvalues of the structure, thus controlling the dimension of the structural response. Data are collected at various stages of structural degradation and a simple nonlinear model, constructed from the undamaged data, is used to make predictions for the damaged response data. Prediction error is then introduced as a "feature" for classifying the magnitude of the damage. Results are presented for an experimental cantilevered beam instrumented with fiber-optic strain sensors.

Attractor reconstruction for non-linear systems: a methodological note

Attractor reconstruction is an important step in the process of making predictions for non-linear time-series and in the computation of certain invariant quantities used to characterize the dynamics of such series. The utility of computed predictions and invariant quantities is dependent on the accuracy of attractor reconstruction, which in turn is determined by the methods used in the reconstruction process. This paper suggests methods by which the delay and embedding dimension may be selected for a typical delay coordinate reconstruction. A comparison is drawn between the use of the autocorrelation function and mutual information in quantifying the delay. In addition, a false nearest neighbor (FNN) approach is used in minimizing the number of delay vectors needed. Results highlight the need for an accurate reconstruction in the computation of the Lyapunov spectrum and in prediction algorithms.

Fatigue of elbow flexor muscles in younger and older adults

The aim of this study was to assess differences in the behavior of fatigue-related measures of neuromuscular function between younger (n = 10; 20-35 years) and older (n = 11; >65 years) healthy adults. Measures reflecting changes in voluntary activation, neuromuscular propagation, metabolite build-up, and excitation-contraction coupling processes were taken before, during, and after a sustained maximum elbow-flexion fatigue task. We found a greater role for a failure in voluntary activation (central fatigue) in about half of the older subjects compared with none of the younger subjects to explain the decrease in force-generating capacity with sustained activity. In contrast, similar behaviors in measures reflecting changes in peripheral mechanisms were noted for the two age groups. These results point to a potential shift in fatigue mechanisms with age, with a greater contribution of central fatigue in some older adults.

The effect of lorazepam on long-term verbal recall in heavy and light social drinkers

Two experiments investigated long-term verbal memory performance in groups of 20-year-old heavy (HSDs) and light social drinkers (LSDs), in the presence and absence of a pharmacological challenge (lorazepam 2 mg). In Experiment 1 (n = 13), a verbal learning task was presented visually and it was found that lorazepam significantly impaired delayed verbal recall performance in both groups. Experiment 2 (n = 14) assessed the effect of presenting the verbal learning task in the auditory compared to the visual modality. Both groups' performance on the delayed trials of the visually presented task was reduced in the lorazepam treatment. However, in the auditory presented task, lorazepam reduced 30-min delayed recall performance in the HSDs but not in the LSDs. The differential effect of lorazepam on HSDs compared to LSDs on delayed recall performance when material is presented in the auditory modality may suggest that frequent heavy social drinking results in changes in CNS functioning.

The effect of lorazepam on memory and event-related potentials in heavy and light social drinkers

To investigate the effects of heavy social drinking on sober cognitive processing, event-related potentials were recorded from 13 heavy social drinkers and 13 light social drinkers in the presence and absence of a pharmacological challenge (i.e., lorazepam). Event-related potentials were elicited by a task requiring continuous recognition memory for visually presented words. The heavy social drinkers exhibited shorter P2 latencies than the light social drinkers and decreased P300 latencies to correctly identified "old" words (i.e., words presented previously in the task) versus "new" words. Lorazepam increased motor reaction time to correctly identified old words and produced a deficit in recognition memory only in the light social drinkers. Light social drinkers had an increased P300 latency and a larger P300 amplitude to new words in the lorazepam treatment. The differences in cognitive functioning evident between heavy and light social drinkers were reflected in event-related potential deviations and appear to indicate a tolerance in heavy social drinkers to the effects of lorazepam.

The effect of heavy social drinking on recall and event-related potentials

OBJECTIVE

It has been suggested that alcohol-related brain impairments progress on a continuum from subtle deficits in social drinkers through to end-state Korsakoff syndrome. The aim of the present study was to investigate whether heavy levels of social drinking have an adverse effect on sober cognitive functioning in young nonalcoholic adults.

METHOD

Event-related potentials (ERPs) were recorded from heavy ( > 200 gm/week) and light ( < 20 gm/week) male social drinkers under the effects of a pharmacological challenge (lorazepam-ATIVAN) and a placebo. The heavy (n = 14) and light (n = 14) social drinkers completed a free-recall task with a rare word probability of .30.

RESULTS

Analysis of the P300 component of the ERP to recalled and not-recalled words found that P300 amplitude in heavy social drinkers (HSDs) was reduced following placebo compared to the light social drinkers (LSDs). Lorazepam produced a distinctive pattern of anterograde memory deficits in both groups and reduced P300 amplitude to rare words in the LSDs compared to both the placebo treatment and the HSDs.

CONCLUSIONS

The differences in central nervous system functioning evident between HSDs and LSDs were reflected in ERP deviations in both the presence and absence of lorazepam. Even though no definite statements can be made because of the small sample size, the results appear to indicate that HSDs have an impairment in their information-processing ability in the absence of an acute inhibitory agent (e.g., alcohol or a benzodiazepine) as well as a differential response to the depressant effects of lorazepam.

P300 in heavy social drinkers: the effect of lorazepam

The P300 component of the event-related potential (ERP) and reaction time (RT) were recorded during a simulated driving task using an oddball paradigm. ERPs and RTs were recorded from heavy social drinkers (n = 11) and low social drinkers (n = 11). A pharmacological challenge (lorazepam-ATIVAN) was administered to both groups in a double-blind procedure. In both groups, P300 amplitude was reduced and RT was increased by the presence of lorazepam; however, heavy social drinkers had longer latency P300 than low social drinkers regardless of the drug condition. The P300 amplitude results are consistent with reduced information processing being induced by lorazepam, or with reduced effectiveness of the eliciting stimuli. On the other hand, the P300 latency results suggest that P300 latency may reflect deficits in information processing induced by alcohol abuse or may have preceded the alcohol abuse. The P300 latency results are consistent with heavy social drinkers occupying an early point on the hypothesized continuum of alcohol-related brain damage.

A comparison of the effect of lorazepam on memory in heavy and low social drinkers

The cognitive deficits, particularly memory impairment, observed in association with organic brain damage caused by chronic alcohol ingestion, are consistent with the profile of benzodiazepine-induced amnesia. This study examined the cognitive capabilities of a group of heavy social drinkers (n = 11) and a group of low social drinkers (n = 11) under the influence of a pharmacological challenge (lorazepam 2 mg) and a placebo treatment. Lorazepam impaired visual memory and verbal learning in both groups, but the effect of lorazepam was exacerbated in the heavy social drinkers for delayed recall of verbal material. Heavy social drinkers had lower verbal fluency scores and were less able to copy complex figures than low social drinkers whether or not the pharmacological challenge was present. Lorazepam induced deficits, in both groups, which confirmed to the classic profile of those observed in benzodiazepine-induced amnesia. The deficits, both in the absence and presence of lorazepam, shown by heavy social drinkers suggest that changes may have occurred in their brain functioning.

Long-term deficits in water maze spatial conditional alternation performance following retrohippocampal lesions in rats

The effects of large bilateral retrohippocampal lesions on long-term performance of conditional spatial alternation, incorporating a strong working memory component, were examined using a T-maze task motivated by swim-escape. The lesions, which included entorhinal cortex, subiculum, pre- and parasubiculum and invaded the molecular layer of the dentate gyrus, completely eliminated the previously acquired conditional alternation learning, and performance failed to recover with 40 days of testing. These findings support the contention that retrohippocampal structures are an important and necessary component of the neural circuitry mediating working memory.

Isolation of mitochondria and mitochondrial RNA from Crithidia fasciculata

Two methods were used to isolate mitochondria from Crithidia fasciculata. In the first method, cells were weakened by exposure to hypotonic conditions and then disrupted by blending; mitochondria were subsequently isolated using disodium 3,5-diacetoamido-2,4,6-triiodobenzoate gradients. In the second, cells were treated with digitonin before disruption; mitochondria were purified by differential centrifugation. Both preparations were examined with the electron microscope and were also shown to possess several characteristic biochemical properties of mitochondria. Kinetoplast DNA was present in the mitochondria, uncontaminated by nuclear DNA. Analysis by polyacrylamide gel electrophoresis showed two RNA components of molecular weights of 0-47 X 10(6) and 0-22 X 10(6), in addition to cytoplasmic RNA contamination. Four mitochondrial components with sedimentation coefficients of 14-6S, 11-4S, 10-1S and 9-9S were identified on sucrose density gradients. Ethidium bromide abolished the incorporation of [5-3H]uridine into the presumed mitochondrial RNA.