Irregular Sampling Research Articles

Thermal Path Reconstruction for Reinforced Concrete Under Fire

In post-fire investigation, the damage of fire-exposed concrete is usually related to the temperature time-history. This paper presents the results of an experimental investigation on reinforced concrete, cement pastes and mortars exposed to fire, aimed at identifying the benchmarks necessary to reconstruct the thermal path 15 dry core samples were obtained from a real fire damaged structure and compared to other reference dry cores collected in not damaged zones of the same structure. In addition, 16 irregular spalling samples were collected and investigated. In order to assess changes in mineralogical composition and microstructure modifications due to temperature, 20 cubic cement pastes samples and 20 prismatic mortars specimens were realized and exposed to temperature ranging from $$200\,^{\circ }\hbox {C}$$ up to $$800\,^{\circ }\hbox {C}$$ with a gradient of $$10\,^{\circ }\hbox {C}$$ /min and keeping the maximum temperature for 1 h. Optical and Scanning Electron Microscopy, X-Ray Diffraction, Thermoanalysis and MIP porosimetry along with Helium picnometry allowed to investigate the damage degree and the mineralogical changes of the concrete and other cement based materials. Calibrated Colorimetry could determine fire temperature in the different parts of the samples due to colour changes in the mineralogical phases and in the microstructure of cement materials. The absence or presence of some specific minerals (like Portlandite), the colorimetric variations and other microstructural features are markers capable of assessing the temperature reached with high accuracy. The approach and the data showcased in this work can be useful for post-fire investigations, for theoretical and numerical models tuning and to optimize the structural retrofitting.

Evaluating consumptive and nonconsumptive predator effects on prey density using field time-series data.

Determining the degree to which predation affects prey abundance in natural communities constitutes a key goal of ecological research. Predators can affect prey through both consumptive effects (CEs) and nonconsumptive effects (NCEs), although the contributions of each mechanism to the density of prey populations remain largely hypothetical in most systems. Common statistical methods applied to time-series data cannot elucidate the mechanisms responsible for hypothesized predator effects on prey density (e.g., differentiate CEs from NCEs), nor can they provide parameters for predictive models. State-space models (SSMs) applied to time-series data offer a way to meet these goals. Here, we employ SSMs to assess effects of an invasive predatory zooplankter, Bythotrephes longimanus, on an important prey species, Daphnia mendotae, in Lake Michigan. We fit mechanistic models in an SSM framework to seasonal time series (1994-2012) using a recently developed, maximum-likelihood-based optimization method, iterated filtering, which can overcome challenges in ecological data (e.g., nonlinearities, measurement error, and irregular sampling intervals). Our results indicate that B.longimanus strongly influences D.mendotae dynamics, with mean annual peak densities of B.longimanus observed in Lake Michigan estimated to cause a 61% reduction in D.mendotae population growth rate and a 59% reduction in peak biomass density. Further, the observed B.longimanus effect is most consistent with an NCE via reduced birth rates. The SSM approach also provided estimates for key biological parameters (e.g., demographic rates) and the contribution of dynamic stochasticity and measurement error. Our study therefore provides evidence derived directly from survey data that the invasive zooplankter B.longimanus is affecting zooplankton demographics and offer parameter estimates needed to inform predictive models that explore the effect of B.longimanus under different scenarios, such as climate change.

Optimal Shadowing Filter for a Positioning and Tracking Methodology with Limited Information.

Positioning and tracking a moving target from limited positional information is a frequently-encountered problem. For given noisy observations of the target’s position, one wants to estimate the true trajectory and reconstruct the full phase space including velocity and acceleration. The shadowing filter offers a robust methodology to achieve such an estimation and reconstruction. Here, we highlight and validate important merits of this methodology for real-life applications. In particular, we explore the filter’s performance when dealing with correlated or uncorrelated noise, irregular sampling in time and how it can be optimised even when the true dynamics of the system are not known.

Dynamic ocean topography of the northern Nordic seas: a comparison between satellite altimetry and ocean modeling

Abstract. The dynamic ocean topography (DOT) of the polar seas can be described by satellite altimetry sea surface height observations combined with geoid information as well as by ocean models. The altimetry observations are characterized by an irregular sampling and seasonal sea ice coverage complicating reliable DOT estimations. Models display various spatiotemporal resolutions but are limited to their computational and mathematical context and introduced forcing models. In the present paper, ALES+ retracked altimetry ranges and derived along-track DOT heights of ESA's Envisat and water heights of the Finite Element Sea Ice-Ocean Model (FESOM) are compared to investigate similarities and discrepancies. The goal of the present paper is to identify to what extent pattern and variability of the northern Nordic seas derived from measurements and model agree with each other, respectively. The study period covers the years 2003–2009. An assessment analysis regarding seasonal DOT variabilities shows good agreement and confirms the dominant impact of the annual signal in both datasets. A comparison based on estimated regional annual signal components shows 2–3 times stronger amplitudes of the observations but good agreement of the phase. Reducing both datasets by constant offsets and the annual signal reveals small regional residuals and highly correlated DOT time series (Pearson linear correlation coefficient of at least 0.67). The highest correlations can be found in areas that are ice-free and affected by ocean currents. However, differences are visible in sea-ice-covered shelf regions. Furthermore, remaining constant artificial elevations in the observational data can be attributed to an insufficient representation of the used geoid. In general, the comparison results in good agreement between simulated and altimetry-based descriptions of the DOT in the northern Nordic seas.

State‐switching continuous‐time correlated random walks

Abstract Continuous‐time models have been developed to capture features of animal movement across temporal scales. In particular, one popular model is the continuous‐time correlated random walk, in which the velocity of an animal is formulated as an Ornstein–Uhlenbeck process, to capture the autocorrelation in the speed and direction of its movement. In telemetry analyses, discrete‐time state‐switching models (such as hidden Markov models) have been increasingly popular to identify behavioural phases from animal tracking data. We propose a multistate formulation of the continuous‐time correlated random walk, with an underlying Markov process used as a proxy for the animal’s behavioural state process. We present a Markov chain Monte Carlo algorithm to carry out Bayesian inference for this multistate continuous‐time model. Posterior samples of the hidden state sequence, of the state transition rates, and of the state‐dependent movement parameters can be obtained. We investigate the performance of the method in a simulation study, and we illustrate its use in a case study of grey seal (Halichoerus grypus) tracking data. The method we present makes use of the state‐space model formulation of the continuous‐time correlated random walk, and can accommodate irregular sampling frequency and measurement error. It will facilitate the use of continuous‐time models to estimate movement characteristics and infer behavioural states from animal telemetry data.

Inferring critical transitions in paleoecological time series with irregular sampling and variable time-averaging

Many ecosystems have abruptly changed in the past and may again in the future, yet prediction and inference of mechanisms causing abrupt changes remains challenging. Critical transitions are one such mechanism, occurring when systems with alternative states cross a threshold. Such transitions are associated with a loss of resilience, often signaled by increasing variability or autocorrelation over time. However, critical transitions are difficult to distinguish from other causal mechanisms, and detection of resilience loss in sedimentary archives can be confounded by time-averaging and discontinuous sampling. Here, we simulate woodland-grassland regime shifts resulting from critical transitions and other mechanisms. We then test the diagnostic ability of two widely-used resilience indicators, standard deviation and autocorrelation time, after alterations common in sedimentary records: time averaging, discontinuous sampling, and varying sedimentation rates. Standard deviation—but not autocorrelation time—still distinguishes gradually forced critical transitions from other regime shifts when sedimentation rates are constant, and can be robust to abrupt changes in sedimentation rate. Unfortunately, shifts in standard deviation alone are rarely definitive evidence of critical transitions. Under exponential sedimentation regimes, which are common in younger upper-column sediments, neither resilience indicator is effective. Discontinuous sampling weakened the strength of resilience indicators. A demonstrative analysis of abrupt early Holocene deforestation recorded at Steel Lake, Minnesota showed signals consistent with resilience loss during early Holocene aridification. Hence, signals of resilience loss can be recovered from sedimentary archives, but efficacy varies among indicators and sedimentation regime. High-resolution and multi-proxy records remain essential to inferring causes, while process-based time series modeling such as this can be calibrated to systems of interest to explicitly test hypotheses about abrupt change causes.

Water/Oil Interfacial Self-Assembled Gold Nanoarrays Modified on Transparent Tape for In Situ Surface-Enhanced Raman Scattering

Herein, a novel in situ surface-enhanced Raman scattering (SERS) method was proposed based on the prepared SERS substrate with good flexibility, excellent optical transparency, and high SERS activity. The SERS substrate was fabricated through the Au colloids injected with an ethanol and hexane mixture to form Au monolayer film (Au MLF) on the water/oil interface and followed by transferred to the sticky side of transparent tape after hexane volatilization. Transmission electron microscopy and scanning electron microscopy revealed that Au NPs with particle sizes of 25 nm were densely assembled and evenly distributed on the flexible and transparent tape. This Au MLF/tape was directly covered on the irregular shape samples and the detection signal was collected by the laser transmitting into the tape and interacting with the analytes. The optimal SERS signal intensity was obtained by tuning the concentration of Au colloid and addition volume of the mixture of ethanol and hexane to regulate the numbers of Au NPs on water/oil interface. The SERS intensity of six sample points selected randomly show a good repeatability with RSD = 4.92% (n = 6 × 8) and satisfying uniformity with RSD of 8.6% within an area of 4.0 × 4.0 μm. Also, the obtained Au MLF/tape SERS substrate showed good detection accuracy and recovery when used to detect residual malachite green (MG) residue on the surfaces of real fish samples with detection limit of 0.04 μmol/L (S/N = 3). Therefore, the developed Au MLF/tape SERS substrate has potential applications in the in situ SERS detection.

Testing the Mantel statistic with a spatially‐constrained permutation procedure

AbstractMantel tests are widely used in ecology to assess the significance of the relationship between two distance matrices computed between pairs of samples. However, recent studies demonstrated that the presence of spatial autocorrelation in both distance matrices induced inflations of parameter estimates and type I error rates. These results also hold for partial Mantel test which is supposed to control for the spatial structures.To address the issue of spatial autocorrelation in testing the Mantel statistic, we developed a new procedure based on spatially constrained randomizations using Moran spectral randomization. A simulation study was conducted to assess the performance of this new procedure. Different scenarios were considered by manipulating the number of variables, the number of samples, the regularity of the sampling design and the level of spatial autocorrelation.As identified by previous studies, we found that Mantel statistic and its associated type I error rate are inflated in simple and partial Mantel tests when both distances matrices are spatially structured. We showed that these biases increased with the number of variables, decreased with the number of samples and were slightly lower for regular than irregular sampling. The new procedure succeeded in correcting the spurious inflations of the parameter estimates and type I error rates in any of the presented scenarios.Our results suggest that studies from several fields (e.g. genetic or community ecology) could have been overestimating the relationship between two distances matrices when both presented spatial autocorrelation. We proposed an alternative solution applicable in every field to correctly compute Mantel statistic with a fair type I error rate.

Seismic Data Quality Control and Interpolation Using Principal Component Analysis

Commonly, seismic data processing procedures, such as stacking and prestack migration, require the ability to detect bad traces/shots and restore or replace them by interpolation, particularly when the seismic observations are noisy or there are malfunctioned components in the recording system. However, currently available trace/shot interpolation methods in the spatial or Fourier domain must deal with requirements such as evenly sampled traces/shots, infinite bandwidth of the signals, and linear seismic events. In this paper, we present a novel method, termed the E-S (eigenspace seismic) method, using principal component analysis (PCA) of the seismic signal to address the issue of reliable detection or interpolation of bad traces/shots. The E-S method assumes the existence of a correlation between the observed seismic entities, such as trace or shot gathers, making it possible to estimate one of these entities from all others for interpolation or seismic quality control. It first transforms a trace (or shot) gather into an eigenspace using PCA. Then in the eigenspace, it treats every trace as a point with its loading scores of PCA as its coordinates. Simple linear, bilinear, or cubic spline 1 dimensional (1D) interpolation is used to determine PCA loading scores for any arbitrary coordinate in the eigenspace, which are then used to construct an interpolated trace for the desired position in physical space. This E-S method works with either regular or irregular sampling and, unlike various other published methods, it is well-suited for band-limited seismic records with curvilinear reflection events. We developed related algorithms and applied these to processed synthetic and offshore seismic survey data with or without simulated noises to demonstrate their performance. By comparing the interpolated and observed seismic traces, we find that the E-S method can effectively assess the quality of the trace, and restore poor quality data by interpolation. The successful processing of synthetic and real data using the E-S method presented in this approach will be widely applicable to seismic trace/shot interpolation and seismic quality control.

Self-Triggered Reduced-Attention Output Feedback Control for Linear Networked Control Systems

A major challenge in networked control systems is to reduce the use of shared communication and control resources without compromising the performance of the closed-loop system. Motivated by this challenge, this paper presents an observer-based controller with irregular sampling that reduces the control attention and guarantees closed-loop stability and global ultimate boundedness of the plant's state vector regardless of bounded input and output disturbances. The feedback control design is based on self-triggered control, whereas the observer is based on the block-form state-space representation of the plant. It is shown via analysis and simulation that the overall control law decreases the number of samples without loosing observability and closed-loop stability, reducing the utilization of communication resources.

The emission and scintillation properties of RRAT J2325−0530 at 154 MHz and 1.4 GHz

AbstractRotating Radio Transients (RRATs) represent a relatively new class of pulsar, primarily characterised by their sporadic bursting emission of single pulses on time scales of minutes to hours. In addition to the difficulty involved in detecting these objects, low-frequency ( $ \lt 300\,\text{MHz}$ ) observations of RRATs are sparse, which makes understanding their broadband emission properties in the context of the normal pulsar population problematic. Here, we present the simultaneous detection of RRAT J2325−0530 using the Murchison Widefield Array (154 MHz) and Parkes radio telescope ( $1.4\,\text{GHz}$ ). On a single-pulse basis, we produce the first polarimetric profile of this pulsar, measure the spectral index ( $\alpha={-2.2\pm 0.1}$ ), pulse energy distributions, and present the pulse rates in the context of detections in previous epochs. We find that the distribution of time between subsequent pulses is consistent with a Poisson process and find no evidence of clustering over the $\sim\!1.5\,\text{h}$ observations. Finally, we are able to quantify the scintillation properties of RRAT J2325−0530 at 1.4 GHz, where the single pulses are modulated substantially across the observing bandwidth, and show that this characterisation is feasible even with irregular time sampling as a consequence of the sporadic emission behaviour.

DtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R

The opening of large archives of satellite data such as LANDSAT, MODIS and the SENTINELs has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyze such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. In this paper we present the R package dtwSat. This package provides an implementation of the time-weighted dynamic time warping method for land cover mapping using sequence of multi-band satellite images. Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant results in time series analysis. Package dtwSat is available from the Comprehensive R Archive Network (CRAN) and contributes to making methods for satellite time series analysis available to a larger audience. The package supports the full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualizing and assessing the results.

Application of Wavelets for Evaluation of Soft Sensors under Irregular Sampling of Output Measurements

Abstract The problem of evaluating soft sensors for plants with irregular output measurement time is considered. In order to find the relationship between inputs and output, the wavelet-transformed inputs are integrated into the regression model framework. A suitable procedure for wavelet type selection is discussed. An example is provided to illustrate successful application of the proposed approach and an industrial case study is given for soft sensor evaluation of a reactive distillation process.

Freehand, Agile, and High-Resolution Imaging With Compact mm-Wave Radar

The aim of this paper is to present a freehand scanning system with a compact mm-wave radar. In order to achieve high-resolution images, the system exploits the free movements of the radar to create a synthetic aperture. However, in contrast to conventional synthetic aperture radar (SAR), in which canonical acquisition surfaces (e.g., planes or cylinders) are used, the system allows for a given tolerance compatible with real hand-made trajectories. Moreover, different techniques are studied to compensate for the impact of irregular sampling to reduce the artifacts in the image. As a result, real-time scanning can be readily performed even by inexperienced users. The scanning system, comprising a commercial motion capture system and an mm-wave module, can be easily deployed and calibrated. Several results involving different objects are shown to illustrate the performance of the system.

Learning Facial Expressions with 3D Mesh Convolutional Neural Network

Making machines understand human expressions enables various useful applications in human-machine interaction. In this article, we present a novel facial expression recognition approach with 3D Mesh Convolutional Neural Networks (3DMCNN) and a visual analytics-guided 3DMCNN design and optimization scheme. From an RGBD camera, we first reconstruct a 3D face model of a subject with facial expressions and then compute the geometric properties of the surface. Instead of using regular Convolutional Neural Networks (CNNs) to learn intensities of the facial images, we convolve the geometric properties on the surface of the 3D model using 3DMCNN. We design a geodesic distance-based convolution method to overcome the difficulties raised from the irregular sampling of the face surface mesh. We further present interactive visual analytics for the purpose of designing and modifying the networks to analyze the learned features and cluster similar nodes in 3DMCNN. By removing low-activity nodes in the network, the performance of the network is greatly improved. We compare our method with the regular CNN-based method by interactively visualizing each layer of the networks and analyze the effectiveness of our method by studying representative cases. Testing on public datasets, our method achieves a higher recognition accuracy than traditional image-based CNN and other 3D CNNs. The proposed framework, including 3DMCNN and interactive visual analytics of the CNN, can be extended to other applications.

TomoSAR Imaging for the Study of Forested Areas: A Virtual Adaptive Beamforming Approach

Among the objectives of the upcoming space missions Tandem-L and BIOMASS, is the 3-D representation of the global forest structure via synthetic aperture radar (SAR) tomography (TomoSAR). To achieve such a goal, modern approaches suggest solving the TomoSAR inverse problems by exploiting polarimetric diversity and structural model properties of the different scattering mechanisms. This way, the related tomographic imaging problems are treated in descriptive regularization settings, applying modern non-parametric spatial spectral analysis (SSA) techniques. Nonetheless, the achievable resolution of the commonly performed SSA-based estimators highly depends on the span of the tomographic aperture; furthermore, irregular sampling and non-uniform constellations sacrifice the attainable resolution, introduce artifacts and increase ambiguity. Overcoming these drawbacks, in this paper, we address a new multi-stage iterative technique for feature-enhanced TomoSAR imaging that aggregates the virtual adaptive beamforming (VAB)-based SSA approach, with the wavelet domain thresholding (WDT) regularization framework, which we refer to as WAVAB (WDT-refined VAB). First, high resolution imagery is recovered applying the descriptive experiment design regularization (DEDR)-inspired reconstructive processing. Next, the additional resolution enhancement with suppression of artifacts is performed, via the WDT-based sparsity promoting refinement in the wavelet transform (WT) domain. Additionally, incorporation of the sum of Kronecker products (SKP) decomposition technique at the pre-processing stage, improves ground and canopy separation and allows for the utilization of different better adapted TomoSAR imaging techniques, on the ground and canopy structural components, separately. The feature enhancing capabilities of the novel robust WAVAB TomoSAR imaging technique are corroborated through the processing of airborne data of the German Aerospace Center (DLR), providing detailed volume height profiles reconstruction, as an alternative to the competing non-parametric SSA-based methods.

An overdetermined linear equations-based time calibration method for fast sampling ASICs

A novel time calibration method for waveform sampling application specific integrated circuits (ASICs) based on switched capacitor arrays (SCAs) is proposed in this paper. Precision timing extraction using SCA ASICs has been proved to be a promising technique in many high energy physics experiments. However, such ASICs suffer from irregular sampling intervals caused by process variations. Therefore, careful calibration is required to improve the time resolution of such systems. We evaluated the limitation of a popular method using the proportionality between voltage amplitude and sampling interval of adjacent switched-capacitor cells responding to either a sawtooth wave or a sine wave. The new time calibration method presented here utilizes the relationship between sampling intervals and the known input signal period to establish overdetermined linear equations, and the roots of these equations correspond to the actual sampling intervals. We applied this new method to a pulse timing test with an ASIC designed by our team, and the test results indicate that the new time calibration method is effective.

On Irregular Sampling and Interpolation in Bernstein Spaces

Sharp estimates of the sampling and interpolation constants in spaces of polynomials are obtained. These estimates are used to deduce asymptotically sharp estimates of the sampling and interpolation constants for Bernstein spaces as the density of a sampling set approaches the critical value.

Least-squares Kirchhoff migration with non-smooth regularisation strategy for subsurface imaging

During the past several decades, many types of wave-equation migration methods arise for subsurface structure imaging. The classical Kirchhoff migration, however, is still widely adopted in the petroleum industry owing to its flexibility and computational efficiency. In constant density isotropic acoustic media, a basic assumption of the Kirchhoff migration is that every point of the subsurface model is supposed to be a diffractor which scatters wavefield energy to every direction, and hence collecting the scattered energy of all directions is the basic requirement for focusing the diffractor. Factors influencing the final image quality include incomplete data acquisition, multipathing from the surface to the imaging point, and insufficient illumination under complex overburden. All these factors can be theoretically taken into account in the migration weighting coefficient. However, computation of the weighting coefficient is hard work. In view of this difficulty, a fast regularising least-squares Kirchhoff migration algorithm is presented in this paper. It not only accounts for the irregular and incomplete data sampling (e.g. limited recording aperture, coarse sampling and acquisition gaps), but also compensates for the anomalous ray coverage and multipathing problem except for the shadow zone in the media. For the purpose of attenuating migration artefacts and providing a clear and accurate image of subsurface reflectivity, regularisation strategies are applied. The classical regularisation strategy may easily lead to over-regularisation or insufficient regularisation; we try to balance these two effects in this paper. The method is called the hybrid regularisation which incorporates smoothing and non-smoothing scale operators. The algorithm is implemented using a fast gradient decent solution method based on the Rayleigh quotient being used. Numerical experiments show that this hybrid regularisation method is powerful in handling the sparsity and smoothness of the model parameters.

Shot repetition: An alternative seismic blending code in marine acquisition

In blended seismic acquisition, or simultaneous source seismic acquisition, source encoding is essential at the acquisition stage to allow for separation of the blended sources at the processing stage. In land seismic surveys, the vibroseis sources may be encoded with near-orthogonal sweeps for blending. In marine seismic surveys, the sweep type of source encoding is difficult because the main source type in marine seismic exploration is the air-gun array, which has an impulsive character. Another issue in marine streamer seismic data acquisition is that the spatial source sampling is generally coarse. This hinders the deblending performance of algorithms based on the random time delay blending code that inherently requires a dense source sampling because they exploit the signal coherency in the common-receiver domain. We have developed an alternative source code called shot repetition that exploits the impulsive character of the marine seismic source in blending. This source code consists of repeated spikes of ones and can be realized physically by activating a broadband impulsive source more than once at (nearly) the same location. Optimization of the shot-repetition type of blending code was done to improve the deblending performance. As a result of using shot repetition, the deblending process can be carried out in individual shot gathers. Therefore, our method has no need for a regular dense source sampling: It can cope with irregular sparse source sampling; it can help with real-time data quality control. In addition, the use of shot repetition is beneficial for reducing the background noise in the deblended data. We determine the feasibility of our method on numerical examples.