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Motivated by the use of unmanned aerial vehicles (UAVs) for buried landmine detection, we consider the spectral classification of dispersive point targets below a rough air-soil interface. The target location can be estimated using a previously developed method for ground-penetrating synthetic aperture radar involving principal component analysis for ground bounce removal and Kirchhoff migration. For the classification problem, we use the approximate location determined from this imaging method to recover the spectral characteristics of the target over the system bandwidth. For the dispersive point target we use here, this spectrum corresponds to its radar cross section (RCS). For a more general target, this recovered spectrum is a proxy for the frequency dependence of the RCS averaged over angles spanning the synthetic aperture. The recovered spectrum is noisy and exhibits an overall scaling error due to modeling errors. Nonetheless, by smoothing and normalizing this recovered spectrum, we compare it with a library of precomputed normalized spectra in a simple multiclass classification scheme. Numerical simulations in two dimensions validate this method and show that this spectral estimation method is effective for target classification. 

Motivated by applications in unmanned aerial based ground penetrating radar for detecting buried landmines, we consider the problem of imaging small point like scatterers situated in a lossy medium below a random rough surface. Both the random rough surface and the absorption in the lossy medium significantly impede the target detection and imaging process. Using principal component analysis we effectively remove the reflection from the air‐soil interface. We then use a modification of the classical synthetic aperture radar imaging functional to image the targets. This imaging method introduces a user‐defined parameter,δ, which scales the resolution by allowing for target localization with sub wavelength accuracy. Numerical results in two dimensions illustrate the robustness of the approach for imaging multiple targets. However, the depth at which targets are detectable is limited due to the absorption in the lossy medium. 

Abstract Mouse tracking is an important source of data in cognitive science. Most contemporary mouse tracking studies use binary-choice tasks and analyze the curvature or velocity of an individual mouse movement during an experimental trial as participants select from one of the two options. However, there are many types of mouse tracking data available beyond what is produced in a binary-choice task, including naturalistic data from web users. In order to utilize these data, cognitive scientists need tools that are robust to the lack of trial-by-trial structure in most normal computer tasks. We use singular value decomposition (SVD) and detrended fluctuation analysis (DFA) to analyze whole time series of unstructured mouse movement data. We also introduce a new technique for describing two-dimensional mouse traces as complex-valued time series, which allows SVD and DFA to be applied in a straightforward way without losing important spatial information. We find that there is useful information at the level of whole time series, and we use this information to predict performance in an online task. We also discuss how the implications of these results can advance the use of mouse tracking research in cognitive science. 

We investigate perceptions of tweets marked with the #BlackLivesMatter and #AllLivesMatter hashtags, as well as how the presence or absence of those hashtags changed the meaning and subsequent interpretation of tweets in U.S. participants. We found a strong effect of partisanship on perceptions of the tweets, such that participants on the political left were more likely to view #AllLivesMatter tweets as racist and offensive, while participants on the political right were more likely to view #BlackLivesMatter tweets as racist and offensive. Moreover, we found that political identity explained evaluation results far better than other measured demographics. Additionally, to assess the influence of hashtags themselves, we removed them from tweets in which they originally appeared and added them to selected neutral tweets. Our results have implications for our understanding of how social identity, and particularly political identity, shapes how individuals perceive and engage with the world. 

We introduce a dispersive point target model based on scattering by a particle in the far-field. The synthetic aperture imaging problem is then expanded to identify these targets and recover their locations and frequency dependent reflectivities. We show that Kirchhoff migration (KM) is able to identify dispersive point targets in an imaging region. However, KM predicts target locations that are shifted in range from their true locations. We derive an estimate for this range shift for a single target. We also show that because of this range shift we cannot recover the complex-valued frequency dependent reflectivity, but we can recover its absolute value and hence the radar cross-section (RCS) of the target. Simulation results show that we can detect, recover the approximate location, and recover the RCS for dispersive point targets thereby opening opportunities to classifying important differences between multiple targets such as their sizes or material compositions. 

Abstract Photocoagulation of blood vessels offers unambiguous advantages to current radiofrequency approaches considering the high specificity of blood absorption at available laser wavelengths (e.g., 532 nm and 1.064 µm). Successful treatment of pediatric vascular lesions, such as port-wine stains requiring microvascular hemostasis, has been documented. Although laser treatments have been successful in smaller diameter blood vessels, photocoagulation of larger sized vessels is less effective. The hypothesis for this study is that a primary limitation in laser coagulation of large diameter blood vessels (500–1000 µm) originates from shear stress gradients associated with higher flow velocities along with temperature-dependent viscosity changes. Laser (1.07 µm) coagulation of blood vessels was tested in the chicken chorio-allantoic membrane (CAM). A finite element model is developed that includes hypothetical limitations in laser coagulation during irradiation. A protocol to specify laser dosimetry is derived from OCT imaging and angiography observations as well as finite element model results. Laser dosimetry is applied in the CAM model to test the experimental hypothesis that blood shear stress and flow velocity are important parameters for laser coagulation and hemostasis of large diameter blood vessels (500–1000 µm). Our experimental results suggest that shear stress and flow velocity are fundamental in the coagulation of large diameter blood vessels (500–1000 µm). Laser dosimetry is proposed and demonstrated for successful coagulation and hemostasis of large diameter CAM blood vessels. 

The study of scattering by a high aspect ratio particle has important applications in sensing and plasmonic imaging. To illustrate the effect of particle’s narrowness (that can be related to parity properties) and the need for adapted methods (in the context of boundary integral methods), we consider the scattering by a penetrable, high aspect ratio ellipse. This problem highlights the main challenge and provides valuable insights to tackle general high aspect ratio particles. We find that boundary integral operators are nearly singular due to the collapsing geometry to a line segment. We show that these nearly singular behaviors lead to qualitatively different asymptotic behaviors for solutions with different parities. Without explicitly taking this into account, computed solutions incur large errors. We introduce the Quadrature by Parity Asymptotic eXpansions (QPAX) that effectively and efficiently addresses these issues. We demonstrate the effectiveness of QPAX through several numerical examples. 

Abstract We have recently introduced a modification of the multiple signal classification method for synthetic aperture radar. This method incorporates a user‐defined parameter,ϵ, that allows for tunable quantitative high‐resolution imaging. However, this method requires relatively large single‐to‐noise ratios (SNR) to work effectively. Here, we first identify the fundamental mechanism in that method that produces high‐resolution images. Then we introduce a modification to Kirchhoff Migration (KM) that uses the same mechanism to produce tunable, high‐resolution images. This modified KM method can be applied to low SNR measurements. We show simulation results that demonstrate the features of this method.