Deployment Geometry Research Articles

Indoor position estimation using angle of arrival measurements: An efficient multi-anchor approach with outlier rejection

This paper addresses the inherent nonlinear problem in indoor position estimation utilizing Angle of Arrival (AoA) measurements. We investigate the influence of deployment geometry on system performance through both analytical methods and Monte-Carlo simulations, shedding light on the limitations of single and multi-anchor setups and underscoring the imperative for advanced multi-anchor localization methods. In response to this problem, we propose a multi-anchor solution with outlier rejection that efficiently considers the nonlinearity of the model. For each anchor, our approach approximates the probability distribution of the node position by leveraging a geometrically-derived unscented transformation of AoA estimates. The approximations are then integrated into a majority voting scheme, effectively eliminating outliers induced by multipath or other adverse effects. To derive the final enhanced position estimate, Bayesian inference is applied to fuse the selected information. Finally, the efficacy of the solution is validated conducting a comparative analysis against commonly used approaches in a real-world Bluetooth indoor localization system. The results obtained solely from high-level angular measurements underscore the practicality, robustness and high accuracy of the proposal.

Experimental Evaluation Performance of Ultrasonic Multisensor Deployment Geometries for a Data Acquisition System

Experimental Evaluation Performance of Ultrasonic Multisensor Deployment Geometries for a Data Acquisition System

Geometric characteristics, deployment mechanisms, and digital fabrication methods of a free-form deployable membrane system based on CNC-knitted fabrics and CNC-bent frames

ABSTRACT Deployable membrane structures have played a vital role in creating flexible and adaptable urban and architectural spaces. However, the geometries of deployable membrane structures are often confined to symmetrical and regular shapes, due to the complex interdependencies between structure, material, motion, and geometry. To overcome this challenge, this research proposes a new category of deployable membrane structures that enables a broad range of deployment geometries, including free forms. The proposed system facilitates the transformation of arbitrary free-form surfaces into structural models, simplifies kinematic analysis for free-form deployment, and reduces structural calculations for free-form deployable structures. This study systematically investigated the proposed deployable membrane system in terms of geometric characteristics, deployment mechanisms, and digital manufacturing methods, including investigations of the generation method and geometric categories, summarizations of the folding mode and foldability conditions, and explorations of the CNC manufacturing logic and the G-code generation method. Finally, a full-scale physical prototype is studied to validate the proposed system and related methods.

Multichannel coherence migration grid search (MCMgs) in locating microseismic events recorded by a surface array

SUMMARY Microseismic monitoring has been used in geo-energy related activities, such as shale-gas exploitation, mining, deep geothermal exploitation, geotechnical and structural engineering, for detecting and locating fractures, rock failures and micro-earthquakes. The success of microseismic monitoring depends on reliable detection and location of the recorded microseismicity. Multichannel coherence migration (MCM) is a detection and location waveform migration-based approach which does not require phase picking, identification and association and performs well on noisy data. Its caveat is a high computational cost, which impedes its application of MCM on large data sets or for real-time monitoring. To address this issue, we propose an improved approach, the multichannel coherence migration grid search (MCMgs), by introducing an adaptive grid optimization technique. Based on results from synthetic and real data, we show that MCMgs reduces the computation time up to 64 times. In addition, MCMgs generates multiple maximum coherence values with various grid sizes instead of a single (maximum) coherence value that links to a single gridpoint and size, thus resulting in more accurate locations. Our simulation results on different deployment geometries demonstrate that MCMgs is effective even with a small number of recordings available—a minimum of seven. We conduct a sensitivity analysis to assess how the detectability of events is affected by the spatial arrangement of the deployed monitoring array. If a limited number of seismometers are available for deployment, our analysis favours a patch array deployment geometry. We show that 12 seismometers deployed at a patch array geometry can have similar detection and localization capability as a large rectangular array of more than 100 seismometers but at a much lower computational and deployment cost.

From strain to displacement: using deformation to enhance distributed acoustic sensing applications

SUMMARY Over a period of less than a decade, distributed acoustic sensing (DAS) has become a well-established technology in seismology. For historical and practical reasons, DAS manufacturers usually provide instruments that natively record strain (rate) as the principal measurement. While at first glance strain recordings seem related to ground motion waveforms (displacement, velocity and acceleration), not all the seismological tools developed over the past century (e.g. magnitude estimation, seismic beamforming, etc.) can be readily applied to strain data. Notably, the directional sensitivity of DAS is more limited than conventional particle motion sensors, and DAS experiences an increased sensitivity to slow waves, often highly scattered by the subsurface structure and challenging to analyse. To address these issues, several strategies have been already proposed to convert strain rate measurements to particle motion. In this study, we focus on strategies based on a quantity we refer to as ‘deformation’. Deformation is defined as the change in length of the cable and is closely related to displacement, yet both quantities differ from one another: deformation is a relative displacement measurement along a curvilinear path. We show that if the geometry of the DAS deployment is made of sufficiently long rectilinear sections, deformation can be used to recover the displacement without the need of additional instruments. We validate this theoretical result using full-waveform simulations and by comparing, on a real data set, the seismic velocity recovered from DAS with that recorded by collocated seismometers. The limitations of this approach are discussed, and two applications are shown: enhancing direct P-wave arrivals and simplifying the magnitude estimation of seismic events. Converted displacement provides better sensitivity to high velocity phases, improves broadside response and permits the direct application of conventional seismological tools that are less effective when applied to strain (rate) data.

Optimizing Simultaneous Water Level and Wave Measurements From Multi‐GNSS Interferometric Reflectometry Over 1 Year at an Exposed Coastal Site

AbstractGlobal Navigation Satellite System Interferometric Reflectometry (GNSS‐IR) measures water level using the interference pattern in signal‐to‐noise ratio (SNR) from direct and reflected signals off the sea surface, retrieved from standard geodetic antennas. Significant wave height is also measured by determining the satellite elevation angles where reflections become incoherent. We developed an approach for standard geodetic antennas to simultaneously measure sea levels and waves using a criterion for identifying coherent reflections. We tested the method at an exposed coastal environment at the E.B. Scripps Memorial Pier in California. The 1‐year test captures a broad range of sea states and benefits from several co‐located standard oceanographic sensors. By including GPS, Galileo, and GLONASS observations, the retrieval rate increases by a factor of ∼2 over GPS alone. Uncorrected water levels are estimated with a root‐mean‐square (RMS) error of 18.2 cm with respect to a conventional tide gauge. We further developed a simplified correction to remove the effect of phenomena altering the SNR oscillatory frequency and phase, which reduces RMS errors to 9.4 cm. We estimate the significant wave height with 15 cm RMS error with respect to a traditional wave gauge. The method, however, requires a short calibration. We find the wave height errors increase abruptly beyond a fixed limit when high waves are present, that may be a result of the particular deployment geometry. With this caveat, the technology could be useful to deploy in under‐sampled regions affected by compounded coastal hazards, such as in areas affected by tropical cyclones and flooding.

Machine-Learning-Accelerated Simulations for the Design of Airbag Constrained by Obstacles at Rest.

Predicting airbag deployment geometries is an important task for airbag and vehicle designers to meet safety standards based on biomechanical injury risk functions. This prediction is also an extraordinarily complex problem given the number of disciplines and their interactions. State-of-the-art airbag deployment geometry simulations (including time history) entail large, computationally expensive numerical methods such as finite element analysis (FEA) and computational fluid dynamics (CFD), among others. This complexity results in exceptionally large simulation times, making thorough exploration of the design space prohibitive. This paper proposes new parametric simulation models which drastically accelerate airbag deployment geometry predictions while maintaining the accuracy of the airbag deployment geometry at reasonable levels; these models, called herein machine learning (ML)-accelerated models, blend physical system modes with data-driven techniques to accomplish fast predictions within a design space defined by airbag and impactor parameters. These ML-accelerated models are evaluated with virtual test cases of increasing complexity: from airbag deployments against a locked deformable obstacle to airbag deployments against free rigid obstacles; the dimension of the tested design spaces is up to six variables. ML training times are documented for completeness; thus, airbag design explorers or optimization engineers can assess the full budget for ML-accelerated approaches including training. In these test cases, the ML-accelerated simulation models run three orders of magnitude faster than the high-fidelity multi-physics methods, while accuracies are kept within reasonable levels within the design space.

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series

Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach need to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise), and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 ∘C at 5 cm below the ground surface, temperature gradients > 2 ∘C m−1, and a sliding time window of at least 4 d the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced precisely (with accuracy to a few millimeters), with a level of noise ≤ 0.02 ∘C, and with a bias defined by a standard deviation ≤ 0.01 ∘C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in bulk density or water content.

Exploiting perturbed and coalescent anchor node geometry with semidefinite relaxation for sensor network localization

Localization in wireless sensor networks (WSNs) suffer from performance issues whenever the anchor nodes (which are aware of their location) are subjected to motion from their usual position. Moreover, accurate localization demands more anchor nodes which is a scarce resource and needs to be used judiciously. In the current work, we propose a novel framework that addresses these two prime concerns by harnessing the inter relationship of anchor node geometry. For an unknown source node surrounded by anchor nodes, the anchors lying on the inner boundary of the deployment geometry may be carrying closely related information about source node, leading to redundancy and inefficient utilization. By anticipating the level of correlation between these anchors, localization can be made more frugal. Rigorous mathematical analysis is carried out to derive lower bounds on estimated locations. Based on fisher information from two proposed models, a convex estimation objective function is formulated using semidefinite programming (SDP) approach to validate the theoretical proceedings. Based on the findings, the proposed method is able to successfully extract useful information about the unknown source node location with limited number of anchor nodes, hence achieving superior localization.

Simulation framework for activity recognition and benchmarking in different radar geometries

Radar micro-Doppler signatures have been proposed for human monitoring and activity classification for surveillance and outdoor security, as well as for ambient assisted living in healthcare-related applications. A known issue is the performance reduction when the target is moving tangentially to the line of sight of the radar. Multiple techniques have been proposed to address this, such as multistatic radar and to some extent, interferometric (IF) radar. A simulator is presented to generate synthetic data representative of eight radar systems (monostatic, circular multistatic and in-line multistatic [IM] and IF) to quantify classification performances as a function of aspect angles and deployment geometries. This simulator allows an unbiased performance evaluation of different radar systems. Six human activities are considered with signatures originating from motion-captured data of 14 different subjects. The classification performances are analysed as a function of aspect angles ranging from 0° to 90° per activity and overall. It demonstrates that IF configurations are more robust than IM configurations. However, IM performs better at angles below 55° before IF configurations take over.

Determination of the seismic signatures of landslides in soft soils: A methodology based on a field scale shear box

We present a novel field experimental setup that can be used for studying the characteristics of landslide seismicity. The setup consists of a concrete, filled with soil, cylinder that moves along a surficial soil corridor. The emitted seismic signals are due to soil friction. The cylinder acts as an upscaled sheer-box allowing control over a number of parameters: the magnitude of normal stress on the failure plane, the degree of saturation and the type of soil. This allows for the simulation of soil friction within, or between, different geological layers under different conditions. Results are site specific, but can be easily reproduced for any geological environment. We validate this methodology by comparing the spectral characteristics of the signals emitted by the movement of the cylinder to those induced by a controlled failure of a 2.5 m high vertical face at a nearby site with very similar geology. We find a very good agreement between the two. This methodology can be used as a site investigation tool for the optimization of the deployment geometry of seismic networks for landslide monitoring, as well as to inform machine learning algorithms on automatic detection and classification of recorded signals during seismic monitoring of landslides.

Optimization of street canyon outdoor channel deployment geometry for mmWave 5G communication

Millimeter Wave (mmWave) channel study is important in 5G communication to assess novel technological solutions in realistic environments. Thus, it is vital to develop reliable channel models integrating the effects of mmWave atmospheric absorption, foliage loss, and the directionality of high gain antenna systems used in mmWave links. In this paper, a design is presented that combines mmWave channel modeling with machine learning for the efficient management of environment geometry and channel specification in a 5G urban microcell (UMi) street canyon (SC) outdoor channel. Accordingly, we first investigate the channel characteristics of mmWave line of sight (LOS) and non-line of sight (NLOS) directional outdoor links in various reference cases. The analysis is conducted for the backhaul and cellular access cases using a low complexity custom channel model based on ray tracing. Additional modeling components such as antenna directionality, oxygen absorption, and foliage loss modeling are also included to enhance accuracy and generality. It is observed that the estimated channel path loss is largely dependent on SC deployment parameters due to the antenna directionality combined with the small wavelength of mmWaves, and the use of correct values for them is essential to obtain optimal channel performance. Hence, we apply a metaheuristic algorithm called particle swarm optimization (PSO) for the optimal placement of position and deployment parameters of SC channel in a mmWave communication system to yield the best channel path loss.

Actual TDoA-based augmentation system for enhancing cybersecurity in ADS-B

Currently, cybersecurity and cyber resilience are emerging and urgent issues in next-generation air traffic surveillance systems, which depend primarily on Automatic Dependent Surveillance-Broadcast (ADS-B) owing to its low cost and high accuracy. Unfortunately, ADS-B is prone to cyber-attacks. To verify the ADS-B positioning data of aircraft, multilateration (MLAT) techniques that use Time Differences of Arrivals (TDoAs) have been proposed. MLAT exhibits low accuracy in determining aircraft positions. Recently, a novel technique using a theoretically calculated TDoA fingerprint map has been proposed. This technique is less dependent on the geometry of sensor deployment and achieves better accuracy than MLAT. However, the accuracy of the existing technique is not sufficiently precise for determining aircraft positions and requires a long computation time. In contrast, this paper presents a reliable surveillance framework using an Actual TDoA-Based Augmentation System (ATBAS). It uses historically recorded real-data from the OpenSky network to train our TDoA fingerprint grid network. Our results show that the accuracy of the proposed ATBAS framework in determining the aircraft positions is significantly better than those of the MLAT and expected TDoA techniques by 56.93% and 48.86%, respectively. Additionally, the proposed framework reduced the computation time by 77% compared with the expected TDoA technique.

Optimizing Sensor Configurations for the Detection of Slow‐Slip Earthquakes in Seafloor Pressure Records, Using the Cascadia Subduction Zone as a Case Study

AbstractWe present seafloor pressure records from the Cascadia Subduction Zone, alongside oceanographic and geophysical models, to evaluate the spatial uniformity of bottom pressure and optimize the geometry of sensor networks for resolving offshore slow‐slip transients. Seafloor pressure records from 2011 to 2015 show that signal amplitudes are depth‐dependent, with tidally filtered and detrended root‐mean‐squares of <2 cm on the abyssal plain and >6 cm on the continental shelf. This is consistent with bottom pressure predictions from circulation models and comparable to deformation amplitudes from offshore slow slip observed in other subduction zones. We show that the oceanographic component of seafloor pressure can be reduced to ≤1‐cm root‐mean‐square by differencing against a reference record from a similar depth, under restrictions that vary with depth. Instruments at 100–250 m require depths matched within 10 m at separations of <100 km, while locations deeper than 1,400 m are broadly comparable over separations of at least 300 km. Despite the significant noise reduction from this method, no slow slip was identified in the dataset, possibly due to poor spatiotemporal instrument coverage, nonideal deployment geometry, and limited depth‐matched instruments. We use forward predictions of deformation from elastic half‐space models and hindcast pressure from circulation models to generate synthetic slow‐slip observational records and show that a range of slip scenarios produce resolvable signals under depth‐matched differencing. For future detection of offshore slow slip in Cascadia, we recommend a geometry in which instruments are deployed along isobaths to optimize corrections for oceanographic signals.

Uplink Interference Mitigation Techniques for Coexistence of 5G Millimeter Wave Users With Incumbents at 70 and 80 GHz

The millimeter wave spectra at 71-76GHz (70GHz) and 81-86GHz (80GHz) have the potential to endow fifth-generation new radio (5G-NR) with mobile connectivity at gigabit rates. However, a pressing issue is the presence of incumbent systems in these bands, which are primarily point-to-point fixed stations (FSs). In this paper, we first identify the key properties of incumbents by parsing databases of existing stations in major cities to devise several modeling guidelines and characterize their deployment geometry and antenna specifications. Second, we develop a detailed uplink interference framework to compute the aggregate interference from outdoor 5G-NR users into FSs. We then present several case studies in dense populated areas, using actual incumbent databases and building layouts. Our simulation results demonstrate promising 5G coexistence at 70GHz and 80GHz as the majority of FSs experience interference well below the noise floor thanks to the propagation losses in these bands and the deployment geometry of the incumbent and 5G systems. For the few FSs that may incur higher interference, we propose several passive interference mitigation techniques such as angular-based exclusion zones and spatial power control. Simulation results show that the techniques can effectively protect FSs, without tangible degradation of the 5G coverage.

Spatial Channel Sounding Based on Bistatic Synthetic Aperture Radar Principles

In this paper, we propose a simplified spatial channel sounding method by utilizing bistatic synthetic aperture radar (BiSAR) principles. Despite the different deployment geometries compared with a conventional BiSAR system, the feasibility of the approach is established by 1) the proposed method achieves a better spatial resolution than conventional directional channel sounders and 2) reconstruction algorithms based on time-domain back-projection in conjunction with a digital elevation model provide a good imaging performance and are suitable for reconstructing the spatial distribution of scatterers. Simulations of a high-speed rail (HSR) scenario demonstrate that the estimated power delay profiles (PDPs) and power angle profiles (PAPs) are close to the actual values.

Angle Feedback for NOMA Transmission in mmWave Drone Networks

In this paper, we consider an unmanned aerial vehicle (UAV) based wireless network using non-orthogonal multiple access (NOMA) transmission in millimeter-wave frequencies to deliver broadband data in a spectrally efficient fashion at hotspot scenarios. The necessity for the NOMA transmitter to gather information on user channel quality becomes a major drawback in practical deployments. We therefore consider various limited feedback schemes for NOMA transmission, to relieve the complexity of tracking and feeding back the full channel state information (CSI) of the users. In particular, through beamforming we allow NOMA to exploit the space domain, and hence the user angle emerges as a promising (yet novel) limited feedback scheme. We show that as the user region for NOMA transmission gets wider, the users become more distinctive at the transmitter side with respect to their angles, making user angle feedback a better alternative than distance feedback in such scenarios. We rigorously derive and analyze the outage sum rate performance for NOMA transmission considering various user ordering strategies involving full CSI, angle, and distance feedback schemes. Our analytical results for NOMA outage sum rates using those feedback schemes match closely with simulations, and provide useful insights on properly choosing a limited feedback scheme for different deployment geometries and operating configurations.

Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar

Passive ocean color remote sensing has revolutionized our ability to quantify the horizontal distribution of phytoplankton across the ocean surface. Lidar technology can provide remotely sensed estimates of the vertical distribution of optical properties and suspended particles in natural waters, significantly improving our ability to model upper ocean biogeochemical processes. In this study, we constructed and deployed a ship-based lidar system to measure laser backscattering and linear depolarization profiles in the coastal Mid-Atlantic ranging from estuarine to oceanic conditions, and across the Gulf of Maine (GoM). The instrument identified layers with different backscattering intensity in stratified waters of the coastal Mid-Atlantic and produced system attenuation coefficients (Ksys) approximating the absorption coefficient (apg) of particulate + dissolved matter. The linear depolarization ratio was strongly related to in situ measurements of the particulate backscattering ratio (bbp/bp). Measurements of Ksys and linear depolarization made across the GoM corresponded well with simultaneous in situ observations performed aboard the M/V Nova Star and by an autonomous glider deployed along the transect. The relationship between Ksys and apg differed between sampling schemes, likely due to differences in the deployment geometries (e.g., height, nadir angle). These results support the proposition that ship-based lidar systems can provide a powerful tool for remotely measuring the vertical distributions of optical properties and geochemical constituents (e.g., particles) in the upper ocean. Continued development of compact lidar systems for deployment on ships, moorings, and autonomous platforms has the potential to greatly improve the quality and scope of a variety of oceanographic investigations.

A <italic>k</italic>-NN-Based Localization Approach for Crowdsourced Air Traffic Communication Networks

In this paper, we argue that current state-of-the-art methods of aircraft localization such as multilateration are insufficient, in particular for modern crowdsourced air traffic networks with random, unplanned deployment geometry. We propose an alternative, a grid-based localization approach using the k -nearest neighbor ( k -NN) algorithm, to deal with the identified shortcomings. Our proposal does not require any changes to the existing air traffic protocols and transmitters, and is easily implemented using only low-cost, commercial-off-the-shelf hardware. Using an algebraic multilateration algorithm for comparison, we evaluate our approach using real-world flight data collected with our collaborative sensor network OpenSky. We quantify its effectiveness in terms of aircraft location accuracy, surveillance coverage, and the verification of false position data. Our results show that the grid-based k -NN approach can increase the effective air traffic surveillance coverage compared to multilateration by a factor of up to 2.5. As it does not suffer from dilution of precision to the same extent, it is more robust in noisy environments and performs better in pre-existing, unplanned receiver deployments. We further find that the mean aircraft location accuracy can be increased by up to 41% in comparison with multilateration while also being able to pinpoint the origin of potential spoofing attacks conducted from the ground.

The Fluid Mechanics of Transcatheter Heart Valve Leaflet Thrombosis in the Neosinus.

Transcatheter heart valve (THV) thrombosis has been increasingly reported. In these studies, thrombus quantification has been based on a 2-dimensional assessment of a 3-dimensional phenomenon. Postprocedural, 4-dimensional, volume-rendered CT data of patients with CoreValve, Evolut R, and SAPIEN 3 transcatheter aortic valve replacement enrolled in the RESOLVE study (Assessment of Transcatheter and Surgical Aortic Bioprosthetic Valve Dysfunction With Multimodality Imaging and Its Treatment with Anticoagulation) were included in this analysis. Patients on anticoagulation were excluded. SAPIEN 3 and CoreValve/Evolut R patients with and without hypoattenuated leaflet thickening were included to study differences between groups. Patients were classified as having THV thrombosis if there was any evidence of hypoattenuated leaflet thickening. Anatomic and THV deployment geometries were analyzed, and thrombus volumes were computed through manual 3-dimensional reconstruction. We aimed to identify and evaluate risk factors that contribute to THV thrombosis through the combination of retrospective clinical data analysis and in vitro imaging in the space between the native and THV leaflets (neosinus). SAPIEN 3 valves with leaflet thrombosis were on average 10% further expanded (by diameter) than those without (95.5±5.2% versus 85.4±3.9%; P<0.001). However, this relationship was not evident with the CoreValve/Evolut R. In CoreValve/Evolut Rs with thrombosis, the thrombus volume increased linearly with implant depth (R2=0.7, P<0.001). This finding was not seen in the SAPIEN 3. The in vitro analysis showed that a supraannular THV deployment resulted in a nearly 7-fold decrease in stagnation zone size (velocities <0.1 m/s) when compared with an intraannular deployment. In addition, the in vitro model indicated that the size of the stagnation zone increased as cardiac output decreased. Although transcatheter aortic valve replacement thrombosis is a multifactorial process involving foreign materials, patient-specific blood chemistry, and complex flow patterns, our study indicates that deployed THV geometry may have implications on the occurrence of thrombosis. In addition, a supraannular neosinus may reduce thrombosis risk because of reduced flow stasis. Although additional prospective studies are needed to further develop strategies for minimizing thrombus burden, these results may help identify patients at higher thrombosis risk and aid in the development of next-generation devices with reduced thrombosis risk.