167 research outputs found
Transmission area and two-photon correlated imaging
The relationship between transmission area of an object imaged and the
visibility of its image is investigated in a lensless system. We show that the
changes of the visibility are quite different when the transmission area is
varied by different manners. An increase of the transmission by adding the slit
number leads to a decrease of the visibility. While, the change is adverse when
the slit width is widened for a given distance between two slits.Comment: 10 pages, 4 figure
Parking Space Management via Dynamic Performance-Based Pricing
In congested urban areas, it remains a pressing challenge to reduce
unnecessary vehicle circling for parking while at the same time maximize
parking space utilization. In observance of new information technologies that
have become readily accessible to drivers and parking agencies, we develop a
dynamic non-cooperative bi-level model (i.e. Stackelberg leader-follower game)
to set parking prices in real-time for effective parking access and space
utilization. The model is expected to fit into an integrated parking pricing
and management system, where parking reservations and transactions are
facilitated by sensing and informatics infrastructures, that ensures the
availability of convenient spaces at equilibrium market prices. It is shown
with numerical examples that the proposed dynamic parking pricing model has the
potential to virtually eliminate vehicle circling for parking, which results in
significant reduction in adverse socioeconomic externalities such as traffic
congestion and emissions
Sub-wavelength Coherent Imaging of a Pure-Phase Object with Thermal Light
We report, for the first time, the observation of sub-wavelength coherent
image of a pure phase object with thermal light,which represents an accurate
Fourier transform. We demonstrate that ghost-imaging scheme (GI) retrieves
amplitude transmittance knowledge of objects rather than the transmitted
intensities as the HBT-type imaging scheme does.Comment: 5 pages, 4 figures; Any comments pls. contact: [email protected]
Optimal Pavement Design and Rehabilitation Planning Using a Mechanistic-Empirical Approach
This paper presents the development of a pavement design and rehabilitation optimization decision-making framework based on Mechanistic-Empirical (ME) roughness transfer models. The AASHTOWare Pavement ME Design (the software of Pavement ME Design) is used to estimate pavement deterioration based on the combined effects of permanent deformation, fatigue, and thermal cracking. The optimization problem is first formulated into a mixed-integer nonlinear programming model to address the predominant trade-off between agency and user costs. To deal with the complexity associated with the pavement roughness transfer functions in the software and to use the roughness values as input to the optimization framework, a dynamic programming subroutine is developed for determining the optimal rehabilitation timing and asphalt concrete design thickness. An application of the proposed model is demonstrated in a case study. Managerial insights from a series of sensitivity analyses on different unit user cost values and model comparisons are presented
Strain Induced One-Dimensional Landau-Level Quantization in Corrugated Graphene
Theoretical research has predicted that ripples of graphene generates
effective gauge field on its low energy electronic structure and could lead to
zero-energy flat bands, which are the analog of Landau levels in real magnetic
fields. Here we demonstrate, using a combination of scanning tunneling
microscopy and tight-binding approximation, that the zero-energy Landau levels
with vanishing Fermi velocities will form when the effective pseudomagnetic
flux per ripple is larger than the flux quantum. Our analysis indicates that
the effective gauge field of the ripples results in zero-energy flat bands in
one direction but not in another. The Fermi velocities in the perpendicular
direction of the ripples are not renormalized at all. The condition to generate
the ripples is also discussed according to classical thin-film elasticity
theory.Comment: 4 figures, Phys. Rev.
Electronic Structures of Graphene Layers on Metal Foil: Effect of Point Defects
Here we report a facile method to generate a high density of point defects in
graphene on metal foil and show how the point defects affect the electronic
structures of graphene layers. Our scanning tunneling microscopy (STM)
measurements, complemented by first principle calculations, reveal that the
point defects result in both the intervalley and intravalley scattering of
graphene. The Fermi velocity is reduced in the vicinity area of the defect due
to the enhanced scattering. Additionally, our analysis further points out that
periodic point defects can tailor the electronic properties of graphene by
introducing a significant bandgap, which opens an avenue towards all-graphene
electronics.Comment: 4 figure
A Two-stage Method with a Shared 3D U-Net for Left Atrial Segmentation of Late Gadolinium-Enhanced MRI Images
Objective: This study was aimed at validating the accuracy of a proposed algorithm for fully automatic 3D left atrial segmentation and to compare its performance with existing deep learning algorithms. Methods: A two-stage method with a shared 3D U-Net was proposed to segment the 3D left atrium. In this architecture, the 3D U-Net was used to extract 3D features, a two-stage strategy was used to decrease segmentation error caused by the class imbalance problem, and the shared network was designed to decrease model complexity. Model performance was evaluated with the DICE score, Jaccard index and Hausdorff distance. Results: Algorithm development and evaluation were performed with a set of 100 late gadolinium-enhanced cardiovascular magnetic resonance images. Our method achieved a DICE score of 0.918, a Jaccard index of 0.848 and a Hausdorff distance of 1.211, thus, outperforming existing deep learning algorithms. The best performance of the proposed model (DICE: 0.851; Jaccard: 0.750; Hausdorff distance: 4.382) was also achieved on a publicly available 2013 image data set. Conclusion: The proposed two-stage method with a shared 3D U-Net is an efficient algorithm for fully automatic 3D left atrial segmentation. This study provides a solution for processing large datasets in resource-constrained applications. Significance Statement: Studying atrial structure directly is crucial for comprehending and managing atrial fibrillation (AF). Accurate reconstruction and measurement of atrial geometry for clinical purposes remains challenging, despite potential improvements in the visibility of AF-associated structures with late gadolinium-enhanced magnetic resonance imaging. This difficulty arises from the varying intensities caused by increased tissue enhancement and artifacts, as well as variability in image quality. Therefore, an efficient algorithm for fully automatic 3D left atrial segmentation is proposed in the present study
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