We report the alliance of vertical-cavity surface-emitting lasers and beam multiplier dammann gratings for built-in structuring of lasing emission. Such ultracompact combined laser–metasurface systems open a new para...
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A novel method mitigating the Lidar performance in fog is presented. It employs a median filter to extract the laser pulses buried in the backscattering returns. Simulation results demonstrate the efficiency of the pr...
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We have built upon an existing freehand3d ultrasound imaging technique to enable display-less scanning at a local site by novice users and remote reading by integrating an electromagnetic tracker with a 2d probe. Sev...
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ISBN:
(数字)9781510649460
ISBN:
(纸本)9781510649460;9781510649453
We have built upon an existing freehand3d ultrasound imaging technique to enable display-less scanning at a local site by novice users and remote reading by integrating an electromagnetic tracker with a 2d probe. Seventy-two volumes are generated using a reconstruction algorithm from data collected by three users in a single longitudinal sweep across a 23week fetus phantom in four different configurations for six scan durations ranging from 5-s to 30-s. The acquisition is semi-blinded: the user knows the fetal orientation but scans without imagedisplay and guidance of a conventional scan. Three non-expert readers and one expert Radiologist extract the clinically relevant planes and measure four key biometric features from the 3dimages. In this paper, we propose (1) a risk metric R to rate the quality of the scan as a function of probe motion and contact and (2) a measurability index M for the availability of the 2d planes within the volume and visibility of the biometric features. Our analysis shows that R is the lowest and M the highest for 15-s acquisitions corresponding to an average transducer sweep speed of 2.4-cm/s. The finding is consistent with a reported speed range of 3-4 cm/s recommended for a low cost teleradiology solution for 2d ultrasound. The errors in average biometric measurements compared to the 50th percentile values in the fetal biometry tables for corresponding gestational week are within -3.8 to 5.7%. R, M, accuracy and precision of measurements are useful indicators of performance of the 3d ultrasound system.
Fluid flow behavior is visualized through particle image velocimetry (PIV) for understanding and studying experimental fluiddynamics. However, traditional PIV methods require multiple cameras and conventional lens sy...
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Fluid flow behavior is visualized through particle image velocimetry (PIV) for understanding and studying experimental fluiddynamics. However, traditional PIV methods require multiple cameras and conventional lens systems for imageacquisition to resolve multi-dimensional velocity fields. In turn, it introduces complexity to the entire system. Meta-lenses are advanced flat optical devices composed of artificial nanoantenna arrays. It can manipulate the wavefront of light with the advantages of ultrathin, compact, and no spherical aberration. Meta-lenses offer novel functionalities and promise to replace traditional optical imaging systems. Here, a binocular meta-lens PIV technique is proposed, where a pair of GaN meta-lenses are fabricated on one substrate and integrated with a imaging sensor to form a compact binocular PIV system. The meta-lens weigh only 116 mg, much lighter than commercial lenses. The 3d velocity field can be obtained by the binocular disparity and particle imagedisplacement information of fluid flow. The measurement error of vortex-ring diameter is approximate to 1.25% experimentally validates via a Reynolds-number (Re) 2000 vortex-ring. This work demonstrates a new development trend for the PIV technique for rejuvenating traditional flow diagnostic tools toward a more compact, easy-to-deploy technique. It enables further miniaturization and low-power systems for portable, field-use, and space-constrained PIV applications. A compact binocular meta-lens PIV system is demonstrated. The 3d velocity field can be obtained by the binocular disparity and particle imagedisplacement information of fluid flow. It's a new development trend for the PIV technique for rejuvenating traditional flow diagnostic tools toward a more compact, easy-to-deploy technique. It enables further miniaturization and low-power systems for portable, field-use, and space-constrained PIV ***
We present a common-path digital holographic microscope (dHM) using a Fresnel biprism suitable for spatially dense samples. The performance of the dHM system for dense samples has been validated calibrated phase image...
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Autonomous Vehicles (AVs) use multiple sensors to gather information about their surroundings. Connected Autonomous Vehicles (CAVs) share sensor data for increased safety and reliability through cooperative perception...
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There are a variety of objects, random postures and multiple objects stacked in a disorganized manner in unstructured home applications, which leads to the object grasping posture estimation and grasping planning base...
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There are a variety of objects, random postures and multiple objects stacked in a disorganized manner in unstructured home applications, which leads to the object grasping posture estimation and grasping planning based on machine vision become very complicated. This paper proposes amethod of cluttering pose detectionbased on convolutional neural network with multiple self-powered sensors information. Firstly, a search strategy for candidate grasping poses based on the 3d point cloud is proposed, and the single-channelgrasping imagedataset representing grasping posture in this paper is established by using Bigbirddataset. Secondly, the ResNet is constructed to rank and filter the single channel capturedimages representing the captured bit pose. It is also compared with three mainstream classification networks, Inception V2, VGG-A and LetNet, and the perception analysis function and the execution planning function are developed under ROS. The effective grasping of the manipulator in the scene of scattered piles is realized based on the detection results of grasping position and combined with the information ofmultiple self-powered sensors, and the ResNet network is comparedwith other three classification networks. In a scattered and stacked environment of objects, the results of experiment show that the method based on ResNet network is superior to the other three networks, and the average success rate of grasping pose detection based on ResNet, InceptionV2, VGGA and LetNet networks is 90.67%, 82.67%, 86.67% and 87.33% respectively, which verifies the effectiveness and superiority of the deep learn-based grasping pose detection model proposed in this paper.
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