A fast-converging iterative method based on weighted feedback for multi-distance phase retrieval

Guo, Cheng; Shen, Cheng; Li, Qiang; Tan, Jiubin; Liu, Shutian; Kan, Xinchi; Liu, Zhengjun

doi:10.1038/s41598-018-24666-8

Cited by 38 publications

(15 citation statements)

References 31 publications

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“…, X (t) }, and P (t) . To enhance the convergence speed of Algorithm 1, the output at the (t − 1)th iteration is regarded as a feedback to the input at the tth iteration [28], such as, the tth input P (t) is defined as…”

Section: B Algorithms Designmentioning

confidence: 99%

Spectrum Management for Multi-Access Edge Computing in Autonomous Vehicular Networks

Peng

Shen

2020

IEEE Trans. Intell. Transport. Syst.

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In this paper, a dynamic spectrum management framework is proposed to improve spectrum resource utilization in a multi-access edge computing (MEC) in autonomous vehicular network (AVNET). To support the increasing data traffic and guarantee quality-of-service (QoS), spectrum slicing, spectrum allocating, and transmit power controlling are jointly considered. Accordingly, three non-convex network utility maximization problems are formulated to slice spectrum among BSs, allocate spectrum among autonomous vehicles (AVs) associated with a BS, and control transmit powers of BSs, respectively. Via linear programming relaxation and first-order Taylor series approximation, these problems are transformed into tractable forms and then are jointly solved through an alternate concave search (ACS) algorithm. As a result, optimal spectrum slicing ratios among BSs, optimal BS-vehicle association patterns, optimal fractions of spectrum resources allocated to AVs, and optimal transmit powers of BSs are obtained. Based on our simulation, a high aggregate network utility is achieved by the proposed spectrum management scheme compared with two existing schemes.

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Section: B Algorithms Designmentioning

confidence: 99%

Spectrum Management for Multi-Access Edge Computing in Autonomous Vehicular Networks

Peng

Shen

2020

IEEE Trans. Intell. Transport. Syst.

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“…However, realistic physical objects always have some finite thickness, and therefore are rather 3D than 2D samples. In optical holography, the complete reconstruction of a wavefront from a sequence of intensity measurements using an iterative procedure has been demonstrated in series of studies between 2003 and 2006 [95][96][97][98][99]. It has been recently demonstrated that 3D samples, including 3D phase objects, can be reconstructed from two or more holograms recorded at different z-distances from the sample [75], as illustrated in Figure 24.…”

Section: D Sample Reconstruction From Two or More In-line Hologramsmentioning

confidence: 99%

Holography and Coherent Diffraction Imaging with Low-(30–250 eV) and High-(80–300 keV) Energy Electrons: History, Principles, and Recent Trends

Latychevskaia

2020

Materials

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In this paper, we present the theoretical background to electron scattering in an atomic potential and the differences between low- and high-energy electrons interacting with matter. We discuss several interferometric techniques that can be realized with low- and high-energy electrons and which can be applied to the imaging of non-crystalline samples and individual macromolecules, including in-line holography, point projection microscopy, off-axis holography, and coherent diffraction imaging. The advantages of using low- and high-energy electrons for particular experiments are examined, and experimental schemes for holography and coherent diffraction imaging are compared.

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“…As a result, the prior knowledge about the object became unnecessary. Multiple measurements have improved the accuracy of the phase retrieval and the unnecessary object prior requirement, and result in a broader application of iterative phase retrieval [ 89 , 90 , 91 ].…”

Section: Wavefront-based Light Field Imaging From Depth Measuremenmentioning

confidence: 99%

3D Imaging Based on Depth Measurement Technologies

Zuo

Lam

et al. 2018

Sensors

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Three-dimensional (3D) imaging has attracted more and more interest because of its widespread applications, especially in information and life science. These techniques can be broadly divided into two types: ray-based and wavefront-based 3D imaging. Issues such as imaging quality and system complexity of these techniques limit the applications significantly, and therefore many investigations have focused on 3D imaging from depth measurements. This paper presents an overview of 3D imaging from depth measurements, and provides a summary of the connection between the ray-based and wavefront-based 3D imaging techniques.

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A fast-converging iterative method based on weighted feedback for multi-distance phase retrieval

Cited by 38 publications

References 31 publications

Spectrum Management for Multi-Access Edge Computing in Autonomous Vehicular Networks

Spectrum Management for Multi-Access Edge Computing in Autonomous Vehicular Networks

Holography and Coherent Diffraction Imaging with Low-(30–250 eV) and High-(80–300 keV) Energy Electrons: History, Principles, and Recent Trends

3D Imaging Based on Depth Measurement Technologies

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