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相关论文: Optimized signal deduction procedure for the MIEZE…

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Modulation of Intensity Emerging from Zero Effort (MIEZE) is a neutron resonant spin echo technique which allows one to measure time correlation scattering functions in materials by implementing radio-frequency (RF) intensity modulation at…

仪器与探测器 · 物理学 2021-05-04 Ryan Dadisman , Georg Ehlers , Fankang Li

Signal recovery from nonlinear measurements involves solving an iterative optimization problem. In this paper, we present a framework to optimize the sensing parameters to improve the quality of the signal recovered by the given iterative…

图像与视频处理 · 电气工程与系统科学 2020-06-09 Zikui Cai , Rakib Hyder , M. Salman Asif

We consider the problem of signal reconstruction from quadratic measurements that are encoded as +1 or -1 depending on whether they exceed a predetermined positive threshold or not. Binary measurements are fast to acquire and inexpensive in…

信息论 · 计算机科学 2018-03-14 Subhadip Mukherjee , Chandra Sekhar Seelamantula

Sparse modeling is one of the efficient techniques for imaging that allows recovering lost information. In this paper, we present a novel iterative phase-retrieval algorithm using a sparse representation of the object amplitude and phase.…

计算机视觉与模式识别 · 计算机科学 2011-08-17 Artem Migukin , Vladimir Katkovnik , Jaakko Astola

The instrumental layout and technical realisation of the neutron resonant spin echo (NRSE) spectrometer RESEDA at the Heinz Maier-Leibnitz Zentrum (MLZ) in Garching, Germany, is presented. RESEDA is based on a longitudinal field…

Conventional sparse phase retrieval schemes can recover sparse signals from the magnitude of linear measurements only up to a global phase ambiguity. This work proposes a novel approach that instead utilizes the magnitude of affine…

信息论 · 计算机科学 2021-05-25 Ming-Hsun Yang , Y. -W. Peter Hong , Jwo-Yuh Wu

Accurate phase extraction from sinusoidal signals is a crucial task in various signal processing applications. While prior research predominantly addresses the case of asynchronous sampling with unknown signal frequency, this study focuses…

信号处理 · 电气工程与系统科学 2024-11-01 Emmanuel Dervieux , Florian Tilquin , Alexis Bisiaux , Wilfried Uhring

In the data analysis of oscillatory systems, methods based on phase reconstruction are widely used to characterize phase-locking properties and inferring the phase dynamics. The main component in these studies is an extraction of the phase…

数据分析、统计与概率 · 物理学 2021-11-22 Erik Gengel , Arkady Pikovsky

We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used…

机器学习 · 统计学 2019-07-18 Viraj Shah , Chinmay Hegde

Exploitation of time-frequency properties of SPDC photon pairs has recently found application in many endeavors. Complete characterization and control over the states in this degree of freedom is of paramount importance for the development…

量子物理 · 物理学 2019-12-18 Ilaria Gianani

Sparse Identification of Nonlinear Dynamics (SINDy) has been shown to successfully recover governing equations from data; however, this approach assumes the initial condition to be exactly known in advance and is sensitive to noise. In this…

动力系统 · 数学 2022-11-23 Baolei Wei

We consider the problem of recovering a $K$-sparse complex signal $x$ from $m$ intensity measurements. We propose the PhaseCode algorithm, and show that in the noiseless case, PhaseCode can recover an arbitrarily-close-to-one fraction of…

信息论 · 计算机科学 2017-04-03 Ramtin Pedarsani , Dong Yin , Kangwook Lee , Kannan Ramchandran

Phase retrieval is a nonlinear inverse problem that arises in a wide range of imaging modalities, from electron microscopy to Fourier ptychography. In particular, the reconstruction is facilitated when the sensing matrix is i.i.d. random,…

Sparse channel estimation problem is one of challenge technical issues in stable broadband wireless communications. Based on square error criterion (SEC), adaptive sparse channel estimation (ASCE) methods, e.g., zero-attracting least mean…

信息论 · 计算机科学 2015-03-04 Guan Gui , Li Xu , Shinya Matsushita

Traditional iterative reconstruction methods are accurate but computationally expensive, limiting their use in high-throughput and real-time ptychography. Recent deep learning approaches improve speed, but often predict phase as a Euclidean…

图像与视频处理 · 电气工程与系统科学 2026-04-30 Carson Yu Liu , Jun Cheng , Chien-Chun Chen , Steve F. Shu

Time domain terahertz spectroscopy typically uses mechanical delay stages that inherently suffer from non-uniform sampling positions. We review, simulate, and experimentally test the ability of corrective cubic spline and Shannon…

光学 · 物理学 2019-03-20 A. M. Potts , T. T. Mai , M. T. Warren , R. Valdés Aguilar

Phase resetting is a common experimental approach to investigating the behaviour of oscillating neurons. Assuming repeated spiking or bursting, a phase reset amounts to a brief perturbation that causes a shift in the phase of this periodic…

动力系统 · 数学 2020-03-17 Peter Langfield , Bernd Krauskopf , Hinke M. Osinga

In this paper, we propose the SPR (sparse phase retrieval) method, which is a new phase retrieval method for coherent x-ray diffraction imaging (CXDI). Conventional phase retrieval methods effectively solve the problem for high…

数据分析、统计与概率 · 物理学 2016-03-29 Shiro Ikeda , Hidetoshi Kono

This paper considers the problem of frequency estimation for a multi-sinusoidal signal consisting of n sinuses in finite-time. The parameterization approach based on applying delay operators to a measurable signal is used. The result is the…

系统与控制 · 电气工程与系统科学 2020-09-15 Anastasiia Vediakova , Alexey Vedyakov , Anton Pyrkin , Alexey Bobtsov , Vladislav Gromov

We extend the data-driven method of Sparse Identification of Nonlinear Dynamics (SINDy) developed by Brunton et al, Proc. Natl. Acad. Sci USA 113 (2016) to the case of delay differential equations (DDEs). This is achieved in a bilevel…

动力系统 · 数学 2022-12-14 Antoine Sandoz , Verena Ducret , Georg A. Gottwald , Gilles Vilmart , Karl Perron
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