11,439 research outputs found
What are Alternatives to Traditional Performance Rating Cycles and Processes?
The dominant format for performance appraisal systems in large U.S. industrial companies continues to be an objective-based approach such as management by objectives (MBO). Most companies conduct formal performance ratings annually or semi-annually. However, the traditional way of performance rating is receiving more and more doubt. With the development of HR theories, practices and technology, many companies are trying to manage employee performance in new ways
Deep learning in computational microscopy
We propose to use deep convolutional neural networks (DCNNs) to perform 2D and 3D computational imaging. Specifically, we investigate three different applications. We first try to solve the 3D inverse scattering problem based on learning a huge number of training target and speckle pairs. We also demonstrate a new DCNN architecture to perform Fourier ptychographic Microscopy (FPM) reconstruction, which achieves high-resolution phase recovery with considerably less data than standard FPM. Finally, we employ DCNN models that can predict focused 2D fluorescent microscopic images from blurred images captured at overfocused or underfocused planes.Published versio
Hamiltonian and Phase-Space Representation of Spatial Solitons
We use Hamiltonian ray tracing and phase-space representation to describe the
propagation of a single spatial soliton and soliton collisions in a Kerr
nonlinear medium. Hamiltonian ray tracing is applied using the iterative
nonlinear beam propagation method, which allows taking both wave effects and
Kerr nonlinearity into consideration. Energy evolution within a single spatial
soliton and the exchange of energy when two solitons collide are interpreted
intuitively by ray trajectories and geometrical shearing of the Wigner
distribution functions.Comment: 12 pages, 5 figure
3D differential phase contrast microscopy
We demonstrate 3D phase and absorption recovery from partially coherent intensity images captured with a programmable LED array source. Images are captured through-focus with four different illumination patterns. Using first Born and weak object approximations (WOA), a linear 3D differential phase contrast (DPC) model is derived. The partially coherent transfer functions relate the sample's complex refractive index distribution to intensity measurements at varying defocus. Volumetric reconstruction is achieved by a global FFT-based method, without an intermediate 2D phase retrieval step. Because the illumination is spatially partially coherent, the transverse resolution of the reconstructed field achieves twice the NA of coherent systems and improved axial resolution
Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media
Imaging through scattering is an important yet challenging problem. Tremendous progress has been made by exploiting the deterministic input–output “transmission matrix” for a fixed medium. However, this “one-to-one” mapping is highly susceptible to speckle decorrelations – small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical “one-to-all” deep learning (DL) technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable DL approach for imaging through scattering media.National Science Foundation (NSF) (1711156); Directorate for Engineering (ENG). (1711156 - National Science Foundation (NSF); Directorate for Engineering (ENG))First author draf
Deep learning approach to scalable imaging through scattering media
We propose a deep learning technique to exploit “deep speckle correlations”. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.Published versio
Sampling and processing for multiple scattering in inline compressive holography
Inline holography is approached from a computational perspective by incorporating a nonlinear forward model based on the iterative Born approximation (IBA). Sampling and its effects on multiple scattering computations are discussed.Published versio
Holographic particle localization under multiple scattering
We introduce a novel framework that incorporates multiple scattering for
large-scale 3D particle-localization using single-shot in-line holography.
Traditional holographic techniques rely on single-scattering models which
become inaccurate under high particle-density. We demonstrate that by
exploiting multiple-scattering, localization is significantly improved. Both
forward and back-scattering are computed by our method under a tractable
recursive framework, in which each recursion estimates the next higher-order
field within the volume. The inverse scattering is presented as a nonlinear
optimization that promotes sparsity, and can be implemented efficiently. We
experimentally reconstruct 100 million object voxels from a single 1-megapixel
hologram. Our work promises utilization of multiple scattering for versatile
large-scale applications
Structured illumination microscopy with unknown patterns and a statistical prior
Structured illumination microscopy (SIM) improves resolution by
down-modulating high-frequency information of an object to fit within the
passband of the optical system. Generally, the reconstruction process requires
prior knowledge of the illumination patterns, which implies a well-calibrated
and aberration-free system. Here, we propose a new \textit{algorithmic
self-calibration} strategy for SIM that does not need to know the exact
patterns {\it a priori}, but only their covariance. The algorithm, termed
PE-SIMS, includes a Pattern-Estimation (PE) step requiring the uniformity of
the sum of the illumination patterns and a SIM reconstruction procedure using a
Statistical prior (SIMS). Additionally, we perform a pixel reassignment process
(SIMS-PR) to enhance the reconstruction quality. We achieve 2 better
resolution than a conventional widefield microscope, while remaining
insensitive to aberration-induced pattern distortion and robust against
parameter tuning
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