De-multiplexing vortex modes in optical communications using transport-based pattern recognition

Park, Se Rim; Cattell, Liam; Nichols, Jonathan M.; Watnik, Abbie T.; Doster, Timothy; Rohde, Gustavo K.

doi:10.1364/oe.26.004004

Cited by 71 publications

(36 citation statements)

References 27 publications

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“…To test and compare the performances of three broad categories of cell image classification algorithms, we selected methods from a few exemplary papers: Wnd‐chrm from NFE; multilayer perceptron and four CNN architectures—an existing shallow CNN , VGG16 , Inception‐V3 , and ResNet from NNs; and the Radon cumulative distribution transform from TBM. Details of all these methods with mathematical descriptions are presented in Appendix A.…”

Section: Methodsmentioning

confidence: 99%

Cell Image Classification: A Comparative Overview

et al. 2020

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Cell image classification methods are currently being used in numerous applications in cell biology and medicine. Applications include understanding the effects of genes and drugs in screening experiments, understanding the role and subcellular localization of different proteins, as well as diagnosis and prognosis of cancer from images acquired using cytological and histological techniques. The article also reviews three main approaches for cell image classification most often used: numerical feature extraction, end-to-end classification with neural networks (NNs), and transport-based morphometry (TBM). In addition, we provide comparisons on four different cell imaging datasets to highlight the relative strength of each method. The results computed using four publicly available datasets show that numerical features tend to carry the best discriminative information for most of the classification tasks. Results also show that NN-based methods produce state-of-the-art results in the dataset that contains a relatively large number of training samples. Data augmentation or the choice of a more recently reported architecture does not necessarily improve the classification performance of NNs in the datasets with limited number of training samples. If understanding and visualization are desired aspects, TBM methods can offer the ability to invert classification functions, and thus can aid in the interpretation of results. These and other comparison outcomes are discussed with the aim of clarifying the advantages and disadvantages of each method. © 2020 International Society for Advancement of Cytometry Key terms image informatics; computational biology; cell biology; digital pathology Interpretation of images of cells has always played important roles in science and medicine. From their discovery in 1665, observation of the spatiotemporal characteristics of cells through microscopic technology has enabled us to better understand the structure of living cells, as well as how they perform certain functions (1,2). In addition, scientists have long used microscopes to evaluate the efficacy of different compounds for drug development (3-9). In medicine, as another example, the observation of cell morphology has long been used to discern malignancy in cancer cells (10-13).Cells are known to exhibit complex phenotypes such as differences in shape, gene expression, subcellular protein localization, and other qualities. In addition, cell cultures, tissues, and organs are known to exhibit complex heterogeneity of phenotypes. The combination of intricate phenotype differences together with their heterogeneous responses to different conditions (e.g., diseases) has made decoding biological processes an increasingly complex task. Thus computational approaches for analyzing images of cells have been used increasingly to aid in the task of decoding the complexity of biological processes. A common task useful in many practical situations is determining the category of a given cell or set of cells: a task known as cell classification.Auto...

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Section: Methodsmentioning

confidence: 99%

Cell Image Classification: A Comparative Overview

et al. 2020

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“…Reasons to learn features from raw data include that doing so often substantially improves performance ( 13 , 25 , 31 ); because such features can be transferred to other domains with small datasets ( 32 , 33 ); because it is time-consuming to manually design features; and because a general algorithm that learns features automatically can improve performance on very different types of data [e.g., sound ( 20 , 34 ) and text ( 23 , 35 )], increasing the impact of the approach. However, an additional benefit to deep learning is that if hand-designed features are thought to be useful, they can be included as well in case they improve performance ( 36 – 40 ).…”

Section: Background and Related Workmentioning

confidence: 99%

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

Norouzzadeh

Nguyen

Kosmala

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

825

749

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SignificanceMotion-sensor cameras in natural habitats offer the opportunity to inexpensively and unobtrusively gather vast amounts of data on animals in the wild. A key obstacle to harnessing their potential is the great cost of having humans analyze each image. Here, we demonstrate that a cutting-edge type of artificial intelligence called deep neural networks can automatically extract such invaluable information. For example, we show deep learning can automate animal identification for 99.3% of the 3.2 million-image Snapshot Serengeti dataset while performing at the same 96.6% accuracy of crowdsourced teams of human volunteers. Automatically, accurately, and inexpensively collecting such data could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences.

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“…Since the FSO channel is typically slow fading and bursty, not all FEC codes are effective at combating the effects of turbulence. There has been some use of machine learning for mode detection, generally making use of OAM superpositions to create an identifiable "petal" structure, but these techniques have not yet been applied to communications (for instance in soft decision decoding) [124], [233], [234].…”

Section: Impact On Optical Signal Processingmentioning

confidence: 99%

Structured Light in Turbulence

Cox

Mphuthi

Nape

et al. 2021

IEEE J. Select. Topics Quantum Electron.

113

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Optical communication is an integral part of the modern economy, having all but replaced electronic communication systems. Future growth in bandwidth appears to be on the horizon using structured light, encoding information into the spatial modes of light, and transmitting them down fibre and free-space, the latter crucial for addressing last mile and digitally disconnected communities. Unfortunately, patterns of light are easily distorted, and in the case of free-space optical communication, turbulence is a significant barrier. Here we review recent progress in structured light in turbulence, first with a tutorial style summary of the core concepts, before highlighting the present state-of-the-art in the field. We support our review with new experimental studies that reveal which types of structured light are best in turbulence, the behaviour of vector versus scalar light in turbulence, the trade-off of diversity and multiplexing, and how turbulence models can be exploited for enhanced optical signal processing protocols. This comprehensive treatise will be invaluable to the large communities interested in free-space optical communication with spatial modes of light.

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De-multiplexing vortex modes in optical communications using transport-based pattern recognition

Cited by 71 publications

References 27 publications

Cell Image Classification: A Comparative Overview

Cell Image Classification: A Comparative Overview

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

Structured Light in Turbulence

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