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1. Curved neuromorphic image sensor array using a MoS2-organic heterostructure inspired by the human visual recognition system

   https://doi.org/10.1038/s41467-020-19806-6  

   Choi, Changsoon; Leem, Juyoung; Kim, Min Sung; Taqieddin, Amir; Cho, Chullhee; Cho, Kyoung Won; Lee, Gil Ju; Seung, Hyojin; Bae, Hyung Jong; Song, Young Min; et al (December 2020, Nature Communications)

   null (Ed.)

   Abstract Conventional imaging and recognition systems require an extensive amount of data storage, pre-processing, and chip-to-chip communications as well as aberration-proof light focusing with multiple lenses for recognizing an object from massive optical inputs. This is because separate chips ( i . e ., flat image sensor array, memory device, and CPU) in conjunction with complicated optics should capture, store, and process massive image information independently. In contrast, human vision employs a highly efficient imaging and recognition process. Here, inspired by the human visual recognition system, we present a novel imaging device for efficient image acquisition and data pre-processing by conferring the neuromorphic data processing function on a curved image sensor array. The curved neuromorphic image sensor array is based on a heterostructure of MoS 2 and poly(1,3,5-trimethyl-1,3,5-trivinyl cyclotrisiloxane). The curved neuromorphic image sensor array features photon-triggered synaptic plasticity owing to its quasi-linear time-dependent photocurrent generation and prolonged photocurrent decay, originated from charge trapping in the MoS 2 -organic vertical stack. The curved neuromorphic image sensor array integrated with a plano-convex lens derives a pre-processed image from a set of noisy optical inputs without redundant data storage, processing, and communications as well as without complex optics. The proposed imaging device can substantially improve efficiency of the image acquisition and recognition process, a step forward to the next generation machine vision. 

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2. Applications and assessment of an excitation-scanning hyperspectral imaging system

   https://doi.org/10.1117/12.2290120  

   Mayes, Sam A.; Leavesley, Silas J.; Rich, Tom C.; Klomkaew, Phiwat; Moore, Kaysie; Browning, Craig (February 2018, Proc. SPIE 10497, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XVI)

   The majority of microscopic and endoscopic technologies utilize white light illumination. For a number of applications, hyper-spectral imaging can be shown to have significant improvements over standard white-light imaging techniques. This is true for both microscopy and in vivo imaging. However, hyperspectral imaging methods have suffered from slow application times. Often, minutes are required to gather a full imaging stack. Here we will describe and evaluate a novel excitation-scanning hyperspectral imaging system and discuss some applications. We have developed and are optimizing a novel approach called excitation-scanning hyperspectral imaging that provides an order of magnitude increased signal strength. This excitation scanning technique has enabled us to produce a microscopy system capable of high speed hyperspectral imaging with the potential for live video acquisition. The excitation-scanning hyperspectral imaging technology we developed may impact a range of applications. The current design uses digital strobing to illuminate at 16 wavelengths with millisecond image acquisition time. Analog intensity control enables a fully customizable excitation profile. A significant advantage of excitation-scanning hyperspectral imaging is can identify multiple targets simultaneously in real time. Finally, we are exploring utilizing this technology for a variety of applications ranging from measuring cAMP distribution in three dimensions within a cell to electrophysiology. 

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3. Field‐based robotic leaf angle detection and characterization of maize plants using stereo vision and deep convolutional neural networks

   https://doi.org/10.1002/rob.22166  

   Xiang, Lirong; Gai, Jingyao; Bao, Yin; Yu, Jianming; Schnable, Patrick S.; Tang, Lie (February 2023, Journal of Field Robotics)

   Maize (Zea mays L.) is one of the three major cereal crops in the world. Leaf angle is an important architectural trait of crops due to its substantial role in light interception by the canopy and hence photosynthetic efficiency. Traditionally, leaf angle has been measured using a protractor, a process that is both slow and laborious. Efficiently measuring leaf angle under field conditions via imaging is challenging due to leaf density in the canopy and the resulting occlusions. However, advances in imaging technologies and machine learning have provided new tools for image acquisition and analysis that could be used to characterize leaf angle using three-dimensional (3D) models of field-grown plants. In this study, PhenoBot 3.0, a robotic vehicle designed to traverse between pairs of agronomically spaced rows of crops, was equipped with multiple tiers of PhenoStereo cameras to capture side-view images of maize plants in the field. PhenoStereo is a customized stereo camera module with integrated strobe lighting for high-speed stereoscopic image acquisition under variable outdoor lighting conditions. An automated image processing pipeline (AngleNet) was developed to measure leaf angles of nonoccluded leaves. In this pipeline, a novel representation form of leaf angle as a triplet of keypoints was proposed. The pipeline employs convolutional neural networks to detect each leaf angle in two-dimensional images and 3D modeling approaches to extract quantitative data from reconstructed models. Our study demonstrates the feasibility of using stereo vision to investigate the distribution of leaf angles in maize under field conditions. The proposed system is an efficient alternative to traditional leaf angle phenotyping and thus could accelerate breeding for improved plant architecture. 

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4. CASPI: collaborative photon processing for active single-photon imaging

   https://doi.org/10.1038/s41467-023-38893-9  

   Lee, Jongho; Ingle, Atul; Chacko, Jenu V.; Eliceiri, Kevin W.; Gupta, Mohit (May 2023, Nature Communications)

   Abstract Image sensors capable of capturing individual photons have made tremendous progress in recent years. However, this technology faces a major limitation. Because they capture scene information at the individual photon level, the raw data is sparse and noisy. Here we propose CASPI: Collaborative Photon Processing for Active Single-Photon Imaging, a technology-agnostic, application-agnostic, and training-free photon processing pipeline for emerging high-resolution single-photon cameras. By collaboratively exploiting both local and non-local correlations in the spatio-temporal photon data cubes, CASPI estimates scene properties reliably even under very challenging lighting conditions. We demonstrate the versatility of CASPI with two applications: LiDAR imaging over a wide range of photon flux levels, from a sub-photon to high ambient regimes, and live-cell autofluorescence FLIM in low photon count regimes. We envision CASPI as a basic building block of general-purpose photon processing units that will be implemented on-chip in future single-photon cameras. 

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5. Ultra-low loss quantum photonic circuits integrated with single quantum emitters

   https://doi.org/10.1038/s41467-022-35332-z  

   Chanana, Ashish; Larocque, Hugo; Moreira, Renan; Carolan, Jacques; Guha, Biswarup; Melo, Emerson G.; Anant, Vikas; Song, Jindong; Englund, Dirk; Blumenthal, Daniel J.; et al (December 2022, Nature Communications)

   Abstract The scaling of many photonic quantum information processing systems is ultimately limited by the flux of quantum light throughout an integrated photonic circuit. Source brightness and waveguide loss set basic limits on the on-chip photon flux. While substantial progress has been made, separately, towards ultra-low loss chip-scale photonic circuits and high brightness single-photon sources, integration of these technologies has remained elusive. Here, we report the integration of a quantum emitter single-photon source with a wafer-scale, ultra-low loss silicon nitride photonic circuit. We demonstrate triggered and pure single-photon emission into a Si₃N₄photonic circuit with ≈ 1 dB/m propagation loss at a wavelength of ≈ 930 nm. We also observe resonance fluorescence in the strong drive regime, showing promise towards coherent control of quantum emitters. These results are a step forward towards scaled chip-integrated photonic quantum information systems in which storing, time-demultiplexing or buffering of deterministically generated single-photons is critical. 

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