All-fiber high-speed image detection enabled by deep learning

Ultrahigh Responsivity and Robust Semiconducting Fiber Enabled by Molecular Soldering-Governed Defect Engineering for Smart Textile Optoelectronics

Semiconducting fibers (SCFs) are of significant interest to design next-generation wearable and comfortable optoelectronics that seamlessly integrate with textiles. However, the practical applications of current SCFs are always limited by poor optoelectronic performance and low mechanical robustness caused by uncontrollable multiscale structural defects. Herein, a versatile in situ molecular soldering-governed defect engineering strategy is proposed to construct ultrahigh responsivity and robust wet-spun MoS2 SCFs, by using a π-conjugated dithiolated molecule to simultaneously patch microscale sulfur vacancies within MoS2 nanosheets, diminish mesoscale interlayer voids/wrinkles, promote macroscale orientation, build long-range photoelectron percolation bridges, and provide n-doping effect. The derived MoS2 SCFs exhibit over two orders of magnitude higher responsivity (144.3 A W-1) than previously reported fiber photodetectors, 37.3-fold faster photoresponse speed (52 ms) than pristine counterpart, and remarkable bending robustness (retain 94.2% of the initial photocurrent after 50 000 bending-flattening cycles). Such superior robustness and photodetection capacity of MoS2 SCFs further enable large-scale weaving of reliable smart textile optoelectronic systems, such as direction-identifiable wireless light alarming system, modularized mechano-optical communication system, and indoor light-controlled IoT system. This work offers a universal strategy for the scalable production of mechanically robust and high-performance SCFs, opening up exciting possibilities for large-scale integration of wearable optoelectronics.

Mid-infrared single-photon upconversion spectroscopy enabled by nonlocal wavelength-to-time mapping

Ultrasensitive spectroscopy is an essential component in mid-infrared (MIR) technology. However, the drawbacks of MIR detectors pose challenges to robust MIR spectroscopy at the single-photon level. We propose an MIR single-photon frequency upconversion spectroscopy nonlocally mapping the MIR information to the time domain. Broadband MIR photons from spontaneous parametric downconversion are frequency-upconverted to the near-infrared band with quantum correlation preservation. Via the group delay of fiber, the MIR spectral information within a 1.18-micrometer bandwidth of 2.76 to 3.94 micrometers is then successfully projected to arrival times of correlated photon pairs. Under the conditions of 6.4 × 106 photons per second illumination, the transmission spectra of polymers with single-photon sensitivity are demonstrated using single-pixel detectors. The developed approach circumvents scanning and frequency selection instability, which stands out for its inherent compatibility for evolving environments and scalability for various wavelengths. Because of its high sensitivity and robustness, characterization of biochemical samples and weak measurement of quantum systems are possible to foresee.

Non-orthogonal optical multiplexing empowered by deep learning

Orthogonality among channels is a canonical basis for optical multiplexing featured with division multiplexing, which substantially reduce the complexity of signal post-processing in demultiplexing. However, it inevitably imposes an upper limit of capacity for multiplexing. Herein, we report on non-orthogonal optical multiplexing over a multimode fiber (MMF) leveraged by a deep neural network, termed speckle light field retrieval network (SLRnet), where it can learn the complicated mapping relation between multiple non-orthogonal input light field encoded with information and their corresponding single intensity output. As a proof-of-principle experimental demonstration, it is shown that the SLRnet can effectively solve the ill-posed problem of non-orthogonal optical multiplexing over an MMF, where multiple non-orthogonal input signals mediated by the same polarization, wavelength and spatial position can be explicitly retrieved utilizing a single-shot speckle output with fidelity as high as ~ 98%. Our results resemble an important step for harnessing non-orthogonal channels for high capacity optical multiplexing.

Photonic Bound States in the Continuum in Nanostructures

Bound states in the continuum (BIC) have garnered considerable attention recently for their unique capacity to confine electromagnetic waves within an open or non-Hermitian system. Utilizing a variety of light confinement mechanisms, nanostructures can achieve ultra-high quality factors and intense field localization with BIC, offering advantages such as long-living resonance modes, adaptable light control, and enhanced light-matter interactions, paving the way for innovative developments in photonics. This review outlines novel functionality and performance enhancements by synergizing optical BIC with diverse nanostructures, delivering an in-depth analysis of BIC designs in gratings, photonic crystals, waveguides, and metasurfaces. Additionally, we showcase the latest advancements of BIC in 2D material platforms and suggest potential trajectories for future research.

Spatiotemporal mode-locking and dissipative solitons in multimode fiber lasers

Multimode fiber (MMF) lasers are emerging as a remarkable testbed to study nonlinear spatiotemporal physics with potential applications spanning from high energy pulse generation, precision measurement to nonlinear microscopy. The underlying mechanism for the generation of ultrashort pulses, which can be understood as a spatiotempoal dissipative soliton (STDS), in the nonlinear multimode resonators is the spatiotemporal mode-locking (STML) with simultaneous synchronization of temporal and spatial modes. In this review, we first introduce the general principles of STML, with an emphasize on the STML dynamics with large intermode dispersion. Then, we present the recent progress of STML, including measurement techniques for STML, exotic nonlinear dynamics of STDS, and mode field engineering in MMF lasers. We conclude by outlining some perspectives that may advance STML in the near future.

Projecting colorful images through scattering media via deep learning

The existence of scatterers in the optical path has been the major obstacle that prohibits one from projecting images through solid walls, turbid water, clouds, and fog. Recent developments in wavefront shaping and neural networks demonstrate effective compensation for scattering effects, showing the promise to project clear images against strong scattering. However, previous studies were mainly restricted to projecting greyscale images using monochromatic light, mainly due to the increased complexity of simultaneously controlling multiple wavelengths. In this work, we fill this blank by developing a projector network, which enables the projection of colorful images through scattering media with three primary colors. To validate the performance of the projector network, we experimentally demonstrated projecting colorful images obtained from the MINST dataset through two stacked diffusers. Quantitatively, the averaged intensity Pearson's correlation coefficient for 1,000 test colorful images reaches about 90.6%, indicating the superiority of the developed network. We anticipate that the projector network can be beneficial to a variety of display applications in scattering environments.

Functionalizing nanophotonic structures with 2D van der Waals materials

The integration of two-dimensional (2D) van der Waals materials with nanostructures has triggered a wide spectrum of optical and optoelectronic applications. Photonic structures of conventional materials typically lack efficient reconfigurability or multifunctionality. Atomically thin 2D materials can thus generate new functionality and reconfigurability for a well-established library of photonic structures such as integrated waveguides, optical fibers, photonic crystals, and metasurfaces, to name a few. Meanwhile, the interaction between light and van der Waals materials can be drastically enhanced as well by leveraging micro-cavities or resonators with high optical confinement. The unique van der Waals surfaces of the 2D materials enable handiness in transfer and mixing with various prefabricated photonic templates with high degrees of freedom, functionalizing as the optical gain, modulation, sensing, or plasmonic media for diverse applications. Here, we review recent advances in synergizing 2D materials to nanophotonic structures for prototyping novel functionality or performance enhancements. Challenges in scalable 2D materials preparations and transfer, as well as emerging opportunities in integrating van der Waals building blocks beyond 2D materials are also discussed.

Optical Fibre-Based Sensors-An Assessment of Current Innovations

Optical fibre sensors are an essential subset of optical fibre technology, designed specifically for sensing and measuring several physical parameters. These sensors offer unique advantages over traditional sensors, making them gradually more valuable in a wide range of applications. They can detect extremely small variations in the physical parameters they are designed to measure, such as analytes in the case of biosensing. This high sensitivity allows them to detect subtle variations in temperature, pressure, strain, the refractive index of analytes, vibration, and other environmental factors with exceptional accuracy. Moreover, these sensors enable remote sensing capabilities. Since light signals are used to carry information, the sensing elements can be placed at distant or inaccessible sites and still communicate the data back to the central monitoring system without signal degradation. In recent times, different attractive configurations and approaches have been proposed to enhance the sensitivity of the optical fibre-based sensor and are briefly explained in this review. However, we believe that the choice of optical fibre sensor configuration should be designated based on the specific application. As these sensors continue to evolve and improve, they will play an increasingly vital role in critical monitoring and control applications across various industries.

Robust real-time imaging through flexible multimode fibers

Conventional endoscopes comprise a bundle of optical fibers, associating one fiber for each pixel in the image. In principle, this can be reduced to a single multimode optical fiber (MMF), the width of a human hair, with one fiber spatial-mode per image pixel. However, images transmitted through a MMF emerge as unrecognizable speckle patterns due to dispersion and coupling between the spatial modes of the fiber. Furthermore, speckle patterns change as the fiber undergoes bending, making the use of MMFs in flexible imaging applications even more complicated. In this paper, we propose a real-time imaging system using flexible MMFs, but which is robust to bending. Our approach does not require access or feedback signal from the distal end of the fiber during imaging. We leverage a variational autoencoder to reconstruct and classify images from the speckles and show that these images can still be recovered when the bend configuration of the fiber is changed to one that was not part of the training set. We utilize a MMF 300 mm long with a 62.5 μm core for imaging [Formula: see text] cm objects placed approximately at 20 cm from the fiber and the system can deal with a change in fiber bend of 50[Formula: see text] and range of movement of 8 cm.

Multimode fiber-based greyscale image projector enabled by neural networks with high generalization ability

Multimode fibers (MMFs) are emerging as promising transmission media for delivering images. However, strong mode coupling inherent in MMFs induces difficulties in directly projecting two-dimensional images through MMFs. By training two subnetworks named Actor-net and Model-net synergetically, [Nature Machine Intelligence2, 403 (2020)10.1038/s42256-020-0199-9] alleviated this issue and demonstrated projecting images through MMFs with high fidelity. In this work, we make a step further by improving the generalization ability to greyscale images. The modified projector network contains three subnetworks, namely forward-net, backward-net, and holography-net, accounting for forward propagation, backward propagation, and the phase-retrieval process. As a proof of concept, we experimentally trained the projector network using randomly generated phase maps and their corresponding resultant speckle images output from a 1-meter-long MMF. With the network being trained, we successfully demonstrated projecting binary images from MNIST and EMNIST and greyscale images from Fashion-MNIST, exhibiting averaged Pearson's correlation coefficients of 0.91, 0.92, and 0.87, respectively. Since all these projected images have never been seen by the projector network before, a strong generalization ability in projecting greyscale images is confirmed.

SMD-YOLO: An efficient and lightweight detection method for mask wearing status during the COVID-19 pandemic

BACKGROUND AND OBJECTIVE

At present, the COVID-19 epidemic is still spreading worldwide and wearing a mask in public areas is an effective way to prevent the spread of the respiratory virus. Although there are many deep learning methods used for detecting the face masks, there are few lightweight detectors having a good effect on small or medium-size face masks detection in the complicated environments.

METHODS

In this work we propose an efficient and lightweight detection method based on YOLOv4-tiny, and a face mask detection and monitoring system for mask wearing status. Two feasible improvement strategies, network structure optimization and K-means++ clustering algorithm, are utilized for improving the detection accuracy on the premise of ensuring the real-time face masks recognition. Particularly, the improved residual module and cross fusion module are designed to aim at extracting the features of small or medium-size targets effectively. Moreover, the enhanced dual attention mechanism and the improved spatial pyramid pooling module are employed for merging sufficiently the deep and shallow semantic information and expanding the receptive field. Afterwards, the detection accuracy is compensated through the combination of activation functions. Finally, the depthwise separable convolution module is used to reduce the quantity of parameters and improve the detection efficiency. Our proposed detector is evaluated on a public face mask dataset, and an ablation experiment is also provided to verify the effectiveness of our proposed model, which is compared with the state-of-the-art (SOTA) models as well.

RESULTS

Our proposed detector increases the AP (average precision) values in each category of the public face mask dataset compared with the original YOLOv4-tiny. The mAP (mean average precision) is improved by 4.56% and the speed reaches 92.81 FPS. Meanwhile, the quantity of parameters and the FLOPs (floating-point operations) are reduced by 1/3, 16.48%, respectively.

CONCLUSIONS

The proposed detector achieves better overall detection performance compared with other SOTA detectors for real-time mask detection, demonstrated the superiority with both theoretical value and practical significance. The developed system also brings greater flexibility to the application of face mask detection in hospitals, campuses, communities, etc.

A Review of Optical Neural Networks

With the continuous miniaturization of conventional integrated circuits, obstacles such as excessive cost, increased resistance to electronic motion, and increased energy consumption are gradually slowing down the development of electrical computing and constraining the application of deep learning. Optical neuromorphic computing presents various opportunities and challenges compared with the realm of electronics. Algorithms running on optical hardware have the potential to meet the growing computational demands of deep learning and artificial intelligence. Here, we review the development of optical neural networks and compare various research proposals. We focus on fiber-based neural networks. Finally, we describe some new research directions and challenges.