The fiercely competitive business environment and increasingly personalized customization needs are driving the digital transformation and upgrading of the manufacturing industry. Industrial Internet of Things (IIoT) intelligence, which can provide innovative and efficient solutions for various aspects of the manufacturing value chain, illuminates the path of transformation for the manufacturing industry. It is time to provide a systematic vision of IIoT intelligence. However, existing surveys often focus on specific areas of IIoT intelligence, leading researchers and readers to have biases in their understanding of IIoT intelligence, that is, believing that research in one direction is the most important for the development of IIoT intelligence, while ignoring contributions from other directions. Therefore, this article provides a comprehensive overview of IIoT intelligence. We first conduct an in-depth analysis of the inevitability of manufacturing transformation and study the successful experiences from the practices of Chinese enterprises. Then, we give our definition of IIoT intelligence and demonstrate the value of IIoT intelligence for industries in functions, operations, deployments, and application. Afterward, we propose a hierarchical development architecture for IIoT intelligence, which consists of five layers. The practical values of technical upgrades at each layer are illustrated by a close look on lighthouse factories. Following that, we identify seven kinds of technologies that accelerate the transformation of manufacturing, and clarify their contributions. The ethical implications and environmental impacts of adopting IIoT intelligence in manufacturing are analyzed as well. Finally, we explore the open challenges and development trends from four aspects to inspire future researches.

Pulse compression is experimentally demonstrated by using a coupled-waveguide structure consisting of an InGaAsP/InP semiconductor as a highly dispersive element. In this structure, waveguide dispersion associated with supermodes is significantly enhanced. The maximum temporal compression factor was 4.3 (compression from 2.2 to 0.514 ps). This result indicates that the coupled-waveguide structure has the potential for use in a simple, compact, transmissive, and sign-free dispersion compensator.

This paper presents a novel surrogate-based cross-correlation (SBCC) framework to improve the correlation performance for practical particle image velocimetry (PIV). The basic idea is that an optimized surrogate filter/image, replacing one raw image, will produce a more accurate and robust correlation signal. Specifically, the surrogate image is encouraged to generate a perfect Gaussian-shaped correlation map to tracking particles (PIV image pair) while producing zero responses to image noise (context images). The problem is formulated with an objective function composed of surrogate loss and consistency loss. As a result, the closed-form solution provides a multivariate operator that could consider other negative context images. Compared with state-of-the-art baseline methods (background subtraction, robust phase correlation, etc.), our SBCC method exhibits significant performance improvement (accuracy and robustness) in the synthetic dataset and several challenging experimental PIV cases. Moreover, our implementation with all details (https://github.com/yongleex/SBCC) is also available for interested researchers.

A prototype transceiver composed of a 1.3-μm-range lens-integrated laser diode and photodiode as well as a complementary metal-oxide-semiconductor (CMOS) laser diode driver and a CMOS transimpedance amplifier for high-speed optical interconnections was developed. It demonstrated 25-Gb/s error-free 100-m multimode fiber transmission, with power dissipation of only 9 mW/Gb/s, for the first time.

The existing particle image velocimetry (PIV) techniques do not consider the curvature effect of the nonstraight particle trajectory because it seems to be impossible to obtain the curvature information from a pair of particle images. In this work, the particle curved trajectory between two recordings is first explained with the streamline segment of a steady flow (diffeomorphic transformation) instead of a single vector, and this novel idea is termed diffeomorphic PIV. Specifically, a deformation field is introduced to describe the particle displacement along the streamline, i.e., we try to find the optimal velocity field, of which the corresponding deformation vector field agrees with the particle displacement. Because the variation of the deformation function can be approximated with the variation of the velocity function, the diffeomorphic PIV can be implemented as special iterative PIV. This says that the diffeomorphic PIV warps the images with deformation vector field instead of velocity field and keeps the rest procedures as the same as a conventional iterative PIV. Two diffeomorphic deformation schemes—forward diffeomorphic deformation interrogation (FDDI) and central diffeomorphic deformation interrogation (CDDI)—are proposed in this article. Tested on synthetic PIV images, the FDDI achieves significant accuracy improvement across different one-pass displacement estimators (cross correlation, optical flow (OF), and deep learning flow). Besides, the results on three real PIV image pairs demonstrate the nonnegligible curvature effect for central difference interrogation (CDI)-based measurement, and our FDDI provides larger velocity estimation—more accurate—in the fast curvy streamline areas. The significant accuracy improvement of the combination of FDDI and accurate dense estimator (e.g., OF) means that our diffeomorphic PIV paves a completely new way for complex flow field measurement.

Due to the nonlinearity in phase shifting profilometry (PSP) system, the captured images are often distorted with fringe harmonics, resulting in inaccurate phase map and measurement. Considering the fact that the phase error can be significantly reduced by modeling high-order fringe harmonics, this work formulates the phase extraction problem — with different frequency images and high-order fringe harmonic model — as a maximum likelihood estimation (MLE). To optimize it efficiently, we thus propose a combined-frequency phase extraction (CFPE) solution by introducing a latent phase map and incorporating the famous expectation-maximization (EM) framework. As a result, our CFPE method only needs ∼5% execution time of a high-order baseline, whilst keeps the high-order accuracy. Tested on synthetic images as well as practical measurements, our CFPE method demonstrated its performance improvement of efficiency and accuracy. In addition, our detailed implementation with experimental arrangement is also provided for interested researchers.