Significance The construction scale of large-scale infrastructure in China has ranked first in the world for many years.Meanwhile,due to construction quality,using environment,natural disasters,and other factors,serious accidents occur frequently.Distributed optical fiber sensing technologies employ optical fibers as signal transmission medium and sensing units to realize continuous distributed measurement of external parameters along the optical fiber.Therefore,it is the most potential non-destructive monitoring technology for large-scale infrastructure health monitoring in real time.However,distributed fiber optic sensing technologies still face various challenges such as reliability,low cost,and intelligence as they move toward the market. Progress At present,distributed optical fiber sensing technologies that have caught extensive attention and research include optical time-domain reflectometer,coherent optical time-domain reflectometer,phase-sensitive optical time-domain reflectometer,optical frequency-domain reflectometer,Raman optical time-domain reflectometer,Brillouin scattering optical time-domain reflectometer,Brillouin optical time-domain analyzer,and optical interferometry.We focus on introducing their working principles,system basic structures,development history,current status,and major research institutions and manufacturers at home and abroad. Based on detailing the application requirements,principles,and methods of distributed optical fiber sensing technologies in communication system monitoring,power system monitoring,coal geology monitoring,oil and gas exploration,transportation field,transportation pipeline monitoring,aerospace equipment monitoring,and perimeter security,we provide several typical application cases. Conclusions and Prospects The future main directions of development are listed: 1)Multi-mechanism integration system.Single sensing parameters make it difficult to represent the true state of the measured object,which can result in false reports and missed reports.Simultaneous measurement of multiple parameters can provide multidimensional and more comprehensive information,thereby more accurately identifying fault events.The key point of the fusion-type distributed optical fiber sensing technology is to employ different scattering lights to respond to different events in the optical fiber to achieve multi-parameter sensing. 2)Specialty sensing fiber cable technology.By changing the fiber material,structure,and packaging,specialty optical fiber cables can overcome the limitations of distributed sensors based on ordinary single-mode optical fibers,and obtain engineering applications in specific sensing parameters and performance in specific fields and scenarios. 3)Sensing signal processing and intelligent perception technology.Due to the weak intensity of scattered light compared to incident light,distributed sensing systems are limited by signal-to-noise ratio.This affects the measurement accuracy,monitoring distance,response speed,spatial resolution,and other key indicators of distributed sensing systems.Signal processing techniques to analyze and enhance collected data are important means to improve the performance of sensing systems. 4)Communication-sensing fusion system.Technologies such as wavelength division multiplexing,polarization diversity,and coherent detection from optical communication systems are applied to distributed fiber optic sensing systems.Additionally,existing optical fiber communication systems can be adopted for synchronous sensing.These are crucial steps towards the practical applications of distributed fiber optic sensing systems. 5)Distributed shape sensing technology.Leveraging distributed fiber optic sensing technology for shape sensing is an important development direction. 6)Ocean state monitoring based on existing optical cables.Existing undersea optical communication networks are employed as sensing networks to achieve intelligent perception of the surrounding environment of the cables.This enables large-scale online monitoring and early warning capabilities with relatively low investment,thus providing rapid and accurate assurance for managing major maritime incidents and maritime disaster risks.

Automatic polarization controllers find broad applications in various fields, including optical communication, quantum optics, optical sensing, and biomedicine. Currently, the predominant integrated automatic polarization controllers employ either lithium niobate or silicon platforms. Devices based on lithium niobate platforms exhibit excellent performance; however, their fabrication complexity hinders widespread commercial deployment. In contrast, silicon-based integrated automatic polarization controllers benefit from complementary metal–oxide–semiconductor compatibility and reduced fabrication costs. Nevertheless, these silicon automatic polarization controllers suffer from low tracking speeds, peaking at merely 1.256 krad/s. In this study, we demonstrated a silicon high-speed automatic polarization controller, incorporating innovative thermal tuning units combined with a sophisticated control algorithm. The response time of these thermal tuning units has been markedly decreased to 3.2 µs. In addition, we have implemented a novel automatic polarization control algorithm, utilizing gradient descent techniques, on a field-programmable gate array control board. The synergy of the rapid thermal tuning unit and the advanced control algorithm has enabled us to attain an unprecedented polarization control speed of up to 20 krad/s, with this rate being solely limited by the capabilities of our characterization equipment. To our knowledge, this speed is the fastest yet reported for a silicon-based integrated automatic polarization control chip. The proposed device represents a significant breakthrough in the field of silicon-based automatic polarization controllers, paving the way for the future integration of additional polarization management devices. Such an advancement would mark a substantial leap in the realm of integrated photonics, bridging the gap between performance efficiency, cost-effectiveness, and technological integration.

Optical neural networks (ONNs) have shown great promise in overcoming the speed and efficiency bottlenecks of artificial neural networks. However, the absence of high-speed, energy-efficient nonlinear activators significantly impedes the advancement of ONNs and their extension to ultrafast application scenarios like real-time intelligent signal processing. In this work, a novel silicon/graphene ultrafast all-optical nonlinear activator, leveraging the hybrid integration of silicon slot waveguides, plasmonic slot waveguides, and monolayer graphene is demonstrated. Exploiting the exceptional picosecond-scale photogenerated carrier relaxation time of graphene, the response time of the activator is markedly reduced to approximate to 93.6 ps, establishing all-optical activator as the fastest known in silicon photonics to knowledge. Moreover, the all-optical nonlinear activator holds a low threshold power of 5.49 mW and a corresponding power consumption per activation of 0.51 pJ. Its feasibility and capability for use in ONNs, manifesting performance comparable with commonly used activation functions are experimentally confirmed. This breakthrough in speed and energy efficiency of all-optical nonlinear activators opens the door to significant improvements in the performance and applicability of ONNs. In this article, an ultrafast and energy-efficient all-optical nonlinear activator is proposed that leverages the saturable absorption and ultrafast carrier relaxation time of monolayer graphene, combined with the strong light confinement of a double-slot structure. This breakthrough in speed and energy efficiency of all-optical nonlinear activators opens the door to significant improvements in the performance and applicability of ONNs. image

Objective Phase -sensitive optical time -domain reflection (& phi;-OTDR) has the advantages of high accuracy, fast response speed, long monitoring distance, and anti -electromagnetic interference and has been widely used in dynamic sensing fields such as perimeter security and railway and pipeline monitoring. For direct detection intensity -demodulation 0-OTDR, the pulse power is limited by the nonlinear effect, which causes a weak signal-to-noise ratio of the end signal, and its sensing distance is usually less than 25 km. Because the optical phase signal is linearly related to the vibration signal imposed on the fiber and coherent detection can significantly improve the detection sensitivity, the long-distance 0-OTDR system mainly uses coherent detection and phase demodulation technology. Most coherent detection phase -demodulation 0-OTDR system model recognition algorithms use phase signal as the input, combined with time -frequency feature extraction methods, such as Fourier transform and wavelet transform. However, interference fading occurs in the coherent detection system, which causes serious deterioration of the intensity signal, resulting in phase demodulation errors and false alarms. Common methods to eliminate interference fading are the frequency diversity, chirped pulses, and other frequency domain regulation technologies, which lead to complex system hardware. Moreover, owing to the variety of the disturbance signals and long sensing distance that results in a low signal-to-noise ratio of the end signal, 0-OTDR systems suffer from false alarms in practical applications. It is of great significance to further improve the accuracy of the vibration signal identification for the timely detection of abnormal events.Methods A pattern recognition method based on a coherent detection 0-OTDR system with mixed intensity and phase signal inputs is proposed, which can effectively reduce the impact of interference fading on the accuracy of event alarms without increasing the hardware complexity. The proposed method uses a hybrid deep neural network (HDNN), which combines a one-dimensional convolutional neural network (1DCNN) and a multi -layer perceptron (MLP), as shown in Fig. 4. The phase and intensity signal vectors are recovered simultaneously using the Hilbert demodulation algorithm. The phase and intensity vectors within a second are simply normalized by the max -min and tanh functions separately and then fed into the model. The model uses MLP to extract the fading noise features of the intensity signal and uses the 1DCNN model as the basic model to extract the disturbance characteristics of the phase signal. After the fusion of two-dimensional features and a classification layer, the model outputs the final detection results.Results and Discussions A long-distance & phi; -OTDR system of more than 25 km was built. An adjustable optical attenuator (VOA) was used to simulate disturbance events occurring at different locations along the fiber, with attenuation of the VOA ranging from 1 dB to 7 dB. Four types of events, such as human beatings, walking, jumping, and machine excavating, are imposed at the outdoor optical cable buried 0.5 m underground. A 1DCNN network with only phase signal input was used as the comparison model. After multiple rounds of training, the experimental results show that the proposed HDNN model with intensity and phase signal inputs can achieve an average accuracy of 98.8%, which is better than the 1DCNN model result of 96.1% with only the phase signal input. Furthermore, comparing the confusion matrix of the two models, the 1DCNN model had the worst recognition accuracy of 91.0% with background noise and human beat events. In contrast, the HDNN model significantly improves the recognition accuracy of the two events to 99.4%. This shows that the interference fading anomalies contained in the background noise can be identified by the HDNN model with additional intensity input. For the other three types of events, the accuracy results of the two models are very close, indicating that the phase signal has a better ability to recover the vibration events than the intensity signal, which is consistent with the previous analysis.Conclusions Aiming to further improve the event alarm accuracy of the long-distance coherent detection 0-OTDR system, a pattern recognition method with a mixed input of intensity and phase signals was proposed. To verify the improvement of the proposed method, a 1DCNN network with only the phase signal input was used as the comparison model. A hybrid deep neural network, combining 1DCNN and MLP, was used for the intensity and phase signal mixed -input classification. The model used MLP to extract the fading noise features of the intensity signal and used 1DCNN to extract the disturbance features of the phase signal. The phase and intensity vectors within a second are simply normalized by the max -min and tanh functions separately and then fed into the model. The experimental results show that the proposed HDNN model can achieve an average accuracy of 98.8% for four types of events, including human beatings, walking, jumping, and machine excavation, which is better than the 1DCNN model detection result of 96.1% with only a phase signal input. The method using intensity signal -assisted phase signal detection can further improve the accuracy of 0-OTDR pattern recognition.

Efficient regulation of thermal radiation is an effective way to conserve energy consumption of buildings. Because windows are the least energy-efficient part of buildings, their thermal radiation regulation is highly demanded, especially in the changing environment, but is still a challenge. Here, by employing a kirigami structure, we design a variable-angle thermal reflector as a transparent envelope of windows for their thermal radiation modulation. The envelope can be easily switched between heating and cooling modes by loading different pre-stresses, which endow the envelope windows with the ability of temperature regulation, and the interior temperature of a building model can be reduced by ~3.3 °C under cooling mode and increased by ~3.9 °C under heating mode in the outdoor test. The improved thermal management of windows by the adaptive envelope provides an extra heating, ventilation, and air-conditioning energy savings percentage of 13% to 29% per year for buildings located in different climate zones around the world, making the kirigami envelope windows a promising way for energy-saving utilization.

Space-division multiplexing (SDM) techniques bring new approaches for various applications in microwave photonic signal processing. In this work, an all-fiber reconfigurable microwave photonic filter (MPF) is realized based on SDM techniques using multicore fiber (MCF) and multimode fiber (MMF). The multimode interference (MMI) generated from the proposed MCF-MMF-MCF structure can be manipulated in dimensions of space and wavelength simultaneously, thus configuring the power distribution in all the taps of the MPF. The trained artificial neural network can accurately predict the operating wavelength and spatial input core for a target power distribution. In a low-cost, light-weight, and compact way, SDM fibers combined with MMI allow for greater flexibility and diversity in the response of the reconfigurable MPF.