The increasing demand for high-precision, real-time sensing in various fields has spurred the development of optical fiber grating sensors (OFGSs). This study reviews the research field of OFGSs, exploring their historical development, current trends, and future opportunities through scientometric analysis utilizing CiteSpace. The research landscape has grown exponentially since the early studies on fiber Bragg gratings and long-period fiber gratings in the 1990s. Bibliometric data reveal that engineering, optics, and instrumentation dominate OFGS research, with emerging interdisciplinary applications in environmental, biological, and medical sensing. Key contributors have advanced OFGSs through femtosecond laser inscription, novel materials, and intelligent system integration, as reflected in co-citation and keywords analyses. Trends such as AI-driven optimization, surface plasmon resonance, and 3D printing signal shift toward adaptive, multifunctional sensing systems capable of addressing diverse challenges. This review also maps the evolution of OFGS research, transitioning from foundational strain and temperature sensing to sophisticated systems for structural health monitoring, biomedical diagnostics, and robotics. Despite global disruptions, the field’s recovery highlights its critical role in advancing sensing technologies. By combining thematic insights from co-citation and keyword analyses, this study identifies both established directions and transformative opportunities, providing a holistic understanding of OFGS research and its trajectory.

This study addresses an adaptive two-stage sliding mode control (SMC) scheme for the state synchronization between two identical systems, which belong to a kind of n-dimensional chaotic system, by considering the appearance of lumped system uncertainties and external disturbances. The controlled system is assumed to be attached to sector nonlinearity for the control input. The proposed adaptive control scheme involves time-variable state-feedback gains, which are updated in accordance with the appropriate adapted rules without foreknown the certain information of nonlinear system dynamics, bounds of lumped system uncertainties and external disturbances, and sector input nonlinearity. The derivation of the control scheme is found based on the introduced sequence of two sliding functions. The stage 1 sliding function is defined by the states of the error dynamical system, where the asymptotical stability is inherent. Then, the stage 2 sliding function is formed by the stage 1 function, where the finite-time stabilization is guaranteed. The proposed adaptive control scheme can cope with the effect of sector nonlinearity for the control to meet the control goal. The sufficient conditions of the stability of the error dynamical system are proven mathematically by means of the Lyapunov theorem. Besides, the capacity of the present scheme is carried out by the numerical studies.

Fiber Bragg gratings (FBGs) have evolved from passive sensing elements into actively programmable photonic components, enabling dynamic wavelength control across diverse applications. This review provides a comprehensive and systematic overview of active wavelength control technologies for FBGs, deliberately excluding passive sensing applications. We systematically categorize the fundamental tuning mechanisms—including mechanical, thermal, optothermal, electro-optic, nonlinear optical, and hybrid approaches—and compare their performance characteristics in terms of tuning range, speed, precision, and trade-offs. Key enhancement techniques, such as mechanical amplification, thermal packaging, femtosecond laser fabrication, and FPGA-based interrogation, are examined. The transformative impact of actively controlled FBGs is elucidated across three major application domains: tunable and narrow-linewidth fiber lasers, reconfigurable microwave photonic systems, and emerging fields including quantum information processing and biomedical imaging. A consolidated technology map visualizes the connections between enabling techniques and applications. Finally, we critically analyze core challenges—performance trade-offs, control complexity, and integration bottlenecks—and outline future research directions driven by novel materials, artificial intelligence, and quantum technologies. This review offers a structured framework for understanding active FBGs as programmable photonic primitives, providing actionable insights for researchers and engineers in academia and industry.

People use a combination of language and gestures to convey intentions, making the generation of natural co-speech gestures a challenging task. In audio-driven gesture generation, relying solely on features extracted from raw audio waveforms limits the model's ability to fully learn the joint distribution between audio and gestures. To address this limitation, we integrated key features from both raw audio waveforms and Mel-spectrograms. Specifically, we employed cascaded 1D convolutions to extract features from the audio waveform and a two-stage attention mechanism to capture features from the Mel-spectrogram. The fused features were then input into a Transformer with cross-dimension attention for sequence modeling, which mitigated accumulated non-autoregressive errors and reduced redundant information. We developed a diffusion model-based Audio to Diffusion Gesture (A2DG) generation pipeline capable of producing high-quality and diverse gestures. Our method demonstrated superior performance in extensive experiments compared to established baselines. Regarding the TED Gesture and TED Expressive datasets, the Fréchet Gesture Distance (FGD) performance improved by 16.8 and 56%, respectively. Additionally, a user study validated that the co-speech gestures generated by our method are more vivid and realistic.

Aiming at the challenging problem of the traditional warp knitting machine electronic jacquard control system with complex structure of multiple circuit boards layered cascade, such as large physical space occupation, high power consumption, and independent high-voltage power supply voltage, we proposed an embedded circuit and control strategy design for the piezoelectric jacquard needle (PJN) with adaptive boost and energy recovery functions. Firstly, the electromechanical dynamics model of PJN was established. Secondly, the fuzzy PI double closed-loop control algorithm driven by a finite state machine is proposed. Thirdly, with the help of a Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET), the PJN is integrated with the drive circuit. The drive circuit of PJN uses an energy storage inductor to replace the current limiting resistor of the traditional drive circuit, which can not only limit the forward charging current of the PJN and reduce energy loss but also can use the energy absorbed from the low-voltage power supply to adaptively boost the power supply of the PJN to the high voltage required for working conditions. The simulation results show that the new PJN drive circuit has an adaptive self-boost function. The PWM signal modulated by the fuzzy PI double closed-loop control algorithm can efficiently and accurately control the adaptive boost power supply and the voltage across the PJN. The mode of the circuit can be correctly switched through the sequential logic of the finite state machine and realize the energy recovery function.