Huimin Lu

In this work, a resource allocation strategy integrating time switching and power splitting (TS-PS) is presented to address the energy efficiency optimization problem in self-powered visible light communication and positioning (SPVLCP) systems. Specifically, for the SPVLCP system employing filter-bank multicarrier (FBMC) modulation, the proposed strategy jointly accounts for energy harvesting requirements, positioning accuracy demands, and transmit-power constraints, formulating the energy efficiency (EE) maximization task as a constrained non-convex optimization problem. To tackle this problem, an iterative algorithm is developed by integrating the concave-convex procedure (CCCP) with successive convex approximation (SCA). Leveraging equivalence transformations and convex surrogate approximations, the original problem is transformed into a series of convex subproblems, which can then be efficiently solved using standard convex optimization solvers. Numerical simulations demonstrate that the proposed strategy significantly enhances the energy efficiency of the SPVLCP system compared with conventional power-splitting (PS) and time-switching (TS) schemes. The performance improvement is especially notable in scenarios with high energy harvesting demands and stringent positioning accuracy requirements. In addition, comparisons with the multi-verse optimizer (MVO) show that the proposed algorithm achieves superior performance and computational efficiency.

Phase-sensitive optical time domain reflectometry ( φ -OTDR) systems frequently encounter a bottleneck where high-accuracy event recognition compromises real-time edge processing capabilities due to the computational demands of complex deep learning models. This paper presents an end-to-end classification framework that resolves this trade-off by integrating discrete first-order difference preprocessing with a lightweight 1D convolutional neural network (1D-CNN). By applying a differential operator to suppress non-stationary baseline drift and Z-score standardization for distributional stability, the proposed method extracts high-frequency transient features from raw time-series data. The subsequent 1D-CNN architecture, characterized by a pyramidal channel expansion and global adaptive average pooling, captures temporal dependencies with minimal structural redundancy. Experimental validation on a public benchmark dataset demonstrates that the model achieves an average accuracy of 99.84%, surpassing state-of-the-art vision transformer (ViT) and MobileNetV2 architectures. Furthermore, the model maintains a parameter footprint of only 42k and achieves an end-to-end inference latency of 0.517 ms on GPU. These results demonstrate that the proposed architecture offers an effective balance between precision and efficiency, enabling robust real-time monitoring on resource-constrained edge devices.

In this work, an integrated ultraviolet (UV) communication– positioning with energy harvesting (UV-CPEH) system in non-line-of-sight (NLOS) channels is considered. Under constraints on positioning error, total transmit power, and harvested energy, a joint optimization model based on the time-switching and power-splitting (TS–PS) mechanism is formulated to enhance the achievable system rate. To address the inherent non-convexity, an efficient iterative algorithm leveraging successive convex approximation (SCA) is proposed to obtain a convergent solution. Numerical results show that the proposed time–power optimization strategy effectively balances the trade-off among communication, positioning, and energy harvesting.

A mesa-sidewall nanoscale reflective structure was introduced on the p-GaN surface of flip-chip deep-ultraviolet light-emitting diodes (DUV-LEDs) to enhance the light extraction efficiency (LEE). In contrast to conventional planar reflectors, the proposed structure was specifically engineered to not only minimize DUV absorption in the p-GaN layer but also establish what is believed to be a novel reflective pathway for photon propagation. Utilizing an artificial intelligence (AI)-assisted inverse design approach based on the Jaya algorithm, the reflective structure was systematically optimized to maximize the LEE through precise modulation of the light propagation mechanism. A comprehensive investigation into the light extraction process was conducted, revealing the effects of structural parameters on the light propagation path and LEE. Remarkably, both simulation and experimental results demonstrated that the optimized mesa-sidewall nanoscale structure achieved an enhancement of more than 200% in LEE over the conventional Al reflective layer, showcasing its significant potential for high-performance DUV optoelectronic applications. This work provides a viable and efficient strategy for developing high-power DUV-LEDs and demonstrates the potential of AI-driven design in advanced photonic devices.

An AlGaN-based p-i-n ultraviolet avalanche photodiode (APD) with double-barrier heterojunctions is proposed in this work. The polarization electric field formed by the heterojunction can significantly enhance the electric field intensity in the intrinsic layer, enabling the designed structure to exhibit superior photoelectric performance. On this basis, both conventional and designed APD structures were experimentally fabricated using metal-organic chemical vapor deposition. A current–voltage (I–V) characteristic measurement system was established to evaluate the photoelectric performance of the APDs with different structures. The measurement results demonstrate that at a reverse bias of 20 V, the designed APD demonstrates approximately 17% higher peak responsivity and 6% greater external quantum efficiency compared to the conventional APD structure.

Based on the scattering characteristics of ultraviolet light, the existing studies on ultraviolet communication (UVC) channels mainly focus on non-line-of-sight (NLOS) links, but lack research on line-of-sight (LOS) links. In this paper, a single-scatter sphere-threshold-based NLOS/LOS UVC channel model is proposed by introducing an imaginary threshold ball. The analytical expressions for the path loss (PL) of the LOS link and the whole link are derived. The simulation results demonstrate that the proposed model aligns closely with the Monte Carlo model, and in the presence of LOS links, its root mean square error (RMSE) is only 0.1138 dB. Further, the expressions for the PL and the channel impulse response (CIR) when the receiver is moving are derived, and the impact of parameters such as motion speed and motion direction is systematically analyzed. The results show that the PL and the CIR are more sensitive to the coplanar-noncoplanar transition (CNT) when the transmitter and receiver are close to a coplanar state at the initial time. In addition, the distance between the transmitter and receiver is the dominant factor affecting channel performance in all scenarios.

In this work, a resource optimization allocation scheme based on the multi-verse optimization (MVO) algorithm is proposed for a self-powered visible light communication and positioning system utilizing a silicon photovoltaic cell as the receiver. A corresponding optimization problem with the objective of minimizing the system bit error rate (BER) is formulated, which incorporates three constraints: positioning error threshold, harvested energy threshold, and maximum transmit electrical power of the light-emitting diode. The MVO algorithm is employed to dynamically adjust both the subcarrier group power at the sending end and the power allocation factor at the receiving end, thereby achieving a synergistic optimization of communication performance, positioning accuracy, and energy transfer efficiency. Simulation results indicate that, compared to the uniform allocation scheme, the proposed scheme significantly reduces the BER and effectively mitigates the high-frequency fading characteristics derived from the silicon photovoltaic cell. The demonstration reveals the trade-off between communication, positioning, and energy harvesting, offering, to our knowledge, novel insights into the design and optimization of future integrated systems.

This Letter proposes an improved Camshift algorithm based on the efficient channel attention (ECA)-ResNet model for laser spot tracking in underwater optical wireless communication (UOWC) systems. Experimental results demonstrate that the proposed Cam-ECANet algorithm can maintain the center location error (CLE) within 12 pixels and support a real-time tracking speed of 50 mm/s. In disturbed underwater environments, it achieves 58.5% and 61.6% BER reduction compared to Cam-ResNet and Cam-Kalm algorithms, effectively ensuring the reliability of the wireless laser communication and tracking system.