Youxing Chen

Accurate classification of carbon deposit severity for automotive engines is critical for routine vehicle inspection and exhaust emission pollution control. Although deep learning-based methods have made certain progress in carbon deposit visual detection, they face prominent drawbacks in real industrial deployment because endoscopic carbon deposit images carry subtle inter-class fine-grained textures and lack sufficient labeled training samples, which severely impair conventional models’ classification performance and prevent them from satisfying the accuracy requirements of practical inspection tasks. This paper proposes an improved EfficientNet-B0 framework for fine-grained identification of cylinder wall carbon deposit severity. By introducing the DiffuseMix data augmentation algorithm, label confusion and overfitting are avoided. At the same time, a dual-path weighted feature fusion structure (DPWFFS) is designed to achieve bidirectional complementary of shallow and deep features, enhancing the representation ability of local discriminative features of carbon deposits. Quantitative experiments conducted on our self-built cylinder wall carbon deposit dataset demonstrate that the proposed improved model achieves a classification accuracy of 90.33%, which substantially outperforms the original EfficientNet-B0 baseline. The presented method provides an effective technical reference for the intelligent automatic identification of engine carbon deposit severity.

Ceramic Matrix Composites (CMC) are widely used in critical applications such as leading edges of aircraft wings and thermal insulation layers of thermal protection systems due to their advantages of being lightweight, high-temperature resistant, and impact-resistant. However, influenced by manufacturing processes and service environments, internal defects such as pores and delamination are prone to occur, significantly compromising the mechanical properties and service reliability of the material. This paper primarily evaluates the feasibility and applicability of using Terahertz Frequency Modulated Continuous Wave (FMCW) technology for the non-contact detection of CMC. First, the measurement principle of FMCW is introduced, and the structure of the detection system, including a two-dimensional mechanical scanning platform, optical lenses, a control platform, and a data acquisition unit, is outlined. Subsequently, scanning imaging was performed on CMC specimens and their bonded thermal protection structure (TPS) specimens, demonstrating the feasibility of Terahertz FMCW technology as an advanced non-destructive testing tool for CMC inspection. The issues of diffraction and the Rayleigh limit inherent in real-aperture terahertz imaging were analyzed and discussed. A multi-scale fusion defect detection method incorporating background estimation is proposed to enable precise delineation of defect regions. Experimental results show that, after processing with the proposed algorithm, the minimum detectable pore diameter at the focal plane is 1 mm, with a regional error of approximately 3%. The detection error for pores and debonding areas in CMC is maintained within 6.44%. Analysis indicates that combining terahertz imaging technology with image processing algorithms enables the quantitative analysis of internal defects in composite materials, offering a new technical approach for defect detection in composite materials.

The coupling efficiency between free-space optics and fiber optics is highly susceptible to environmental disturbances (e.g., beam displacement, angular jitter, and wavefront aberrations), which has become a critical bottleneck in high-precision optical transmission systems. To address this issue, a cascaded adaptive feedback control system integrated into the optical transmission path is proposed. Targeting the dual requirements of “rapid response” and “long-term stability” in practical optical transmission, a power-feedback-based ramp-up algorithm is developed to simultaneously meet these needs. The algorithm dynamically adjusts piezoelectric-driven reflective mirrors to achieve multi-dimensional autonomous beam alignment and real-time optimization of coupling states. The system is validated on a rubidium Raman photon source experimental platform, demonstrating its applicability to demanding quantum and sensing platforms. Experimental results show that the system increases multimode (single-mode) optical coupling efficiency from below 1% to over 80% (70%) within 10 s (20 s) and enters a long-term stable transmission state after 65 s (70 s). Additionally, real-time link monitoring effectively suppresses coupling attenuation caused by beam jitter, achieving a balance between response speed and transmission stability without the need for independent mode optimization. This scheme provides practical technical support for efficient coupling in high-precision optical transmission systems such as laser communication and optical sensing.

This study addresses the issue of cross-interference that occurs when locked continuous light and signal photons are collinear during interferometer measurements. To tackle this, a temperature-controlled Fabry–Pérot cavity filter with a heterogeneous cascaded structure is proposed and applied. The system consists of six filtering stages, created by designing Fabry–Pérot cavities of three different lengths, each used twice (to match optical frequencies), along with temperature control settings. By applying differentiated linewidth regulation, the approach effectively suppresses interference from locked light while significantly enhancing the signal-to-noise ratio in photon detection. This method overcomes the challenge of interference from same-frequency noise photons in atomic ensemble-entangled sources, achieving a noise–photon extinction ratio on the order of 10⁶ and surpassing the frequency resolution limit of a single filter. Experimental results demonstrate that the system reduces the noise floor in the detection optical path to below 10⁻¹⁶, while maintaining a photon transmission efficiency above 53% for the signal. This technology effectively addresses key challenges in noise suppression and photon state fidelity optimization in optical fiber quantum communication, offering a scalable frequency–photon noise filtering solution for long-distance quantum communication. Furthermore, its multi-parameter cooperative filtering mechanism holds broad potential applications in areas such as quantum storage and optical frequency combs.

Crop chlorophyll contents affect growth, and accurate assessment aids field management. SPAD (Soil Plant Analysis Development) values of leaves were mainly used to estimate chlorophyll content. Background interference affects the accuracy of SPAD value inversion. To address this issue, a rice leaf SPAD inversion method combining deep learning and feature selection is proposed. First, a leaf segmentation model based on U-Net was established. Then, the color features of leaf images were extracted. Seven color features highly correlated with SPAD were selected via the Pearson correlation coefficient and recursive feature elimination optimization. Finally, leaf SPAD inversion models based on random forest, support vector regression, BPNNs, and XGBoost were established. The results demonstrated that the U-Net could achieve accurate segmentation of leaves with a maximum mean intersection over union (MIoU) of 88.23. The coefficients of determination R² between the anticipated and observed SPAD values of the four models were 0.819, 0.829, 0.896, and 0.721, and the root mean square errors (RMSEs) were 2.223, 2.131, 1.564, and 2.906. Through comparison, the method can accurately predict SPAD in different low-definition and saturation images, showing a certain robustness. It can offer technical support for accurate, nondestructive, and expedited evaluation of crop leaves’ chlorophyll content via machine vision.

In the realm of quantum communication and photonic technologies, the extension of coherence time for photon wave packets is essential for improving system efficacy. This research introduces a methodology for measuring coherence time utilizing an unbalanced fiber interferometer, specifically designed for tunable pulse-width photon wave packets produced by cold atoms. By synchronously generating write pulses, signal light, and frequency-locking light from a single laser source, the study effectively mitigated frequency discrepancies that typically arise from the use of multiple light sources. The implementation of frequency-resolved photon counting under phase-locked conditions was accomplished through the application of polarization filtering and cascaded filtering techniques. The experimental results indicated that the periodicity of frequency shifts in interference fringe patterns diminishes as the differences in delay arm lengths increase, while fluctuations in fiber length and high-frequency laser jitter adversely affect interference visibility. Through an analysis of the correlation between delay and photon counts, the coherence time of the write laser was determined to be 2.56 µs, whereas the Stokes photons produced through interactions with cold atoms exhibited a reduced coherence time of 1.23 µs. The findings suggest that enhancements in laser bandwidth compression and fiber phase stability could further prolong the coherence time of photon wave packets generated by cold atoms, thereby providing valuable technical support for high-fidelity quantum information processing.

Most existing studies on the optimal bandwidth selection for plant nitrogen are based on the sensitive band center, and determine the optimal bands by manually adjusting the bandwidth, step by step. However, this method has a high level of manual involvement and is time-consuming. This paper focused on rice as the research subject, based on determining the center of the rice plant nitrogen-sensitive bands and the maximum region Ω of the fitted R² between the narrow-band vegetation indices (N-VIs) and plant nitrogen, a method was proposed to automatically select the optimal bandwidth by constructing inscribed rectangles. UAV hyperspectral images were used to carry out the spatial inversion and precision verification of the rice plant nitrogen, based on the optimal width of sensitive bands. The results revealed that the optimal bandwidths, automatically selected on the basis of N-VIs via the inscribed rectangle method, achieved good results in the remote sensing inversion of plant nitrogen at the rice jointing and flowering stages, with the coefficient of determination (R²) greater than 0.49 to satisfy the requirement of significance (p < 0.05) and the normalized root mean square error (NRMSE) and mean relative error (MRE) of less than 13%. These findings indicate that the method of crop plant nitrogen inversion band center screening and automatic search for the optimal bandwidth in this study has certain feasibility, which provides a new idea for screening the optimal bandwidth on the basis of the sensitive band center and provides technical support for the design of satellite band parameters.

Advanced artificial intelligence (AI) and multi-access edge computing (MEC) technologies facilitate the development of edge intelligence, enabling the intelligence learned from remote cloud to network edge. To achieve automatic decision-making, the training efficiency and accuracy of AI models are crucial for edge intelligence. However, the collected data volume of each network edge node is limited, which may cause the over-fitting of AI models. To improve the training efficiency and accuracy of AI models for edge intelligence, intelligence networking-empowered edge computing (INEEC) is a promising solution, which enables each network edge node to improve its AI models quickly and economically with the help of other network edge nodes’ sharing of their learned intelligence. Sharing intelligence among network edge nodes efficiently is essential for INEEC. Thus in this paper, we study the intelligence sharing scheme, which aims to maximize the system energy efficiency while ensuring the latency tolerance via jointly optimizing intelligence requesting strategy, transmission power control and computation resource allocation. The system energy efficiency is defined as the ratio of model performance to energy consumption. Taking into account the dynamic characteristics of edge network conditions, the intelligence sharing problem is modeled as a Markov decision process (MDP). Subsequently, a twin delayed deep deterministic policy gradient (TD3)-based algorithm is designed to automatically make the optimal decisions. Finally, by extensive simulation experiments, it is shown that: 1) compared with DDPG and DQN, the proposed algorithm has a better convergence performance; 2) jointly optimizing intelligence requesting strategy, transmission power control and computation resource allocation helps to improve intelligence sharing efficiency; 3) under different parameter settings, the proposed algorithm achieves better results than the benchmark algorithms.

Satellite networks can enhance global network coverage without geographical restrictions. By applying edge computing paradigm to satellite networks, they can provide communication and computation services for Internet of Remote Things (IoRT) mobile devices (IMDs) at any time. Thus, in 6G mobile systems, satellite networks serve as a complement to terrestrial networks. This paper places its emphasis on satellite-terrestrial integrated networks (STINs). Computation offloading and resource allocation (CORA) play a crucial role in STINs given the limited communication and computation resources of satellites. In this paper, we investigate the CORA to minimize system energy consumption while ensuring the latency tolerance via jointly optimizing the offloading decision and the allocation of radio and computation resources. Taking into account the dynamic characteristics of network conditions, the CORA problem is formulated as a Markov decision process (MDP). Subsequently, an algorithm based on twin delayed deep deterministic policy gradient (TD3) is designed to automatically determine the optimal decisions. Finally, the convergence and superiority of the proposed algorithm in terms of energy efficiency are evaluated through extensive simulation experiments.