Traditional unmanned aerial vehicle (UAV) detection methods struggle with multi-scale variations during flight, complex backgrounds, and low accuracy, whereas existing deep learning detection methods have high accuracy but high dependence on equipment, making it difficult to detect small UAV targets efficiently. To address the above challenges, this paper proposes an improved lightweight high-precision model, YOLOv8-E (Enhanced YOLOv8), for the fast and accurate detection and identification of small UAVs in complex environments. First, a Sobel filter is introduced to enhance the C2f module to form the C2f-ESCFFM (Edge-Sensitive Cross-Stage Feature Fusion Module) module, which achieves higher computational efficiency and feature representation capacity while preserving detection accuracy as much as possible by fusing the SobelConv branch for edge extraction and the convolution branch to extract spatial information. Second, the neck network is based on the HSFPN (High-level Screening-feature Pyramid Network) architecture, and the CAA (Context Anchor Attention) mechanism is introduced to enhance the semantic parsing of low-level features to form a new CAHS-FPN (Context-Augmented Hierarchical Scale Feature Pyramid Network) network, enabling the fusion of deep and shallow features. This improves the feature representation capability of the model, allowing it to detect targets of different sizes efficiently. Finally, the optimized detail-enhanced convolution (DEConv) technique is introduced into the head network, forming the LSCOD (Lightweight Shared Convolutional Object Detector Head) module, enhancing the generalization ability of the model by integrating a priori information and adopting the strategy of shared convolution. This ensures that the model enhances its localization and classification performance without increasing parameters or computational costs, thus effectively improving the detection performance of small UAV targets. The experimental results show that compared with the baseline model, the YOLOv8-E model achieved (mean average precision at IoU = 0.5) an mAP@0.5 improvement of 6.3%, reaching 98.4%, whereas the model parameter scale was reduced by more than 50%. Overall, YOLOv8-E significantly reduces the demand for computational resources while ensuring high-precision detection.

The FeCo/Fe₃O₄ nanocomposite was synthesized using the hydrothermal approach, in which the FeCo alloy and Fe₃O₄ are formed by one step. The structure of the FeCo/Fe₃O₄ nanocomposite was characterized by means of Scanning electron microscopy (SEM), X-ray diffraction (XRD) and X-ray energy-dispersive spectrometer spectroscopy (EDX). They show that the mass ratio of FeCo/Fe₃O₄ strongly depends on the reaction temperature. Such various architectures follow a stepwise growth mechanism of the composites prepared in various reaction temperatures were also discussed. It indicates that this strategy is facile, effective and controllable for the synthesis of FeCo/Fe₃O₄ by the one-step method. Furthermore, the magnetic and wave-absorbing properties of the nanocomposites with various structures were investigated in detail. The results show that the FeCo/Fe₃O₄ with higher mass ratio has higher magnetic properties. Moreover, the FeCo/Fe₃O₄ nanocomposite shows high wave-absorbing properties (e.g., –37.9 dB), which are expected to apply in microwave absorbing materials.

The measurement of cutting force is an effective method for machining condition monitoring in intelligent manufacturing. Titanium nitride films and silicon nitride films were prepared on 304 stainless steel substrates by DC-reactive magnetron sputtering and plasma-enhanced chemical vapor deposition (PECVD). The effects of substrate negative bias and nitrogen flow on the surface microstructures of TiN film were investigated. The smoothness of the film is optimal when the bias voltage is −60 V. X-ray diffraction (XRD) analysis was performed on the samples with the optimal smoothness, and it was found that when the nitrogen flow rate was higher than 2 sccm, the titanium nitride film had a mixed phase of TiN(111) and (200). It is further revealed that the change of peak intensity of TiN(200) can be enhanced by nitrogen flow. Through atomic force microscopy (AFM), it is found that the stronger the intensity of the TiN (200) peak, the smoother the surface of the film is. Finally, the effect of different film thicknesses on the hardness and toughness of the TiN/Si₃N₄ film system was studied by nanoindentation experiments. The nanohardness (H) of the TiN/Si₃N₄ film can reach 39.2 GPa, the elastic modulus (E) is 480.4 GPa, the optimal toughness value (H³/E²) is 0.261 GPa, and the sample has good insulation performance. Linear fitting of the film’s toughness to nanohardness shows that TiN/Si₃N₄ films with higher hardness usually have a higher H³/E² ratio.

The thin-film strain sensor is a cutting-force sensor that can be integrated with cutting tools. The quality of the alloy film strain layer resistance grid plays an important role in the performance of the sensor. In this paper, the two film patterning processes of photolithography magnetron sputtering and photolithography ion beam etching are compared, and the effects of the geometric size of the thin-film resistance grid on the resistance value and resistance strain coefficient of the thin film are compared and analyzed. Through orthogonal experiments of incident angle, argon flow rate, and substrate negative bias in the ion beam etching process parameters, the effects of the process parameters on photoresist stripping quality, etching rate, surface roughness, and resistivity are discussed. The effects of process parameters on etching rate, surface roughness, and resistivity are analyzed by the range method. The effect of substrate temperature on the preparation of Ni Cr alloy films is observed by scanning electron microscope. The surface morphology of the films before and after ion beam etching is observed by atomic force microscope. The influence of the lithography process on the surface quality of the film is discussed, and the etching process parameters are optimized.

Given the growing need for high-performance operation of 4WID-UGVs, coordinated optimization of trajectory tracking, vehicle stability, and energy efficiency poses a challenge. Existing control strategies often fail to effectively balance these multiple objectives, particularly in integrating energy-saving goals while ensuring precise trajectory following and stable vehicle motion. Thus, a hierarchical control architecture based on Model Predictive Control (MPC) is proposed. The upper-level controller focuses on trajectory tracking accuracy and computes the optimal longitudinal acceleration and additional yaw moment using a receding horizon optimization scheme. The lower-level controller formulates a multi-objective allocation model that integrates vehicle stability, energy consumption, and wheel utilization, translating the upper-level outputs into precise steering angles and torque commands for each wheel. This work innovatively integrates multi-objective optimization more comprehensively within the intelligent vehicle context. To validate the proposed approach, simulation experiments were conducted on S-shaped and circular paths. The results show that the proposed method can keep the average lateral and longitudinal tracking errors at about 0.2 m, while keeping the average efficiency of the wheel hub motor above 85%. This study provides a feasible and effective control strategy for achieving high-performance, energy-saving autonomous driving of distributed drive vehicles.

Traditional unmanned aerial vehicle (UAV) detection and tracking methods have long faced the twin challenges of high cost and poor efficiency. In real-world battlefield environments with complex backgrounds, occlusions, and varying speeds, existing techniques struggle to track small UAVs accurately and stably. To tackle these issues, this paper presents an enhanced YOLOv8N-Drone-based algorithm for improved target tracking of small UAVs. Firstly, a novel module named C2f-DSFEM (Depthwise-Separable and Sobel Feature Enhancement Module) is designed, integrating Sobel convolution with depthwise separable convolution across layers. Edge detail extraction and multi-scale feature representation are synchronized through a bidirectional feature enhancement mechanism, and the discriminability of target features in complex backgrounds is thus significantly enhanced. For the feature confusion problem, the improved lightweight Context Anchored Attention (CAA) mechanism is integrated into the Neck network, which effectively improves the system’s adaptability to complex scenes. By employing a position-aware weight allocation strategy, this approach enables adaptive suppression of background interference and precise focus on the target region, thereby improving localization accuracy. At the level of loss function optimization, the traditional classification loss is replaced by the focal loss (Focal Loss). This mechanism effectively suppresses the contribution of easy-to-classify samples through a dynamic weight adjustment strategy, while significantly increasing the priority of difficult samples in the training process. The class imbalance that exists between the positive and negative samples is then significantly mitigated. Experimental results show the enhanced YOLOv8 boosts mean average precision (Map@0.5) by 12.3%, hitting 99.2%. In terms of tracking performance, the proposed YOLOv8 N-Drone algorithm achieves a 19.2% improvement in Multiple Object Tracking Accuracy (MOTA) under complex multi-scenario conditions. Additionally, the IDF1 score increases by 6.8%, and the number of ID switches is reduced by 85.2%, indicating significant improvements in both accuracy and stability of UAV tracking. Compared to other mainstream algorithms, the proposed improved method demonstrates significant advantages in tracking performance, offering a more effective and reliable solution for small-target tracking tasks in UAV applications.