Brain tumors are highly invasive neurological diseases, and accurate early diagnosis is crucial for developing personalized treatment plans. Computer-aided diagnosis (CAD) based on deep learning techniques has achieved significant progress in medical image analysis, but limitations remain in terms of classification accuracy, computational efficiency, and interpretability. To address these issues, this study proposes an optimized EfficientNet model based on transfer learning and fine-tuning strategies. The model improves certain convolutional and fully connected layers and adds a global average pooling layer and a Dropout layer at the top of the network to enhance feature extraction capability and classification performance. Additionally, gradient-weighted class activation mapping (Grad-CAM) is introduced to visualize the model's decision-making process, effectively highlighting key discriminative regions of brain tumors, thereby improving interpretability and clinical reliability. Experimental results on the Figshare dataset demonstrate that the proposed model achieves an accuracy of 99.35% on the test set while significantly reducing parameter count and computational complexity, outperforming baseline models including VGG16, ResNet152V2, and Vision Transformer across all major metrics. Furthermore, cross-dataset validation shows that the model attains an accuracy of 92.51%, further demonstrating its robust stability and generalization capability.

To address the limitations of existing weakly supervised semantic segmentation models for point clouds, which struggle to balance local feature correlation, generalization, and feature utilization. This paper proposes WS-MLF, a weakly supervised point cloud semantic segmentation model via multi-scale local feature fusion, based on the RAC-Net baseline. Firstly, the raw point cloud data is taken as input, and a multi-scale spherical sampling methods (MSSM) is employed to capture hierarchical features across varying spatial radii. Secondly, a multi-local feature aggregation enhancement module (MFA) is designed to refine geometric context within neighborhoods. Thirdly, a spatial-channel-fused hybrid attention module (SCH-Att) is proposed to prioritize discriminative channels and key points. Finally, a decoder is utilized for upsampling to generate point-level semantic labels, thereby completing the semantic segmentation task. The proposed model is evaluated on large-scale indoor scene datasets, S3DIS and ScanNet-v2. Experimental results demonstrate that on the S3DIS dataset, when the label ratios are 0.02% and 0.06%, the mIoU surpasses RAC-Net by 2.71% and 0.54%, respectively. On the ScanNet-v2 dataset, with a label ratio of 20 pt, the mIoU increases by 1.55% compared with RAC-Net. These results validate WS-MLF's effectiveness in extracting key features under weak supervision, enhancing segmentation accuracy.

Fire and smoke detection is a critical component of intelligent surveillance and disaster early warning systems, with wide applications in forest fire prevention, industrial safety and other fields. However, existing algorithms often suffer from low detection precision, slow speed, and large model size under natural environments. To address these issues, this paper proposes a fire and smoke detection method based on the lightweight YOLOv8n. The proposed model replaces the original backbone with PP-LCNet to reduce model size, introduces the CARAFE upsampling operator to enhance feature reconstruction, and integrates the EMA attention mechanism to improve target perception capability. Experimental results show that, compared with the original YOLOv8n, the improved model reduces parameters by 1.01 M and computational cost by 2.2 G, while achieving a detection precision of 94.8% and an mAP50 of 93.6%. It outperforms other mainstream lightweight detection models, achieving an excellent balance between precision and real-time performance, and demonstrates strong practical value.

To address the low search efficiency, slow convergence speed, and limited path expansion diversity of the RRT family of algorithms, an adaptive multi-strategy dynamic step-size algorithm, AMDS-Bi-RRT^*, is proposed. Based on the Bi-RRT^* framework, the algorithm enhances convergence efficiency through a dynamic goal-directed extension strategy and an adaptive step-size evaluation function. A multi-directional emergency maneuver strategy is designed to improve adaptability in complex environments. Meanwhile, node sampling is optimized using an improved artificial potential field method, and a three-stage path smoothing approach is introduced to ensure path feasibility. Comparative experiments conducted in four simulation environments of varying complexity against five benchmark algorithms—Bi-APF-RRT^*, Bi-RRT^*, APF-RRT^*, RRT^*, and goal-biased RRT^*—demonstrate that AMDS-Bi-RRT^* reduces average planning time by 12.22%~23.45%, shortens average path length by 0.88%~1.89%, and decreases the average number of nodes by 6.69%~22.85%. The results verify that AMDS-Bi-RRT^* outperforms the comparison algorithms in planning efficiency, path quality, and convergence speed, confirming its superior performance across diverse environments.

Accurate assessment of pilot cognitive states is critical for ensuring flight safety, yet existing methods exhibit limitations in fusing multimodal physiological signals. To address this, this paper proposes a dual-stream deep learning network based on bidirectional cross-modal attention. The model adopts a parallel dual-branch architecture: The electroencephalography (EEG) branch quantifies brain functional connectivity through phase locking value (PLV) features and employs a densely connected network enhanced with squeeze-and-excitation (SE) modules for deep feature extraction; the electrocardiogram (ECG) branch extracts heart rate variability (HRV) and waveform features, processed by a residual-connected multilayer perceptron to characterize autonomic nervous system activity. Building upon this, an innovatively designed bidirectional cross-modal attention module dynamically weights and fuses the dual-path deep features to achieve precise classification of three states—concentrated attention, distracted attention, and startle/surprise. Experimental results on the NASA public dataset demonstrate an overall recognition accuracy of 97.44%. Ablation and comparative analyses confirm that the fusion strategy significantly outperforms single-modality analysis and simple feature concatenation methods. The study reveals that deep integration of EEG functional connectivity and ECG physiological information via attention mechanisms effectively enhances cognitive state recognition performance. This approach provides reliable technical support for developing objective and efficient pilot state monitoring systems, holding significant application value for improving flight safety.

In order to meet the requirements of large-scale infrastructure and supporting equipment for corrosion monitoring in atmospheric environment, a multi-channel atmospheric corrosion monitoring system based on STM32F407ZGT6 is designed and implemented, aiming at the shortcomings of poor real-time performance and low monitoring accuracy in the existing corrosion monitoring system. The system combines the electrochemical impedance spectroscopy measurement technology and the theory of the equivalent circuit model of the galvanic probe of the double electrode primary battery, uses the time division multiplexing method to control the multi-channel excitation signal generation module to generate the excitation signal to act on the electrode system of each channel, uses the multi-channel response signal acquisition module to collect the response voltage data generated by the electrode system of each channel in real time, and transmits the calculated and processed electrochemical impedance spectroscopy data to the upper computer software deployed in the cloud server through the wireless communication module, and finally analyzes and processes the electrochemical impedance spectroscopy data to obtain the corrosion state information. The experimental results show that the system can realize multi-point corrosion status analysis and monitoring in atmospheric environment, the accuracy of corrosion rate is more than 90%, and the monitoring data can be accurately transmitted to the monitoring platform in real time.

This paper proposes a superconducting parallel-nanowire dual-resolution single-photon detector capable of simultaneously achieving photon-number resolution and spatial-position resolution under a single-output readout scheme. The detector consists of N superconducting nanowire units connected in parallel. Each unit incorporates a uniquely valued marking resistor in parallel to form an asymmetric resistor network, along with a series resistor of identical value. The entire array is biased by a common current source and read out through a single output channel. Taking a four-pixel structure as an example, with gradient-distributed shunt resistors (100, 200, 400, and 800 Ω) and a 50 Ω series resistor, LTspice simulations demonstrate that the superposition of response pulse amplitudes enables simultaneous discrimination of both photon number and spatial location, allowing up to 4-photon events and 15 distinct spatial response patterns to be identified. Further analysis indicates that the proposed structure effectively suppresses current shunting and latching effects commonly found in conventional parallel-nanowire detectors, thereby enhancing operational stability, albeit at the cost of reduced output signal amplitude and signal-to-noise ratio. This study provides a novel and feasible technical pathway for developing dual-resolution PNDs, offering a new perspective for future large-scale, high-count-rate, and low-SWaP-C multifunctional PNDs with full-information acquisition capabilities, thereby broadening potential applications in quantum imaging, lidar, and quantum communication.

Electric vehicle charging load forecasting supports power dispatch decisions by addressing load fluctuations from widespread EV grid integration. A new method for predicting short-term EV charging loads is proposed to enhance power grid stability and reliability by improving load forecasting accuracy. First, historical load data is decomposed into subcomponents using VMD, then combined with temperature data and input into multiple TCN-LSTM branches for feature extraction, simplifying EV load sequence complexity. Secondly, a two-stage attention mechanism enhances the LSTM structure, improving load characteristic capture at specific times and feature dimension fusion, boosting complex load pattern recognition. Finally, a time conversion prediction module integrates results via a fully connected layer to enhance prediction accuracy and reduce errors. Case study analyzes real EV charging station load data from a Shaoxing community. Experimental results show the proposed method reduces MSE by 68%, MAE by 60%, and improves the performance index by 4%, demonstrating strong predictive performance.

Aiming at the problems that the electromagnetic levitation system is easily affected by external disturbances and the inherent contradiction of the integer order PD in the traditional linear active disturbance rejection control, this paper proposes a fractional order linear active disturbance rejection control method. The linear extended state observer is used to estimate the total disturbance of the system in real time, and a fractional order differential operator is introduced into the position loop control law. By utilizing the characteristic that its order can be continuously adjusted within the interval (0, 2), the requirements of phase and amplitude in the frequency domain are flexibly adapted. Theoretical analysis shows that fractional-order linear active disturbance rejection controller can simultaneously enhance the disturbance suppression ability in the low-frequency band and suppress the high-frequency noise amplification effect. Simulation and experimental results show that, compared with linear active disturbance rejection control, fractional-order linear active disturbance rejection controller, reduces the position deviation by 48.72%, shortens the adjustment time by 80.28%, and can effectively deal with stronger disturbances and improve the tracking accuracy, significantly enhancing the anti-interference and tracking performance of the system.

In recent years, the application of 3D Gaussian splatting technology in simultaneous localization and mapping systems has made it possible to perform high-quality image rendering using explicit 3D Gaussian models, significantly improving the fidelity of environmental reconstruction. However, the existing methods based on 3DGS have problems such as limited tracking accuracy and lack of global consistency in the 3D reconstruction of complex indoor environments. For this purpose, this paper proposes a dense SLAM algorithm based on 3D Gaussian splatting—SNGO-SLAM. This algorithm combines the advantages of both frame-to-model and frame-to-frame tracking methods, and uses surface normal perception to obtain richer geometric information, significantly improving the tracking accuracy. To address the tracking error that occurs over time, the algorithm introduces a loop closure process and optimizes the 3D Gaussian point representation problem, further enhancing the tracking accuracy. In addition, this algorithm also introduces a dual Gaussian pruning strategy, optimizing memory usage and ensuring precise camera tracking. Experiments on the Replica, ScanNet and TUM RGBD datasets show that while maintaining high rendering quality, the absolute root mean square error of the trajectory of this algorithm on the Replica dataset reaches 0.27 cm. Compared with NICE SLAM, Vox-Fusion, Gaussian SLAM and SplaTAM, the tracking accuracy has increased by 74.53%, 91.26%, 12.90% and 28.95% respectively, providing new ideas for SLAM technology.