To address the problem of excessive outlier errors caused by noise in indoor non-line-of-sight (NLOS) environments—particularly in positioning and robot localization—two Chan–Taylor cooperative algorithms are proposed and implemented to suppress NLOS-induced errors. The first approach integrates Kalman filtering for error mitigation, while the second reconstructs distance measurements based on statistical characteristics. By combining multiple positioning algorithms, the proposed methods effectively reduce the impact of high noise levels and NLOS interference. Dynamic and static positioning tests were conducted to evaluate the accuracy of both schemes. The results indicate that localization errors in NLOS environments can be significantly reduced by both approaches, with the NLOS error suppression algorithm exhibiting superior performance and adaptability.

This paper proposes an AI-enhanced QKD protocol, which uses machine learning-based adaptive control to dynamically optimize error correction and entanglement quality in view of the dynamics of network conditions. The proposed framework integrates the AI prediction model with Low-Density Parity-Check codes and entanglement swapping, aiming at the intelligent regulation of photon loss, QBER, and the key generation rate. An AI model predicts real-time channel noise and thus dynamically adjusts LDPC parameters and entanglement fidelity thresholds to reach a self-optimizing QKD system. Simulation results show that the proposed protocol has improved the achievable transmission distance by up to 120% (up to 220 km) and suppressed QBER by 65% compared to standard BB84 and previously optimized QKD protocols. This work underscore the crucial steps toward a more autonomous, scalable, and resilient quantum network, enabling secure communication over global distances.

The performance of visual SLAM is strongly influenced by the quality of front-end feature detection and correspondence matching. To improve ORB-SLAM3 in weak-texture environments, under feature clustering, and in the presence of mismatches, this paper optimizes the front-end pipeline in three stages. First, an adaptive threshold is introduced into FAST detection to improve keypoint extraction in weak-texture regions. Second, an improved quadtree-based distribution strategy is adopted to reduce feature over-concentration in strongly textured areas while retaining more valid keypoints in weak-texture regions. Third, PROSAC is used for correspondence verification to remove mismatches with lower iterative cost than conventional random sampling. The improved front-end is integrated into ORB-SLAM3 and evaluated on the EuRoC Vicon Room1 03 sequence. Experimental results show clear gains in feature extraction and matching quality, reducing the mean Absolute Trajectory Error (ATE) by 81.8% and the mean Relative Pose Error (RPE) by 89.4% relative to the baseline, thereby improving localization and mapping accuracy.

Spam email has long threatened communication security and work efficiency. To address this, we design and implement an email spam-detection agent that integrates the DeepSeek large language model, the Dify agent-development platform, and a fine-tuned BERT model. The system uses BERT as the core classifier, leveraging its strengths in semantic understanding and deep feature extraction; it adopts a binary scheme (label 0 = ham, 1 = spam), and fine-tuning enables effective recognition of email text features. Meanwhile, the DeepSeek LLM is introduced to exploit its capabilities in reasoning and generation: for ham messages, DeepSeek produces key-point summaries, and for spam it provides risk explanations and safety recommendations. With Dify’s tool orchestration and human–computer interaction interface, the system automates the entire pipeline from parsing email content to intelligent decision-making and interactive feedback, forming an end-to-end agentic framework for spam detection.For experiments, we train and validate on the Kaggle email dataset (33,715 messages: 17,170 spam / 16,545 ham), using a 70%/15%/15% train/validation/test split. On the spam-detection task, the system achieves 99.49% accuracy; precision, recall, and F1 reach 99.42%, 99.57%, and 99.49%, respectively. These results demonstrate excellent detection performance and strong generalization. In summary, the proposed DeepSeek–Dify–BERT integrated agent effectively safeguards user communications, reduces potential information-security risks, and substantially improves the intelligence and automation of the detection workflow.

To improve the efficiency and reliability of industrial digital instrument reading, this paper proposes an automatic recognition method based on YOLOv8. Aiming at the low efficiency and poor robustness of traditional manual and rule-based methods, YOLOv8 is used to accurately detect the digital display area of instruments, benefiting from its fast inference speed and strong adaptability to complex environments. Subsequently, image preprocessing operations, including grayscale conversion, denoising, binarization, and morphological processing, are applied to enhance digital features, and individual digits are segmented and recognized using a ResNet34 classifier. Experimental results show that the proposed method achieves a detection accuracy of over 98.20% for digital display areas and a digit recognition accuracy of over 98.60%, demonstrating good robustness and practical applicability in complex industrial scenarios.

In the digital transformation of the judiciary, legal entity recognition is a foundational prerequisite for building intelligent judicial systems. To address the limited domain adaptability of generic pre-trained models and the computational burden of training large legal models, this paper proposes a lightweight yet effective legal entity recognition optimizer built upon the BERT-BiLSTM-CRF architecture. Empirical results demonstrate substantial gains in accuracy, efficiency, and deployability. With a legal-specialized adapter, the model attains 97.63% F1 on the CAIL2021-IE corpus, 2.68 pp above the baseline. Progressive unfreezing coupled with mixed-precision training substantially reduces training time and GPU memory footprint on a single consumer-grade GPU. Finally, the clause-aware attention mechanism further improves extraction quality on longer documents while reducing inference overhead in extended-context settings. Collectively, these innovations overcome the challenges of domain adaptation, resource overhead, and long-text processing in legal entity recognition, offering a practical deployment solution for resource-constrained deployments of legal AI.