Partial fingerprint recognition has emerged as a critical method for verifying user authenticity during mobile transactions. As a result, there is a pressing need to develop techniques that effectively and accurately authenticate users, even when the scanner only captures a limited area of the finger. A key challenge in partial fingerprint matching is the inevitable loss of features when a full fingerprint image is reduced to a partial one. To address this, we propose a method that integrates deep learning with feature descriptors for partial fingerprint matching. Specifically, our approach employs a Siamese Network based on a CNN architecture for deep learning, complemented by a SIFT-based feature descriptor to extract minimal yet significant features from the partial fingerprint. The final matching score is determined by combining the outputs from both methods, using a weighted scheme. The experimental results, obtained from varying image sizes, sufficient epochs, and different datasets, indicate that our combined method achieves an Equal Error Rate (EER) of approximately 4% for databases DB1 and DB3 in the FVC2002 dataset. Additionally, validation at FRR@FAR 1/50,000 yields results of about 6.36% and 8.11% for DB1 and DB2, respectively. These findings demonstrate the efficacy of our approach in partial fingerprint recognition. Future work could involve utilizing higher-resolution datasets to capture more detailed fingerprint features, such as pore structures, and exploring alternative deep learning techniques to further streamline the training process.

Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds—outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system’s suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives.

Real-time traffic flow prediction plays a vital role in alleviating urban congestion and improving transportation efficiency. However, urban traffic data are often subject to sensor anomalies, missing values, and unstable model performance. To address these challenges, this paper proposes a lightweight and robust recurrent neural network architecture designed to enhance the accuracy and reliability of traffic flow forecasting under data incompleteness. We introduce two enhanced recurrent models—Extended Long Short-Term Memory (xLSTM) and Extended Gated Recurrent Unit (xGRU)—which incorporate exponential gating mechanisms and matrix-valued memory updates. These enhancements significantly reduce model complexity and training overhead while preserving high prediction accuracy. Furthermore, a three-tier imputation strategy is proposed to handle missing data, adaptively applying linear interpolation, temporal averaging, or seasonal decomposition based on the length and characteristics of the missing intervals. Extensive experiments were conducted on a six-month multivariate traffic sensor dataset collected from Taichung City, Taiwan. The results demonstrate that xGRU achieves comparable or superior forecasting accuracy to mainstream Transformer-based models, such as Informer, Autoformer, and TFT, despite using significantly fewer parameters. These findings highlight the proposed architecture’s practical potential for real-world urban traffic forecasting with enhanced efficiency, robustness, and data resilience.

The field of autonomous driving has seen continuous advances, yet achieving higher levels of automation in real-world applications remains challenging. A critical requirement for autonomous navigation is accurate map construction, particularly in novel and unstructured environments. In recent years, Simultaneous Localization and Mapping (SLAM) has evolved to support diverse sensor modalities, with some implementations incorporating machine learning to improve performance. However, these approaches often demand substantial computational resources. The key challenge lies in achieving efficiency within resource-constrained environments while minimizing errors that could degrade downstream tasks. This paper presents an enhanced ORB-SLAM2 (Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping, version 2) algorithm implemented on a Raspberry Pi 3 (ARM A53 CPU) to improve mapping performance under limited computational resources. ORB-SLAM2 comprises four main stages: Tracking, Local Mapping, Loop Closing, and Full Bundle Adjustment (BA). The proposed improvements include employing a more efficient feature descriptor to increase stereo feature-matching rates and optimizing loop-closing parameters to reduce accumulated errors. Experimental results demonstrate that the proposed system achieves notable improvements on the Raspberry Pi 3 platform. For monocular SLAM, RMSE is reduced by 18.11%, mean error by 22.97%, median error by 29.41%, and maximum error by 17.18%. For stereo SLAM, RMSE decreases by 0.30% and mean error by 0.38%. Furthermore, the ROS topic frequency stabilizes at 10 Hz, with quad-core CPU utilization averaging approximately 90%. These results indicate that the system satisfies real-time requirements while maintaining a balanced trade-off between accuracy and computational efficiency under resource constraints.

Context-aware driving assistance must do more than detect objects: it has to identify the cues that materially affect risk, separate observable evidence from inference, and produce recommendations that humans can audit. This paper presents a grounded multi-agent multimodal large language model (MLLM) framework for interpretable risk assessment in driving scenes. The framework decomposes reasoning into four stages—context relevance evaluation, visual interpretation, factual verification with anomaly extraction, and risk assessment with action recommendation—so that the final advisory is generated only from a verified intermediate representation rather than directly from a free-form scene description. We evaluate the framework on a manually labeled benchmark derived from BDD100K covering traffic-sign interpretation, traffic-density assessment, and pedestrian–vehicle interaction risk. The benchmark contains 600 frames with three-rater annotation and majority-vote labels (Fleiss’ $\kappa =0.79$ on risk levels); we explicitly discuss the implications of this scale for generalization and complement it with a multi-backbone stress test. Across five independent runs, the proposed framework improves risk accuracy from $74.3\pm 0.9\mathrm{\%}$ to $84.8\pm 0.6\mathrm{\%}$ and macro-F1 from $72.8\pm 1.1\mathrm{\%}$ to $83.1\pm 0.7\mathrm{\%}$ over a single-agent MLLM baseline. The hallucination rate—defined as the fraction of outputs containing at least one entity, attribute, or relation that has no visual support in the source frame—drops from $18.7\mathrm{\%}$ to $8.9\mathrm{\%}$, and the actionability score—a five-point human rating averaged over usefulness, specificity, and visual consistency—rises from $3.62$ to $4.28$. McNemar tests confirm that the gain in risk accuracy is statistically significant ($p<0.001$). The framework is intended as a semantic decision-support layer for explainable advanced driver-assistance systems and human-centered autonomous-driving interfaces.