信息工程最新文献

{"title":"RISMO: Riemannian Information-Geometric Swarm Motion Orchestration for Heterogeneous UAV–UGV Cooperative Exploration in Unknown Environments","authors":"Fenghe Guo,&nbsp;Runjie Shen,&nbsp;Junjie Zhang,&nbsp;Yongchun Wang,&nbsp;Chenyang Sun,&nbsp;Zishan Wang,&nbsp;Huiming Li","doi":"10.1007/s43684-026-00143-2","DOIUrl":"10.1007/s43684-026-00143-2","url":null,"abstract":"<div><p>The orchestration of heterogeneous swarm systems, such as Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) teams, in unknown, GPS-denied environments requires coordination among active perception, collision avoidance, and topological flexibility. Traditional Artificial Potential Field (APF) methods can suffer from local-minimum deadlocks, while optimization-based Control Barrier Function Quadratic Programs (CBF-QPs) may produce high-frequency control variation in narrow corridors. To address these limitations, this paper proposes the Riemannian Information-geometric Swarm Motion Orchestration (RISMO) framework. RISMO integrates objective-driven navigation, perception-aware motion generation, and obstacle avoidance within a common non-Euclidean metric representation. We construct a Riemannian metric tensor that incorporates the Fisher Information Matrix (FIM) to encourage the UGVs to preserve line-of-sight tracking of the UAVs. To accommodate formation deformation in narrow passages, the swarm topology is modeled as a Semi-Markov Jump System (S-MJS). Under the enforced positive per-agent inter-event bound and the stated Average Dwell Time (ADT) condition, the modeled switching sequence has a bounded transition frequency and cannot exhibit Zeno accumulation. Furthermore, a goal-aligned tangential flux supplies a deterministic local directional bias near saddle-like stagnation. The numerical and ablation results show that this mechanism facilitates escape from the tested non-convex traps. In the planar simulations, RISMO reduces the observed gradient stagnation of APF and the high-frequency control variation of CBF-QP while preserving positive obstacle clearance and cooperative perception.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00143-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV-based dynamic crowd monitoring for rescue prioritization using subspace-driven localization","authors":"Zhitong Zhang,&nbsp;Mingliang Xiong,&nbsp;Wen Fang,&nbsp;Qingwei Jiang,&nbsp;Siyuan Du,&nbsp;Tianyi Lv","doi":"10.1007/s43684-026-00142-3","DOIUrl":"10.1007/s43684-026-00142-3","url":null,"abstract":"<div><p>Rapid and reliable localization of survivors is critical in post-disaster search and rescue operations. However, conventional sensing methods such as visual imaging are often ineffective due to smoke, dust, or debris, and traditional RSSI-based localization suffers from severe multipath interference and noise. This paper proposes a subspace-driven multi-UAV system for dynamic crowd monitoring and rescue prioritization. The system employs Boustrophedon scanning to acquire spatially continuous RSSI sequences from survivors’ Wi-Fi devices. A spatial resampling technique is first applied to regularize the irregular measurements, followed by Hankel matrix construction and Singular Value Decomposition (SVD) to separate signal from noise, yielding robust coarse device locations. A multi-pass scanning strategy tracks devices over time, detects mobility as an indicator of vitality, and maintains device records with expiration mechanisms. Detected devices are mapped to predefined rescue zones, and a weighted priority score is computed for each zone based on device count, mobility, and temporal trends. Simulation results under low and high noise conditions demonstrate that the proposed method significantly outperforms Kalman-filtered trilateration and SVD without resampling. At 6 dB noise, it achieves a 46.7% reduction in RMSE and a 60.0% reduction in outlier ratio. Furthermore, spatial resampling reduces computational time by 17% per device-line, with greater gains expected in high-frequency scenarios. The proposed framework transforms raw RSSI measurements into actionable intelligence for rescue command, enabling efficient resource allocation.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00142-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OCE: an optimization convergence metric for LiDAR SLAM degeneracy detection","authors":"Junrui Zhang,&nbsp;Mengfan Zhu,&nbsp;Chenyang Sun,&nbsp;Fenghe Guo,&nbsp;Quanxi Zhan,&nbsp;Runjie Shen","doi":"10.1007/s43684-026-00144-1","DOIUrl":"10.1007/s43684-026-00144-1","url":null,"abstract":"<div><p>LiDAR-based Simultaneous Localization and Mapping (SLAM) systems suffer from environment-induced degeneracy in geometrically self-similar scenes such as tunnels and cylindrical structures, where the point cloud registration problem becomes ill-conditioned. Existing degeneracy detection methods predominantly rely on eigenvalue analysis of the Hessian matrix, which assesses geometric conditioning at a fixed linearization point. However, in semi-degenerate environments containing partial but sufficient geometric constraints, these methods tend to produce false positive detections because measurement noise inflates eigenvalues and obscures the contribution of minor structural features. In this paper, we propose the Optimization Convergence Efficiency (OCE) metric, which evaluates the relative contraction of the directional optimizer gradient across Levenberg-Marquardt evaluations and directly characterizes the convergence response of LiDAR registration along the monitored state direction. The proposed method is evaluated through controlled simulation with ground-truth-based quantitative assessment and real-world UAV experiments in a wind-turbine tower and a long tunnel. In the nominal simulation, OCE achieves 98.21% recall with a 2.33% false-positive rate, compared with 100% recall and a 49.42% false-positive rate for the conventional eigenvalue-ratio detector. Controlled measurement-noise experiments further demonstrate that OCE maintains substantially lower false-alarm rates as spectral discrimination becomes less reliable. These results show that OCE can retain usable LiDAR pose estimates in semi-degenerate environments while reliably identifying actual registration failure.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00144-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149132702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An auditable confidentiality protocol for blockchain transactions","authors":"Aoxuan Li,&nbsp;Gabriele D'Angelo,&nbsp;Su-Kit Tang,&nbsp;Frank Fang,&nbsp;Baron Gong","doi":"10.1016/j.bcra.2025.100380","DOIUrl":"10.1016/j.bcra.2025.100380","url":null,"abstract":"<div><div>Blockchain technology inherently exposes user payment details, including account balances, asset holdings, and transaction histories, posing significant security and privacy risks. Moreover, no existing cross-chain bridges support private transactions that protect users' trading histories. This paper introduces a novel confidentiality protocol that conceals user payments while ensuring auditability. The protocol employs a privacy committee, where encrypted secret shares of transaction data are distributed among committee members. Auditors can access private transaction data only with majority approval from the committee. Additionally, the protocol utilizes a zero-knowledge rollup scheme, batching multiple transactions to reduce private transaction costs by 90% compared to non-batched approaches. Implemented using ZoKrates and Solidity, the proposed scheme has been evaluated on the Ethereum test network, demonstrating a one-to-one private transaction latency of approximately 5 s. The protocol's security is rigorously analyzed using the standard real/ideal world paradigm.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"7 5","pages":"Article 100380"},"PeriodicalIF":6.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149211213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CAVer: Compacting accumulation to reduce cross-chain verification overhead","authors":"Yi Guo,&nbsp;Yuying Wang,&nbsp;Jinyue Huang,&nbsp;Tiantian Duan,&nbsp;Linpeng Jia,&nbsp;Hanwen Zhang,&nbsp;Yi Sun","doi":"10.1016/j.bcra.2025.100381","DOIUrl":"10.1016/j.bcra.2025.100381","url":null,"abstract":"<div><div>The data silos between heterogeneous blockchain platforms have given rise to cross-chain protocols. These cross-chain protocols enable secure interoperability between multiple distinct blockchains. Security requires that the target chain has the ability to verify whether cross-chain messages have been finalized on the source chain. Existing cross-chain protocols first synchronize source chain block headers to the target chain and validate them, then verify cross-chain messages included in the header's commitment value using native accumulators, like a Merkle tree. Cross-chain verification incurs significant on-chain overhead. Existing solutions primarily focus on optimizing header verification, while neglecting verification overhead for messages. It is worth noting that as the number of on-chain states increases, the number of elements in the accumulator multiplies, leading to a larger proof size that escalates message verification overhead on the target chain. To reduce message verification overhead on the target chain, we present CAVer. It removes non-cross-chain states from accumulators to minimize message proof size. CAVer incorporates three schemes: Fill-Weak, Fill-Mid, and Fill-Pro. Fill-Weak abandons blockchain's native accumulators by creating a new contract-based compacting accumulator exclusively for cross-chain messages on the source chain. Based on Fill-Weak, Fill-Mid, and Fill-Pro employ block-isolation and target-isolation, respectively, to further reduce verification overhead. Additionally, we design the algorithm <span>AppendAndTailor</span> to reduce storage cost on the source chain. It aims at trimming historical states when accumulating cross-chain messages. Evaluations on Ethereum-like blockchains demonstrate that CAVer reduces message verification overhead on the target chain by 76% compared to the existing state-of-the-art scheme.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"7 5","pages":"Article 100381"},"PeriodicalIF":6.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149211224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing health data integrity through on-chain verification and cryptographic proofs","authors":"Fausto Neri da Silva Vanin,&nbsp;Blanda Helena de Mello,&nbsp;Rodrigo da Rosa Righi,&nbsp;Cristiano André da Costa,&nbsp;Rodolfo Stoffel Antunes","doi":"10.1016/j.bcra.2025.100373","DOIUrl":"10.1016/j.bcra.2025.100373","url":null,"abstract":"<div><div>The integration of blockchain technology in healthcare has gained significant attention due to its potential to address critical challenges such as data privacy, interoperability, and health record integrity. Although electronic health record (EHR) standards such as health level 7 fast healthcare interoperability resources (HL7 FHIR) and OpenEHR provide frameworks for data consistency and interoperability, concerns persist regarding the privacy and security of sensitive patient information, particularly in compliance with regulations. Existing solutions often store only hashed representations of data on blockchain nodes, making direct on-chain validation impossible and increasing the risk of invalid or malicious data entering the system. This paper introduces maximize, encrypt, prove, comply and adapt (MEPCA), a novel blockchain-based framework designed to enhance on-chain EHR data processing using cryptographic techniques. The model incorporates an innovative method to generate cryptographic proofs of data validity, enabling direct on-chain verification of hash digests. We present practical use case implementations and perform a technical evaluation of a fully on-chain MEPCA approach within the HL7 FHIR ecosystem. Our analysis examines the processing time of the hash proof algorithm, comparing it with existing methods based on transactions per second (TPS), latency, and computational efficiency in a multi-host network environment. The experimental results show that the MEPCA framework achieves a maximum of 83.2 TPS, with a latency below 1.5 s and the hash-proof computation taking 1.6 ms. Our primary contribution is to demonstrate viable healthcare scenarios for a fully on-chain approach, providing strategic guidance for the adoption of decentralized solutions in healthcare through technical evaluation and real-world applications.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"7 5","pages":"Article 100373"},"PeriodicalIF":6.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149211212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual influence, real impact: Deciphering social media sentiment and its effects on cryptocurrency market dynamics","authors":"Iheb Ghazouani,&nbsp;Ines Ghazouani,&nbsp;Abdelwahed Omri","doi":"10.1016/j.bcra.2025.100375","DOIUrl":"10.1016/j.bcra.2025.100375","url":null,"abstract":"<div><div>This research investigates the effect of social media sentiment on the cryptocurrency market, particularly focusing on Bitcoin and Ethereum. Using TensorFlow as a machine learning tool, we developed a sentiment index from 66,582 Reddit posts about Bitcoin and 23,231 about Ethereum, collected in 2022. The sentiment scores, ranging from −1 (negative) to 1 (positive), were categorized into positive, neutral, and negative classes and analyzed alongside daily return and volatility metrics for both cryptocurrencies using a Vector Autoregression (VAR) model. Our study identifies significant impacts of social media sentiment on cryptocurrency markets. Specifically, Bitcoin’s returns show a heightened sensitivity to negative sentiment, whereas Ethereum’s returns remain unaffected by any sentiment type. However, the volatility of both cryptocurrencies is affected by neutral sentiment. These findings highlight distinct behavioral patterns across cryptocurrencies and uncover a bidirectional relationship between market dynamics and social media sentiment. This study offers novel insights into how public perception influences digital asset markets, thereby contributing to the behavioral finance literature and providing practical implications for investors and policymakers.</div></div>","PeriodicalId":53141,"journal":{"name":"Blockchain-Research and Applications","volume":"7 5","pages":"Article 100375"},"PeriodicalIF":6.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149159523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive emergency braking for highway safety: a simulation framework for performance and reliability evaluation","authors":"Ricardo Ribeiro,&nbsp;Catarina Gonçalves,&nbsp;Nuno Pombo","doi":"10.1007/s43684-026-00140-5","DOIUrl":"10.1007/s43684-026-00140-5","url":null,"abstract":"<div><p>Highway safety is a critical concern for automated vehicles, particularly in dynamic and high-risk scenarios requiring precise braking and trajectory adjustments. This paper presents a comprehensive evaluation of an Automated Emergency Braking (AEB) system designed to enhance safety in diverse highway conditions. Using a Matlab Simulink-based test bench, the study explores Car-to-Car Rear Moving, Car-to-Car Rear Stationary, and Car-to-Car Rear Braking scenarios, adhering to ISO 8855 standards and leveraging a 3 Degrees of Freedom (3DOF) vehicle model.</p><p>The analysis highlights the AEB system’s ability to mitigate collisions through adaptive Model Predictive Control and dynamic Path Planning, ensuring safe and efficient navigation. Results demonstrate the AEB system’s effectiveness in handling challenges such as deadlocks, rapid deceleration, and complex lane-change scenarios while balancing safety and performance trade-offs. Despite its strengths, limitations related to real-world unpredictability and computational demands are acknowledged, with recommendations for future work to refine integration with predictive algorithms and enhance scalability.</p><p>This study underscores the importance of advanced AEB systems in improving highway driving safety and provides a robust framework for evaluating their performance in simulated environments. By addressing critical gaps in current research, this work contributes to the development of more reliable and adaptable braking systems for automated highway driving.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00140-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148986583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CDJMP: an adaptive conditional diffusion model for multi-agent joint motion prediction in autonomous driving","authors":"Wenwen Zheng,&nbsp;Chao Huang,&nbsp;Hao Zhang,&nbsp;Huilin Yin","doi":"10.1007/s43684-026-00138-z","DOIUrl":"10.1007/s43684-026-00138-z","url":null,"abstract":"<div><p>In autonomous driving trajectory prediction, it is important to generate multi-modal trajectories. As a generative method, diffusion model has been increasingly adopted in the field of trajectory prediction. In this paper, we propose CDJMP (Conditional Diffusion model-based Joint Motion Prediction), an adaptive conditional diffusion framework designed for multi-agent trajectory forecasting in autonomous driving. However, when conventional diffusion models generate multi-modal trajectories, particularly for multi-agent joint prediction, they often fail to balance the diversity of generated trajectories and the accuracy of prediction. At the same time, diffusion models also suffer from time-consuming inference. To address these issues, CDJMP utilizes a two-stage framework to generate diverse and highly accurate trajectories. In the first stage, a probabilistic initializer predicts proposal trajectories together with adaptive denoising steps. In the second stage, a group-aware conditional encoder captures dynamic multi-agent interactions and guides the diffusion process to produce coherent multimodal outcomes. Experiments on the INTERACTION and Argoverse datasets demonstrate that CDJMP achieves state-of-the-art performance, reducing minADE and minFDE by up to 9% and 10%, respectively, on the INTERACTION dataset. Ablation experiments demonstrate that CDJMP can effectively reduce inference time while maintaining prediction accuracy. These results highlight the potential of CDJMP as an efficient and accurate framework for real-time multi-agent trajectory prediction in autonomous driving.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00138-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148926493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV-SGDFNet: structured guided dual-level feature fusion for low-altitude anti-UAV detection","authors":"Yang Yu,&nbsp;Kuixian Cai,&nbsp;Rui Fan,&nbsp;Brian C. W. Kei,&nbsp;Bohuan Xue","doi":"10.1007/s43684-026-00141-4","DOIUrl":"10.1007/s43684-026-00141-4","url":null,"abstract":"<div><p>In complex low-altitude environments, illegally operated UAVs are typically small and can easily be confused with background clutter, leading to reduced detection stability. To address this challenge, we propose UAV-SGDFNet, an efficient small UAV detector. The model enhances robustness by jointly optimizing feature representation and multi-scale fusion. In the feature extraction phase, we introduce a spatial-channel collaborative attention mechanism to strengthen discriminative features for the target and suppress background interference. In the feature fusion phase, we design a structurally guided dual-level fusion module that enables structured cross-level interaction through the cooperation of a guidance path and a fidelity path, mitigating semantic dilution and improving the localization of small targets. Experimental results on a complex-scene dataset show that the proposed method outperforms several mainstream detectors, achieving an mAP@0.5 of 0.748 and increasing Recall to 0.692, while maintaining high inference efficiency, indicating a favorable accuracy-efficiency trade-off. Ablation studies and visual analyses further confirm the effectiveness of the proposed components for stable small-target detection and indicate its potential for real-time deployment.</p></div>","PeriodicalId":71187,"journal":{"name":"自主智能系统(英文)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43684-026-00141-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}