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International Conference on Intelligent Transportation

作者： Jose Matute Mario Rodriguez-Arozamena Joshue Perez Ali Karimoddini Department of Electrical and Computer Engineering North Carolina Agricultural and Technical State University Greensboro NC USA TECNALIA Research & Innovation Basque Research and Technology Alliance (BRTA) Derio Bizkaia Spain Department of Automatic Control and Systems Engineering University of the Basque Country (UPV/EHU) Bilbao Bizkaia Spain Department of Electrical and Computer Engineering North Carolina A&T State University Greensboro NC US

One major hurdle for the deployment of autonomous vehicles in rural environments is achieving accurate localization in areas with tree-canopied roads or outdated point cloud maps. The presence of limited visibility and high variability renders standalone sensor localization unreliable in such situations. To tackle these issues, this paper presents a sensor fusion-based localization framework that integrates data from GNSS, LiDAR, INS, and vehicle odometry. The proposed approach uses a loosely-coupled Extended Kalman Filter for sensor fusion and a weighted gate approach for accurate state estimations. Compared to a state-of-the-art technique, the proposed method achieves a reduction of around 71% in maximum lateral deviations. This method successfully enables a safe and reliable localization in challenging scenarios that are frequently found in the rural and inter-urban sectors.

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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Consistent Kalman flters for nonlinear uncertain systems over sensor networks

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control Theory and technology 2020年第4期18卷 399-408页

作者： Xingkang He Wenchao Xue Haitao Fang Xiaoming Hu Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044 StockholmSweden LSC NCMISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Optimization and Systems Theory Department of MathematicsKTH Royal Institute of Technology10044 StockholmSweden

In this paper,we study how to design filters for nonlinear uncertain systems over sensor *** intoduce two Kalmantype nonlinear fitrs in centralied and dstrbute ***,the tuning method for the parameters of the filteres is established to ensure the consistency,i.e..the mean square error is upper bounded by a known parameter matrix at each *** apply the consistent fiters to the track to-track association analysis of multi targets with uncertain dynamics.A novel track to-track asocaion algoritm is proposed to idenify whether two tracks are from the same *** is proven that the resulting probability of *** is lower than the desired *** simulations on *** track association are given to show the ffetives of the methods.

关键词： Kalman fiter Consistencey Distributed fiter Track-to-rack asociation

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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Chinese Automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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OA-ECBVC: A Cooperative Collision-Free Encirclement and Capture Approach in Cluttered Environments

OA-ECBVC: A Cooperative Collision-Free Encirclement and Capt...

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IEEE Conference on Decision and control

作者： Xinyi Wang Yulong Ding Yizhou Chen Ruihua Han Lele Xi Ben M. Chen Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China Department of Control Science and Engineering Tongji University Shanghai China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Department of Computer Science The University of Hong Kong Hong Kong China Hebei University of Science and Technology Shijiazhuang China

This article investigates the practical scenarios of chasing an adversarial evader in an unbounded environment with cluttered obstacles. We propose a Voronoi-based decentralized algorithm for multiple pursuers to encircle and capture the evader by reacting to collisions. An efficient approach is presented for constructing an obstacle-aware evader-centered bounded Voronoi cell (OA-ECBVC), which strictly ensures collision avoidance in various obstacle scenarios when pursuing the evader. The evader can be efficiently enclosed in a convex hull given random initial configurations. Furthermore, to cooperatively capture the evader, each pursuer continually compresses the boundary of its OA-ECBVC to quickly reduce the movement space of the evader while maintaining encirclement. Our OA-ECBVC algorithm is validated in various simulated environments with different dynamic systems of robots. Real-time performance of resisting uncertainties shows the superior reliability of our method for deployment on multiple robot platforms.

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A Trustless Federated Framework for Decentralized and Confidential Deep Learning

A Trustless Federated Framework for Decentralized and Confid...

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Global Emerging technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Chao Li Qiuyu Shen Cheng Xiang Bharath Ramesh Department of Electrical and Computer Engineering Advanced Control Technology Lab National University of Singapore Singapore International Center for Neuromorphic Systems The MARCS Institute for Brain Behaviour and Development Western Sydney University Sydney Australia

Nowadays, deep learning models can be trained on large amounts of web data on power hungry servers and be deployment-ready for specific real-world applications. With a state-of-the-art model architecture and a large publicly available dataset for pre-training, convolutional neural network models can be further fine-tuned via transfer learning for a related task. Nonetheless, the training process required of privacy-sensitive applications needs to protect data confidentiality and simultaneously boost performance using limited training data. For such data-deprived and privacy-centric learning, we introduce a trustless federated learning framework that seamlessly integrates deep learning models from different edge nodes using a blockchain-based architecture. Our framework performs federated learning without the need of a central server by leveraging a smart contract blockchain platform with a distributed file system for model storage. Users can locally train on their data while routinely benefiting from an enhanced model obtained by merging the models from all users in a decentralized fashion. Most importantly, this framework is free of the potential single point of failure in centralized federated learning. Besides, our framework has in-built incentive mechanisms to prevent model corruption and temper bad actors. We tested our framework on various computer vision datasets. The experimental results show that the merged model accuracy is on-par compared to a centralized federated training setup. To the best of our knowledge, this work represents the first systematic attempt at building a blockchain-based federated deep learning framework for computer vision. The code is publicly made available at: https://***/s-elo/DNN-Blockchain.

关键词： Deep learning Training Data privacy Federated learning Computational modeling Smart contracts Data models

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Evolving Neuromorphic systems on the Ethereum Smart Contract Platform

Evolving Neuromorphic Systems on the Ethereum Smart Contract...

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Global Emerging technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Hongchi Wu Binhao Fang Cheng Xiang Gregory Cohen André van Schaik Bharath Ramesh Department of Electrical and Computer Engineering Advanced Control Technology Lab National University of Singapore Singapore International Center for Neuromorphic Systems The MARCS Institute for Brain Behaviour and Development Western Sydney University Sydney Australia

Neuromorphic intelligent systems are motivated by the observation that biological organisms - from algae to primates - excel in swiftly sensing their environment, reacting promptly to its perils and opportunities. Furthermore, biological organisms function more resiliently than our most advanced machines, with a fraction of their power requirements. Taking inspiration from how primates and humans have successfully evolved higher cognitive intelligence within social constructs, this paper proposes neuromorphic systems to be built and governed on a public distributed ledger platform. However, following in the footsteps of generic AI research, neuromorphic benchmarks and algorithms are developed in isolation. Furthermore, as a relatively niche research field, there is limited access to the actual neuromorphic sensors and large publicly available curated data, exacerbating the slow research progress. Nonetheless, centralized neuromorphic datasets and algorithms pose a threat to secure closed-loop behavior and learning outcomes, both commonly modulated in biological organisms via social interactions. This paper makes the case for early adoption of distributed ledger technology by neuromorphic systems and benchmarks to avoid the pitfalls endured by AI research – showcasing competing event-based gesture recognition systems on the Ethereum smart contract platform. This shift towards real-world and dynamic systems on a distributed ledger platform will improve collaboration among neuromorphic researchers while enabling healthy competition via incentives. Smart contract protocols allow model behavior monitoring, setting new learning tasks and increase in baseline performance, and naturally provides a governance framework for evolving neuromorphic systems. The code is publicly made available at: https://***/BruceFang123.

关键词： Neuromorphics Distributed ledger Smart contracts Biological systems Benchmark testing Blockchains Sensors

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Real-time Seamless Image Stitching in Autonomous Driving

Real-time Seamless Image Stitching in Autonomous Driving

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International Conference on Information Fusion

作者： Christian Kinzig Irene Corté s Carlos Ferná ndez Martin Lauer Institute of Measurement and Control Systems Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Department of Computer Science and Engineering Universidad Carlos III de Madrid (UC3M) Madrid Spain

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

Autonomous vehicles depend on an accurate perception of their surroundings. For this purpose, different approaches are used to detect traffic participants such as cars, cyclists, and pedestrians, as well as static objects. A commonly used method is object detection and classification in camera images. However, due to the limited field of view of camera images, detecting in the entire environment of the ego-vehicle is an additional challenge. Some solutions include the use of catadioptric cameras or clustered surround view camera systems that require a large installation height. In multi-camera setups, an additional step is required to merge objects from overlapping areas between cameras. As an alternative to these systems, we present a real-time capable image stitching method to improve the horizontal field of view for object detection in autonomous driving. To do this, we use a spherical camera model and determine the overlapping area of the neighboring images based on the calibration. Furthermore, lidar measurements are used to improve image alignment. Finally, seam carving is applied to optimize the transition between the images. We tested our approach on a modular redundant sensor platform and on the publicly available nuScenes dataset. In addition to qualitative results, we evaluated the stitched images using an object detection network. Moreover, the real-time capability of our image stitching method is shown in a runtime analysis.

关键词： Runtime Laser radar Pipelines Merging Object detection Cameras Real-time systems

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A Sample-Based Algorithm for Approximately Testing r-Robustness of a Digraph

A Sample-Based Algorithm for Approximately Testing r-Robustn...

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IEEE Conference on Decision and control

作者： Yuhao Yi Yuan Wang Xingkang He Stacy Patterson Karl H. Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Electrical Engineering University of Notre Dame Notre Dame IN USA Department of Computer Science Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

One of the intensely studied concepts of network robustness is r-robustness, which is a network topology property quantified by an integer r. It is required by mean subsequence reduced (MSR) algorithms and their variants to achieve resilient consensus. However, determining r-robustness is intractable for large networks. In this paper, we propose a sample-based algorithm to approximately test r-robustness of a digraph with n vertices and m edges. For a digraph with a moderate assumption on the minimum in-degree, and an error parameter 0

关键词： Network topology Approximation algorithms Robustness Testing

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Predicting the critical behavior of complex dynamic systems via learning the governing mechanisms

arXiv

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arXiv 2025年

作者： Wang, Xiangrong Lu, Dan Wu, Zongze Xu, Weina Hou, Hongru Hu, Yanqing Moreno, Yamir College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China School of Reliability and Systems Engineering Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Southern University of Science and Technology Shenzhen China University of Zaragoza Zaragoza50018 Spain Department of Theoretical Physics University of Zaragoza Zaragoza50009 Spain

Critical points separate distinct dynamical regimes of complex systems, often delimiting functional or macroscopic phases in which the system operates. However, the long-term prediction of critical regimes and behaviors is challenging given the narrow set of parameters from which they emerge. Here, we propose a framework to learn the rules that govern the dynamic processes of a system. The learned governing rules further refine and guide the representative learning of neural networks from a series of dynamic graphs. This combination enables knowledge-based prediction for the critical behaviors of dynamical networked systems. We evaluate the performance of our framework in predicting two typical critical behaviors in spreading dynamics on various synthetic and real-world networks. Our results show that governing rules can be learned effectively and significantly improve prediction accuracy. Our framework demonstrates a scenario for facilitating the representability of deep neural networks through learning the underlying mechanism, which aims to steer applications for predicting complex behavior that learnable physical rules can drive. © 2025, CC BY.

关键词： Deep neural networks

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