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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Mahyar Pourjabar Ahmed AlKatheeri Manuele Rusci Agata Barcis Vlad Niculescu Eliseo Ferrante Daniele Palossi Luca Benini Department of Electrical Electronic and Information Engineering (DEI) University of Bologna Italy Autonomous Robotics Research Center (ARRC) Technology Innovation Institute UAE Department of Electrical Engineering KU Leuven Belgium Integrated Systems Laboratory (IIS) ETH Zürich Switzerland Dalle Molle Institute for Artificial Intelligence (IDSIA) USI-SUPSI Switzerland

Relative localization is a crucial functional block of any robotic swarm. We address it in a fleet of nano-drones characterized by a 10 cm-scale form factor, which makes them highly versatile but also strictly limited in their onboard power envelope. State-of-the-Art solutions leverage Ultra-WideBand (UWB) technology, allowing distance range measurements between peer nano-drones and a stationary infrastructure of multiple UWB anchors. Therefore, we propose an UWB-based infrastructure-free nanodrones swarm, where part of the fleet acts as dynamic anchors, i.e., anchor-drones (ADs), capable of automatic deployment and landing. By varying the ADs' position constraint, we develop three alternative solutions with different trade-offs between flexibility and localization accuracy. In-field results, with four flying mission- drones (MDs), show a localization root mean square error (RMSE) spanning from 15.3 cm to 27.8 cm, at most. Scaling the number of MDs from 4 to 8, the RMSE marginally increases, i.e., less than 10 cm at most. The power consumption of the MDs' UWB module amounts to 342mW. Ultimately, compared to a fixed-infrastructure commercial solution, our infrastructure-free system can be deployed anywhere and rapidly by taking 5.7 s to self-localize 4 ADs with a localization RMSE of up to 12.3% in the most challenging case with 8 MDs.

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Dynamically Adaptive Cascading Updates for Hierarchical Digital Twin

SSRN

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SSRN 2024年

作者： Guo, Haoyu Wang, Shaoping Shi, Jian Lizzio, Fausto Guglieri, Giorgio School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Mechanical and Aerospace Engineering Polytechnic University of Turin Turin10129 Italy Beihang Ningbo Research Institute Ningbo315800 China Tianmushan Laboratory Hangzhou310023 China

Digital twin (DT) has emerged as a focal point of current research due to its advanced capability to dynamically map the evolution of physical equipment. The dynamic updating feature stands as one of DT’s core technological strengths. However, existing methods for updates often face challenges in efficiency and accuracy when dealing with complex model update tasks. To address the above fundamental challenges, this paper introduces a hierarchical DTs cascading update strategy to coordinate complex update tasks, thereby enhancing the efficiency of updates. It also provides the dynamically adaptive update algorithm for error threshold and batch size to optimize the update process based on signal change rates which helps to enhance the fidelity and real-time performance of the DT system. Finally, the paper validates the effectiveness of the proposed updating method by using oil leakage from the barrel-port plate of a piston pump as an illustrative example. The results confirm the significant improvement of this updating method in enhancing DT accuracy and updating efficiency. © 2024, The Authors. All rights reserved.

关键词： Leakage (fluid)

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Learning Fine-Grained Alignment for Aerial Vision-Dialog Navigation 39

Learning Fine-Grained Alignment for Aerial Vision-Dialog Nav...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Su, Yifei An, Dong Chen, Kehan Yu, Weichen Ning, Baiyang Ling, Yonggen Huang, Yan Wang, Liang School of Artificial Intelligence University of Chinese Academy of Sciences China MAIS Institute of Automation of Chinese Academy of Sciences China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Electrical and Computer Engineering Department Carnegie Mellon University United States Robotics X Tencent Shenzhen China

ISBN: (纸本)157735897X

Aerial Vision-Dialog Navigation (AVDN) is a new task that requires drones to navigate to a target location based on human-robot dialog history. This paper focuses on the critical fine-grained cross-modal alignment problem in AVDN, requiring the drone to align language entities with visual landmarks in top-down views. To achieve this, we first construct a Fine-Grained AVDN (FG-AVDN) dataset via a semi-automatic annotation pipeline, providing diverse multimodal annotations at the entity-landmark level. Based on this, a novel Fine-grained Entity-Landmark Alignment (FELA) method is proposed to learn the cross-modal alignment explicitly. Concretely, FELA first boosts the drone’s visual understanding with a precise semantic grid representation, which captures the environmental semantics and spatial structure simultaneously. Subsequently, to learn the entity-landmark alignment, we devise cross-modal auxiliary tasks from three perspectives, including grounding, captioning, and contrastive learning. Extensive experiments demonstrate that our explicit entity-landmark alignment learning is beneficial for AVDN. As a result, FELA achieves leading performance with 3.2% SR and 4.9% GP improvements over prior arts. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Robots

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Awgunet: Attention-Aided Wavelet Guided U-Net for Nuclei Segmentation in Histopathology Images

Awgunet: Attention-Aided Wavelet Guided U-Net for Nuclei Seg...

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IEEE International Symposium on Biomedical Imaging

作者： Ayush Roy Payel Pramanik Dmitrii Kaplun Sergei Antonov Ram Sarkar Department of Electrical Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Accurate nuclei segmentation in histopathological images is crucial for cancer diagnosis. Automating this process offers valuable support to clinical experts, as manual annotation is time-consuming and prone to human errors. However, automating nuclei segmentation presents challenges due to uncertain cell boundaries, intricate staining, and diverse structures. In this paper, we present a segmentation approach that combines the U-Net architecture with a DenseNet-121 backbone, harnessing the strengths of both to capture comprehensive contextual and spatial information. Our model introduces the Wavelet-guided channel attention module to enhance cell boundary delineation, along with a learnable weighted global attention module for channel-specific attention. The decoder module, composed of an upsample block and convolution block, further refines segmentation in handling staining patterns. The experimental results conducted on two publicly accessible histopathology datasets, namely Monuseg and TNBC, underscore the superiority of our proposed model, demonstrating its potential to advance histopathological image analysis and cancer diagnosis. The code is made available at: https://***/AyushRoy2001/AWGUNET

关键词： Image segmentation Convolution Histopathology Microprocessors Noise Computer architecture Feature extraction

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Developing an Integrated Cognitive Test (ICT) for Computerized Assessment of Cognitive Impairment Risk

Developing an Integrated Cognitive Test (ICT) for Computeriz...

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Biomedical engineering International Conference (BMEiCON)

作者： Warissara Limpornchitwilai Chatchai Paengkumhag Wisanu Jutharee Kosin Chamnongthai Boonserm Kaewkamnerdpong Biological Engineering Program Faculty of Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Institute of Field Robotics and Automation King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Electrical Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

In an aging society, dementia is a notable issue that occurs as individuals grow older, impacting cognitive ability and emotional well-being. This has led to the development of cognitive assessment tools to facilitate early detection of cognitive impairment. Limited utilization of computerized tests across different countries is due to the mismatch of existing computerized tests between the educational and cultural backgrounds of the elderly; this study sought to address this issue by developing the Integrated Cognitive Test (ICT). ICT is designed as a computerized tool for assessing the risk of cognitive impairment in Thai older adults with an easy-to-use interface. Thirty-six elderly participants with diverse education levels and without prior cognitive impairment diagnoses participated in the study. They completed MoCA, MMSE, and ICT in a single-day experiment. The scores, reaction time, and overall completion time were recorded for analysis. The study found significant differences between ICT and MMSE scores (p<0.001) but not with MoCA (p=0.421); the results indicated ICT's potential alignment with MoCA for cognitive assessment. The decision tree attained 80.60% accuracy in cognitive impairment risk classification using MoCA output. Key features impacting classification included average reaction time in working memory, memory recall, and standard deviation in language tasks. Overall, the analysis demonstrated that ICT is an effective tool for assessing cognitive impairment, for older adults. Furthermore, designing a cognitive test that considers the unique education and cultural backgrounds of the Thai elderly can serve as a model for creating similar tests for other populations.

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Novel Intensity Mapping Functions: Weighted Histogram Averaging

Novel Intensity Mapping Functions: Weighted Histogram Averag...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yilun Xu Zhengguo Li Weihai Chen Changyun Wen School of Automation Science and Electrical Engineering Beihang University Beijing SRO Department Institute for Infocomm Research Singapore School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665409858

It is challenging to align the brightness distribution of the images with different exposures due to possible color distortion and loss of details in the brightest and darkest regions of input images. In this paper, a novel intensity mapping algorithm is first proposed by introducing a new concept of weighted histogram averaging (WHA). The proposed WHA algorithm leverages the correspondence between the histogram bins of two images which are built up by using the non-decreasing property of the intensity mapping functions (IMFs). Extensive experiments indicate that the proposed WHA algorithm significantly surpasses the related state-of-the-art intensity mapping methods.

关键词： Industrial electronics Histograms Image color analysis Brightness Distortion

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive Control for Tractor-Trailer Trajectory Tracking with Unknown Parameters

arXiv

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arXiv 2023年

作者： Wang, Zehao Zhang, Han Wang, Jingchuan Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive Control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC. © 2023, CC BY-NC-SA.

关键词： Model predictive control

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

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High performance efficiency of optical networking infrastructure characteristics with maximum speed of commercial fiber optic line cables employment

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Journal of Optical Communications 2024年第s1期45卷 s2679-s2688页

作者： Gopalan, Anitha Arjunsingh, Kishore Kumar Ramya, Mohanraj Thangaraj, Jayakumar Venkatanaresh, Mandyam Rajendran, Kanchana Omran, Omar Karem Hossain, Md. Amzad Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Tamil Nadu Chennai India Department of Robotics and Automation Sri Ramakrishna Engineering College Tamil Nadu Coimbatore India Department of ECE S.A. Engineering College Tamil Nadu Chennai India Department of Mechatronics Engineering KCG College of Technology Tamil Nadu Chennai India Andra Pradesh Tirupati India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Naser Institute of Engineering Portsaid Egypt Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh

Fiber optic communications have made great strides, and it is now the foundation of our telecommunications and data networking infrastructures. Due to its beneficial characteristics, including low attenuation, a large bandwidth, and immunity to electromagnetic interference, optical fiber is frequently acknowledged as a superior transmission medium. Optical fibers have been widely used to create high speed lines that can transmit either a single wavelength channel or many wavelength channels using wavelength division multiplexing due to their special features (WDM). The maximum speed of commercial fiber-optic cables was only 2.5 Gb/s and can be extended to 1 Tb/s for short and medium distance. The study clarifies the possible transmission distance for different transmission media silica/plastic optical fibers with various optical fiber window transmissions versus environmental ambient temperature variations. The study demonstrated the bad effects of the temperature effects on the optical fiber transmission media (silica/plastic fibers) for various transmission windows. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Optical fibers

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A Distributed Framework for Deep Reinforcement Learning by Consensus

A Distributed Framework for Deep Reinforcement Learning by C...

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Chinese Control and Decision Conference, CCDC

作者： Bo Liu Shuang Zhu Peng Sun Qisheng Huang Zhengtao Ding Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China PowerChina Huadong Engineering Corporation Limited Hangzhou China College of Computer Science and Electronic Engineering Hunan University Changsha China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China Department of Electrical and Electronic Engineering University of Manchester Manchester UK

This paper proposes a distributed training framework for deep reinforcement learning algorithms to address large-scale problems with privacy protection. First, we design a hierarchical decentralized communication topology with a server to alleviate the heavy burden on the central server. By pushing the model updating and data storage to the edge side, it not only unleashes the computing potential of the terminal devices but also shortens the response time. Second, the consensus algorithm is applied for all agents over the decentralized topology to approach each other, where each agent only requires neighboring information to achieve consensus. Then, the distributed training framework for deep Q-networks is designed based on the consensus algorithm, which consists of local learning and global consensus. Finally, the simulation demonstrates that the proposed distributed learning deep Q-networks shows better performance than its centralized learning counterpart.

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