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International Conference on advanced Technologies for Signal and Image Processing (ATSIP)

作者： Karam Abdullah Imen Jegham Mohamed Ali Mahjoub Anouar Ben Khalifa Computer Science Department Collage of Education for Pure Science University of Mosul Iraq Ecole Nationale d’Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Institut National des Technologies et des Sciences du Kef Université de Jendouba Le Kef Tunisie

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

As big data continues to rise, the fusion of information emerges as one of the primary concerns in the fields of computer vision and intelligent transportation systems. A multi-view driver action recognition system can be a key to road safety, especially at nighttime. In fact, driver monitoring from a single view is one of the most challenging task due to the high amount of issues present including occlusion and illumination variation. A multiview driver action recognition system can provide more abundant and complementary driver behavior information which helps to prevent dramatic traffic injuries. In this work, we introduce a multi-viewpoint information fusion system that reliably classifies drivers’ actions at nighttime. Experiments on the public and unique realistic dataset, 3MDAD, demonstrate that our proposed approach achieves high recognition accuracy compared to state-of-the-art methods.

关键词： computer vision Image recognition Lighting Road safety Reliability Task analysis Monitoring

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Semantic-focused Patch Tokenizer with Multi-branch Mixer for Visual Place Recognition

Semantic-focused Patch Tokenizer with Multi-branch Mixer for...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhenyu Xu Ziliang Ren Qieshi Zhang Jie Lou Dacheng Tao Jun Cheng CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology Dongguan University of Technology Dongguan China Chinese University of Hong Kong Hong Kong China China Nuclear Power Operations Co. Ltd. Shenzhen China Nanyang Technological University Singapore

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Visual Place Recognition (VPR) is critical for navigation and loop closure in autonomous driving tasks, mitigating the impact of shift errors caused by dynamic changes in the environment. Due to the limited ability of backbone networks and extreme environmental changes, current methods fail to capture foundational semantic details that include the distinctive attributes for unique place identification. To address this problem, we propose a new visual token-guided VPR framework that contains a semantic-focused patch tokenizer and a multi-branch Mixer. To mitigate the inference from place-unrelated objects, the semantic-focused patch tokenizer exploits attention-based channel selection and spatial partition, which efficiently captures important semantic information within the channels and preserve spatial relationships among the backbone features. To extract abstract features with spatial structure information, the multi-branch Mixer utilizes a multi-branch structure to aggregate local and global position information, improving the robustness of global representations to environmental changes. Experimental results demonstrate that our method outperforms state-of-the-art methods, achieving 85.3% Recall@1 on the MSLS val dataset and 59.1% Recall@1 on the Nordland dataset when using ResNet18 as the backbone.

关键词： Visualization Navigation Semantics Feature extraction Robustness Data mining Task analysis

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A comprehensive survey of oracle character recognition: challenges, benchmarks, and beyond

arXiv

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arXiv 2024年

作者： Li, Jing Chi, Xueke Wang, Qiufeng Huang, Kaizhu Wang, Da-Han Liu, Yongge Liu, Cheng-Lin College of Science & Technology Ningbo University Ningbo China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Duke Kunshan University Suzhou China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen China Anyang Normal University Anyang China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Oracle character recognition—an analysis of ancient Chinese inscriptions found on oracle bones—has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Hybrid-Excited, Variable-Flux, and Inter-Modular Biased-Flux Motors: A Comparative Analysis

Hybrid-Excited, Variable-Flux, and Inter-Modular Biased-Flux...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Mohammad Amirkhani Ehsan Farmahini Farahani Alireza Eikani Mojtaba Mirsalim Javad Shokrollahi Moghani Department of Electrical Engineering Electrical Machines and Transformers Research Laboratory Amirkabir University of Technology Tehran Iran Electrical Power Research Group School of Engineering Newcastle University Newcastle upon Tyne U.K Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper analyses three prominent biased-flux permanent magnet (BFPM) motors and compares them with each other. The structures of these BFPMs are different from each other, but they share the same operation principle. These structures are, namely, hybrid-excited multi-tooth slot-PM (HETSPM), multi-tooth variable flux reluctance (TVFR), and inter-modular PM (IMPM). The reason for this selection of structure is because of complementary features that each one possesses. For example, the HETSPM motor places in between the TVFR and IMPM motors in terms of PM consumption and demagnetization. However, the IMPM structure produces high amount torque at high efficiency. The demagnetization of the HETSPM and IMPM structures is also investigated and is shown that, in the similar situation, the HETSPM provides a safer operation environment for the PMs. Two-dimensional finite element method is used to extract the important profiles of the mentioned structures.

关键词： Electrical engineering Magnetic flux Torque Motors Permanent magnets Finite element analysis Topology

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Distributed service-level agreement management with smart contracts and blockchain

Distributed service-level agreement management with smart co...

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作者： Uriarte, Rafael Brundo Zhou, Huan Kritikos, Kyriakos Shi, Zeshun Zhao, Zhiming De Nicola, Rocco IMT School for Advanced Studies Lucca Luccca Italy Vienna University of Technology Vienna Austria School of Computer Science National University of Defense Technology Changsha China Informatics Institute University of Amsterdam Amsterdam Netherlands Information Systems Laboratory ICS-FORTH Heraklion Greece Cybersecurity National Laboratory CINI Roma Italy

The current cloud market is dominated by a few providers, which offer cloud services in a take-it-or-leave-it manner. However, the dynamism and uncertainty of cloud environments may require the change over time of both application requirements and service capabilities. The current service-level agreement (SLA) management solutions cannot easily guarantee a trustworthy, distributed SLA adaptation due to the centralized authority of the cloud provider who could also misbehave to pursue individual goals. To address the above issues, we propose a novel SLA management framework, which facilitates the specification and enforcement of dynamic SLAs that enable one to describe how, and under which conditions, the offered service level can change over time. The proposed framework relies on a two-level blockchain architecture. At the first level, the smart SLA is transformed into a smart contract that dynamically guides service provisioning. At the second level, a permissioned blockchain is built through a federation of monitoring entities to generate objective measurements for the smart SLA/contract assessment. The scalability of this permissioned blockchain is also thoroughly evaluated. The proposed framework enables creating open distributed clouds, which offer manageable and dynamic services, and facilitates cost reduction for cloud consumers, while it increases flexibility in resource management and trust in the offered cloud services. © 2020 The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons, Ltd.

关键词： Smart contract

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Integrated Sensing and Communications Towards Proactive Beamforming in mmWave V2I via Multi-Modal Feature Fusion (MMFF)

arXiv

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arXiv 2023年

作者： Zhang, Haotian Gao, Shijian Cheng, Xiang Yang, Liuqing State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China Guangzhou511400 China Guangzhou511400 China Department of Electronic and Computer Engineering the Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong

The future of vehicular communication networks relies on mmWave massive multi-input-multi-output antenna arrays for intensive data transfer and massive vehicle access. However, reliable vehicle-to-infrastructure links require exact alignment between the narrow beams, which traditionally involves excessive signaling overhead. To address this issue, we propose a novel proactive beamforming scheme that integrates multi-modal sensing and communications via Multi-Modal Feature Fusion Network (MMFF-Net), which is composed of multiple neural network components with distinct functions. Unlike existing methods that rely solely on communication processing, our approach obtains comprehensive environmental features to improve beam alignment accuracy. We verify our scheme on the Vision-Wireless (ViWi) dataset, which we enriched with realistic vehicle drifting behavior. Our proposed MMFF-Net achieves more accurate and stable angle prediction, which in turn increases the achievable rates and reduces the communication system outage probability. Even in complex dynamic scenarios with adverse environment conditions, robust prediction results can be guaranteed, demonstrating the feasibility and practicality of the proposed proactive beamforming approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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WaveFormer: A Wavelet Transformer for Parkinson Disease’s Retinal Layer Segmentation in OCT

WaveFormer: A Wavelet Transformer for Parkinson Disease’s R...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yanlin Chen Xiaoqing Zhang Tianao Wang Chen Tang Haili Ye Jiang Liu Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Shenzhen Institute of Advanced Technology Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Chinese Academy of Sciences Shenzhen China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou China The Oujiang Laboratory The Affiliated Eye Hospital Wenzhou Medical University Wenzhou China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Pathology symptoms of Parkinson disease (PD) are different from those of retinal diseases in the retinal layers, which are subtle. However, segmenting pathology information of PD from retinal layers automatically based on optical coherence tomography (OCT) images has not been studied before. Although existing Transformer-based segmentation methods have achieved good segmentation results, they have limitations in capturing local context information. Convolutional neural networks (CNNs) can construct local context dependencies among pixels, which is complementary to Transformers. Particularly, edge information extraction is significant for accurate retinal layer segmentation, which is ignored by both Transformers and CNNs but can be captured by frequency domain learning methods. To fully leverage the advantages of Transformers, CNNs, and frequency domain learning methods, we propose a Wavelet Transformer (WaveFormer) for retinal layer segmentation based on OCT images. In the WaveFormer, we design a Wavelet Spatial Attention block to exploit the potential of frequency information. Based on these advantages, WaveFormer can be data-efficient in limited OCT images of PD. The experimental results on the OCT-PD segmentation dataset show that our WaveFormer outperforms existing Transformers and CNNs. For example, WaveFormer outperforms Swin-UNet by 3.41% of IoU.

关键词： Learning systems Image segmentation Pathology Wavelet domain Image edge detection Frequency-domain analysis Transformers

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A portable rotational sensor based on the dual-polarization fiber optic gyroscope

A portable rotational sensor based on the dual-polarization ...

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2021 Asia Communications and Photonics Conference, ACP 2021

作者： Cao, Yuwen Zhu, Lanxin Zhou, Tong Yang, Chunxia Chen, Yanjun Li, Zhengbin State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Beijing100871 China School of Software and Microelectronics Peking University Beijing102600 China Youfu Technology Co. Ltd Hangzhou311200 China

A portable rotational sensor based on a modified dual-polarization fiber optic gyroscope is demonstrated, in which the excess relative intensity noise is effectively compensated achieving an enhanced sensitivity of 70... 详细信息

ISBN: (纸本)9781957171005

关键词： Gyroscopes

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CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation

CertainOdom: Uncertainty Weighted Multi-task Learning Model ...

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IEEE International Conference on Robotics and Biomimetics

作者： Leyuan Sun Guanqun Ding Yusuke Yoshiyasu Fumio Kanehiro Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan CNRS-AIST Joint Robotics Laboratory (JRL) IRL National Institute of Advanced Industrial Science and Technology (AIST). Digital Architecture Research Center (DARC) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Computer Vision Research Team Artificial Intelligence Research Center (AIRC) National Institute of Advanced Industrial Science and Technology (AIST) Japan

ISBN: (纸本)9781665481106

As a basic and indispensable module, LiDAR odom-etry estimation is widely used in robotics. In recent years, learning-based modeling approaches for odometry estimation have been validated to be feasible. However, it is necessary to consider security factors as the highest priorities when we apply the learning-based model to certain high-risk real-world scenarios, such as autonomous driving. The odometry uncertainty estimation provides more valuable information for downstream tasks, such as route planning and navigation. In this paper, we propose an end-to-end neural network (namely CertainOdom) to solve odometry and uncertainty estimation tasks by applying multi-task learning. Instead of using the manually-tuned hyper-parameters, we employ the learnable uncertainties to weigh the balance between the error of translation and orientation in the loss function. We evaluate the estimated trajectory and uncertainty on KITTI dataset. We also compare the robustness against the traditional geometry-based methods on our artificially degraded KITTI LiDAR dataset. Extensive experimental results show that our model with uncertainty weighted loss achieves competitive performance in LiDAR odometry estimation. We also explain our uncertainties qualitatively and quantitatively.

关键词： Uncertainty Laser radar Biological system modeling Estimation Multitasking Robustness Trajectory

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Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records

arXiv

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arXiv 2022年

作者： Takagi, Yusuke Hashimoto, Noriaki Masuda, Hiroki Miyoshi, Hiroaki Ohshima, Koichi Hontani, Hidekata Takeuchi, Ichiro Department of Computer Science Nagoya Institute of Technology Japan RIKEN Center for Advanced Intelligence Project Japan Department of Pathology Kurume University School of Medicine Japan Department of Mechanical Systems Engineering Nagoya University Japan

In medical image diagnosis, identifying the attention region, i.e., the region of interest for which the diagnosis is made, is an important task. Various methods have been developed to automatically identify target regions from given medical images. However, in actual medical practice, the diagnosis is made based not only on the images but also on a variety of clinical records. This means that pathologists examine medical images with some prior knowledge of the patients and that the attention regions may change depending on the clinical records. In this study, we propose a method called the Personalized Attention Mechanism (PersAM), by which the attention regions in medical images are adaptively changed according to the clinical records. The primary idea of the PersAM method is to encode the relationships between the medical images and clinical records using a variant of Transformer architecture. To demonstrate the effectiveness of the PersAM method, we applied it to a large-scale digital pathology problem of identifying the subtypes of 842 malignant lymphoma patients based on their gigapixel whole slide images and clinical records. Copyright © 2022, The Authors. All rights reserved.

关键词： Diagnosis

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