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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Moritz Eckhoff Dennis Knobbe Henning Zwirnmann Abdalla Swikir Sami Haddadin Technical University of Munich Germany Department of Computer Engineering Chair of Robotics and Systems Intelligence TUM School of Computation Information and Technology Centre for Tactile Internet with Human-in-the-Loop (CeTI) of Technische Universität Dresden Germany Department of Electrical and Electronic Engineering Omar Al-Mukhtar University Albaida Libya

One of the most important aspects of autonomous systems is safety. This includes ensuring safe human-robot and safe robot-environment interaction when autonomously performing complex tasks or in collaborative scenarios. Al-though several methods have been introduced to tackle this, most are unsuitable for real-time applications and require carefully handcrafted obstacle descriptions. In this work, we propose a method combining high-frequency and real-time self and environment collision avoidance of a robotic manipulator with low-frequency, multimodal, and high-resolution environmental perceptions accumulated in a digital twin system. Our method is based on geometric primitives, so-called primitive skeletons. These, in turn, are information-compressed and real-time compatible digital representations of the robot's body and environment, automatically generated from ultra-realistic virtual replicas of the real world provided by the digital twin. Our approach is a key enabler for closing the loop between environment perception and robot control by providing the millisecond real-time control stage with a current and accurate world description, empowering it to react to environmental changes. We evaluate our whole-body collision avoidance on a 9-DOFs robot system through five experiments, demonstrating the functionality and efficiency of our framework.

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Relation Regularized Scene Graph Generation

arXiv

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arXiv 2022年

作者： Guo, Yuyu Gao, Lianli Song, Jingkuan Wang, Peng Sebe, Nicu Shen, Heng Tao Li, Xuelong The Future Media Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computing and Information Technology The University of Wollongong NSW2170 Australia The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy Northwestern Polytechnical University Xi'An710072 China

Scene graph generation (SGG) is built on top of detected objects to predict object pairwise visual relations for describing the image content abstraction. Existing works have revealed that if the links between objects are given as prior knowledge, the performance of SGG is significantly improved. Inspired by this observation, in this paper, we propose a Relation Regularized Network (R2-Net), which can predict whether there is a relationship between two objects and encode this relation into object feature refinement and better SGG. Specifically, we first construct an affinity matrix among detected objects to represent the probability of a relationship between two objects. Graph Convolution Networks (GCNs) over this relation affinity matrix are then used as object encoders, producing relation-regularized representations of objects. With these relation-regularized features, our R2-Net can effectively refine object labels and generate scene graphs. Extensive experiments are conducted on the Visual Genome dataset for three SGG tasks (i.e., PREDCLS, SGSLS, and SGDET), demonstrating the effectiveness of our proposed method. Ablation studies also verify the key roles of our proposed components in performance improvement. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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Implementing Deep Learning Techniques into Healthcare System Improvement

Implementing Deep Learning Techniques into Healthcare System...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Harmeet Kaur Kochhar Mrudula Kilaru Nasser Aljohani Himanshu Rai Goyal Devendra Singh DilipKumar Jang Bahadur Saini University Institute of Media Studies Chandigarh University Velagapudi Ramakrishna Siddhartha Engineering College Information Systems Department Islamic University of Madinah Madinah Saudi Arabia Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India Department of Computer Science & Engineering Sawmi Rama Himalayan University Dehradun India

Artificial intelligence (AI) has the potential to transform clinical decision-making and assistance. In any case, the utilization of current AI methods remains only as a supportive solution to make clinical decisions. The existing methodologies fail to perform in-depth analysis to deliver accurate predictions. This study reviews the integration of advanced technologies AutoML in healthcare sector by considering the data privacy, heterogeneity and interpretability. This review will be a useful resource for medical practitioners interested in applying information science methods to their field of study.

关键词： Technological innovation Renewable energy sources Medical services Transforms System improvement Writing Software

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Relationship between number of annotations and accuracy in segmentation of the erector spinae muscle using Bayesian U-Net in torso CT images

Relationship between number of annotations and accuracy in s...

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International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Wakamatsu, Yuichi Kamiya, Naoki Zhou, Xiangrong Hara, Takeshi Fujita, Hiroshi Department of Media Informatics Graduate School of Information Science and Technology Aichi Prefectural University 1522-3 Ibaragabasama Nagakute-shi Aichi480-1198 Japan Department of Information Systems Graduate School of Information Science and Technology Aichi Prefectural University 1522-3 Ibaragabasama Nagakute-shi Aichi480-1198 Japan Department of Electrical Electronic and Computer Engineering Faculty of Engineering Gifu University 1-1 Yanagido Gifu-shi Gifu501-1193 Japan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Supervised learning for image segmentation requires annotated images. However, image annotation has the problem that it is time-consuming. This problem is particularly significant in the erector spinae muscle segmentation due to the large size of the muscle. Therefore, this study considers the relationship between the number of annotated images used for training and segmentation accuracy of the erector spinae muscle in torso CT images. We use Bayesian U-Net, which has shown high accuracy in thigh muscle segmentation, for the segmentation of the erector spinae muscle. In the network training, we limit the number of slices for each case and the number of cases to 100%, 50%, 25%, and 10%. In the experiment, we use 30 torso CT images, including 6 cases for the test dataset. Experimental results are evaluated by the mean Dice value of the test dataset. Using 100% of the slices per case, the segmentation accuracy with 100%, 50%, 25%, and 10% of the cases was 0.934, 0.927, 0.926, and 0.890, respectively. On the other hand, using 100% of the cases, the segmentation accuracy with 100%, 50%, 25%, and 10% of the slices per case was 0.934, 0.934, 0.933, and 0.931, respectively. Furthermore, the segmentation accuracy with 100% of the cases and 10% of the slices per case was higher than that of the previous method. We showed that it is feasible to achieve high segmentation accuracy with a limited number of annotated images by selecting several slices from a limited number of cases for training. © 2021 SPIE.

关键词： Image segmentation

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A General Framework of Reversible Data Hiding with Controlled Contrast Enhancement

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computers, Materials & Continua 2020年第1期62卷 157-177页

作者： Shaowei Weng Yiyun Liu Yunqing Shi Bo Ou Chunyu Zhang Cuiping Wang School of Information Engineering Guangdong University of TechnologyGuangzhou510006China Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security Shenzhen UniversityShenzhen518060China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07103 USA College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China School of Information Engineering Xizang Minzu UniversityXianyang712082China School of Electronic Information Qingdao UniversityQingdao266071China

This paper proposes a two-step general framework for reversible data hiding(RDH)schemes with controllable contrast *** first step aims at preserving visual perception as much as possible on the basis of achieving high embedding capacity(EC),while the second step is used for increasing image *** the second step,some peak-pairs are utilized so that the histogram of pixel values is modified to perform histogram equalization(HE),which would lead to the image contrast ***,for HE,the utilization of some peak-pairs easily leads to over-enhanced image contrast when a large number of bits are ***,in our proposed framework,contrast over-enhancement is avoided by controlling the degree of contrast *** the second step can only provide a small amount of data due to controlled contrast enhancement,the first one helps to achieve a large amount of data without degrading visual *** RDH method which can achieve high EC while preserve good visual quality,can be selected for the first *** fact,Gao et al.’s method is a special case of our proposed *** addition,two simple and commonly-used RDH methods are also introduced to further demonstrate the generalization of our framework.

关键词： Reversible data hiding controlled contrast enhancement general framework PEE-based RDH method IT-based RDH method

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On the importance of AI research beyond disciplines

arXiv

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arXiv 2023年

作者： Dignum, Virginia Casey, Donal Cerratto-Pargman, Teresa Dignum, Frank Fantasia, Valentina Formark, Bodil Hammarfelt, Björn Holmberg, Gunnar Holzapfel, André Larsson, Stefan Lagerkvist, Amanda Lakemond, Nicolette Lindgren, Helena Lorig, Fabian Marusic, Ana Rahm, Lina Razmetaeva, Yulia Sikström, Sverker Tatar, Kıvanç Tucker, Jason Department of Computing Science Umeå University Sweden Department of Philosophy Lund University Sweden Department of Business Studies Uppsala University Sweden Dept. of Computer and Systems Sciences Stockholm University Sweden Umeå University Sweden Swedish School of Library and Information Science University of Borås Sweden Department of Management and Engineering Linköping University Sweden Division of Media Technology and Interaction Design KTH Sweden Department of Technology and Society Lund University Sweden Department of Informatics and Media Uppsala University Sweden Department of Computer Science and Media Technology Malmö University Sweden University of Split School of Medicine Split Croatia Department of Philosophy and History KTH Sweden Department of Law Uppsala University Sweden Department of Psychology Lund University Sweden Chalmers University of Technology Sweden Department of Global Political Studies Malmö University Sweden

As the impact of AI on various scientific fields is increasing, it is crucial to embrace interdisciplinary knowledge to understand the impact of technology on society. The goal is to foster a research environment beyond disciplines that values diversity and creates, critiques and develops new conceptual and theoretical frameworks. Even though research beyond disciplines is essential for understanding complex societal issues and creating positive impact it is notoriously difficult to evaluate and is often not recognized by current academic career progression. The motivation for this paper is to engage in broad discussion across disciplines and identify guiding principles fir AI research beyond disciplines in a structured and inclusive way, revealing new perspectives and contributing to societal and human wellbeing and sustainability. © 2023, CC BY.

关键词： Sustainable development

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Incorporating simulated spatial context information improves the effectiveness of contrastive learning models

arXiv

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arXiv 2024年

作者： Zhu, Lizhen Wang, James Z. Lee, Wonseuk Wyble, Brad Data Science and Artificial Intelligence Area College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Human-Computer Interaction Area College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Department of Communication and Media School of Social Sciences and Humanities Loughborough University Leicestershire Loughborough United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States Department of Psychology The Pennsylvania State University University ParkPA United States

Visual learning often occurs in a specific context, where an agent acquires skills through exploration and tracking of its location in a consistent environment. The historical spatial context of the agent provides a similarity signal for self-supervised contrastive learning. We present a unique approach, termed Environmental Spatial Similarity (ESS), that complements existing contrastive learning methods. Using images from simulated, photorealistic environments as an experimental setting, we demonstrate that ESS outperforms traditional instance discrimination approaches. Moreover, sampling additional data from the same environment substantially improves accuracy and provides new augmentations. ESS allows remarkable proficiency in room classification and spatial prediction tasks, especially in unfamiliar environments. This learning paradigm has the potential to enable rapid visual learning in agents operating in new environments with unique visual characteristics. Potentially transformative applications span from robotics to space exploration. Our proof of concept demonstrates improved efficiency over methods that rely on extensive, disconnected datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： computer vision

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Wirelessly Enabled Beam Steering MSF Antenna for Biomedical Imaging Applications

Wirelessly Enabled Beam Steering MSF Antenna for Biomedical ...

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URSI International Symposium on Electromagnetic Theory (EMTS)

作者： Asif Bilal Abdul Quddious Amin Darvazehban Sasan Ahdi Rezaeieh Stavros Iezekiel Marco A. Antoniades EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus KIOS Research Center for Excellence University of Cyprus Nicosia Cyprus Chair of RF and Photonics Engineering TU Dresden Dresden Germany School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia Department of Electrical Computer & Biomedical Engineering Toronto Metropolitan University Toronto ON Canada

ISBN: (数字)9798350399288

ISBN: (纸本)9798350399295

In this work, a wirelessly enabled beam steering metasurface (MSF) antenna for bio-imaging is proposed. The design consists of multiple layers, the top layer consists of an MSF layer, while the bottom layer consists of two layers, one that contains the feeding antennas and the second consists of the feeding network. In the feeding network of the MSF antenna, an RF energy harvesting (EH) system has been incorporated that allows dynamic switching to achieve the desired beam steering required for torso scanning. In the designed EH system, a receiving antenna and rectifying circuit harvests the low power received RF signal at three different frequencies in the 2.4 GHz ISM band. In order to avoid coupling between the EH system and MSF antenna, the former operates in the 2.4 GHz ISM band, while the latter operates in the 900 MHz ISM band. The proposed approach has potential to be used in biomedical imaging applications and to provide an alternative to complex biasing networks required to reconfigure devices.

关键词： Microwave antennas Torso Beam steering Antenna theory Switches Wireless power transfer Antenna feeds Metasurfaces Antennas Biomedical imaging

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Ambiguity learning and data correlation in multi-cross modal cyber-physical systems for detecting fake information

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Cyber-Physical Systems 2025年

作者： Byeon, Haewon AlMahadin, Ghayth Bsoul, Qusay Quraishi, Aadam Soni, Mukesh Khan, Shahi Raza Shabaz, Mohammad Department of AI and Software Inje University Gimhae Korea Republic of Data Science Department College of Information Technology Mutah University Mutah Jordan Department of Cybersecurity College of Computer Sciences and Informatics Amman Arab University Amman Jordan M.D. Research Intervention Treatment Institute HoustonTX United States Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune Pune India MJMC Centre for Media and Mass Communication Jamia Hamdard Delhi India Model Institute of Engineering and Technology Jammu India

This paper proposes an improved cyber-physical false information detection model based on cross-modal ambiguous learning. This work introduced the IC2 LFD model with an emphasis on ‘text-image’ ambiguity, focusing on the main goal of intelligent multi-cross modal data fake information identification. The experiment revealed that the significance of text and images differs in intelligent multi-cross modal data fake information detection. The effectiveness of the model is verified on the twitter dataset, showing a 6% accuracy improvement compared to the baseline model and a 1.6% performance improvement over detection methods without dynamic weight allocation. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Adversarial machine learning

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3D object recognition system based on local shape descriptors and depth data analysis

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Recent Patents on computer Science 2019年第1期12卷 18-24页

作者： Chowdhary, Chiranji Lal Department Computer Applications and Creative Media School of Information Technology and Engineering Vellore Institute of Technology Vellore India

Background: A physical object, which is actually in 3D form, is captured by a sen-sor/camera (in case of computer vision) and seen by a human eye (in case of a human vision). When someone is observing something, many other things are also involved there which make it more challenging to recognize. After capturing such a thing by a camera or sensor, a digital image is formed which is nothing other than a bunch of pixels. It is becoming important to know that how a computer understands images. Objective: This paper is for highlighting novel techniques on 3D object recognition system with local shape descriptors and depth data analysis. Methods: The proposed work is applied to RGBD and COIL-100 datasets and this is of four-fold as preprocessing, feature generation, dimensionality reduction, and classification. The first stage of preprocessing is smoothing by 2D median filtering on the depth (Z-value) and registration by orientation correction on 3D object data. The next stage is of feature generation and having two phases of shape map generation with shape index map and SIFT/SURF descriptors. The dimensionality reduction is the third stage of this proposed work where linear discriminant analysis and principal component analysis are used. The final stage is fused on classification. Results: Here, calculation of the discriminative subspace for the training set, testing of object data and classification is done by comparing target and query data with different aspects for finding proper matching tasks. Conclusion: This concludes with new proposed approach of 3D Object Recognition. The local shape descriptors are used for 3D object recognition system to implement and test. This system is achieves 89.2% accuracy for Columbia object image library-100 images by using local shape descriptors. © 2019 Bentham Science Publishers.

关键词： Principal component analysis

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