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International Conference on Sensing Technology, ICST

作者： Dayarnab Baidya Karthick Thiyagarajan Mitradip Bhattacharjee Yang An Department of Electrical Engineering and Computer Science i-Lab Indian Institute of Science Education and Research Bhopal Bhopal Madhya Pradesh India Smart Sensing and Robotics Laboratory (SensR Lab) Centre for Advanced Manufacturing Technology School of Engineering Design and Built Environment Western Sydney University Penrith Australia

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

The design of a wireless sensor network receiver requires an efficient Low Noise Amplifier (LNA), with the transistor being a critical component that influences its performance. Selecting an optimal transistor for LNA design is a complex and time-consuming process due to the conflicting criteria involved. This study utilizes the Analytic Hierarchy Trade-off Ranking Method (AH-TOR), an integrated Multi-Criteria Decision-Making (MCDM) approach, to facilitate the selection of a suitable transistor for LNA design. Four types of transistors were evaluated as alternatives based on four criteria, with the Pseudomorphic High Electron Mobility Transistor (PHEMT) emerging as the optimal choice. The weight of PHEMT was calculated to be 1.0787, and its performance was benchmarked against other existing methods. Further analysis demonstrated the robustness of the AH-TOR method across various scenarios, consistently identifying PHEMT as the most suitable option. These findings highlight the reliability of the AH-TOR method for transistor selection in diverse applications.

关键词： Low-noise amplifiers Wireless sensor networks PHEMTs Decision making Receivers Electron mobility Robustness Sensors Transistors Integrated circuit reliability

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Applications of Transformer Attention Mechanisms in Information Security: Current Trends and Prospects 2

Applications of Transformer Attention Mechanisms in Informat...

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2nd IEEE International Conference on Artificial Intelligence of Things and Crowdsensing, AIoTCs 2022

作者： Vubangsi, M. Abidemi, Sarumi Usman Akanni, Olukayode Mubarak, Auwalu Saleh Al-Turjman, Fadi Artificial Intelligence Engineering Dept. Ai and Robotics Institute Near East University Mersin 10 Nicosia Cyprus Research Center for Ai and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Cyprus Computational Materials Science Lab Department of Computer Science Htttc Bambili University of Bamenda NWR Cameroon

ISBN: (纸本)9798350334104

In this work, we present a comprehensive survey on applications of the most recent transformer architecture based on attention in information security. Our review reveals three primary areas of application: Intrusion detection, Anomaly Detection and Malware Detection. We have presented an overview of attention-based mechanisms and their application in each cybersecurity use case, and discussed open grounds for future trends in Artificial Intelligence enabled information security. © 2022 IEEE.

关键词： Anomaly detection

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Design and Implementation of a Conference Attendance Monitoring System Using Blockchain and AI Technologies 2

Design and Implementation of a Conference Attendance Monitor...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Vubangsi, Mercel Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin 10 Nicosia Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin 10 Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we design and implement a conference attendance monitoring system based on blockchain and Artificial Intelligence (AI) technologies. This system is built with *** for front-end and makes use of Google's real-time database for storage. The system is designed to auto-track the users device location and screen behavior during a time slot allocated to a conference or conference session for which the user has subscribed. AI powered analytics of the data using custom mathematical models and JavaScript Machine Learning Libraries permits decision making on the data, facilitating the task on the conference organizers with regards to rating attendee performances. © 2022 IEEE.

关键词： Smart contract

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A Unified Deep Imitation Learning and Control Framework for Robot-Assisted Sonography

A Unified Deep Imitation Learning and Control Framework for ...

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IEEE International Conference on Development and Learning, ICDL

作者： Weiyong Si Cheng Guo Ning Wang Minyu Yang Rebecca Harris Chenguang Yang Bristol Robotics Lab University of the West of England School of Computer Science and Electronic Engineering University of Essex Faculty of Engineering University of Bristol Department of Engineering University of Cambridge University Hospitals Bristol and Weston.

Medical ultrasound scanning is a challenging dexterous manipulation task for robots, even for experienced sonogra-phers, since it involves motion control and force regulation based on the real-time ultrasound image and patient feedback. In this work, we proposed a novel robot-assisted ultrasound scanning framework, integrating deep multimodal imitation learning and model-based compliant control. We investigated the deep imitation learning model to fuse multimodal information, including the RGB image, force profile, ultrasound image, and proprioceptive information for robot-assisted ultrasound scanning artery. The deep imitation learning module predicts reference motion and force command. We designed a compliant controller in Cartesian space to track reference trajectory and desired force. The generalization capability of the deep multimodal imitation learning module and control performance and the quality of the acquired ultrasound image through Phantom. The results show that the proposed approach is able to improve the success rate of procedure completion, and the complete time is reduced.

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Robot Laughter: Does an appropriate laugh facilitate the robot’s humor performance?

Robot Laughter: Does an appropriate laugh facilitate the rob...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Heng Zhang Xiaoxuan Hei Junpei Zhong Adriana Tapus Computer Science and System Engineering Department (U2IS) Autonomous Systems and Robotics Lab ENSTA Paris Institut Polytechnique de Paris Paris France Department of Rehabilitation Sciences The Hong Kong Polytechnic University Hong Kong

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Laughter serves as a subtle social signal in human interaction, playing an essential role in expressing emotions and facilitating social connections. However, laughter comes in various forms and is usually accompanied by different non-verbal expressions, such as facial expressions and gestures. These accompanying factors can significantly influence the effect of laughter in diverse contexts, thus complicating the research on laughter, especially in understanding its role in social dynamics. Consequently, endowing robots with the ability to appropriately use laughter in interactions with humans is still a big challenge. Our current study focuses on the effect of robot laughter on robot humor expression. Our objective is to investigate whether and how two factors, the type of laughter and the robot laughter gesture, impact the overall humor performance. In this study, we selected four types of laughter (sarcastic, joyful, embarrassed, and relieved laughter) from a laughter corpus based on four specific types of jokes (Affiliative, Aggressive, Self-enhancing, and Self-defeating). For each type of laughter, we designed distinct robot gestures. During the humor performance, the robot NAO delivered jokes accompanied by matching or mismatching laughter, with or without corresponding gestures. To enhance the quantity and diversity of experimental data, we conducted an online survey utilizing recordings of the robot’s humor performance. The experimental findings indicate that when the robot’s laughter matches the type of humor in the joke, participants rate the humor performance significantly higher compared to situations where there is a mismatch. Additionally, the results confirm the positive impact of robot laughter gestures on humor performance.

关键词： Surveys Recording Robots

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HGIC: A Hand Gesture Based Interactive Control System for Efficient and Scalable Multi-UAV Operations

HGIC: A Hand Gesture Based Interactive Control System for Ef...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mengsha Hu Jinzhou Li Runxiang Jin Chao Shi Lei Xu Rui Liu Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Department of Computer Science Kent State University Kent OH USA College of Engineering Cornell University Ithaca NY USA Department of Systems Science and Industrial Engineering Binghamton University Binghamton NY USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

As technological advancements continue to expand the capabilities of multi unmanned-aerial-vehicle systems (mUAV), human operators face challenges in scalability and efficiency due to the complex cognitive load and operations associated with motion adjustments and team coordination. Such cognitive demands limit the feasible size of mUAV teams and necessitate extensive operator training, impeding broader adoption. This paper developed a Hand Gesture Based Interactive Control (HGIC), a novel interface system that utilize computer vision techniques to intuitively translate hand gestures into modular commands for robot teaming. Through learning control models, these commands enable efficient and scalable mUAV motion control and adjustments. HGIC eliminates the need for specialized hardware and offers two key benefits: 1) Minimal training requirements through natural gestures; and 2) Enhanced scalability and efficiency via adaptable commands. By reducing the cognitive burden on operators, HGIC opens the door for more effective large-scale mUAV applications in complex, dynamic, and uncertain scenarios. HGIC will be open-sourced after the paper being published online for the research community, aiming to drive forward innovations in human-mUAV interactions.

关键词： Training Technological innovation computer vision Scalability Robot kinematics Computational modeling Control systems Hardware Motion control Faces

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A Soft Micro-Robotic Catheter for Aneurysm Treatment: A Novel Design and Enhanced Euler-Bernoulli Model with Cross-Section Optimization

A Soft Micro-Robotic Catheter for Aneurysm Treatment: A Nove...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Emanuele Nicotra Chi Cong Nguyen James Davies Phuoc Thien Phan Trung Thien Hoang Bibhu Sharma Adrienne Ji Kefan Zhu Trung Dung Ngo Van Anh Ho Hung Manh La Nigel H. Lovell Thanh Nho Do Graduate School of Biomedical Engineering Faculty of Engineering UNSW Sydney Kensington Campus Australia More-Than-One Robotics Laboratory Faculty of Sustainable Design Engineering University of Prince Edward Island Charlottetown Canada Soft Haptics Laboratory School of Materials Science Japan Advanced Institute of Science and Technology Kawaguchi Saitama Japan Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Aneurysms, balloon-like bulges in blood vessels, present a significant health risk due to their potential to rupture, leading to life-threatening internal bleeding. Current treatments often involve delivering embolic materials or metal coils to fill these bulges, occluding them from the pressure of blood flow. However, clinical micro-catheters that deploy embolic materials used today face limitations, primarily their rigidity and the lack of active control over the bending tip of the catheter. This paper introduces a new soft micro-robotics catheter, with diameter of only 0.8 mm, equipped with a hollow channel. With this new design, the new device can induce bending motions at its tip for active steerability to reach desired aneurysm targets and then perform the delivery of embolic materials and tools. To enhance the control and precise navigation during procedures, a robust mathematical model and image processing techniques are also introduced and validated. Experiments are also performed to characterise and validate the model’s accuracy and the steerability and navigation capabilities of the new micro-catheter.

关键词： Performance evaluation Navigation Metals Aneurysm Bending Mathematical models Rigidity

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Salient Sparse Visual Odometry with Pose-only Supervision

arXiv

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arXiv 2024年

作者： Chen, Siyu Liu, Kangcheng Wang, Chen Yuan, Shenghai Yang, Jianfei Xie, Lihua The School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore The Spatial AI & Robotics Lab The Department of Computer Science & Engineering State University of New York at Buffalo BuffaloNY14260 United States

Visual Odometry (VO) is vital for the navigation of autonomous systems, providing accurate position and orientation estimates at reasonable costs. While traditional VO methods excel in some conditions, they struggle with challenges like variable lighting and motion blur. Deep learning-based VO, though more adaptable, can face generalization problems in new environments. Addressing these drawbacks, this paper presents a novel hybrid visual odometry (VO) framework that leverages pose-only supervision, offering a balanced solution between robustness and the need for extensive labeling. We propose two cost-effective and innovative designs: a self-supervised homographic pre-training for enhancing optical flow learning from pose-only labels and a random patch-based salient point detection strategy for more accurate optical flow patch extraction. These designs eliminate the need for dense optical flow labels for training and significantly improve the generalization capability of the system in diverse and challenging environments. Our pose-only supervised method achieves competitive performance on standard datasets and greater robustness and generalization ability in extreme and unseen scenarios, even compared to dense optical flow-supervised state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical flows

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Attention in Attention for PET-CT Modality Consensus Lung Tumor Segmentation

Attention in Attention for PET-CT Modality Consensus Lung Tu...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yuzhou Zhao Xinyu Zhou Haijing Guo Qianyu Guo Yan Zuo Shaoli Song Shuyong Gao Wenqiang Zhang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Department of Nuclear Medicine Fudan University Shanghai Cancer Center China Shanghai Engineering Research Center of AI Robotics Academy for Engineering and Technology Fudan University

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Combination of multi-modal PET-CT imaging for lung tumor segmentation is significant for clinical treatment. Existing methods have not fully considered the impact of noise in PET-CT on the multi-modal interaction. To address this, we propose a novel Attention in Attention Network (AiANet). AiANet can mutually learn multi-modal characteristics for segmentation through its cross-learning modules. Within the cross-learning module, we introduce two nested-attention blocks, namely Attention in Self-Attention (AiSA) and Attention in Cross-Attention (AiCA), for multi-scale feature enhancement and multi-modal feature interaction. Importantly, since traditional attention weights calculated solely or unilaterally based on PET or CT can be vulnerable to the inevitable noisy information, we embed a novel Attention in Attention (AiA) module into AiSA and AiCA. The AiA module can seek cross-modal consensus for attention weights to alleviate their noise. Experimental results on clinical PET-CT data of lung cancer demonstrate the superiority of our method.

关键词： Image segmentation Computed tomography Aggregates Noise Lung cancer Imaging Noise measurement Tumors

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UC-NeRF: Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views

arXiv

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arXiv 2024年

作者： Guo, Jiaxin Wang, Jiangliu Wei, Ruofeng Kang, Di Dou, Qi Liu, Yun-Hui CUHK T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong China Tencent AI Lab Shen Zhen China CUHK T Stone Robotics Institute The Chinese University of Hong Kong with Hong Kong Center for Logistics Robotics Hong Kong

Visualizing surgical scenes is crucial for revealing internal anatomical structures during minimally invasive procedures. Novel View Synthesis is a vital technique that offers geometry and appearance reconstruction, enhancing understanding, planning, and decision-making in surgical scenes. Despite the impressive achievements of Neural Radiance Field (NeRF), its direct application to surgical scenes produces unsatisfying results due to two challenges: endoscopic sparse views and significant photometric inconsistencies. In this paper, we propose uncertainty-aware conditional NeRF for novel view synthesis to tackle the severe shape-radiance ambiguity from sparse surgical views. The core of UC-NeRF is to incorporate the multi-view uncertainty estimation to condition the neural radiance field for modeling the severe photometric inconsistencies adaptively. Specifically, our UC-NeRF first builds a consistency learner in the form of multi-view stereo network, to establish the geometric correspondence from sparse views and generate uncertainty estimation and feature priors. In neural rendering, we design a base-adaptive NeRF network to exploit the uncertainty estimation for explicitly handling the photometric inconsistencies. Furthermore, an uncertainty-guided geometry distillation is employed to enhance geometry learning. Experiments on the SCARED and Hamlyn datasets demonstrate our superior performance in rendering appearance and geometry, consistently outperforming the current state-of-the-art approaches. Our code will be released at https://***/wrld/UC-NeRF. Copyright © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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