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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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AirLine: Efficient Learnable Line Detection with Local Edge Voting

arXiv

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arXiv 2023年

作者： Lin, Xiao Wang, Chen College of Computing Georgia Institute of Technology AtlantaGA30332 United States Spatial AI & Robotics Lab The Department of Computer Science and Engineering State University of New York BuffaloNY14260 United States

Line detection is widely used in many robotic tasks such as scene recognition, 3D reconstruction, and simultaneous localization and mapping (SLAM). Compared to points, lines can provide both low-level and high-level geometrical information for downstream tasks. In this paper, we propose a novel learnable edge-based line detection algorithm, AirLine, which can be applied to various tasks. In contrast to existing learnable endpoint-based methods, which are sensitive to the geometrical condition of environments, AirLine can extract line segments directly from edges, resulting in a better generalization ability for unseen environments. To balance efficiency and accuracy, we introduce a region-grow algorithm and a local edge voting scheme for line parameterization. To the best of our knowledge, AirLine is one of the first learnable edge-based line detection methods. Our extensive experiments have shown that it retains state-of-the-art-level precision, yet with a 3 − 80× runtime acceleration compared to other learning-based methods, which is critical for low-power robots. Copyright © 2023, The Authors. All rights reserved.

关键词： robotics

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USED: Universal Speaker Extraction and Diarization

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2024年 33卷 96-110页

作者： Junyi Ao Mehmet Sinan Yıldırım Ruijie Tao Meng Ge Shuai Wang Yanmin Qian Haizhou Li Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Shenzhen Research Institute of Big Data Shenzhen China Auditory Cognition and Computational Acoustics Lab Department of Computer Science and Engineering and the MoE Key Laboratory of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Speaker extraction and diarization are two enabling techniques for real-world speech applications. Speaker extraction aims to extract a target speaker's voice from a speech mixture, while speaker diarization demarcates speech segments by speaker, annotating ‘who spoke when’. Previous studies have typically treated the two tasks independently. In practical applications, it is more meaningful to have knowledge about ‘who spoke what and when’, which is captured by the two tasks. The two tasks share a similar objective of disentangling speakers. Speaker extraction operates in the frequency domain, whereas diarization is in the temporal domain. It is logical to believe that speaker activities obtained from speaker diarization can benefit speaker extraction, while the extracted speech offers more accurate speaker activity detection than the speech mixture. In this paper, we propose a unified model called Universal Speaker Extraction and Diarization (USED) to address output inconsistency and scenario mismatch issues. It is designed to manage speech mixtures with varying overlap ratios and variable number of speakers. We show that the USED model significantly outperforms the competitive baselines for speaker extraction and diarization tasks on LibriMix and SparseLibriMix datasets. We further validate the diarization performance on CALLHOME, a dataset based on real recordings, and experimental results indicate that our model surpasses recently proposed approaches.

关键词： Speech recognition Data mining Training Multitasking Speech enhancement Time-domain analysis Recording Predictive models Particle separators Oral communication

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Effective Searching for the Honeybee Queen in a Living Colony

Effective Searching for the Honeybee Queen in a Living Colon...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Jan Blaha Jan Mikula Tomáš Vintr Jiří Janota Jiří Ulrich Tomáš Rouček Fatemeh Rekabi-Bana Laurenz Alexander Fedotoff Martin Stefanec Thomas Schmickl Farshad Arvin Miroslav Kulich Tomáš Krajník Faculty of Electrical Engineering Czech Technical University in Prague Czechia Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czechia Computer Science Department Durham University United Kingdom Department of Zoology Artificial Life Lab Institute of Biology University of Graz Graz Austria

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Despite the importance of honeybees as pollinators for the entire ecosystem and their recent decline threatening agricultural production, the dynamics of the living colony are not well understood. In our EU H2020 RoboRoyale project, we aim to support the pollination activity of the honeybees through robots interacting with the core element of the honeybee colony, the honeybee queen. In order to achieve that, we need to understand how the honeybee queen behaves and interacts with the surrounding worker bees. To gather the necessary data, we observe the queen with a moving camera, and occasionally, we instruct the system to perform selective observations elsewhere. In this paper, we deal with the problem of searching for the honeybee queen inside a living colony. We demonstrate that combining spatio-temporal models of queen presence with efficient search methods significantly decreases the time required to find her. This will minimize the chance of missing interesting data on the infrequent behaviors or queen-worker interactions, leading to a better understanding of the queen’s behavior over time. Moreover, a faster search for the queen allows the robot to leave her more frequently and gather more data in other areas of the honeybee colony.

关键词： computer aided software engineering Automation Biological system modeling Ecosystems Robot vision systems Production Search problems Cameras Path planning Robots

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A Novel Hierarchical Discourse Model for Scientific Article and It's Efficient Top-K Resampling-based Text Classification Approach

A Novel Hierarchical Discourse Model for Scientific Article ...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Gao, Min Chen, Chun-Hua Gao, Zhi-Han Chen, Wei-Long Ren, Yuan Kwong, Sam Zhan, Zhi-Hui South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Guangzhou510006 China South China University of Technology School of Software Engineering Guangzhou510006 China South China University of Technology School of Environment and Energy Guangzhou510006 China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Scientific articles contain rich knowledge that can significantly assists scientific research, but it is difficult to precisely extract knowledge information due to the complexity of the discourse structure of scientific articles. To provide more accurate scientific research knowledge for researchers in a specific academic domain, it is necessary to study the discourse structure of domain scientific articles and to propose an automatic annotation approach to automatically annotate discourse information from articles. Unfortunately, few works have studied the discourse structure of domain scientific articles and the corresponding automatic discourse annotation. To fill this gap, we take scientific articles of the wastewater-based epidemiology domain as a case to study how to automatically and efficiently annotate discourse information. This paper has three contributions. Firstly, we propose a hierarchical discourse model with two layers to cover all potential discourses in various domain scientific articles. Specifically, the first layer defines four core discourse concepts to describe the main process of a scientific research which can be applied in all scientific articles in various domains. The second layer defines fine-granular domain-specific structure, which can accurately describe the entire research contents of a specific domain. Secondly, based on the proposed model, we build a corpus dataset of 100 annotated scientific articles in the wastewater-based epidemiology domain. Thirdly, based on the model and dataset, we propose a simple yet efficient Top-K resampling-based approach to train a more effective classifier for automatic annotation. Extensive experiments verify the effectiveness and efficiency of our proposed hierarchical discourse model and the Top-K resampling-based classification approach. © 2022 IEEE.

关键词： Classification (of information)

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Toward Perpetual Occlusion-Aware Observation of Comb States in Living Honeybee Colonies

Toward Perpetual Occlusion-Aware Observation of Comb States ...

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

作者： Jan Blaha Tomáš Vintr Jan Mikula Jiří Janota Tomáš Rouĉek Jiří Ulrich Fatemeh Rekabi-Bana Laurenz Alexander Fedotoff Martin Stefanec Thomas Schmickl Farshad Arvin Miroslav Kulich Tomáš Krajník Faculty of Electrical Engineering Czech Technical University in Prague Czechia Computer Science Department Durham University United Kingdom Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czechia Department of Zoology Artificial Life Lab Institute of Biology University of Graz Graz Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Honeybees are one of the most important pollinators in the ecosystem. Unfortunately, the dynamics of living honeybee colonies are not well understood due to their complexity and difficulty of observation. In our project “RoboRoyale”, we build and operate a robot to be a part of a bio-hybrid system, which currently observes the honeybee queen in the colony and physically tracks it with a camera. Apart from tracking and observing the queen, the system needs to monitor the state of the honeybee comb which is most of the time occluded by workerbees. This introduces a necessary tradeoff between tracking the queen and visiting the rest of the hive to create a daily map. We aim to collect the necessary data more effectively. We evaluate several mapping methods that consider the previous observations and forecasted densities of bees occluding the view. To predict the presence of bees, we use previously established maps of dynamics developed for autonomy in human-populated environments. Using data from the last observational season, we show significant improvement of the informed comb mapping methods over our current system. This will allow us to use our resources more effectively in the upcoming season.

关键词： Biological system modeling Robot vision systems Ecosystems Data collection Cameras Data models Complexity theory Biomedical monitoring Monitoring Intelligent robots

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Particle Filter Based Localization of Access Points Using Direction of Arrival on Mobile Robots 92

Particle Filter Based Localization of Access Points Using Di...

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92nd IEEE Vehicular Technology Conference, VTC 2020-Fall

作者： Parashar, Ravi Parasuraman, Ramviyas University of Georgia Heterogeneous Robotics Lab Department of Computer Science AthensGA30602 United States

ISBN: (纸本)9781728194844

Localization of autonomous vehicles in unknown and unstructured GPS-denied environments is still a relevant and major research challenge in the field of robotics. Applications of such research can be found in search and rescue missions and connected vehicles, where multiple robots need an efficient solution for simultaneous localization through multi-sensor integration so that they can effectively cooperate and coordinate tasks amongst themselves. In this paper, we propose a novel method for estimating the position of a WiFi access point in relation to a moving robot. Specifically, we exploit the integration of two sensors: Direction-of-arrival (DOA) of WiFi signals and the robot's odometry and combine them with Gaussian probabilistic sampling in a Particle Filter framework. We evaluate the proposed method in terms of accuracy and computational efficiency through extensive trials on datasets gathered from real-world measurements with mobile robots and compared our method against standard approaches. The results demonstrate superior localization accuracy (up to 3x improvement) and capability for most practical applications. © 2020 IEEE.

关键词： Direction of arrival

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Mining Railway Grievances on Twitter for Efficient E-Governance in India

Mining Railway Grievances on Twitter for Efficient E-Governa...

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IEEE International Conference on Big Data

作者： Swati Agarwal Ashrut Kumar Rijul Ganguly Department of Computer Science and Information Systems Computational Linguistics and Social Networks Lab BITS Pilani K K Birla Goa Campus Goa India

ISBN: (纸本)9781665480468

Recently, Twitter has been used as a citizen-engaging platform by Indian Railway Ministry (IRM) for collecting civic issues. However, due to the manual inspection model, a large percentage of complaints are left unaddressed affecting the credibility of the citizen-sourcing mediums. The existing solutions are for English reports and unable to capture the diverse range of code-mixed languages used in the complaints. In this paper, we developed a multilingual cased version of BERT (mBERT) for automated identification of monolingual and multilingual (aka code- mixed) complaints. The proposed mBERT is a transformers- based model pre-trained on multilingual Wikipedia corpus. In addition to the grievances classification, we also employ a BERT multi-label classifier for labelling the tweets with the issues reported in the complaints. The proposed solution approach obtains an accuracy of 82% with the binary classifier and overall accuracy of 95% with the multi-label model. Additionally, a critical analysis is presented on the classified reports and a location-based analysis to visualise the velocity and veracity of complaints across India.

关键词： Social networking (online) Blogs Bit error rate Text categorization Manuals Big Data Transformers

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

Applications of Transformer Attention Mechanisms in Informat...

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2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs)

作者： M. Vubangsi Sarumi Usman Abidemi Olukayode Akanni Auwalu Saleh Mubarak Fadi Al-Turjman Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Mersin Turkey Department of Computer Science HTTTC Bambili Computational materials Science lab University of Bamenda NWR Cameroon Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin Turkey

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.

关键词： Neural networks Intrusion detection Transformers Market research Real-time systems Malware Internet of Things

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Development and performance evaluation of a Bengali-adapted mHealth app for early detection of impaired visual acuity

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Heliyon 2024年第23期10卷 e40853页

作者： Haque, Monzurul Zaman, Marzia Islam, Ashraful Sarker, Farhana Ferdausi, Nahid Mamun, Khondaker A. AIMS Lab IRIIC United International University Dhaka Bangladesh Center for Computational & Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Southeast University Dhaka Bangladesh Gopalganj Eye Hospital and Training Institute Gopalganj Bangladesh Department of Computer Science and Engineering United International University Dhaka Bangladesh CMED Health Limited Dhaka Bangladesh

This paper presents a comprehensive study on the development and performance evaluation of “Dristi,” a mobile health (mHealth) app designed to facilitate eye screening for impaired visual acuity (VA) in Bangladesh. Recognizing the critical importance of vision and the challenges posed by visual impairments, particularly in low-resource settings, this study explores an innovative solution to enhance eye care accessibility. The app, developed for Android devices, integrates features such as VA testing using adapted Bengali letters, color blindness tests, and eye health education, aiming to make eye care more user-friendly and accessible. Inspired by the Snellen chart, the app employs a simplified method for VA assessment, suitable for the constraints of mobile devices. The study involved a cross-sectional comparative design, conducted between June and September 2022, to clinically validate the app's feature of testing distant vision and near vision. Participants were recruited from one government eye hospital, one semi-private eye hospital, one high school, and a private clinic in Bangladesh, encompassing a diverse demographic. The validation study revealed promising results in terms of the app's accuracy, sensitivity, and specificity in both distant and near vision testing, although some variability was observed across different data sources. The study also identified a significant number of participants with diabetes and hypertension, common comorbidities affecting eye health. Overall, “Dristi” demonstrates potential as a valuable tool in the early detection and management of visual impairments in Bangladesh. Its development marks a significant step towards utilizing mHealth solutions to overcome healthcare barriers and improve eye care services in low-resource settings. Future directions include expanding the app's features, enhancing its integration with healthcare systems, and extending its reach to other similar contexts. © 2024 The Author(s)

关键词： Distant vision Eye mHealth Near vision Vision problem Visual acuity

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