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arXiv 2024年

作者： de Lima, Lucas Carvalho Lawrance, Nicholas Khosoussi, Kasra Borges, Paulo Brünig, Michael The School of Electrical Engineering and Computer Science The University of Queensland BrisbaneQLD4072 Australia CSIRO Robotics Data61 PullenvaleQLD4069 Australia

Autonomous navigation in unstructured natural environments poses a significant challenge. In goal navigation tasks without prior information, the limited look-ahead of onboard sensors utilised by robots compromises path efficiency. We propose a novel approach that leverages an above-the-canopy aerial map for improved ground robot navigation. Our system utilises aerial lidar scans to create a 3D probabilistic occupancy map, uniquely incorporating the uncertainty in the aerial vehicle's trajectory for improved accuracy. Novel path planning cost functions are introduced, combining path length with obstruction risk estimated from the probabilistic map. The D* Lite algorithm then calculates an optimal (minimum-cost) path to the goal. This system also allows for dynamic replanning upon encountering unforeseen obstacles on the ground. Extensive experiments and ablation studies in simulated and real forests demonstrate the effectiveness of our system. Copyright © 2024, The Authors. All rights reserved.

关键词： Air navigation

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High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor 16

High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Park, Nu-Ri Kim, Min-Joong Lee, Jongsu Seo, Minah Ryu, Yong-Sang Kim, Myung-Ki KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Korea Institute of Science and Technology Sensor System Research Center Seoul02792 Korea Republic of Northwestern University Department of Electrical Engineering and Computer Science EvanstonIL60208 United States School of Biomedical Engineering Korea University Seoul02481 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02481 Korea Republic of

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen ( ©... 详细信息

ISBN: (纸本)9798350372076

关键词： Nanoclay

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Disclosure Privacy Information Social Media on TikTok 9

Disclosure Privacy Information Social Media on TikTok

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Syamsuar, Dedy Fahrezi, Fazri Favian, Ahnaf Irwansyah, Edy Irawan, Achmad Fakhri School of Information System Bina Nusantara University Faculty of Engineering Professional Engineer Program Dept. Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Information System Department Jakarta11480 Indonesia School of Information System Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information;nevertheless, perceived risks can greatly discourage users. © 2024 IEEE.

关键词： Differential privacy

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Hyperbolic Delaunay Geometric Alignment

arXiv

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arXiv 2024年

作者： Medbouhi, Aniss Aiman Marchetti, Giovanni Luca Polianskii, Vladislav Kravberg, Alexander Poklukar, Petra Varava, Anastasia Kragic, Danica Division of Robotics Perception and Learning Department of Intelligent Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

Hyperbolic machine learning is an emerging field aimed at representing data with a hierarchical structure. However, there is a lack of tools for evaluation and analysis of the resulting hyperbolic data representations. To this end, we propose Hyperbolic Delaunay Geometric Alignment (HyperDGA) – a similarity score for comparing datasets in a hyperbolic space. The core idea is counting the edges of the hyperbolic Delaunay graph connecting datapoints across the given sets. We provide an empirical investigation on synthetic and real-life biological data and demonstrate that HyperDGA outperforms the hyperbolic version of classical distances between sets. Furthermore, we showcase the potential of HyperDGA for evaluating latent representations inferred by a Hyperbolic Variational Auto-Encoder. © 2024, CC BY.

关键词： Geometry

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An Adversarial Approach to Register Extreme Resolution Tissue Cleared 3D Brain Images

arXiv

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arXiv 2025年

作者： Nazib, Abdullah Fookes, Clinton Perrin, Dimitri School of Computer Science Queensland University of Technology BrisbaneQLD Australia School of Electrical Engineering and Robotics Queensland University of Technology BrisbaneQLD Australia Centre for Data Science Queensland University of Technology BrisbaneQLD Australia

We developed a generative patch based 3D image registration model that can register very high resolution images obtained from a biochemical process name tissue clearing. Tissue clearing process removes lipids and fats from the tissue and make the tissue transparent. When cleared tissues are imaged with Light-sheet fluorescent microscopy, the resulting images give a clear window to the cellular activities and dynamics inside the *** the images obtained are very rich with cellular information and hence their resolution is extremely high (eg .2560 × 2160 × 676). Analyzing images with such high resolution is a difficult task for any image analysis *** registration is a common step in image analysis pipeline when comparison between images are required. Traditional image registration methods fail to register images with such extant. In this paper we addressed this very high resolution image registration issue by proposing a patch-based generative network named InvGAN. Our proposed network can register very high resolution tissue cleared images. The tissue cleared dataset used in this paper are obtained from a tissue clearing protocol named CUBIC. We compared our method both with traditional and deep-learning based registration *** different versions of CUBIC dataset are used, representing two different resolutions 25% and 100% respectively. Experiments on two different resolutions clearly show the impact of resolution on the registration quality. At 25% resolution, our method achieves comparable registration accuracy with very short time (7 minutes approximately). At 100% resolution, most of the traditional registration methods fail except Elastix registration *** takes 28 hours to register where proposed InvGAN takes only 10 minutes. © 2025, CC BY.

关键词： Image registration

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ClickVOS: Click Video Object Segmentation

arXiv

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arXiv 2024年

作者： Guo, Pinxue Hong, Lingyi Zhou, Xinyu Gao, Shuyong Li, Wanyun Li, Jinglun Chen, Zhaoyu Li, Xiaoqiang Zhang, Wei Zhang, Wenqiang the Shanghai Engineering Research Center of AI&Robotics Academy for Engineering&Technology Fudan University Shanghai China the Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China the School of Computer Engineering and Science Shanghai University Shanghai China Engineering Research Center of AI&Robotics Ministry of Education Academy for Engineering&Technology Fudan University Shanghai China

—Video Object Segmentation (VOS) task aims to segment objects in videos. However, previous settings either require time-consuming manual masks of target objects at the first frame during inference or lack the flexibility to specify arbitrary objects of interest. To address these limitations, we propose the setting named Click Video Object Segmentation (ClickVOS) which segments objects of interest across the whole video according to a single click per object in the first frame. And we provide the extended datasets DAVIS-P and YouTubeVOS-P that with point annotations to support this task. ClickVOS is of significant practical applications and research implications due to its only 1-2 seconds interaction time for indicating an object, comparing annotating the mask of an object needs several minutes. However, ClickVOS also presents increased challenges. To address this task, we propose an end-to-end baseline approach named called Attention Before Segmentation (ABS), motivated by the attention process of humans. ABS utilizes the given point in the first frame to perceive the target object through a concise yet effective segmentation attention. Although the initial object mask is possibly inaccurate, in our ABS, as the video goes on, the initially imprecise object mask can self-heal instead of deteriorating due to error accumulation, which is attributed to our designed improvement memory that continuously records stable global object memory and updates detailed dense memory. In addition, we conduct various baseline explorations utilizing off-the-shelf algorithms from related fields, which could provide insights for the further exploration of ClickVOS. The experimental results demonstrate the superiority of the proposed ABS approach. Extended datasets and codes will be available at https://***/PinxueGuo/ClickVOS. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion compensation

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DeepExtract: Neural Networks Based Image Keypoints Extraction for Indirect SLAM Algorithms

DeepExtract: Neural Networks Based Image Keypoints Extractio...

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IEEE International Conference on Artificial Intelligence in engineering and Technology (IICAIET)

作者： Maurya Gurram Prakash Kumar Uttam Shantipal S. Ohol School of Computer Science and Engineering Vellore Institute of Technology Amaravathi Andhra Pradesh Robotics and Artificial Intelligence RnDE (Engrs) DRDO Pune Maharashtra School of Mechanical Engineering COEP Technological University Pune Maharashtra

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

Indirect SLAM (Simultaneous Localization and Mapping) algorithms primarily rely upon image features to estimate poses and to perform loop closure. Traditional image key point extraction algorithms fail to detect features in the image in some cases due to various reasons like low exposure, lack of contours and hardware constraints. To tackle this issue, we propose a U-Net neural network based image keypoint extraction algorithm that works on low cost camera hardware. We utilize an unconventional approach by applying image segmentation techniques to extract Shi- Tomashi features for training the neural network. It is an attempt to building innovative approaches for Deep learning-based visual odometry problem. The primary focus of this work is to establish a new research direction for deep learning based keypoint extraction tailored for real-time SLAM on low-cost and low-power devices. We aim to lay the foundations for further investigation into segmentation-based key-point modeling and its impact on critical SLAM components like feature tracking, mapping, and loop closure detection.

关键词： Training Image segmentation Tracking loops Solid modeling Electric potential Simultaneous localization and mapping Neural networks Feature extraction Cameras Hardware

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Rearranging the Environment to Maximize Energy with a Robotic Circuit Drawing

Rearranging the Environment to Maximize Energy with a Roboti...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Tan, Xianglong Liu, Zhikang Yu, Chen Schwertfeger, Sören Rosendo, Andre Hamlyn Centre Imperial College London United Kingdom University of York Department of Computer Science United Kingdom School of Information Science and Technology ShanghaiTech University China Worcester Polytechnic Institute Robotics Engineering United States

ISBN: (纸本)9781665481090

The ability of acquiring energy form environments is greatly beneficial for robot to function in uncertain environments. In this work, we present a robot capable of drawing circuits with conductive ink while also rearranging the visual world to receive maximum energy from a power source. A range of circuit drawing tasks is designed to simulate real-world scenarios, including avoiding physical obstacles and regions that would discontinue drawn circuits. We adopt the state-of-the-art Transporter networks for pick-and-place manipulation from visual observation. We conduct experiments in both simulation and real-world settings, and our results show that, with a small number of demonstrations, the robot learns to rearrange the placement of objects (removing obstacles and bridging areas unsuitable for drawing) and to connect a power source with a minimum amount of conductive ink. As autonomous robots become more and more involved in our daily lives, our proposed approach brings a novel way for machines to keep themselves functional by rearranging their surroundings to create their own electric circuits. © 2022 IEEE.

关键词： Timing circuits

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Shape Sensing Stretchable Sensor Grid Towards Camera-Less Motion Capture

Shape Sensing Stretchable Sensor Grid Towards Camera-Less Mo...

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IEEE SENSORS

作者： Joseph Karam Aiden Shaevitz Joseph R. Davidson Matthew L. Johnston School of Electrical Engineering and Computer Science Oregon State University Corvallis OR USA Collaborative Robotics and Intelligent Systems Institute Oregon State University Corvallis OR USA

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

Stretchable electronics and conformal sensors are valuable for applications in soft robotics and wearable devices. Here, we demonstrate a stretchable sensor sheet with integrated readout for measuring strain and orientation across the sheet. Conductive liquid metal wires encased in silicone measure multi-axis strain. These strain sensors, paired with precision 9-axis IMUs mounted on PCBs embedded in the silicone, enable a shape-sensing, stretchable sensor grid. A prototype is presented first with a single strain sensor and IMU pair, which is charac-terized and calibrated for higher accuracy. Then, a larger scale stretchable grid with 24 strain sensors and 16 IMUs is presented. A four-wire liquid metal bus delivers power to the system and serves as a data communication bus. Long-term, our approach may enable motion capture and shape reconstruction without the need for external cameras or instrumented environments.

关键词： Liquids Shape Metals Soft robotics Strain measurement Capacitive sensors Motion capture Wearable devices Wearable sensors Strain

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SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARNING

SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARN...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Liu, Linfeng Juwono, Filbert H. Wong, W.K. Purwanto, Erick Jin, Tingyan Zhen, Yuyang Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing China School of Instrument Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781837242689

In this study, we explore the classification and prediction capabilities of three models-Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)-on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction. © The Institution of engineering & Technology 2024.

关键词： Genetic programming

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